No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Analysis of cooperative driving strategies at road network level with macroscopic fundamental diagram
Abstract
Cooperative driving, especially the passing order, is the critical link to improve the traffic efficiency of the road network by using automated vehicles (AVs). However, most studies have only considered the performance of the passing order at the isolated intersection and have not yet investigated its impact on the road network. In this paper, we will focus on the performance of the passing orders derived from different cooperative driving strategies on the network traffic through a series of simulation experiments. Meanwhile, we will compare the impacts of the passing order at intersections and the car-following gap in straight links on the network traffic efficiency. The experiments results show that the passing order has a dominant impact on the network traffic efficiency, and a better order can significantly raise the curve of the macroscopic fundamental diagram (MFD); due to the inevitable conflicts in the two-dimensional traffic, the choice of the car-following gap within a reasonable range has a relatively small improvement on the network traffic efficiency. The findings in this paper have instructive significance for the rising research on network-wide cooperative driving and provide a systematical perspective for network traffic control.
... However, the previous studies [1], [30], [31] have demonstrated that in urban road networks, which encompass various types of right-of-way conflicts like intersections, the twodimensional right-of-way conflicts play a dominant role in influencing the efficiency and capacity of the traffic system compared with one-dimensional car-following performance. Consequently, the dedicated lane strategy has limited effectiveness in enhancing the efficiency of urban network-wide mixed traffic systems. ...
... In each round of planning, the time for CAVs to arrive at the conflict zone is estimated, and then the passing order is arranged in ascending order of arrival time. The advantage of this category of algorithm is that it requires less computation cost, but the disadvantage is that there is a certain performance gap between its corresponding passing order and the optimal solution [30], [37], [38]. ...
... Compared to traditional traffic signal strategy, signal-free cooperative driving strategy can comprehensively improve the efficiency of the traffic system [30]. However, the cooperative driving strategy cannot be directly deployed into mixed traffic environments where HDVs exist. ...
The proper management of mixed traffic is crucial for unleashing the benefits of connected and automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into one-dimensional benefits in car-following performance and two-dimensional benefits in efficiently addressing right-of-way conflicts. Currently, the most effective approach to achieve this is by establishing a dedicated right-of-way for CAVs. However, existing strategies are limited to dedicated lane strategy, which can only unleash the one-dimensional benefits of CAVs while the two-dimensional benefits remain untapped. Therefore, this paper proposes a novel management approach for mixed traffic called the dedicated link strategy. The dedicated link refers to the road link that only allows CAVs to use. This strategy can unleash both the one-dimensional and two-dimensional benefits of CAVs via: (i) dedicated link deployment at the road network level and (ii) a novel intersection management approach. Specifically, at the macroscopic road network level, we introduce a bi-level dedicated link deployment model and design an artificial bee colony based algorithm to solve the optimal dedicated link deployment. At the microscopic intersection level, we develop a novel intersection management approach that integrates traditional traffic signal strategy with the emerging signal-free cooperative driving method, thereby boosting the efficiency of intersections. The macroscopic and microscopic methods will complement each other to achieve efficient management of network-wide mixed traffic systems. Finally, we verify the performance of the dedicated link strategy through comprehensive experiments. In essence, the proposed dedicated link strategy unifies the existing dedicated lane strategy and dedicated intersection strategy, providing a general solution for mixed traffic management.
... However, on an urban road network with numerous right-of-way conflicts (e.g., ramps, intersections, etc.), a shorter car-following gap itself is not sufficient to significantly improve traffic efficiency. This is because two-dimensional right-of-way conflicts have a dominant effect on the efficiency of network-wide traffic compared to one-dimensional car-following gaps (Zhang et al., 2022b). Related research (Yu et al., 2021;Zhang et al., 2023c;Wang et al., 2020;Meng et al., 2017;Zhang et al., 2023d) has demonstrated that in urban network-wide traffic, better intersection control strategies can substantially improve the efficiency of individual vehicles and the overall traffic system performance compared to shorter car-following gaps. ...
... The studies (Yu et al., 2021;Ye and Yamamoto, 2018;Chen et al., 2015) have shown that compared with heterogeneous mixed traffic, homogeneous CAV traffic has obvious advantages in terms of road capacity and free-flow speed, which will lead to a significant difference in travel time. On the other hand, dedicated intersections can deploy more efficient signal-free cooperative driving algorithms than traffic signal control at regular intersections, which can equivalently improve the average free-flow speed on the corresponding links (Zhang et al., 2022b;Xu et al., 2021). Therefore, we distinguish the travel time cost for road links based on the different intersection settings, and these differences are specifically implemented on the constraints of the lower-level traffic assignment problem. ...
... Constraints (2f-2 h) use the well-known Bureau of Public Roads (BPR) function (Zheng et al., 2007;Wu et al., 2021;Wu et al., 2022;Yao et al., 2021) for evaluating the travel time through link (i, j) ∈ A, which is proposed by the United States Department of Transportation. t k ij = l ij /u k ij (k = 0, 1, 2) denotes the freeflow travel time on link (i, j) ∈ A. l ij and u k ij denote the length and free-flow speed of link (i, j) ∈ A. Here, based on the study in Zhang et al., (2022b), dedicated intersections deployed with signal-free cooperative driving algorithms will lead to greater average free-flow speeds compared to traffic signal control, i.e., u 0 ij < u 1 ij < u 2 ij . Constraints (2i) and (2j) determine the flow capacity of link (i, j) ∈ A. Similar to related studies (Guo et al., 2021;Yao et al., 2021;Lazar et al., 2017;Levin and Boyles, et al., 2016;Levin, 2017), we adopt the model in Levin and Boyles (2016), which models the flow capacity of a link as a dynamic reciprocal function of the CAV penetration. ...
The management of mixed traffic systems is critical to realize the benefits of connected and automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into the one-dimensional benefits of improving car-following performance and the two-dimensional benefits of efficiently addressing right-of-way conflicts. Researchers have proposed dedicated lanes that can exploit the one-dimensional benefits of CAVs, but the two-dimensional benefits remain unexplored in mixed traffic environments. To fully release the benefits of CAVs, we introduce an innovative approach for managing mixed traffic in road networks, known as the dedicated intersection strategy. Specifically, the dedicated intersection strategy refers to constructing two-dimensional CAV-dedicated right-of-way within the road network, achieving separation between CAVs and human driven vehicles (HDVs) at the intersection level. However, the deployment problem of dedicated intersections is an NP-hard problem. Therefore, we propose a bi-level solving framework, in which the upper level determines the dedicated intersection deployment scheme and the lower level solves the corresponding traffic assignment problem. To quickly find adequate deployment schemes, we propose an artificial bee colony based intelligent algorithm. Our numerical experiments demonstrate that the proposed algorithm can quickly find near-optimal dedicated intersection deployment schemes within a small number of iterations. Compared to regular intersections, the deployment of dedicated intersections in the road network can improve overall traffic system efficiency, particularly for CAVs. Finally, microscopic traffic simulation experiments further verify the superior performance of the proposed dedicated intersection strategy.
... C ONNECTED and automated vehicles (CAVs) are expected to significantly improve the safety and efficiency of autonomous driving and are considered the crucial component of the next-generation transportation system [1]- [7]. With cooperative driving of vehicles to fully utilize the advantages of CAVs [8]- [15]. ...
... Pei et al. [29] design a distributed strategy for the coordination of adjacent intersections. However, due to the complexity of the road network planning problem and the severe coupling between sub-problems, there are still gaps in network-wide approaches [15], [16]. ...
... spatial-temporal range. Deploying these approaches directly to road network scenarios will lead to a prominent issue, i.e., many vehicles will queue beyond the control zone, and thus their efficiency will be seriously weakened [15]. Second, and more prominently, there are still gaps in network-wide planning problems due to the complexity of the problem. ...
Cooperative driving of connected and automated vehicles (CAVs) has attracted extensive attention and researchers have proposed various approaches. However, existing approaches are limited to small-scale isolated scenarios and gaps remain in network-wide cooperative driving, especially in routing. In this paper, we decompose the network-level cooperative driving problem into two dominant sub-problems and accordingly propose a bi-level network-wide cooperative driving approach. The dynamic routing problem is considered in the upper level and we propose a multi-agent deep reinforcement learning (DRL) based routing model. The model can promote the equilibrium of network-wide traffic through distributed self-organized routing collaboration among vehicles, thereby improving efficiency for both individual vehicles and global traffic systems. In the lower level, we focus on the right-of-way assignment problem at signal-free intersections and propose an adaptive cooperative driving algorithm. The algorithm can adaptively evaluate priorities of different lanes, and then uses the lane priorities to guide the Monte Carlo tree search (MCTS) for better right-of-way assignments. Essentially, the upper level determines which conflict areas the vehicles will pass through, and the lower level addresses how the vehicles use the limited road resources more efficiently in each conflict area. The experimental results show that the upper and lower levels complement each other and work together to significantly improve the network-wide traffic efficiency and reduce the travel time of individual vehicles. Moreover, the results demonstrate that microscopic and mesoscopic cooperative driving behaviors of vehicles can significantly benefit the macroscopic traffic system.
... years [1], [2], [3], [4]. With the aid of V2X, vehicles can communicate with roadside units (RSUs) and surrounding vehicles, sharing spatiotemporal state information and driving intentions, which is expected to address the safety challenges faced by autonomous driving effectively [5], [6], [7], [8]. ...
... CAV-involved traffic has changed from the traditional traffic-responsive feedback control to emerging planning based feed-forward control [18], and the corresponding input and output have changed substantially. Researchers have proposed various feed-forward methods for multi-CAV cooperative driving in typical traffic scenarios, which include cooperative adaptive cruise control (CACC) for CAV platoons [19], [20], [21], cooperative driving at signal-free intersections [1], [11], [22], [23], multi-CAV merging at on-ramps [24], [25], [26], [27], [28], lane-changing scenarios [29], [30], [31], [32], etc., covering various driving scenarios within a general traffic system. The simulation and evaluation of these methods are a major demand for a CAV-oriented traffic simulator. ...
