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A game theory approach to measuring the performance reliability of transport networks
Abstract
Establishing the performance reliability of a transport network is an important practical problem for engineers and planners involved in network design. Methods proposed hitherto have assumed knowledge of link performance frequency distributions (usually delay, travel time or capacity distributions), information that is in many cases absent. In this paper, a two-player non-cooperative game is envisaged between on the one hand the network user seeking a path to minimise the expected trip cost and on the other hand an "evil entity" choosing link performance scenarios to maximise the expected trip cost. At the Nash mixed strategy equilibrium, the user is unable to reduce the expected trip cost by changing his path choice probabilities while the evil entity is unable to increase the expected trip cost by changing the scenario probabilities, without cooperating. The Nash equilibrium measures network performance when users are extremely pessimistic about the state of the network and may therefore be used as a basis for a cautious approach to network design.
... The source of uncertainty in game theory is the stochastic traffic supplies that are the result of link failures. In particular, compared to the route choice criterion used in traditional user equilibrium or stochastic user equilibrium, the route choice criterion in game theory assumes that the demons select links that will cause the maximum damage to travelers, while travelers accordingly seek the best routes to avoid link failures (e.g., Bell, 2000;Bell and Cassir, 2002;Szeto et al., 2006;Szeto, 2011). The resulting trip cost obtained by the game theory model is used as the measure to evaluate the network performance reliability. ...
... The procedure of determining the optimal reliable path corresponds to the reliable path finding problem in the literature, which is an active research problem conducted by many researchers from different aspects. For example, Miller-Hooks and her colleagues propose several efficient procedures for finding the reliable paths with the least expected time as reliability measure in stochastic and time-varying networks (Miller-Hooks and Mahmassani, 1998, 2000Miller-Hooks, 2001;Opasanon and Miller-Hooks, 2006). Shahabi et al. (2013Shahabi et al. ( , 2015 discuss the robustness of the reliable path finding problem and design solution algorithms for this problem. ...
... Game theory-based measure Bell (2000); Bell and Cassir (2002); Szeto et al. (2006) Demons select links to cause the maximum damage to travelers, while travelers seek the best routes to avoid link failures. 48 There are also some risk measures in the route choice criteria that do not fall into the above two types. ...
The unavoidable travel time variability in transportation networks, resulted from the widespread supply side and demand side uncertainties, makes travel time reliability (TTR) be a common and core interest of all of the stakeholders in transportation systems, including planners, travelers, service providers, and managers. This common and core interest stimulates extensive studies on modeling TTR. Researchers have developed a range of theories and models of TTR, many of which have been incorporated into transportation models, transport policies, and project appraisals. Adopting the network perspective, this paper aims to provide an integrated framework for summarizing the methodological developments of modeling TTR in transportation networks, including its characterization, evaluation and valuation, and traffic assignment. Specifically, the TTR characterization provides a whole picture of travel time distribution in transportation networks. TTR evaluation and TTR valuation (known as the value of reliability, VOR) interpret abstract characterized TTR in a simple and intuitive way to be well understood by different stakeholders of transportation systems. Lastly TTR-based traffic assignment investigates the effects of TTR on the individual users travel behavior and consequently the collective network flow pattern. As the above three topics are mainly separately studied in different disciplines and research areas, the integrated framework allows us to better understand their relationships and may contribute to developing possible combinations of TTR modeling philosophy. Also, the network perspective enables to focus on common challenges of modeling TTR, especially the uncertainty propagation from the uncertainty sources to the TTR at spatial levels including link, route, and the entire network. Some potential directions for future research are discussed in the era of new data environment, applications, and emerging technologies.
... It is common to define connectivity as the existence of at least one path that connects each pair of vertices (Garrison and Marble, 1965). On the other hand, a network is said to be disconnected if it becomes impossible to reach a given destination departing from a given origin (Bell, 2000). According to Scott et al. (2006), connectivity depends solely on the structure of the network, and it is independent of its dynamic features (e.g.traffic demand, capacity, or congestion). ...
... Reliability is defined by Bell (2000) as the ability of a transport network to keep acceptable travel costs under pessimistic conditions. Also, reliability refers to the network's capability to cope with travel demand in case of disruptions (Jaller, González-Calderón, Yushimito, & Sánchez-Díaz, 2015;Luathep, Sumalee, Ho, & Kurauchi, 2011;Sharov & Mikhailov, 2017;Taylor and D'este, 2003;Xi-qiao et al., 2009). ...
