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Traffic congestion has been a challenging problem in urban areas during rush hours. Transit priority (especially transit signal priority) strategy can provide smooth flow for decreasing travel times, stops, and delay. However, on urban arterials, the performance of transit signal priority scenario along arterial corridors of urban traffic network i...
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Citations
... Li et al. [45] proposed an adaptive TSP optimization model that optimizes the green time splitting for three consecutive cycles to minimize the weighted sum of bus delays and other traffic delays, taking into account safety and other optimization constraints under the double-loop structure of signal control. Li et al. [46] simulated the physical queuing phenomenon of vehicles by considering a dynamic traffic model with bus and car interactions and proposed an adaptive transit priority signal optimization setting method. Moghimi et al. [47] proposed an adaptive signal control system with selforganizing logic based on exclusive bus lanes, which reduced bus delays and improved bus operation reliability. ...
Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.
... Delay is the most commonly used performance index in many of the signal priority strategies. Researchers used minimization of vehicle delay [11,[19][20][21][22][23] and minimization of person delay [13,[24][25][26][27][28][29][30] as objective functions. Chow et al. [31] used minimization of schedule and headway deviation as the objective function. ...
Bus signal priority (BSP) is a traffic management measure which enhances the efficiency and service reliability of the buses. An efficient BSP helps in improving the bus travel time and reduces its delay at the signalized intersection while at the same time does not have an adverse impact on the overall network performance. This paper aims to provide a comprehensive review of the bus signal priority process by presenting the priority selection criteria, which discusses the different types of priority control strategy i.e. active priority and passive priority used and the criteria using which the priority is granted. It also gives an insight into the system architecture required for BSP implementation. The paper further discusses the model formulations for different priority control strategies focussing on the input data required, objective functions and constraints used in the formulation. Finally, the different methods used for BSP model evaluation in the literature along with the various performance indices used are presented.
... Liu and Qiu [35] minimized the weighted sum of the signal control delay and transit vehicle delay through trade-off optimizations. Li and Jin [36] adopted a simulation method with the objective of reducing both the total passenger travel delay and passenger waiting time at downstream bus stops within a network level. To date, multiobjective optimization is an appropriate method to deal with the trade-off between transit and private vehicles in TSP problems. ...
Bus rapid transit (BRT) as a valid means of public transportation for overpopulated country of China is an especial mode of travel to relieve traffic congestion. Despite utilizing exclusive bus line, BRT constantly experiences delay at intersections when encountering a red signal. Transit signal priority (TSP) considered as a promising control strategy to improve the operation efficiency of BRT has been widely used at isolated intersections or arterials to reduce travel delay. However, as a transport with schedule, the unexpected arrival time of transit vehicle at stop lines of consecutive intersections inevitably affects the operation reliability of the BRT system. To solve the problem, this study develops an optimization model of conditional TSP for bus rapid transit with the objective of improving the on-time performance of the BRT system while mitigating adverse impacts on private vehicles. A stop-to-stop segment is established with both road sections and intersections for the consideration of the transit operation reliability. The constraints on the degree of saturation and queue overflow were considered for mitigating adverse impacts of TSP. A mixed-integer non-linear programming procedure is adopted to formulate the optimization model. The mathematical model is linearized and solved by the branch-and-bound method. Extensive numerical analyses are conducted, and the proposed conditional TSP strategy is compared with unconditional TSP and no-TSP control to evaluate its performance under various traffic and signal conditions. A case study of a BRT line in Shanghai, China, was selected to illustrate the effectiveness of the proposed model. For the tested scenarios, the transit on-time performance was improved by 21%, with an incurred cost of 3.4% increase in the delay of private vehicles. This indicates that the proposed model performs well in maintaining the reliability of the transit system with the least impact on general traffic.
... Wilbur Smith's publication in 1968 [13] is often referred to as one of the first publications on transit signal priority, e.g. [1,9]. Since then the technology has been developed and is practically in use [2,12]. ...
... The static traffic assignment procedure is ued to describe the traffic flow. There are also some researchers (Li and Jin, 2017;Zhang et al., 2018) assume that the traffic flow can be obtained accurately to study the MPC. ...