... Here, the output of the centralized algorithm is usually represented by a sequence consisting of vehicle indexes, which is figuratively called the passing order [51], as illustrated in Fig. 5. In particular, it has been shown in [1] that the passing order plays a dominant impact on the efficiency of the signal-free intersection system. Moreover, it should be noted that cooperative driving at signal-free intersections in CAVSim is uniformly planned in a time-driven manner with a time step T planning inter section . ...
Connected and automated vehicles (CAVs) are expected to play a vital role in the emerging intelligent transportation system. In recent years, researchers have proposed various cooperative driving methods for CAVs, and there is an urgent need for a generic and unified traffic simulator to simulate and evaluate these methods. However, traditional traffic simulators have two critical deficiencies for CAV simulation needs: 1) the planning and dynamical modeling of vehicles in traditional simulators are based on a feedback mode, which is incompatible with the feed-forward decision and planning that CAVs commonly adopt; 2) the traditional simulators cannot provide typical traffic scenarios and corresponding standardized algorithms for multi-CAV cooperative driving. In this paper, we introduce CAVSim, a novel microscopic traffic simulator for CAVs, to address these deficiencies. CAVSim is developed modularly according to the emerging technology of the CAV environment, emphasizes feed-forward decision and planning for CAVs, and highlights the cooperative decision and planning components in the CAV environment. CAVSim incorporates rich and typical traffic scenarios and provides standardized cooperative driving algorithms and comparable performance metrics for multi-CAV cooperative driving. With CAVSim, researchers can conveniently deploy decision, planning, and control methods for CAVs at different levels, evaluate their performance, compare them with the standardized algorithms incorporated in CAVSim, and even further explore their impact on traffic flow. As a unified platform for CAVs, CAVSim can facilitate the studies on CAVs and promote the advancement of methods and techniques for CAVs.
... One typical example of such right-of-way arrangements is to find the optimal passing order that can minimize the total delay of all CAVs for a signal-free intersection. It has been shown in papers [3], [9], [10] that a better passing order can significantly improve the performance of the CAVs system, while a poor order may cause traffic congestion. ...
... In each round of planning, the arrival time of CAVs to the conflict area will be estimated, and then the passing order is arranged in ascending order of their arrival times. Although the FCFS based algorithms have a low computational burden, their generated passing orders are usually far from the optimal solution [3], [9], [14]. The second category is mathematical programming algorithms which transfer the whole problem into an optimization problem with several integer variables introduced to denote the relative order between vehicles [15], [16], [17], [18]. ...
... Here, J Delay (V i , π | s) is the delay of Vehicle i with the passing order π ∈ , and thus the first term is the delay-sum of all vehicles. For a given passing order, the computational complexity of calculating objective value J is O(n), where n is the number of vehicles [3]. Besides, since it is unenforceable for passing orders that violate the order of vehicles in front and behind at the same lane, the second bool function f Enforceable (π | s) is used to guarantee the enforceability of π . ...
Connected and automated vehicles (CAVs) have the potential to significantly improve the safety and efficiency of traffic. One revolutionary CAV’s impact on transportation system is cooperative driving that turns signalized intersections to be signal-free and boosts traffic efficiency by better organizing the passing order of CAVs. However, how to get the optimal passing order is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. For the scenarios with 40 CAVs, AlphaOrder reduces the travel delay by more than
$20\%$
on average compared to the best-so-far MCTS based algorithm. Moreover, our algorithm provides a general approach to managing preemptive resource sharing between multi-agents (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas).
... The key characteristic of such systems is that vehicles are discharged as discrete "customers" as opposed to traffic flows at conventional signalized intersections. Hence, signal-free intersections are more flexible and thus potentially more efficient than signalized intersections [5][6][7]. ...
... Extensive results have been developed recently for the planning layer [10][11][12][13][14][15][16] and driving layer [17][18][19][20][21][22]. There also exists a body of work on the sequencing layer [6,23,24], but, to the best of our knowledge, very limited results are available for macroscopic evaluation of various sequencing policies. Specifically, the following two questions have not been well understood from a theoretical, quantitative perspective: ...
... Lioris et al [5] used a classical queuing model to estimate intersection capacity with the introduction of CAVs. Zhang et al [6] simulated and evaluated various sequencing policies in typical scenarios. Miculescu and Karaman [24] proposed an online algorithm that provides guarantees on safety and efficiency under the first-in-first-out (FIFO) policy. ...
Signal-free intersections are a representative application of smart and connected vehicle technologies. Although extensive results have been developed for trajectory planning and autonomous driving, the formulation and evaluation of vehicle sequencing have not been well understood.In this paper, we consider theoretical guarantees of macroscopic performance (i.e., capacity and delay) of typical sequencing policies at signal-free intersections. We model intersection traffic as a piecewise-deterministic Markov process (PDMP). We analytically characterize the intersection capacity regions and provide upper bounds on travel delay under three typical policies, viz. first-in-first-out, min-switchover, and longer-queue-first. We obtain these results by constructing policy-specific Lyapunov functions and computing mean drift of the PDMP. We also validate the results via a series of micro-simulation-based experiments.
... C ONNECTED and automated vehicle (CAV) swarms are expected to be among the first large-scale robotic systems to enter and widely affect existing social systems, and to be the key participants in next-generation intelligent transportation system [1]- [3]. With the aid of vehicle-to-vehicle communication, CAVs can share their states (e.g., position, velocity, etc.) and intentions (e.g., behaviors, path, etc.) to achieve cooperative driving, improving efficiency while ensuring safety. ...
... One typical example for such right-of-way arrangements is to find the optimal passing order that can minimize the total delay of all CAVs for a signal-free intersection. It has been shown in papers [3], [9], [10] that a better passing order can improve the performance of the CAV swarms system, while a poor order may cause traffic congestion. It is extremely challenging to find the (near-)optimal passing order because: (i) the number of passing orders grows exponentially with the number of vehicles, e.g., there are approximately 8.159 × 10 47 passing orders for the scenario with 40 vehicles; (ii) algorithm is generally deployed at a roadside unit and the edge computing capability is limited; (iii) algorithm should satisfy the real-time requirement since vehicles are moving. ...
... In each round of planning, the arrival time of CAVs to the conflict area will be estimated, and then the passing order is arranged in ascending order of their arrival times. Although the FCFS based algorithms have a low computational burden, its generated passing order is usually far from the optimal solution [3], [9], [14]. The second category is mathematical programming algorithms which transfer the whole problem into an optimization problem with several integer variables introduced to denote the relative order between vehicles [15]- [18]. ...
Connected and automated vehicles (CAVs) are viewed as a special kind of robots that have the potential to significantly improve the safety and efficiency of traffic. In contrast to many swarm robotics studies that are demonstrated in labs by employing a small number of robots, CAV studies aims to achieve cooperative driving of unceasing robot swarm flows. However, how to get the optimal passing order of such robot swarm flows even for a signal-free intersection is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. Furthermore, our approach provides a general approach to managing preemptive resource sharing between swarm robotics (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas
... Further, we incorporate the domain knowledge of traffic flow dynamics into the interval division of traffic density in each cell, which facilitates enhancing the accuracy of traffic density description when implementing one-hot encoding. According to the traffic fundamental diagram shown in Fig. 3(b), one can notice that the relationship between the actual traffic density and critical density n c has a large impact on traffic efficiency [40], and more details on the calculation of the critical density can refer to [33], [41], [42]. Therefore, we incorporate the information of critical density into state space design to dynamically determine the interval division of traffic density in each cell. ...
... The headway h a between autonomous vehicles is set as 1s. Similar to the previous studies [40], [45], we assume that vehicles arrive in a Poisson Process at each input link. In this experiment, the arrival rate λ of vehicles at each input link is set as 1000veh/link/h. ...
Routing for autonomous vehicles with global traffic information and sufficient direct cooperation among vehicles has been widely studied to relieve traffic congestion in recent years. However, the assembly rate of Vehicle-to-Everything (V2X) equipment in practical traffic systems is currently and could be at a low level in near future. Accordingly, autonomous vehicles can only access localized traffic information, and direct cooperation among them cannot always be guaranteed. Thus, how to optimize the routing choices in such scenarios is worthy of particular attention. In this paper, we propose a self-organized routing strategy based on deep reinforcement learning (DRL). Under the condition of limited traffic information, the proposed self-organized mechanism well organizes localized traffic conditions through vehicle-level routing decisions, which are able to achieve network-wide benefits gains. In the specified DRL, we propose a novel reward mechanism to harmonize indirect interactions among vehicles by jointly learning individual and overall efficiency, even if each vehicle is modified to make individual decisions independently, rather than only focusing on individual interests as in the greedy strategy. Numerical experiments demonstrate that the proposed self-organized strategy is promising to resolve the routing problem from the perspective of individual decision-making with limited traffic information.
... To whom correspondence should be addressed. Manuscript received: 2022-09-08; revised: 2022-11-09; accepted: 2022- [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] cooperative driving plan for multiple vehicles, so that these vehicles can pass a certain conflict point (e.g., unsignalized intersection, ramp areas, and working zones) as quickly as possible. In this study, we also address this issue. ...
... Generally, cooperative driving planning can be roughly categorized into two kinds, namely, studies of non-idealized traffic scenarios [8][9][10][11] and studies of idealized traffic scenarios. In the first kind of study, researchers assume that the vehicles' lengths, arrivals, driving directions, and speeds are all random and timevarying [12][13][14] . Under such assumptions, we need to design adaptive and intelligent planning algorithms to schedule a short-term feasible passing order for the investigated vehicles, so that their delay can be minimized. ...
Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these approaches are lacking. In this study, we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance. We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles. The obtained conclusions shed light on future cooperative driving studies.
... V2X based connected and automated vehicles (CAVs) are one of the leading technologies to achieve autonomous driving and have received extensive research attention in recent years [1]- [4]. With the aid of V2X, vehicles can communicate with roadside units (RSUs) and surrounding vehicles, sharing their state information and driving intentions, which is expected to address the safety challenges faced by autonomous driving effectively. ...