... Reliability is a relevant factor in the selection of the preferred route, mode or schedule choice, since when a threshold is not reached within the regular route/mode, users starts searching for alternative means to complete their trips (Susilawati et al., 2013). Bell (2000) and Al-Deek and Emam (2006) have pointed out that the complexity of reliability is that it involves both the network infrastructure and the behavioral response of the users. ...
The proper functioning of road networks is fundamental for an adequate development of daily socio-economic activities and for the sustainability of modern communities. Road network performance (RNP) has received increased attention from researchers and practitioners during the past three decades. The paper provides insights regarding common elements and possible overlapping among the definitions of eleven RNP concepts (connectivity, redundancy, accessibility, reliability, connectivity reliability, travel time reliability, capacity reliability, flexibility, robustness, vulnerability, and resilience). For doing so, the authors develop a classification scheme that maps possible relationships and boundaries between them. Finally, the paper gives a discussion and puts forward promising avenues of future research.
... As noted in Sect. 1 above, connectivity has been regarded as one of the determinants when assessing the reliability of transport networks. The relationship between reliability and connectivity can be derived from the concept of the network, which is concerned with the probability of components in the network being connected (Bell 2000). The literature reviewed in Sects. ...
... From the perspective of supply chain management, the term connectivity reliability is defined as the collaboration of partners upstream and downstream in the supply chain (Fawcett et al. 2007), relating to information sharing among stakeholders and the interactions among firms. The measurement of connectivity reliability involves both the infrastructure and the behavioural responses of the users (Bell 2000). The impact of disruptions depends on how well the stakeholders can adapt. ...
Improving reliability is increasingly regarded as an important topic in maritime transportation, especially given the significant impact that both uncertainty and delays in shipping and at ports have on the efficient flow of freight along wider supply chains. The term ‘reliability’ appears in different academic fields and with a variety of different meanings and interpretations. In transportation, reliability has been studied in most modes, but less so in the case of maritime containerisation. This paper reports on a systematic literature review of the concept of reliability in transportation, with a focus on reliability in container shipping networks. The selected papers were analysed to extract information according to the three identified sub-networks: (1) ports, including studies with a focus on infrastructure, service availability and risks in ports and hinterlands; (2) network structures, including the configuration of the networks, the vulnerability and resilience of the existing networks; (3) supply chains, including connectivity and planning of activities that integrate stakeholders within the supply chain. These sub-networks were then used to further query the database, searching for papers relevant to the research problem. Two research questions are addressed: (1) How is reliability best understood in the context of container shipping networks? (2) What are the determinants that affect container shipping network reliability? The review showed that there is no uniform definition of reliability in container shipping networks, but different approaches to understand it, depending on the theoretical perspective, have been adopted. Influencing factors and relevant metrics are discussed and a framework combining different dimensions of reliability, expressed as three themes, i.e., infrastructure reliability, network configuration reliability, and connectivity reliability, is developed. This can help both practitioners and researchers to understand in more detail the various dimensions and nuances of reliability specifically in the context of container shipping, its interrelationship with wider logistics systems and how, where possible, reliability can be improved.
... The maximum total system payoff at CE is at least that any NE (Marris et al., 2021). And User Equilibrium (UE) must be a NE (Bell, 2000). Hence, CE offers the theoretical foundation to mitigate the UO-SO conflict in traffic systems since it provides the opportunity that the system and individual travelers both get better off compared to NE. ...
... It is crucial for enhancing transportation system accessibility as it directly affects how easily different areas can be reached via road networks [64]. A highly connected traffic network can maintain operational efficiency even when some routes are closed [65]. Redundancy refers to the presence of extra or backup routes in the traffic network, which can be utilized when primary routes are unavailable [66]. ...
Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable.
... Meanwhile, game theory has been applied to various areas in transportation systems, such as decision-making, policy formulation, and individual behavior research. For example, (1) assesses the effects of different traffic management policies on congestion (Bell, 2000); (2) predicts headways facilitates the optimization of routes and schedules to cater to the diverse travel requirements of passengers in urban public transportation systems (Dai et al., 2019). ...