Accurately modeling signalized intersections is of great significance for improving traffic signal control. It is challenging to capture Congestion Propagation among different facilities of the intersection in Dynamic Stochastic traffic Environments (CPDSE), especially in the case of oversaturated conditions. Most model-based predictive control methods for traffic signal ignore the aforementioned CPDSE. In this paper, from a mesoscopic level, we propose a M_t/ G(x)/C/C feedback fluid queueing network model for the first time to consider the CPDSE where the random traffic demand and the transition probability are time-varying, and the stochastic service ability is state-dependent. We prove that the signalized intersection feedback queuing network with N facilities can be decomposed into N feedback queues. A recursive algorithm is developed to solve the feedback queuing network model. The algorithm's computational complexity is only a linear function of the number of time slices when the number of facilities is given. Simulation experiments show that the proposed model and algorithm perform well regardless of variations in traffic intensity. Compared with the mean result of the 200 simulations, the average absolute error is 0.5152 vehicles, and the average relative error is 6.43% under three demand scenarios. Based on the proposed feedback queueing network model, two optimization frameworks are developed for traffic signal control by minimizing the vehicle's average delay time or total costs including fuel consumption. A rolling optimization strategy that embeds the mesh adaptive direct search algorithm is proposed to realize the real-time traffic signal control. Numerical experiments based on the actual survey data in Kunshan City reveal some interesting findings: (1) There exists an moderate-sized time step for rolling optimization to minimize the average delay time or the total costs. That is, too small time step of rolling optimization may increase the vehicle’s average delay time or total costs; (2) The percentage of delay reduced by our method compared with the Synchro software reaches the maximum (around 70%) when the traffic demand (initial state) is moderate and the initial state (traffic demand) is low; and (3) The percentage of average delay or total costs reduced by our method compared with Synchro climbs up and then declines with the traffic intensity.
... This can be done by adjusting the offsets of green times based on distance between intersections and speed on the section. Studies have shown that signal coordination results in 10 to 30% reduction in travel time [25][26][27][28]. For present study, firstly, timings of all the intersections were optimized according to current traffic volumes. ...
Every year, large scale migration to urban metropolitans in Pakistan is causing uncontrolled urban sprawl which is responsible for huge social costs incurred in terms of traffic congestion and higher housing prices in these metropolitans. Due to rapid increase in motorization, current transport infrastructure of cities, such as Rawalpindi, is insufficient to meet the ever-increasing traffic demand. This study aims to identify prevailing traffic congestion problems and signals' coordination issues on 2.7 km section of highway (from Qasim Market Intersection to Pearl Continental Intersection) near Rawalpindi Saddar through advanced traffic simulation setup. Different cost-effective infrastructure interventions are proposed and compared in terms of potential for improvement (PI) through various attributes such as; travel time, fuel consumption, operation and maintenance costs etc. Simulation results for proposed infrastructure interventions proved their adequacy by reducing travel time, vehicle operating costs and environmental benefits. However, providing signal coordination was shown to be an effective solution, than infrastructure interventions, in terms of cost and time of project.
... • priority directions [15,20]. ...
We have analysed the signal coordination along the segment of the arterial Todor Aleksandrov - Tzaritza Ioanna boulevards, from Ploshtad Nezavisimost to the intersection with Pancho Vladigerov boulevard. We have found out that two groups of intersections in it are coordinated while the other intersections are not. As a result, the whole arterial is not coordinated. Using Green Traffic Software (GTS) we’ve evaluated the efficiency of the current timing plan and developed a more effective plan. It provides a lower average number of vehicle stops at the intersections and reduces the total engine emissions, the latter being of particular interest in Sofia. Unlike the current plan, it produces a non-zero bandwidth along the whole arterial, in the priority direction. We limit ourselves with CO2 emissions though GTS can be used for any other sort of emissions.
... Wu et al. proposed to optimize the holding time at bus stops, signal timings, and bus speed in order to minimize bus delay so that buses can pass through signalized intersections without stopping [4]. Li and Jin regarded intersection and the downstream bus stop as a control unit and established an optimization model of bus priority green signal duration considering passenger delay at intersection and bus stops [5]. Considering the influence of bus priority strategy on nonpriority phase, Wang et al. established a bi-level programming model with the upper-level model aiming at minimizing the vehicle delay in the nonpriority direction and the lower-level model aiming at minimizing the average passenger delay in the entire intersection [6]. ...