... (1) where ,max f a is the maximum acceleration/deceleration of the following vehicle and is determined by its physical properties; ,0 f v is the desired speed of the following vehicle; is the free acceleration exponent. The desired headway * () St of the following vehicle is defined as where 0 S is the minimum desired distance headway; 0 T is the desired time headway;a is the comfortable acceleration; b is the comfortable braking deceleration.Second, the modified IDM model proposed in [19] aims to simulate the car-following behaviors of CAVs. ...
Connected and automated vehicles (CAVs) are expected to play a vital role in the next-generation intelligent transportation system. In recent years, researchers have proposed various cooperative driving methods for CAVs' decisions and planning, and there is an urgent need for a suitable and unified traffic simulator to evaluate and test these methods. However, existing traffic simulators have three critical deficiencies for CAV simulation needs: (1) most of them are from the perspective of traffic flow simulation and have strong simplification and assumptions for vehicle modeling; (2) CAVs are different from traditional human-driven vehicles (HVs) and have new properties, which require new driving models; (3) the existing traffic simulators are inconvenient to deploy the emerging cooperative driving methods because their modeling of the traffic system architecture is traditional. In this paper, we introduce CAVSim, a novel microscope traffic simulator for the CAV environment, to address these deficiencies. CAVSim is modularly developed according to the emerging architecture for the CAV environment, emphasizes more detailed driving behaviors of CAVs, and highlights the decision and planning components in the CAV environment. With CAVSim, researchers can quickly deploy decision and planning methods at different levels, evaluate and test their performance, and explore their impact on the traffic flow in the CAV environment.
... For example, the driving safety metrics, Time-To-Collision (TTC), Time Headway (THW), critical states are used to estimate and extract the risky driving scenarios [36]- [38]. The traffic flow metrics, vehicle speed and density, are used to classify the scenarios under dense and sparse vehicle flow [39]- [40]. However, the parameters are always fixed and may lack accuracy and flexibility in some types of driving environments [41]. ...
Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation.
... One level is to control the trajectory speed before reaching the intersection (Xu et al., 2020). The other level is to schedule the crossing sequence when crossing the intersection (Zhang et al., 2022). In order to optimize the overall intersection efficiency, it draws attention to controlling the two levels simultaneously since Au and Stone (2010) based on the Little's Law (Little, 1961). ...
Cooperative CAV crossing a reservation-based intersection is an integrated control problem that consists of the trajectory modeling and scheduling optimization. In order to achieve the best system performance in the intersection, a final speed is traditionally modeled as large as possible when reaching the intersection in the trajectory modeling level, coupled with a delay-minimization problem. However, the delay optimization problem may not find the best solution in terms of the system efficiency. This paper aims to theoretically justify the best objective function and propose an optimal integrated control model. A speed-maximization trajectory optimization model is proposed based on the queue theory. The optimization model is formulated as a discrete-time mixed integer programming model based on the trajectory analysis. Through extensive numerical simulations with platooning and turning movements, the optimization model achieves better system performance than state-of-the-art methods. The results validate the advantages of maximizing the average speed using the discrete-time trajectory modeling method.
... We conduct experiments with the simulator presented in Refs. [33] and [34]. A pure tracking algorithm is used as the lateral control of the vehicle, referenced in Ref. [35], with the longitudinal model referenced in Ref. [36], which is used to generate continuous trajectories, also as an extension of the collision avoidance model. ...
Virtual simulation testing of Autonomous Vehicles (AVs) is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs. Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets. However, the criticalities defined in their testing tasks are based on fixed assumptions, the obtained scenarios cannot pose a challenge to AVs with different strategies. To fill this gap, we propose an intelligent testing method based on operable testing tasks. We found that the driving behavior of Surrounding Vehicles (SVs) has a critical impact on AV, which can be used to adjust the testing task difficulty to find more challenging scenarios. To model different driving behaviors, we utilize behavioral utility functions with binary driving strategies. Further, we construct a vehicle interaction model, based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty. Finally, by adjusting SV's strategies, we can generate more corner cases when testing different AVs in a finite number of simulations.
... In this paper, we consider the possible influence of recently emerging automated vehicles to traffic breakdown probability [18,19]. Usually, researchers find that shorter car-following gaps between automated vehicles at the microscopic level could directly lead to larger capacity and lower breakdown probability at the macroscopic level. ...
Automated vehicles are expected to greatly boost traffic efficiency. However, how to estimate traffic breakdown probability for the mixed flow of autonomous vehicles and human driven vehicles around ramping areas remains to be answered. In this paper, we propose a stochastic temporal queueing model to reliably depict the queue dynamics of mixed traffic flow at ramping bottlenecks. The new model is a specified Newell’s car-following model that allows two kinds of vehicle velocities and first-in-first-out (FIFO) queueing behaviors. The jam queue join time is supposed to be a random variable for human driven vehicles but a constant for automated vehicles. Different from many known models, we check the occurrence of significant velocity drop along the road instead of examining the duration of the simulated jam queue so as to avoid drawing the wrong conclusions of traffic breakdown. Monte Carlo simulation results show that the generated breakdown probability curves for pure human driven vehicles agree well with empirical observations. Having noticed that various driving strategy of automated vehicles exist, we carry out further analysis to show that the chosen car-following strategy of automated vehicles characterizes the breakdown probabilities. Further tests indicate that when the penetration rate of automated vehicles is larger than 20%, the traffic breakdown probability curve of the mixed traffic will be noticeably shifted rightward, if an appropriate car-following strategy is applied. This indicates the potential benefit of automated vehicles in improving traffic efficiency.
... Further, we can achieve a better trade-off between fairness and traffic efficiency by appropriately modifying the DP-based and the rule-based strategies. These cooperative driving strategies compared in this study will be integrated into CAVSim, the simulation platform developed in [20], [21], to facilitate the researchers to conduct further studies. ...
This paper focuses on cooperative driving strategies at on-ramps and comprehensively compares the performance of five representative strategies. The simulation results show that the dynamic programming (DP)-based, the grouping-based, and the rule-based strategies perform well in computation time and traffic efficiency, so all three strategies are recommended for practical use. We also show that improving traffic efficiency is usually at the expense of fairness, but a better trade-off between them can be realized in the modified DP-based and rule-based strategies. All cooperative driving strategies compared in this study will be integrated into CAVSim (a simulation platform dedicated to CAVs) for the convenience of researchers and the community.
... In order to train and evaluate the lane-change behavior planning strategy proposed in this paper, we build a multi-lane highway traffic environment with the simulator presented in [51] and [52]. The simulator emphasizes the driving behavior characteristics of automated vehicles from the traffic perspective, which is in line with the focus of this paper. ...
Lane change is a common-yet-challenging driving behavior for automated vehicles. To improve the safety and efficiency of automated vehicles, researchers have proposed various lane-change decision models. However, most of the existing models consider lane-change behavior as a one-player decision-making problem, ignoring the essential multi-agent properties when vehicles are driving in traffic. Such models lead to deficiencies in interaction and collaboration between vehicles, which results in hazardous driving behaviors and overall traffic inefficiency. In this paper, we revisit the lane-change problem and propose a bi-level lane-change behavior planning strategy, where the upper level is a novel multi-agent deep reinforcement learning (DRL) based lane-change decision model and the lower level is a negotiation based right-of-way assignment model. We promote the collaboration performance of the upper-level lane-change decision model from three crucial aspects. First, we formulate the lane-change decision problem with a novel multi-agent reinforcement learning model, which provides a more appropriate paradigm for collaboration than the single-agent model. Second, we encode the driving intentions of surrounding vehicles into the observation space, which can empower multiple vehicles to implicitly negotiate the right-of-way in decision-making and enable the model to determine the right-of-way in a collaborative manner. Third, an ingenious reward function is designed to allow the vehicles to consider not only ego benefits but also the impact of changing lanes on traffic, which will guide the multi-agent system to learn excellent coordination performance. With the upper-level lane-change decisions, the lower-level right-of-way assignment model is used to guarantee the safety of lane-change behaviors. The experiments show that the proposed approaches can lead to safe, efficient, and harmonious lane-change behaviors, which boosts the collaboration between vehicles and in turn improves the safety and efficiency of the overall traffic. Moreover, the proposed approaches promote the microscopic synchronization of vehicles, which can lead to the macroscopic synchronization of traffic flow.
... Cooperative driving at signal-free intersections focuses on coordinating the vehicles that move in different directions within the conflict areas so that these vehicles can pass through the conflict area safely and efficiently [11]- [14]. Recently, several effective approaches are designed to well resolve the problems of cooperative driving around an isolated intersection and even in large-scale networks [15]- [22]. However, there is a critical situation that often appears in the practical traffic systems and yet has not been considered in most existing studies about cooperative driving, i.e., the presence of potential vehicle failures. ...
Cooperative driving shows great potential to improve traffic safety and efficiency and has been well discussed in recent years. However, most existing studies focus on ideal traffic environments and ignore potential vehicle failures in traffic systems, which pose significant threats to traffic safety. Therefore, the fault-tolerant capacity of the existing cooperative driving strategies is questionable. To fill this research gap, this paper proposes a fault-tolerant cooperative driving strategy for signal-free intersections by modeling potential vehicle failure types, aiming to keep a good balance between traffic safety and efficiency. Notably, a rule-based fault-tolerant model is constructed to mitigate the threat of potential vehicle failures to traffic safety and efficiency, and to effectively recover the cooperative driving system after vehicle failures occur. Theoretical analysis and simulation results jointly demonstrate the promising performance of the proposed model in achieving fault tolerance and improving traffic efficiency.