The ramp merging zone of highways is a bottleneck for interweaving traffic flow, which can easily cause traffic turbulence and accidents. However, many studies fail to account for the reactions of human-driven vehicles to the merging process and the differences among drivers. The research purpose of this article is to explore how ramp CAVs can reasonably and efficiently merge into the mixed traffic flow of the mainline in the future. To address this issue, this paper proposes a game theory-based on-ramp merging controller for connected automated vehicles (CAVs) in mixed traffic flow. First, a two-layer decision-making framework based on the Stackelberg game is designed to consider the fuel consumption and safety payoffs of mixed traffic flow under different driving behaviors. The upper layer of the framework determines the optimal merging decision (i.e., merging time and location) for on-ramp vehicles (RVs) based on the Stackelberg game. The lower layer optimizes the merging trajectory of CAVs to reduce energy consumption and safety risks during the ramp-merging process. Then, a driving behavior estimation algorithm is developed to describe the differences in mainline vehicles (MLVs) response to the merging behavior of RVs. Finally, the simulation experiments are adopted to verify the effectiveness and stability of the proposed framework. The results indicated that (1) estimating the driving behavior in advance can shorten the time it takes to merge, thereby reducing fuel consumption and achieving higher fuel efficiency; (2) the traffic emissions of CO2, NOx, and PM have been reduced by 14.1%, 18.6%, and 14.3%, respectively, based on the proposed on-ramp merging controller; (3) the multi-scenario simulation results show that that the strategy proposed in this paper exhibits noteworthy robustness in mitigating the issue of ramp merging. Therefore, the proposed framework promotes environmental protection, 2 operational efficiency, and traffic flow stability in different traffic scenarios.
... In the intersection of urban planning and disaster management, combining flood risk assessments with road network analysis is pivotal (Bell, 2001). Within this synthesis, Path-based traffic assignment models are indispensable, offering detailed insights into real-world intricacies such as spillback effects and traffic congestions, which are particularly valuable in emergency situations (Sheffi, 1985). ...
... Typically, low-vulnerability networks have three characteristics: 1) a low probability of disruptive events, 2) an ability to resist and absorb the effects of such events (often called "robustness"), and 3) the ability to adapt and recover from adverse situations over time (often called "resilience") ( Zhang et al., 2015). One indicator of functionality is origin-destination (OD) connectivity, which measures the probability of OD pairs becoming disconnected due to a disruption (Bell, 2000;Iida and Wakabayashi, 1989;Miller-Hooks et al., 2012;Morelli and Cunha, 2021). Real-world travel demand and capacity constraint data, which are often unavailable, may provide even more realistic measures (Mattsson and Jenelius, 2015). ...
... Bell [21] proposed an evaluation method of road network reliability based on game theory to predict the travel path of users and evaluate the cost. Jiang et al. [22] proposed a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the infuence of the trafc information. ...
Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.
... First, the proposed methodology focuses on one specific impact dimension, that is the impact on users. This dimension has to be complemented in order to consider the broader socioeconomic impact, including the impact on the economic activities operating in the area related to supply chain management (Bell, 2000;Smith et al., 2003). The proposed approach could also be enhanced by considering elastic demand function. ...
... The game theory was used to provide reliable route choice for commuters in traffic congestion during peak hours. In measuring the reliability of transport network performance, Bell [17] found that when users are extremely pessimistic about the state of the network, the Nash equilibrium can be used to measure network performance and can indeed be used as a basis for a cautious approach to network design.Škrinjar et al. [18] conducted a study in urban transport planning using game theory. Game theory was found to be an effective tool for decision makers to make optimal decisions in dealing with traffic in large cities. ...
Game theory models provide very powerful tools for evaluating strategies that are beneficial to both rail and road operators competing for passengers on parallel routes. This study examines how game theory can help rail operators who are incurring losses on passenger transport to identify strategies that can minimise costs, using the methodology of dual linear programming to analyse strategies. In identifying the best strategies for minimising costs for the railway operator, the best strategies for maximising profits for the road operators are also identified. The game model is set up between two passenger transport operators (rail and road) and is based on the income earned by the road operators from passengers. This study illustrates the following: how the strategies of the two competitors (rail and road) are determined; the formation of the payoff matrix and the presentation of the mathematical problem for the two competitors; and the results and verification of the best strategies for both competitors. The Leonid Hurwicz criterion was used to verify the optimal strategies.