In this paper, a bus priority signal control (BPSC) method based on delays of passengers and pedestrians at adjacent intersections, is proposed. The influences of BPSC on passenger and pedestrian delay at adjacent intersections under the condition of coordinated control of green waves are studied. The implementation of BPSC at intersections not only reduces the delay of bus passengers, social vehicle passengers and pedestrians, but also improves the traffic flow of priority buses and social vehicles at downstream intersections. This study takes the green phase extension as an example of the active BPSC strategy, and analyzes three cases of priority vehicles reaching downstream intersection. Firstly, passenger and pedestrian delays at adjacent intersections are calculated under different traffic situations. Secondly, models with the goal of maximizing the reduced total delays are established. Thirdly, three algorithms are used to solve the problem to obtain the optimal signal timing adjustment scheme at upstream intersections. Ultimately, the result shows that the BPSC can effectively reduce pedestrian delays at intersections, protect the rights and interests of pedestrians, reduce the delays of priority vehicles, and maximize the reduced total delay.
... In actual applications, the arterial coordination problem should also be considered, where the background signal time plan includes a coordinated phase (CP) for the green wave control [25] or queue length inhibition strategy [26]. Recently, the research on TSP concerning the coordination problem mainly focuses on the transit green-wave offset optimization when the coordinated phase is the transit phase (TP) itself [13], [16], [27]- [29]. In this scenario, the coordination effect is consistent with the priority effect and there is less restrictions on phase time adjustment process. ...
Transit signal priority (TSP) is a typical strategy used to reduce the delay in public transit and it has been used extensively to improve the transit operations in urban traffic systems. This paper proposes a transit signal priority controlling method considering both the non-transit traffic benefits and the coordinated phase states for multi-ring time plan. The integer linear programming method is used to build the green time extension and red time truncation models. In the proposed model, the optimization object includes two levels: the maximum priority effect for the first level and minimum time deviations for non-transit phases for the second level. Based on this, the semi-ring structure and the backward/forward migration time parameters of coordinated phases are further presented for establishing the constraints. The overall scheme is thoroughly tested and demonstrated in a realistic experimental scenario. Compared with the TSP scheme which takes the coordinated phase as the deadline for adjusting phases, the proposed method can maximize the priority effect by taking full advantage of the migration state of coordinated phases. Moreover, it also reduces the effects to non-transit phases by compensation and traffic-state-based compression mechanisms.
... And a bandwidth approach to arterial signal optimisation with transit priority is proposed, which can generate green bands for both bus systems and general vehicles with the same timing plan [21]. Moreover, the arterial coordination of TSP by regarding the transit phase (TP) as the coordinated phase (CP), which can optimize TSP control easily because of the method has fewer restrictions on phase time adjustment process is analyzed [22][23][24][25]. But the troubles will appear when the CP differs from TP. ...
... N is the number of phases between P.1 and P'.1.(3) The P.'1 in the next NC is extended and the phase time should be less than the maximum green time, total length of the CC should not be changed and the constraint is presented by(24). ...
Real-time transit signal priority (TSP) control is affected by coordination phase and deprive non-transit traffic benefits. In this paper, fully considering the migration states of coordinated phases and queuing states of non-transit vehicles, a transit signal priority controlling method is proposed for the single-ring sequential phasing under the connected vehicle (CV) environment. The queue length of the non-transit phase is estimated using real-time parking position of the probe vehicles in CV environment, and then the compression capability of phase can be confirmed. Under the premise of not destroying the green wave band of the intersection group, three models are proposed, including the green time extension, red time truncation and phase insertion models. In the proposed model, the maximum priority effect and the minimum time deviations for non-transit phase are taken as the optimization object. The maximum/minimum green time, total cycle length and the backward/forward migration time of coordinated phases are taken as the constraints. Through the analysis of experimental results with examples, the transit priority control model proposed in this paper effectively improves the transit priority efficiency, and minimizes the impact on the non-transit traffic benefits while considering the coordinated phase state and not destroying the coordination effect.