Under dynamic traffic demand conditions, two issues are usually of concern when implementing perimeter control for congested areas of a road network. One is to avoid intersection spillback at the boundaries and expansion of the congestion; the other is to improve the output traffic efficiency of the congested areas to quickly relieve traffic congestion. To simultaneously overcome these two issues and solve the traffic congestion problem, in this paper, we adopt a dynamic buffer area to store boundary queuing vehicles and uses connected and autonomous vehicle (CAV) technology to improve the traffic flow transmission efficiency of a congested area. First, the establishment method and traffic flow transmission model of the dynamic buffer area are built. Second, the traffic flow transmission characteristics of the dynamic buffer area in a mixed CAV and human-driven vehicle (HV) traffic environment are analysed. Third, a hierarchical control method based on model prediction (HCMMP) is proposed for the scenario of a single kernel network. The HCMMP’s upper level adopts MPC to adjust the traffic flow into the dynamic buffer area, and the lower level optimizes the signal timing in the dynamic buffer area using real-time CAV information. Finally, a reality urban road network is selected as a study case, and the proposed HCMMP is analysed and compared with proportional integral (PI) control and MPC (both without a dynamic buffer area) and with PI control with the dynamic buffer area (PIBA). The results show that the proposed HCMMP can reduce the accumulated delay and fuel consumption of vehicles compared with PI control, MPC and PIBA at low and medium CAV penetration rates.
Birds-Eye-View (BEV) perception can naturally represent natural scenes, which is conducive to multimodal data processing and fusion. BEV data contain rich semantics and integrate the information of driving scenes, which play an important role in researches related to autonomous driving. However, BEV constructed by single vehicle perception encounter certain issues, such as low accuracy and insufficient range, and thus cannot be well applied to scenario understanding and driving situation prediction. To address the challenges, this paper proposes a novel data-driven approach based on vehicle-to-everything (V2X) communication. The roadside unit or cloud center collects local BEV data from all connected and automated vehicles (CAVs) within the control area, then fuses and predicts the future global BEV occupancy grid map. It provides powerful support for driving safety warning, cooperative driving planning, cooperative traffic control and other applications. More precisely, we develop an attention-based cooperative BEV fusion and prediction model called BEV-V2X. We also compare the performance of BEV-V2X with that of single vehicle prediction. Experimental results demonstrate that our proposed method achieves higher accuracy. Even in cases where not all vehicles are CAVs, the model can still comprehensively estimate and predict global spatiotemporal changes. We also discuss the impact of the CAV rate, single vehicle perception ability, and grid size on the fusion and prediction results.
Cooperative driving has great potential to improve traffic safety and efficiency and has been widely discussed in recent years. However, most existing researches only focus on the ideal communication environment and ignore the potential vehicle communication failure, which poses serious safety threats to traffic safety. To fill this research gap, we propose three designing principles for a fault-tolerant cooperative driving model considering communication failure. We let vehicles make real-time decision adjustments according to changes in communication conditions. Fault and non-fault vehicles can execute different decision models in a distributed manner. We test with on-ramps scenarios in simulations. Experiment results demonstrate the promising performance of the proposed model in achieving fault tolerance while reducing the impacts on traffic efficiency.
The Macroscopic Fundamental Diagram (MFD) represents an increasingly established model for assessing the quality of traffic flow in networks. However, the uniqueness of an empirically estimated MFD cannot be guaranteed due to the problem of detector selection. Instationarity and varying flow patterns make it difficult to select the link flows that are representative of the traffic state in the whole network. This paper developed a new method for selecting loop detectors that represent a particular traffic state of a road network. The method relies on a metric of heterogeneity characterizing the role of a network link over the time of a day. The dispersion indicates the heterogeneity in traffic conditions and the dynamic role of each time interval. The heterogeneity-weighted saturation level of links is used to determine a ranking of links. The high-ranked links in the ranking represent the most homogenous sample of subset links. The study used the loop detector data of Zurich and London and a simulated network to compare both equal (classical) and dynamic weights (proposed) by selecting the sample links based on different saturation levels. Moreover, associating the saturation level with the heterogeneity level specified the links creating the heterogeneity in the road network primarily.
With the gradual popularization of automobiles, it has become a very important means of transportation for people, bringing a lot of convenience to people’s travel. In the field of driving, with the development of intelligent communication technology, intelligent driving vehicles “drive into” people’s field of vision. In contemporary society, intelligence is integrated into all aspects of life and is more and more inseparable from intelligence. In order to research multi-vehicle coordination-enhanced intelligent driving framework based on human–machine hybrid intelligence, from the perspective of human–machine hybrid intelligence, this paper takes multi-vehicle coordination as the starting point, introduces the fish swarm effect and builds a multi-vehicle coordinated, intelligent driving system with strong self-adaptability and scalability, so that it can adapt to more complex and real road environments.
Named-data networking (NDN) is a promising communication technology for vehicular networks because it offers mobility support and inherent en route content security. However, vanilla NDN uses pull-based communication that experiences long communication delays that make it ineligible for the cooperative driving applications requiring real-time communications, i.e., platooning. Additionally, it requires a proper content naming convention to enable content communication. Therefore, in this article, we propose a platoon-specific naming and name-based content communication scheme. The proposed protocol minimizes the communication overhead with minimum delay in platooning scenario. The simulation results show that our proposed name-based protocol offers better performance in terms of low commu-nication overhead, delay, and strongly correlated platoon dynamics compared to the vanilla NDN.
Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years. However, existing cooperative driving strategies either suffer from computational complexity or cannot guarantee global optimality. To fill this research gap, this paper proposes an optimal and computationally efficient cooperative driving strategy with the polynomial-time complexity. By modeling the conflict relations among the vehicles, the solution space of the cooperative driving problem is completely represented by a newly designed small-size state space. Then, based on dynamic programming, the globally optimal solution can be searched inside the state space efficiently. It is proved that the proposed strategy can reduce the time complexity of computation from exponential to a small-degree polynomial. Simulation results further demonstrate that the proposed strategy can obtain the globally optimal solution within a limited computation time under various traffic demand settings.
Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years. However, cooperative driving in multi-intersection road networks remains to be further investigated, because many algorithms for isolated intersection cannot be directly adopted for road networks. In this paper, we propose a distributed strategy to appropriately decompose the problem into small-scale sub-problems that address vehicle cooperation within limited temporal-spatial areas and meanwhile assure appropriate coordination between adjacent areas by specially designed information exchange. Simulation results demonstrate the efficiency-complexity balanced advantage of the proposed strategy under various traffic demand settings.
Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing road capacity, and stabilizing traffic. To materialize these widely expected potentials of AVs, a sound understanding of AVs’ impacts on traffic flow is essential. Not surprisingly, despite the relatively short history of AVs, there have been numerous studies in the literature focusing on understanding and modeling various aspects of AV-involved traffic flow and significant progresses have already been made. To understand the recent development and ultimately inspire new research ideas on this important topic, this survey systematically and comprehensively reviews the existing AV-involved traffic flow models with different levels of details, and examines the relationship among the design of AV-based driving strategies, the management of transportation systems, and the resulting traffic dynamics. The pros and cons of the existing models and approaches are critically discussed, and future research directions are also provided.
Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram (MFD) provides an efficient description of network-wide traffic performance. In this paper, we show how temporal patterns of vehicle traffic define the performance of urban road networks. We present two high-resolution traffic datasets covering a year each. We introduce a methodology to quantify the similarity of macroscopic traffic patterns. We do so by using the concepts of the MFD and a dynamic time warping (DTW) based algorithm for time series. This allows us to derive a few representative MFD clusters that capture the essential macroscopic traffic patterns. We then provide an in-depth analysis of traffic heterogeneity in the network which is indicative of the previously found clusters. Thereupon, we define a parsimonious classification approach to predict the expected MFD clusters early in the morning with high accuracy.
The advent of shared-economy and smartphones made on-demand transportation services possible, which created additional opportunities, but also more complexity to urban mobility. Companies that offer these services are called Transportation Network Companies (TNCs) due to their internet-based nature. Although ride-sourcing is the most notorious service TNCs provide, little is known about to what degree its operations can interfere in traffic conditions, while replacing other transportation modes, or when a large number of idle vehicles is cruising for passengers. We experimentally analyze the efficiency of TNCs using taxi trip data from a Chinese megacity and an agent-based simulation with a trip-based MFD model for determining the speed. We investigate the effect of expanding fleet sizes for TNCs, passengers’ inclination towards sharing rides, and strategies to alleviate urban congestion. We observe that, although a larger fleet size reduces waiting time, it also intensifies congestion, which, in turn, prolongs the total travel time. Such congestion effect is so significant that it is nearly insensitive to passengers’ willingness to share and flexible supply. Finally, parking management strategies can prevent idle vehicles from cruising without assigned passengers, mitigating the negative impacts of ride-sourcing over congestion, and improving the service quality.
Data-driven surveys show that drivers do not always choose the shortest-path for their travels. The ideas of bounded rationality have been used to model this behavior, and relax the main assumption of travel time minimization of the User Equilibrium principle. In this paper, we propose an extension of an existing dynamic traffic assignment framework, for aggregated traffic models based on the Macroscopic Fundamental Diagram and regional networks, that extends the principle of the User Equilibrium to account for bounded rational drivers. We focus on drivers with indifferent preferences, and with preferences for more reliable travel times. The network equilibrium is calculated through Monte Carlo simulations and the classical Method of Successive Averages. We first investigate how the drivers' preferences for reliable travel times influences the traffic dynamics in the regional network. We then discuss a potential application example of the proposed methodological framework for estimating the emissions of Carbon Dioxide CO2 and Monoxide NOx at the network level. The results shed light on the importance of properly accounting for more realistic drivers' behavior for estimating emissions. The main contributions of this study lie on the edge between the disciplines of traffic flow theory and network modeling, with a great potential of application for practitioners to assess traffic emissions on large metropolitan areas.
In this paper, we propose a control and optimization framework to reduce network-wide emissions in an urban traffic network. The framework is comprised of two layers. The first layer (optimal green routing) predicts the optimal splitting coefficients for all Origin–Destination (OD) pairs crossing the city center or bypassing. As implementing optimal routing strategies on the field is almost impossible as we do not have direct control over the user’s decisions, we choose to balance travel time through gating at the city perimeter so that the usual Dynamic User Equilibrium (DUE) discipline matches the optimal splitting coefficients. The second layer then adjusts inflows at the city gates to the DUE solution based on the instantaneous travel times corresponding to the optimal routing strategy for each OD pair. The accumulation-based Macroscopic Fundamental Diagram (MFD) model of a single reservoir city with seven arterial routes and six bypass alternatives is developed. A Linear programming problem is formulated to determine the optimal splitting coefficients and a Nonlinear Model Predictive Control (NMPC)-based gating control strategy is designed to track the optimal splitting coefficients. The network-wide emission control framework is compared to three other gating strategies aiming to (i) optimize traffic conditions or (ii) minimize emissions, in the inner-city only, and (iii) optimize traffic conditions in the whole network. A comprehensive analysis conducted on all four approaches is presented. We compare the results of the controlled case with respect to the uncontrolled case and with respect to each other. The comparison results show that: (i) the proposed network-wide emission control strategy significantly outperforms the other two simpler control strategies focusing on inner-city only in reducing the total emission, (ii) but also improves the in total time spent and mean speed at the network-level.