... The goal of the research is to tackle the problem as a flow-shop and profit from its advantages to propose a solution that minimizes the completion time in the case of delays, through the rescheduling of jobs on machines to avoid bottlenecks and limit the emergent behaviours caused by high utilization of a specific machine. Moreover, the network's reliability is measured using a minimax problem similar to the two-player non-cooperative approach shown in [14]. In this preliminary work, short-term scheduling has been considered with deterministic knowledge of the flow of the products. ...
... Game theory (GT), developed by Von Neumann and Morgenstern (1944), provides insights to understand interactions among multiple agents. It has wide application in recent transport studies (Littlechild and Thompson, 1977;Bell, 2000;Chen et al., 2018;Fisk, 1984;Ji and Levinson, 2020c), which complements the advantages of microscopic and macroscopic models and allows consideration of interactive behaviors. Although the LC maneuver is complicated in the real world, we argue modeling with simple rules of game theory helps reveal how drivers make decisions under different conditions and build on it to develop micro-pricing of LC to manipulate the frequency of lane changes. ...
Risky and aggressive lane changes on highways reduce capacity and increase the risk of collision. We propose a lane-changing pricing scheme as an effective tool to penalize those maneuvers to reduce congestion as a societal goal while aiming for safe driving conditions. In this paper, we first model driver behavior and their payoffs under a game theory framework and find optimal lane-changing strategies for individuals and their peers in multiple pairwise games. Payoffs are estimated for two primary evaluation criteria: efficiency and safety, which are quantified by incorporating driver tradeoffs. After that, the discretionary lane-changing (DLC) model is calibrated and validated by real-world vehicular trajectory data. To manipulate drivers’ DLC behaviors, two types of lane-changing tolls based on local-optimal and global-optimal rules are introduced to align individual preferences with social benefits. We find prices can close this gap and achieve ‘win-win’ results by reducing drivers’ aggressive lane changes in the congested traffic. Meanwhile, the tolls collected can be used to compensate drivers who get delayed when yielding, to encourage appropriate yielding behavior and a pseudo-revenue neutral tolling system.
... Game playing or scenario testing is then undertaken to illustrate particular views within certain cohorts or policy positions and their likely success in achieving objectives. Game theory (originally developed by Von Neumann and Morgenstern, 1944) has been applied to transport modelling and route choice (Bell, 2000; and others), but has seldom been used in transport futures studies and strategy development. It has much potential in exploring whether certain policy positions are likely to succeed in achieving certain objectives. ...
... Simulation methods usually analyze the impacts of network indicators on road network vulnerability using computer software (Zhong et al. 2018;Hardiansyah et al. 2020). In addition, other studies have also established a vulnerability assessment model based on game theory and fuzzy logic theory (Bell 2000;El-Rashidy and Muller 2014). ...
... Indeed, network reliability assessment, as well as identification of vulnerability and design of resilient transportation networks, is an important topic of current research. Bell (2000) developed a model for measuring the performance reliability of transportation networks, based on link failure. The failure occurs with a given probability and causes an additional travel time. ...
A game-theoretic approach based on the framework of transferable-utility cooperative games is developed to assess the reliability of transfer nodes in public transportation networks in the case of stochastic transfer times. A cooperative game is defined, whose model takes into account the public transportation system, the travel times, the transfers and the associated stochastic transfer times, and the users’ demand. The transfer stops are modeled as the players of such a game, and the Shapley value – a solution concept in cooperative game theory – is used to identify their centrality and relative importance. Theoretical properties of the model are analyzed. A two-level Monte Carlo approximation of the vector of Shapley values associated with the nodes is introduced, which is efficient and able to take into account the stochastic features of the transportation network. The performance of the algorithm is investigated, together with that of its distributed computing variation. The usefulness of the proposed approach for planners and policy makers is shown with a simple example and on a case study from the public transportation network of Auckland, New Zealand.
... Kita [25] explored the lane-changing decision-making process of vehicles from the perspective of the game. Bell [26] coupled the idea of game theory into the lane-changing process of vehicles in the expressway scene and studied its impact on the traffic flow. Banjanovic et al. [27] established the cooperative lane-changing behavior strategy model based on game theory and showed the gains of participants by analyzing the behavior information of lane-changing vehicles and surrounding vehicles. ...