In this paper, we analyse the performance of a model predictive controller for coordination of connected, automated vehicles at intersections. The problem has combinatorial complexity, and we propose to solve it approximately by using a two stage procedure where (1) the vehicle crossing order in which the vehicles cross the intersection is found by solving a mixed integer quadratic program and (2) the control commands are subsequently found by solving a nonlinear program. We show that the controller is persistently safe and compare its performance against traffic lights and two simpler optimisation-based coordination schemes. The results show that our approach outperforms the considered alternatives in terms of both energy consumption and travel-time delay, especially for medium to high traffic loads.
Bi-modal or 3D-MFD relates the accumulation of cars, buses to total production at the network level. The current work provides a detailed discussion of extended MFD-based models namely, accumulation-based and trip-based models that accounts for bi-modal flows through 3DMFD. In addition, delay accumulation-based models, also known as exit-flow models in classical traffic flow theory, are revisited. Fundamental modeling differences between different MFDbased models are illustrated using a benchmark test case. A new FIFO-based entry flow function is also proposed in order to address the inconsistencies of the conventional entry flow function in the context of the 3D-MFD case. A novel weak internal FIFO discipline is proposed to circumnavigate the violation of internal FIFO order during network unloading in the delay accumulation based model. MFD-based models are verified using the solutions of micro-simulations performed on an idealized grid network. The importance of separating the 3D-MFD into partial car and bus 3D-MFDs is highlighted. Moreover, it is also shown that partial bus 3D-MFD should be further split when dedicated bus lanes are present in the network to account for unequal mean speeds between different bus lanes.
Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network’s critical number of vehicles and the network’s traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.
In this paper, we revisit the principle of bounded rationality applied to dynamic traffic assignment to evaluate its influences on network performance. We investigate the influence of different types of bounded rational user behavior on: (i) route flows at equilibrium; and (ii) network performance in terms of its internal, inflow and outflow capacities. We consider the implementation of a bounded rational framework based on Monte Carlo simulation. A Lighthill-Whitham-Richards (LWR) mesoscopic traffic simulator is considered to calculate time-dependent route costs that account for congestion, spillback and shock-wave effects. Network equilibrium is calculated using the Method of Successive Averages. As a benchmark, the results are compared against both Deterministic and Stochastic User Equilibrium. To model different types of bounded rational user behavior we consider two definitions of user search order (stochastic and strict preferences) and two settings of the indifference band. We also test the framework on a toy Braess network to gain insight into changes in the route flows at equilibrium for both search orders and increasing values of aspiration levels.
Connected vehicles will change the modes of future transportation management and organization, especially at intersections. In this paper, we propose a distributed conflict-free cooperation method for multiple connected vehicles at unsignalized intersections. We firstly project the approaching vehicles from different traffic movements into a virtual lane and introduce a conflict-free geometry topology considering the conflict relationship of involved vehicles, thus constructing a virtual platoon. Then we present the modeling of communication topology to describe two modes of information transmission between vehicles. Finally, a distributed controller is designed to stabilize the virtual platoon for conflict-free cooperation at intersections. Numerical simulations validate the effectiveness of this method.
We propose an urban traffic management scheme for an all connected vehicle environments. If all the vehicles are autonomous, for example in smart city projects or future's dense city centers, then such an environment does not need a physical traffic signal. Instead, an intersection control server processes data streams from approaching vehicles, periodically solves an optimization problem, and assigns to each vehicle an optimal arrival time that ensures safety while significantly reducing number of stops and intersection delays. The scheduling problem is formulated as a Mixed-Integer Linear Program (MILP), and is solved by IBM CPLEX optimization package. The optimization outputs (scheduled access/arrival times) are sent to all approaching vehicles. The autonomous vehicles adjust their speed accordingly by a proposed trajectory planning algorithm with the aim of accessing the intersection at their scheduled times. A customized traffic microsimulation environment is developed to determine the potentials of the prosed solution in comparison to two baseline scenarios. In addition, the proposed MILP-based intersection control scheme is modified and simulated for a mixed traffic consisting of autonomous and human-controlled vehicles, all connected through a wireless communication to the intersection controller of a signalized intersection.
Increasingly, commercial vehicles are equipped with automated vehicle (AV) features such as adaptive cruise control systems. The AV feature can automatically control the headway between the current vehicle and the preceding vehicle in an adaptive manner. The automatic control may lead to significantly different car- following motions compared with those of human-driven vehicles, which challenges the applicability of classic traffic flow theory to emerging road traffic with AVs. To investigate the impacts of commercial AVs on traffic flow, this paper proposes a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram (FD), i.e., the foundation for traffic flow theory for AV traffic. To demonstrate the empirical experiment settings, we collected high-resolution trajectory data with multiple commercial AVs following one another in a platoon with different headway settings. The field experiment results revealed that the traditional triangular FD structure remains applicable to describe the traffic flow characteristics of AV traffic. Further, by comparing the FDs between AVs and human-driven vehicles, it was found that although the shortest AV headway setting can significantly improve road capacity, other headway settings may decrease road capacity compared with existing human-driven-vehicle traffic. It was also found that headway settings may affect the stability of traffic flow, which has been revealed by theoretical studies but was first verified by empirical AV data. With these findings, mixed traffic flow FDs were derived by incorporating different headway settings and AV penetration rates. The method proposed in this paper, including experiment designs, data collection approaches, traffic flow characteristics analyses, and mixed traffic flow FD construction approaches, can serve as a methodological foundation for studying future mixed traffic flow features with uncertain and evolving AV technologies.
City-scale control of urban road traffic poses a challenging problem. Dynamical models based on the macroscopic fundamental diagram (MFD) enable development of model predictive perimeter control methods for large-scale urban networks, representing an advanced control solution carrying substantial potential for field implementation. In this paper we develop a multi-region approximation of the trip-based model, which describes in more details the trip length characteristics compared to existing accumulation-based models. The proposed M-model includes effects of the total remaining travel distance on the transfer flows driving the vehicle accumulation dynamics, potentially yielding improved accuracy over the standard production-over-trip length approximation of the outflow MFD considered in many works on MFD-based modeling and control. We explain that to properly perform perimeter control, boundary queue dynamics have to be integrated. Furthermore, model-based parameter estimation (MBPE), nonlinear moving horizon observer (MHO), and model predictive control (MPC) formulations for the proposed models are presented, forming an integrated traffic control framework. Microsimulation-based case studies, considering an urban network with 1500 links, where the model parameters obtained by MBPE method are used in MHO and MPC design, demonstrate the efficient operation of the proposed framework.
In urban areas, the number of cars has increased significantly in recent years, resulting in frequent traffic congestion in parking lots. Automated valet parking (AVP) system based on automated guided vehicles (AGVs) can relieve human from parking and improve efficiency to a certain extent due to their fully automatic control and operation. However, with the expansion of the scale of the whole parking lot, the current AGV based AVP system is facing the disadvantage of long-time queue congestion and even deadlock. In this paper, we systematically consider the traffic congestion faced by the AGV based AVP system and introduce a bi-level cooperative operation approach. The global cooperative parking space allocation is considered in the upper-level, and the cooperative driving of multiple AGVs in the conflict zone is resolved in the lower-level. The upper-level problem is formulated as a Markov decision process, and a global cooperative allocation method is obtained by using deep reinforcement learning (DRL). In the lower-level, with the modified planning based cooperative driving method, multiple AGVs can drive efficiently without collision and deadlock in the conflict zone. Experiment results show that the proposed cooperative operation approach can significantly alleviate the congestion problem in the AGV based parking lot and improve the AVP system’s efficiency.
The properties of cooperative driving strategies for planning and controlling Connected and Automated Vehicles (CAVs) at intersections range from some that achieve highly efficient coordination performance to others whose implementation is computationally fast. This paper comprehensively compares the performance of four representative strategies in terms of travel time, energy consumption, computation time, and fairness under different conditions, including the geometric configuration of intersections, asymmetry in traffic arrival rates, and the relative magnitude of these rates. Our simulation-based study has led to the following conclusions: 1) The Monte Carlo Tree Search (MCTS)-based strategy achieves the best traffic efficiency and has great performance in fuel consumption; 2) MCTS and Dynamic Resequencing (DR) strategies both perform well in all metrics of interest. If the computation budget is adequate, the MCTS strategy is recommended; otherwise, the DR strategy is preferable; 3) An asymmetric intersection has a noticeable impact on the strategies, whereas the influence of the arrival rates can be neglected. When the geometric shape is asymmetrical, the modified First-In-First-Out (FIFO) strategy significantly outperforms the FIFO strategy and works well when the traffic demand is moderate, but their performances are similar in other situations; and 4) Improving traffic efficiency sometimes comes at the cost of fairness, but the DR and MCTS strategies can be adjusted to realize a better trade-off between various performance metrics by appropriately designing their objective functions.
Accumulation-based Macroscopic Fundamental Diagram (MFD) model is widely employed to design perimeter control methods to improve traffic operation in urban networks. While the accumulation-based MFD assumes a low-scatter, non-linear relationship between region production and accumulation, the outflow relationship in formulating dynamics of multi-region networks requires simplifying assumptions. The existing perimeter control methods are grounded on accumulation-based MFD models where the number of transferring vehicles is approximated by the ratio of the instantaneous number of vehicles based on their destinations. Moreover, perimeter control may lead to more vehicles queuing at the region boundary (i.e. cordon queues) which add local impediments on traveling vehicles and impact the accuracy of well-defined MFDs. To address these shortcomings under time-varying conditions, this paper develops a robust perimeter control method based on the Sliding Mode Control to minimize total travel time in the entire network. To test the performance of the proposed control method, a trip-based MFD model is developed that accounts for cordon queues and various trip lengths of individual travelers. In this paper, two-region accumulation-based and trip-based MFD models are compared through numerical experiments. The results pinpoint the proposed robust perimeter control method can effectively alleviate congestion and improve network efficiency during traffic rush hours.