Autonomous driving vehicles have some advantages, such as alleviating tasks of drivers and reducing carbon emissions. With the advancement of intelligent network connection technology, autonomous driving vehicles are showing a trend of practicality and popularization, so it is crucial to study the decision-making mechanism of autonomous driving vehicles. This paper focuses on the lane-changing decision-making behavior of autonomous driving vehicles. Firstly, the objective quantification of lane-changing intention is carried out to reasonably show the lane-changing intention of autonomous driving vehicles as a prerequisite for lane-changing decision-making. Besides, the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced, and explore the dynamic influencing factors of lane-changing process for autonomous driving vehicles. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous driving vehicles is established. Analyze the decision-making behavior mechanism of lane-changing, so that the autonomous driving vehicle can change lanes safely and reasonably. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that under the decision-making behavior model of the lane-changing game, the average speed of vehicles increases by 3.6%, and the average number of passed vehicles increases by 10.3%, which has higher stability, safety, speed gains, and lane utilization. The modeling of lane-changing game strategy for autonomous driving vehicles comprehensively considers the dynamic factors in the traffic environment, and scientifically shows the lane-changing decision-making mechanism of autonomous driving vehicles.
... The capacity reliability which is the probability that the capacity of the network is greater than or equal to the required demand (Chen et al., 2002Kuang et al., 2013;Church and Scaparra, 2007) and finally the flow-decrement reliability, introduced by Du and Nicholson (1991), which is the probability that the flow reduction is lower than a specific threshold under a degradable network . Other aspects of reliability, including the behavioral-related reliability based on the behavioral responses (Chen et al., 2002;Shariat-Mohaymany and Babaei, 2010;Bell, 2000), are related to the reliability definition which aims at computing the level of service whatever the traffic conditions (Calvert and Snelder, 2018). ...
In addition to operating close to their maximum capacity, transport networks, and especially the urban ones, are subject to various disruptions induced by human, technical or natural factors, which often generate loss of performance, damages and high maintenance costs. Introduced in the 70's, the notion of resilience represents the ability of a system to maintain an acceptable level of performance in presence of a disruption. Modeling and quantifying the resilience of multimodal, large-scale, urban transport networks is expected to allow cities guaranteeing higher-quality of service and seamless mobility, even in the presence of disruptions and major, predictable events. The research presented in this dissertation is motivated by the need of proper defining the resilience of the transport network in order to understand their vulnerabilities. Such indication aims at improving the functioning of the network under disruption and anticipating the loss of performance by means of a resilient-oriented transport network design. In the literature, two major approaches aim at quantifying the network resilience. On the one hand, the topological approach, based on graph theory, which characterizes the static components of transport resilience, as issued from the redundancy of the network and its connectivity. On the other hand, the dynamic approach, which takes into account the traffic dynamics and leverages traffic theory for quantifying resilience induced by the network users behaviors and the transport network performances. The combination of the static and the dynamic approaches for resilience characterization is promising and provides deeper insights in the properties of a network, both in terms of its topology and performance. Centrality measures, aiming at ranking the importance of the graph components and issued from graph theory, are mainly analyzed to characterize the transport networks in static settings. By computing them on dynamic weighted graphs that capture traffic conditions and by adapting their formulation to consider the users’ demand, we are able to jointly consider network topology and traffic dynamics in resilience characterization. To emulate the impact of disruptions, both simulated and real data are considered. A stress test methodology, mostly used in the bank and nuclear sectors, which aims at simulating the worst scenarios in order to analyze the impact and the reaction of the network, is developed to observe the transport network behavior. Finally, we develop a methodology, quick-to-compute, which aims at prioritizing the construction of some new transport mode lines, by maximizing the performance improvement in a resilience context. We also propose an algorithm for the optimal deployment of a disruption-adapted park-and-ride system.
... In some other studies, the total utility of the passengers, the stochastic traffic supply and demand models, and the dynamic route choice behavior are integrated into vulnerability measure (Cats and Jenelius, 2014). More recently, game theory and optimization methods are used to find the vulnerable components in the traffic network (Bell, 2000). ...
As a complex system, the bike-sharing system suffers from system failures, which can increase travel costs and impair user satisfaction. We proposed a concept of the vulnerability of bike-sharing system and a method to measure it. The method depends on the cost changes due to additional travel time induced by the failure of bike docking stations. It can capture the traffc mode transfer in the context
of multi-modal traffc system, such as walking, bus, and subway. Moreover, to investigate the impact of network structure on the vulnerability, we developed the centrality measuring methods, and a community detection model for the bike-sharing system. Subsequently, the proposed methods are applied to Citi Bike in New York City, the largest bike-sharing system in the USA. The results show that the most vulnerable bike docking stations are located far from bus and railway stations, with low docking station density in their surrounding areas. We also found that the number of nearby bicycle stations, bus stops, and subway stations have a negative correlation with the vulnerability index. In contrast, the degree centrality and trip betweenness centrality are positively associated
with the index. The proposed vulnerability analysis method can help urban planners to evaluate the design of a bike-sharing system and buttress operators to optimize maintenance planning.
... Vulnerability focuses on the weaknesses of the network and the consequences of failure. According to [8][9][10], the vulnerability of urban road networks could be viewed in a similar manner to measure the risk. e concept of vulnerability can be separated into two parts, using both the product of the probability of an event occurring and the outcome. ...
Malignant traffic accidents are typical devastating events suffered by the urban road network. They cause severe functional loss when loading on the urban road network is high, exerting a significant impact on the operation of the city. The resilience of a road network refers to its ability to maintain a certain level of capacity and service when disturbed by external factors and to recover after a disturbance event, which is a crucial factor in the construction of transportation infrastructure systems. A comprehensive understanding of the adverse effects of malignant traffic accidents on the urban road network is imperative, and resilience is a concept employed to systematically explain this. This study investigates the impact of malignant traffic accidents on the resilience of the urban road network. A simulation is carried out focusing on an ideal urban road network, describing the temporal and spatial distribution of the average speed of road sections in the network. Inspired by the simulation experiment results, the ideal resilience curve is summarized, and the theory of resilience concept portrayal is innovatively developed into “6R” (redundancy, reduction, robustness, recovery, reinforcement, and rapidity). Combining the topological and “6R” resilience attributes of the urban road network, the urban road network resilience evaluation system is constructed, which yields an all-round and full-process evaluation for the urban road network with malignant traffic accidents. Results show that under malignant traffic accidents, the resilience of high-class surface roads, such as primary roads, is the poorest, suggesting that more attention and resources must be devoted to high-class surface roads. This study on the urban road network deepens the understanding and portrayal of its resilience and proposes an evaluation method to analyze its performance under disruption events.
... Game theory (GT), developed by Von Neumann and Morgenstern (Von Neumann and Morgenstern 1944), provides insights to understand manoeuvre-level interactions among multiple players. It has wide application in recent transport studies (Littlechild and Thompson 1977;Fisk 1984;Bell 2000;Chen et al. 2018;Ji and Levinson 2020a), which complements the advantages of microscopic and macroscopic models and allows consideration of interactive behaviors. Although the LC manoeuvre is complicated in the real world, we argue modeling with simple rules in game theory helps reveal how drivers make decisions under different conditions and find possible improvements for those decisions. ...
This dissertation explores the rationality of drivers’ risky and aggressive behaviors in
lane-changing scenarios and discusses some feasible ways to hold selfish drivers accountable for their decisions. Regardless of potential congestion and crashes suffering by other road users, rational drivers prefer to maximize their gains and demand others’ yielding. However, when all of them have such thoughts, conflicts (dilemmas) are embedded in their interactions, leading to unexpected consequences for the whole traffic. This question is investigated analytically by exploiting the game theory concept. A simplified 2 * 2 non-cooperative game is built to model strategies executed by human drivers without communications.
This research learns driver behavior in two predefined sub-phases: ‘Stay’ and ‘Execution’
from empirical data. This procedure examines the factors that impact drivers’ execution of lane changes. From the results, we understand that lane-changing is motivated by the urgency to change and the dissatisfaction with current circumstances. The analytical model is then established by integrating driver incentives into payoff functions. The ‘greed’ and ‘fear’ of drivers in this process are quantified by speed advantages and possible crash costs respectively, so they trade off these factors and make decisions based on their own and opponents’ estimated payoffs. Using a numerical case study, we find that social gaps exist between user-optimal and system-optimal strategies when drivers mostly engage in selfish behaviors, significantly deteriorating the total system benefit. Pricing can be a sufficient tool to incentivize users to cooperate with others and achieve win-win outcomes. It is posited that the designed pricing schemes may promote the negotiation between drivers, reducing collision risks and improving operational traffic efficiency. Several simulation experiments are then conducted to evaluate this dissertation’s hypotheses on the performance of pricing rules. Overall, the proposed framework develops a behavioral model and improvement schemes from the perspective of microscopic vehicular interactions. The conclusions will hopefully find their applications in autonomous vehicle-human interaction algorithms and future transportation systems.