Since the introduction of the Macroscopic Fundamental
Diagram (MFD), many traffic control strategies and algorithms
have been developed to implement MFD-based perimeter
control over a specific urban region. A model-based controller
consists of two components: a plant model that represents reality;
and a prediction model used to determine optimal control actions.
In most studies, the authors assume a constant average trip
length for all drivers traveling within the same region, for the
prediction model. In these studies about perimeter control and
MFD traffic models, the controllers show a good performance
because accumulations, i.e. traffic states, from the plant are
used to reflect the initial state of the prediction model with a
high frequency (about a few seconds). However, this average
trip length changes over time as it depends on the Origin-
Destination flow decomposition, playing an important role in
real applications. The main contributions of this paper are
twofold. First, we show that the assumption about constant trip
lengths used in the prediction model deteriorates the controller’s
performance for low frequency updates of the optimal control
actions. Second, we propose a methodological framework based
on the Unscented Kalman Filter (UKF) for dynamically adjusting
the average trip lengths and accumulations. Our test results
on a real city network show that applying this methodological
framework significantly improves the controller’s performance.
Enabling technologies of connected and automated vehicles (CAVs) bring new opportunities to signalized intersection control. CAVs not only provide a new source of data for traffic management but also can be controlled as actuators to improve traffic flow. This study proposes a hierarchical and implementation-ready cooperative driving framework with a mixed traffic composition of CAVs, connected vehicles (CVs), and regular vehicles (RVs) for urban arterials. The proposed framework combines centralized and distributed control concepts, where the infrastructure generates optimal signal timing plans and provides high-level trajectory guidance to the CAVs while detailed trajectories are generated by each vehicle. The system consists of three levels of models. At the vehicle level, a state transition diagram is designed for different modes of operations of CAVs including eco-trajectory planning, cooperative adaptive cruise control (CACC) and collision avoidance. At the intersection level, a mixed-integer linear programming (MILP) problem is formulated to optimize the signal timing plan and arrival time of CAVs, with consideration of CACC platooning behaviors. At the corridor level, link performance functions are applied to calculate the total delay of the coordinated phases of each intersection, and a linear programming (LP) problem is formulated to optimize the offsets for every cycle, which are then passed to the intersection level. Simulation results from a calibrated real-world arterial corridor show that both mobility and fuel economy benefits from the cooperative driving framework. The total delay is reduced by 2.2%−33.0% and fuel consumption by 3.9%−7.4%, with different mixture of vehicle compositions and CAV penetration rates (e.g., 0%−100%). Sensitivity analysis on volume fluctuation is performed, which confirms the benefits of the dynamic offset optimization.
This paper studies the cooperative driving of connected and automated vehicles (CAVs) at conflict areas (e.g., non-signalized intersections and ramping regions). Due to safety concerns, most existing studies prohibit lane change since this may cause lateral collisions when coordination is not appropriately performed. However, in many traffic scenarios (e.g., work zones), vehicles must change lanes. To solve this problem, we categorize the potential collision into two kinds and thus establish a bi-level planning problem. The right-of-way of vehicles for the critical conflict zone is considered in the upper-level, and the right-of-way of vehicles during lane changes is then resolved in the lower-level. The solutions of the upper-level problem are represented in tree space, and a near-optimal solution is searched for by combining Monte Carlo Tree Search (MCTS) with some heuristic rules within a very short planning time. The proposed strategy is suitable for not only the shortest delay objective but also other objectives (e.g., energy-saving). Numerical examples show that the proposed strategy leads to good traffic performance in real-time.
Recent advances in the network-level traffic flow modelling provide an efficient tool for analyzing traffic performance of large-scale networks. A relationship between density and flow at the network level is developed and widely studied, namely the macroscopic fundamental diagram (MFD). Nevertheless, few empirical studies have been dedicated on the empirical evidence on the properties of the MFD for multiple modes of transport and to the best knowledge not yet at the scale of a megacity. This work combines rich, but incomplete data from multiple sources to investigate the vehicle and passenger MFDs for cars and buses in the road network of Shenzhen. A novel algorithm is proposed for partitioning bimodal network considering the homogeneous distribution of link-level car speeds and bus speeds.
Furthermore, this paper sheds light on the passenger MFD for mixed car-bus networks. We propose an algorithm to estimate alighting passenger flow and passenger density on bus, by fusing smart card data (i.e. records for boarding passengers) and bus GPS data. We analyze the complexities of passenger flow and the impact of weather on traffic demand and bus occupancy. The results provide an empirical knowledge on multimodal traffic performance with respect to passenger flow. The existence of double hysteresis loops in bus passenger MFD is observed and the causes are explained by considering the influence of service operational features. The three-dimensional vehicle and passenger MFDs are also presented for revealing the complex dynamics characteristics of bimodal road network.
This study models a multi-modal network with ridesharing services. The developed model reproduces the scenario where travelers with their own cars may choose to be a solo-driver, a ridesharing driver, a ridesharing rider, or a public transit passenger while travelers without their own cars can only choose to be either a ridesharing rider or a public transit passenger. The developed model can capture the (clock) time-dependent choices of travelers and the evolution of traffic conditions, i.e., the within-day traffic dynamics. In particular, the within-day traffic dynamics in a city region is modeled through an aggregate traffic representation, i.e., the Macroscopic Fundamental Diagram (MFD). This paper further develops a doubly dynamical system that examines how the within-day time-dependent travelers’ choices and traffic conditions will evolve from day to day, i.e., the day-to-day dynamics. Based on the doubly dynamical framework, this paper proposes two different congestion pricing schemes that aim to reduce network congestion and improve traffic efficiency. One scheme is to price all vehicles including both solo-driving and ridesharing vehicles (for ridesharing trips, the price is shared by the driver and rider), while the other scheme prices the solo-driving vehicles only in order to encourage ridesharing. The pricing levels (under each scheme) can be determined either through an adaptive adjustment mechanism from period to period driven by observed traffic conditions, or through solving a bi-level optimization problem. Numerical studies are conducted to illustrate the models and effectiveness of the pricing schemes. The results indicate that the emerging ridesharing platform may not necessarily reduce traffic congestion, but the proposed congestion pricing schemes can effectively reduce congestion and improve system performance. While pricing solo-driving vehicles only may encourage ridesharing, it can be less effective in reducing the overall congestion when compared to pricing both solo-driving and ridesharing vehicles.
Equity issues among travellers are critical in congestion pricing. Failure to treat equity can lead to low acceptability towards pricing. In this paper, we develop congestion pricing schemes to improve both equity and traffic performance, for multimodal networks. We consider the equity issue by the existence of heterogeneous population, with respect to income level and value-of-time (VOT). An optimization framework is formulated for obtaining optimal toll schemes. We apply an aggregated network-level traffic flow model to reproduce congestion dynamics and mode choice behavior. The gain and loss for VOT-based user groups are investigated and discussed. We carry out simulation analysis over two pricing schemes: a unified toll and a VOT-based toll. In particular, we allow during the optimization process the VOT-based toll to obtain negative values for some users if it meets some system objectives. Our results confirm that significant differences in behavior and cost savings exist among groups, which justifies the need for a VOT-based pricing. It also demonstrates that properly-designed VOT-based tolls can improve the inequity in savings, e.g. users whose VOT values are lower may receive larger travel cost savings. Furthermore, a policy-oriented discussion on the design and implementation of the proposed equitable pricing schemes is provided.
Macroscopic fundamental diagram (MFD) has been receiving increasing attention recently due to its potential to describe traffic dynamics and guide the design of traffic control schemes at the network level. Perimeter control and route guidance are two main MFD-based traffic control approaches. However, current MFD-based perimeter control seldom considers travelers’ route choice behavior, while MFD-based route guidance studies usually assume directly that travelers would follow the guidance and neglect the effects of traffic control. This paper aims to integrate the MFD-based perimeter control (i.e., the behavior of a system manager) and the dynamic user equilibrium based route choice behavior (i.e., the behavior of travelers) into one rigorous mathematical framework. Given a traffic network that has been divided into multiple homogeneous regions, we use MFD to describe the dynamics of each region, and use point queue model to capture the dynamics of queues formed at the boundaries. Besides, we model travelers' route choice behavior by the instantaneous dynamic user equilibrium (IDUE) principle, and design an efficient range perimeter control method from the system perspective. We model the interactions between the system manager and the travelers as a non-zero sum, non-cooperative differential game, where the system manager aims to improve the system performance while travelers try to minimize their own travel times. Meanwhile, they share the common constraints (i.e., MFD dynamics and point queue dynamics at boundaries). Mathematically, this leads to a differential complementarity system (DCS). We propose a time-stepping approach to discretize and solve the DCS model, based on which the solution existence and convergence are also established. Numerical results show that the proposed method can limit the vehicle accumulations within the efficient range of each region, which helps improve the network performance. Compared with the condition without perimeter control, the proposed control method can improve network-wide traffic performance up to 14.18%.
Estimation of vehicular emissions at network level is a prominent issue in transportation planning and management of urban areas. For large networks, macroscopic emission models are preferred because of their simplicity. However, these models do not consider traffic flow dynamics that significantly affect emissions production. This study proposes a network-level emission modeling framework based on the network-wide fundamental diagram (NFD), via integrating the NFD properties with an existing microscopic emission model. The NFD and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogeneous vehicle types with different emission generation rates in a network-level model. This framework is applied to the large-scale network of Chicago as well as its central business district. Non-linear and support vector regression models are developed using simulated trajectory data of 13 simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from the underlying microscopic emissions model regardless of the simulation tool that is used to calibrate the network-level emissions model. The microscopic traffic simulation is appropriate for smaller networks, while mesoscopic traffic simulation is a proper means to calibrate models for larger networks. The proposed model is also used to demonstrate the relationship between macroscopic emissions and flow characteristics in the form of a network emissions diagram. The results of this study provide a tool for planners to analyze vehicular emissions in real time and find optimal policies to control the level of emissions in large cities.