... Bell et al incorporated commuter flow into the analysis of network reliability [24] and vulnerability [25] using a novel game theoretic approach, where commuters and disruptions in the network are modelled as two non-cooperative players. The overall network reliability and vulnerability is then evaluated using the expected trip cost at the Nash equilibrium. ...
Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model incorporating the network structure of stations, differences in line capacities, and travel demand. Our method provides a holistic assessment of network resilience and allows for straightforward comparisons of different scenarios including rail expansions and changes in demand. Applying our methodology to multiple configurations of Singapore's rapid transit system, we demonstrate its effectiveness in capturing the impact of planned future lines. We also showcase through simulated studies how tipping points in resilience arise when demand varies. Furthermore, we demonstrate that system resilience could be unintentionally reduced by redistributing commuting demand to peripheral areas. Our methodology is easily applied to other rapid transit systems around the world.
... Some scholars have studied the integrated management system on the traffic management system and the traveler information system based on the agent technology, and the information communication platform between them is constructed (Ezzedine et al. [9,10]; Liu and Wang [11]; Zhang et al. [12]; Zhen [13]; Yuan et al. [14]; Zhou et al. [15]; Zhang and Gao [16]). According to the noncooperative game between two agents, the reliability of transportation network performance was measured by Bel [17] in 2000. Miyagi et al. [18] showed a pure user Nash equilibrium which describes the route-choice behavior of a user in a traffic network comprising several discrete, interactive decision-makers, and the agents use only the utility information of the previous action to get a congestion game. ...
To distribute the right-of-way of intersection reasonably, the game model between each adjacent agent was obtained through agent technology. The Nash equilibrium model to measure the negotiation effects was proposed, and the existence of the Nash equilibrium solution was proved. We obtained the game equilibrium solution between each adjacent agent and thus got the equilibrium price (cost) in the network. According to the equilibrium price, travelers will choose the best path in transportation networks by adopting the path strategy with the minimum cost or fuzzy-comparison strategy. The results indicate that the contract mode algorithm makes signal control of each intersection coordinated and unified and shows subjective initiative of traffic control and management fully. The contract-based algorithm combining the management initiatives with the driver’s rational behavior will make the control effectiveness of the road network system increase by 55.58%. Hence, it is an effective measurement for studying coordination between system optimality and user optimality.
... There have been similar applications in public transport that inspired this paper. Bell (2000) uses game theory to assess the reliability of a transport network when one player (individual network user) aims to minimize their expected trip costs, while an "evil entity" imposes link costs on the network user to maximize the expected trip cost. Barabino et al. (2014) examine the optimum inspection level of passengers in Proof-of-payment transit systems. ...
The global sulphur cap is the final step in a series of regulations that aim to reduce SOx emissions from shipping. It affects international shipping and requires all vessels to use fuel with a maximum of 0.5% sulphur content or use abatement technologies that achieve a similar reduction in SOx emissions. The existing legislative framework poses several challenges, stemming mainly from a highly non-homogeneous and spatially differentiated system, with cases where the penalty fines are as low as the benefit that the violator enjoyed from non-compliances. The purpose of this paper is to develop a game theoretic modelling framework that improves the effectiveness of sulphur regulations enforcement and proposes a uniform violation fine system. A mixed strategy game with two players is formulated, representing the ship operator (who can either comply or not with the regulation), and an enforcement agency (that can opt to inspect or not inspect the ship) respectively. The proposed model can improve compliance rates and increase societal environmental benefits through reduced sulphur emissions. We also consider a new system with warnings issued for repeated violations of the regulation that would lead to a mandatory retrofit of the vessel with sulphur abatement technologies. Such models can ensure a level playing field for ship operators that currently have invested heavily in abatement options to comply with the sulphur regulations.
... Al-Deek and Emam [135] apply a statistical analysis to road networks using the Weibull and the exponential distribution to compute travel time and capacity reliability. Also using travel time to asses reliability, Bell [147] presents a completely different two-player non-cooperative game theory approach: On the one hand, the network user tries to seek a path to minimize the expected trip cost, on the other hand, another entity chooses link degradation scenarios to maximize the expected trip cost. The effects of road closure on traffic flow patterns in New Zealand are assessed by Dalziell and Nicholson [137] to analyze the risk and impact of natural hazards on a road network. ...