The Macroscopic Fundamental Diagram (MFD) describes the relation of average network flow, density and speed in urban networks. It can be estimated based on empirical or simulation data, or approximated analytically. Two main analytical approximation methods to derive the MFD for arterial roads and urban networks exist at the moment. These are the method of cuts (MoC) and related approaches, as well as the stochastic approximation (SA). This paper systematically evaluates these methods including their most recent advancements for the case of an urban arterial MFD. Both approaches are evaluated based on a traffic data set for a segment of an arterial in the city of Munich, Germany. This data set includes loop detector and signal data for a typical working day. It is found that the deterministic MoC finds a more accurate upper bound for the MFD for the studied case. The estimation error of the stochastic method is about three times higher than the one of the deterministic method. However, the SA outperforms the MoC in approximating the free-flow branch of the MFD. The analysis of the discrepancies between the empirical and the analytical MFDs includes an investigation of the measurement bias and an in-depth sensitivity study of signal control and public transport operation related input parameters. This study is conducted as a Monte-Carlo-Simulation based on a Latin Hypercube sampling. Interestingly, it is found that applying the MoC for a high number of feasible green-to-cycle ratios predicts the empirical MFD well. Overall, it is concluded that the availability of signal data can improve the analytical approximation of the MFD even for a highly inhomogeneous arterial.
Connected automated vehicles (CAVs) have been currently considered as promising solutions for realization of envisioned autonomous traffic management systems in the future. CAVs can achieve high desired traffic efficiency and provide safe, energy-saving, and comfortable ride experience for passengers. However, in order to practically implement such autonomous systems based on CAVs, there exist several significant challenges to be dealt with, such as coupled spatiotemporal constraints on CAVs’ trajectories at unsignalized intersections, multiple objectives for trajectory optimization in road segments, and heterogeneous decision-making behaviors of CAVs in road networks with highly dynamic traffic demand. In this paper, we propose a cooperative autonomous traffic organization method for CAVs in multi-intersection road networks. The methodological framework consists of threefold components: an autonomous crossing strategy based on a conflict resolution approach at unsignalized intersections, multi-objective trajectory optimization in road segments, and a composite strategy for route planning considering heterogeneous decision-making behaviors of CAVs based on social and individual benefit, respectively. Specifically, we first identify a set of potential conflict points of different CAVs’ spatial trajectories at the intersection, and then design different minimum safe time headways to resolve conflicts. Under the constraints of entry and exit conditions at adjacent intersections, we propose a multi-objective optimal control model by jointly considering vehicle safety, energy conservation, and ride comfort, and then analytically derive a closed-form solution for optimizing the CAVs’ trajectories. Furthermore, with the purpose to adapt dynamic traffic demand, we propose a composite strategy for route planning by coordinating heterogeneous decision-making behaviors of CAVs in road networks. Finally, extensive simulation experiments have been performed to validate our proposed method and to demonstrate its advantage over conventional baseline schemes in terms of global traffic efficiency. Additional numerical results are also provided to shed light on the impact of the proportion of CAVs with heterogeneous decision-making behaviors on the global system performance.
In urban road networks, the interactions between different modes can clearly impact the overall travel production. Although those interactions can be quantified with the multi-modal macroscopic fundamental diagram; so far, no functional form exists for this diagram to explicitly capture operational and network properties. In this paper, we propose a methodology to generate such functional form, and we show its applicability to the specific case of a bi-modal network with buses and cars. The proposed functional form has two components. First, a three dimensional lower envelope limits travel production to the theoretical best-case situation for any given number of vehicles for the different modes. The lower envelope’s parameters are derived from topology and operational features of the road network. Second, a smoothing parameter quantifies how interactions between all vehicle types reduce travel production from the theoretical best-case. The smoothing parameter is estimated with network topology and traffic data. In the case no traffic data is available, our functional form is still applicable. The lower envelope can be approximated assuming fundamental parameters of traffic operations. For the smoothing parameter, we show that it always hold similar values even for different networks, making its approximation also possible. This feature of the proposed functional form is an advantage compared to curve fitting, as it provides a reasonable shape for the multi-modal macroscopic fundamental diagram irrespective of traffic data availability. The methodology is illustrated and validated using simulation and empirical data sets from London and Zurich.
Cooperative driving emerges as a promising way to improve efficiency and safety for Connected and Automated Vehicles (CAVs). Its key idea is to design a strategy to schedule the movements of neighboring vehicles. The typical cooperative driving strategies can be categorized into two categories. The first category is the optimal strategy, which aims to find the globally optimal passing order of vehicles, but the computational cost of this strategy grows significantly with the increasing number of vehicles. The second category is sub-optimal strategy, which uses heuristic rules or other methods to export an acceptable local optimal solution within a limited computation time. However, there usually lacks a rigorous theoretical guarantee of the performances, and further validation is always required for practical applications. To overcome all these limitations, a computationally efficient strategy is proposed to obtain the globally optimal passing order based on dynamic programming (DP). Specifically, the problem of merging at on-ramps is resolved by a DP method, which uses the domain knowledge to reduce the complexity by well defining the state space, state transition, and criterion function. With the DP method, it is proved that the globally optimal passing order can be obtained with the quadratic polynomial computational complexity of O(N2), where N denotes the number of vehicles. Simulation results demonstrate the performances of the proposed strategy regarding optimality and efficiency.
Perimeter control schemes proposed to alleviate congestion in large-scale urban networks usually assume perfect knowledge of the accumulation state together with current and future inflow demands, requiring information about the origins and destinations (OD) of drivers. Such assumptions are problematic for practice due to: (i) Measurement noise, (ii) difficulty of measuring OD-based accumulation states and inflow demands. To address these, we propose a nonlinear moving horizon estimation (MHE) scheme for large-scale urban road networks with dynamics described via macroscopic fundamental diagram. Furthermore, we consider various measurement configurations likely to be encountered in practice, such as measurements on regional accumulations and transfer flows without OD information, and provide results of their observability tests. Simulation studies, considering joint operation of the MHE with a model predictive perimeter control scheme, indicate substantial potential towards practical implementation of MFD-based perimeter control.
Bimodal urban networks are complex systems operating within multiple constraints. This paper develops an integrated and systematic framework for the optimization of bimodal urban networks using 3D-MFDs, considering the complexities of bimodality. With the proposed framework, effective strategies can be designed for the planning, management, and control of bimodal networks. In particular, strategies to provide public transport priority on the network level can be holistically evaluated. We apply this methodological framework to propose, model, and analyze one such strategy to provide public transport priority in the perimeter of urban networks. The proposed strategy addresses a pressing problem of the existing perimeter control (i.e. gating) schemes: public transport vehicles will be queuing with the cars in the perimeter and hence blocked from entering the network. This impairs the service quality of public transport. Adopting our proposed strategy, the inflows of public transport and cars can be regulated independently (i.e. both inflows are controllable), the network traffic can be managed more efficiently, and public transport priority can be provided. The performance of the proposed strategy is evaluated both analytically and with simulations. Results show that the proposed strategy always performs better than existing perimeter control schemes in terms of passenger mobility. Most importantly, it differentiates the public transport mode and the car mode, with much smaller queueing time outside the network for public transport. This can shift the transportation system to a more sustainable state in the long run. Policy recommendations are provided for a large range of traffic scenarios.
In this paper, we propose a new cooperative driving strategy for connected and automated vehicles (CAVs) at unsignalized intersections. Based on the tree representation of the solution space for the passing order, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal passing order (leaf node) within a very short planning time. Testing results show that this new strategy can keep a good tradeoff between performance and computation flexibility.
In this paper, we propose a regional dynamic traffic assignment framework for macroscopic fundamental diagram (MFD) models that explicitly accounts for trip length distributions. The proposed framework considers stochasticity on both the trip lengths and the regional mean speed. Consequently, we can define utility functions to assess the cost on alternatives, depending on which terms are considered stochastic. We propose a numerical resolution scheme based on Monte Carlo simulations and use the method of successive averages to solve the network equilibrium. Based on our test scenarios, we show that the variability of trip lengths inside the regions cannot be neglected. Moreover, it is also important to consider the stochasticity on the regional mean speeds to account for correlation between regional paths. We also discuss an implementation of the proposed dynamic traffic assignment framework on the sixth district of the Lyon network, where trip lengths are explicitly calculated. The traffic states are modeled by considering the accumulation-based MFD model. The results highlight the influence of the variability of trip lengths on the predicted traffic states.
This paper presents a concept, herein called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC), for organizing directionally unrestricted traffic flows in automated vehicle environment. In the proposed concept, vehicles can use lanes traditionally reserved for the opposite direction of travel. This concept allows left and right turning vehicles to align themselves in an appropriate lane before reaching the downstream intersection, so that they can smoothly go through the intersection without having any conflicts with vehicles from the other movements. Conflicts between through movements are handled by a reservation-based algorithm. To simulate the proposed concept, i.e. allow flexibility of such driving maneuvers that cannot be accomplished in the other existing simulation tools, a new microsimulation platform is
developed. The proposed CADLARIC control scheme is evaluated through a comparison with a conventional fixed-time signal control and Fully Reservation-Based Intersection Control (FRIC). The results show that CADLARIC: (i) significantly outperforms other control methods in terms of the traffic performance (i.e. delays and stops); and (ii) generates a reduction of conflicting situations at the network level when compared to FRIC.
Real-time control of large-scale urban networks has been attracting significant research attention. This paper, using the information provided by connected vehicles, proposes a novel control approach based on the concept of perimeter control to maximize the social welfare of all vehicles. The contributions of this paper are threefold. First, we consider vehicle heterogeneity (i.e. corresponding to different transportation modes, or with different occupancies, values of time, priority levels, etc.) and integrate a priority scheme into perimeter control to improve both the traffic performance and the social welfare. This is achieved by installing priority lanes at some of the perimeter intersections. Unlike the existing research works that provide priority to certain traffic modes, we dynamically identify the groups of vehicles that we should prioritize, in order to maximize the social welfare. Second, we develop a model predictive control approach to simultaneously optimize the toll for using the priority lanes and the traffic signal timings at the perimeter intersections. This approach can explicitly handle the constraint of the storage capacity of each intersection link. Third, we propose a recursive estimation algorithm to update our knowledge on the distribution of the value of times (VOTs), using the lane choice information of connected vehicles. The proposed approach is tested in a simulated network which resembles the main features of the city center of Zurich, Switzerland. By using the proposed strategy, the traffic accumulation inside the network is still stabilized, and the monetary costs due to delay are significantly reduced at the entire network (up to 25.8%) compared to the strategy without priority. The distribution of the combined cost (including cost due to delay and tolls) is more uniform across VOT groups than that resulting from the strategy without priority. It is also shown that the proposed recursive estimation algorithm quickly converges and further improves the social welfare.
Conventional intersection managements, such as signalized intersections, may not necessarily be the optimal strategies when it comes to connected and automated vehicles (CAVs) environment. Autonomous intersection management (AIM) is tailored for CAVs aiming at replacing the conventional traffic control strategies. In this work, using the communication and computation technologies of CAVs, the sequential movements of vehicles through intersections are modelled as multi-agent Markov decision processes (MAMDPs) in which vehicle agents cooperate to minimize intersection delay with collision-free constraints. To handle the huge dimension scale incurred by the nature of multi-agent decision making problems, the state space of CAVs are decomposed into independent part and coordinated part by exploiting the structural properties of the AIM problem, and a decentralized coordination multi-agent learning approach (DCL-AIM) is proposed to solve the problem efficiently by exploiting both global and localized agent coordination needs in AIM. The main feature of the proposed approach is to explicitly identify and dynamically adapt agent coordination needs during the learning process so that the curse of dimensionality and environment nonstationarity problems in multi-agent learning can be alleviated.
The effectiveness of the proposed method is demonstrated under a variety of traffic conditions. The comparison analysis is performed between DCL-AIM and the First-Come-First-Serve based AIM (FCFS-AIM), with Longest-Queue-First (LQF-AIM) policy and the signal control based on the Webster’s method (Signal) as benchmarks. Experimental results show that the sequential decisions from DCL-AIM outperform the other control policies.
The perimeter control concept based on macroscopic fundamental diagram (MFD) modeling is now well-established in various literature. Recent research efforts are devoted to develop perimeter control schemes, which can be eventually applied to urban traffic control systems. Modern urban traffic control systems increasingly rely on information technology infrastructure, combining various network and physical facilities and use of communication technologies, which increases the vulnerability for cyberattacks. As witnessed in recent real-life events, modern urban traffic control systems are vulnerable and not protected from internal and external, malicious and accidental threats. Hence, applicable perimeter control algorithms should be robust not only against dynamic uncertainties, but in addition, it should be resilient against cyberattack issues for real future implementations.
In this paper, a resilient perimeter control scheme is developed for a modified multi-region MFD system model. The developed perimeter control is a fully decentralized scheme, which operates based on online local regional information only. It achieves an a priori desired tracking performance with simultaneous compensation of (i) cyberattacks in the channels of control signals transmission, (ii) model parameter uncertainties, (iii) unknown but bounded disturbances, and (iv) control input constraints. A framework of adaptive control techniques is utilized to design the perimeter control. The proposed fault-tolerant perimeter control does not require on-line fault detection, as the fault effects caused by cyberattacks are continuously and adaptively compensated by the developed algorithm. Stability proof of the closed-loop system in terms of the tracking and parameter errors are introduced, and simulation results for three aggregate urban traffic regions are presented.
Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.
This paper studies a trajectory-based traffic management (TTM) problem for the purpose of managing traffic in a road facility reserved exclusively for autonomous vehicles (AV). The base TTM model aims to find optimal trajectories for multiple AVs while resolving inter-vehicle conflicts in the most generic way. The model is formulated as a mixed in- teger program (MIP) that can be solved using off-the-shelf solvers. To improve compu- tational efficiency, a specialized algorithm based on the rolling horizon approach is also developed. We then show that the base TTM model can be easily extended to first accom- modate scheduling decisions (the TTMS model) and to further impose equity constraints (the TTMSE model). For the simplest network and homogeneous users, solutions to TTMS and TTMSE are similar, respectively, to system optimal (SO) and user equilibrium (UE) so- lutions of Vickrey’s bottleneck model. Numerical experiments highlight TTM’s ability to simultaneously generate optimal trajectories for multiple vehicles. They also show that, while solving TTM exactly is computationally demanding, obtaining good approximate so- lutions can be accomplished efficiently by the rolling horizon algorithm.
In view of the advantages and a promising market prospect of the emerging connected automated vehicles (CAVs), the number of CAVs will keep increasing rapidly in the coming decade. Meanwhile, the regular human-piloted vehicles (RHVs) may still play a significant role in the roadway traffic. Therefore, it will be very likely that the roadway is shared by CAVs and RHVs in the near future. To support traffic control design, this paper develops a multiclass multilane cell transmission model (CTM) to simulate traffic flow dynamics mixed with CAVs and RHVs by capturing the interaction between the two vehicle classes. First, headway distributions and variations in the fundamental diagram with respect to different penetration rates of CAVs are quantified. Then, the minimum headway acceptance criteria are determined for the lane changing (LC) maneuvers proposed by CAVs and RHVs with consideration on drivers’ anticipation. An advanced priority incremental transfer (PIT) principle is then proposed to evaluate the sending flows. Finally, the cell-lane-specific multiclass flow conservation law is developed to propagate traffic flow and density considering the vehicle LC maneuvers. Numerical simulations explore the potential operational capacity increase, delay reduction, and traffic flow smoothing under several penetration scenarios.
The macroscopic fundamental diagram (MFD) relates vehicle accumulation and production of travel in an urban network with a well-defined and reproducible curve. Thanks to this relationship, the MFD offers a wide range of applications, most notably for traffic control. Recently, more and more empirical MFDs have been documented, providing further insights and facilitating their application in real urban networks. So far, however, no generally accepted functional form has been identified. This paper proposes a functional form for the MFD that is based on the smooth approximation of an upper bound of technologically feasible traffic states (uMFD). In this functional form, the uMFD can be either estimated from MFD measurements or defined a-priori, either analytically or with additional measurements in the network, while the smoothing to the uMFD is quantified with a single parameter λ. The uMFD can in principle be any multi-regime function, but we find that a trapezoidal shape with only four parameters, all physically meaningful, models the familiar shape of the MFD very well as shown with empirical data from Marseille, London, Lucerne, Yokohama, and Zurich. Further, we point to novel applications and analyses based on the interpretation of λ that would otherwise not be possible without this new functional form.
In this paper, we discuss how to develop an appropriate collision avoidance strategy for car-following. This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position speed perception measurement of vehicles and other drivers. Both theoretical analysis and numerical testing results are provided to show the effectiveness of the proposed strategy.
Given the efficiency and equity concerns of a cordon toll, this paper proposes a few alternative distance-dependent area-based pricing models for a large-scale dynamic traffic network. We use the Network Fundamental Diagram (NFD) to monitor the network traffic state over time and consider different trip lengths in the toll calculation. The first model is a distance toll that is linearly related to the distance traveled within the cordon. The second model is an improved joint distance and time toll (JDTT) whereby users are charged jointly in proportion to the distance traveled and time spent within the cordon. The third model is a further improved joint distance and delay toll (JDDT) which replaces the time toll in the JDTT with a delay toll component. To solve the optimal toll level problem, we develop a simulation-based optimization (SBO) framework. Specifically, we propose a simultaneous approach and a sequential approach, respectively, based on the proportional-integral (PI) feedback controller to iteratively adjust the JDTT and JDDT, and use a calibrated large-scale simulation-based dynamic traffic assignment (DTA) model of Melbourne, Australia to evaluate the network performance under different pricing scenarios. While the framework is developed for static pricing, we show that it can be easily extended to solve time-dependent pricing by using multiple PI controllers. Results show that although the distance toll keeps the network from entering the congested regime of the NFD, it naturally drives users into the shortest paths within the cordon resulting in an uneven distribution of congestion. This is reflected by a large clockwise hysteresis loop in the NFD. In contrast, both the JDTT and JDDT reduce the size of the hysteresis loop while achieving the same control objective.
Connected autonomous vehicles are considered as mitigators of issues such as traffic congestion, road safety, inefficient fuel consumption, and pollutant emissions that current road transportation system suffers from. Connected Autonomous vehicles utilises communication systems to enhance the performance of autonomous vehicles and consequently improve transportation by enabling cooperative functionalities, namely, cooperative sensing and cooperative manoeuvring. The former refers to the ability to share and fuse information gathered from vehicle sensors and road infrastructures to create a better understanding of the surrounding environment while the latter enables groups of vehicles to drive in a co-ordinated way which ultimately results in a safer and more efficient driving environment. However, there is a gap in understanding how and to what extent connectivity can contribute in improving the efficiency, safety and performance of autonomous vehicles. Therefore, the aim of this paper is to investigate the potential benefits that can be achieved from connected autonomous vehicles through analysing five use-cases: (i) intersection management, (ii) energy management, (iii) lane changing (iv) vehicle platooning, and (v) road friction estimation. The current paper highlights that although connectivity can enhance the performance of autonomous vehicles and contribute to improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, the number of autonomous vehicles, traffic scenarios, and the way of augmenting off-board information into vehicle control systems.
We address the problem of optimally controlling connected and automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling, so as to minimize energy consumption subject to a throughput maximization requirement. We show that the solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization. We present a complete analytical solution of these decentralized problems and derive conditions under which feasible solutions satisfying all safety constraints always exist. The effectiveness of the proposed solution is illustrated through simulation which shows substantial dual benefits of the proposed decentralized framework by allowing CAVs to conserve momentum and fuel while also improving travel time.