The frequency of natural disasters is increasing all over the world, which can cause immense damage to road infrastructure and its functionality. Therefore, it is crucial to consider the functionality of critical road infrastructure before, during, and after a disaster. For that, global road network data, which is usable for routing applications, is required. OpenStreetMap (OSM) provides global, crowd-sourced road network data that is free and accessible for everyone. However, the usability for routing applications is often an issue. Two main gaps in related studies are identified: the intrinsic improvement of certain aspects of OSM road data for navigational purposes, and missing approaches for the assessment of critical road infrastructure in disaster cases that can handle limited global data availability. Therefore, the aim of this thesis is to develop a generic, multi-scale concept to assess critical road infrastructure in a disaster context using OSM data. For this main objective, two consecutive research goals are identified: (i) improving the routability of OSM data intrinsically, and (ii) assessing critical road infrastructure in a disaster context. Therefore, this thesis and the developed concept are divided into two main parts, each addressing one research goal.
In the first part of this thesis, the OSM road network data is enhanced by improving its routability. The quality of the OSM road network is analyzed in detail, which leads to the identification of two major challenges for the applicability of OSM data in routing applications: missing speed information and road classification errors. To address the first challenge, a Fuzzy Framework for Speed Estimation (Fuzzy-FSE) is developed that employs fuzzy control to estimate average speed based on the parameters road class, road slope, road surface, and link length. The Fuzzy-FSE consists of two parts: a rule and knowledge base, which decides on the output membership functions, and multiple Fuzzy Control Systems, which calculate the output average speeds. Results demonstrate that even using only OSM data, the Fuzzy-FSE performs better than existing methods such as fixed speed profiles. The second challenge of road classification errors is addressed by developing a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are combined in a rating system to obtain an error probability. The rating system can then suggest possible misclassifications to a human user. The results indicate that more classification errors are found at gaps than at disconnected parts. Furthermore, the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. An enhanced OSM road network dataset results from the first part of this thesis.
In the second part of this thesis, the enhanced OSM data is applied to assess critical road infrastructure in a disaster context. The second part of the generic, multi-scale concept is developed, which consists of multiple, interconnected modules. One module implements two accessibility indices, which highlight different aspects of road network accessibility. A basic travel demand model is developed in another module, which estimates daily intercity traffic solely based on OSM data. A third module uses the above-described modules to estimate different natural disaster impacts on the road network. Finally, the vulnerability of the road network towards further disruptions during long-term disasters is analyzed in a fourth module. The generic concept with all modules is applied exemplarily in two different case study regions for two wildfire scenarios. As a result, the concept provides a valuable, flexible, and data-sparse decision aid tool for regional planners and disaster management that can be applied globally and enables country- or region-specific adaptations.
... It is noted that the DUE is a special case of the SUE. Besides, there exist some other transport equilibria, such as pure strategy Nash equilibrium (PSNE) and mixed strategy Nash equilibrium (MSNE) (Bell 2000). Dixit and Denant-Boemont (2014) argued that the SUE is more reliable to accurately predict average choices and choice variances than PSNE and MSNE. ...
This paper studies the impact of route choice set of networks on the day-to-day traffic dynamics through conducting a group of experiments, in which four traffic networks with different sets for choice are used. It is observed that a deterministic user equilibrium (DUE) state is difficult to sustain and the probability of reaching DUE is larger in a network with smaller route choice set. Three day-to-day models are calibrated by using the experimental data. Results reveal that (1) there are linear relationships between traffic volume/flow changes obtained from the experiments and models; (2) when the same model is applied to different networks, the calibrated adjustment parameters are statistically different; and (3) the same model reveals different performances for different networks. Besides, it is shown the participants are more likely to change route choices in a network with larger route choice set and tend to switch away after experiencing a longer travel time.
... One of the groundbreaking studies in the area of hazmat logistics was found in Bell (2000) in which a two-player non-cooperative game is envisaged between on the one hand the network user seeking a path to minimise the expected trip cost and on the other hand an 'evil entity' choosing link performance scenarios to maximise the expected trip cost. The applications of the classis two-player non-cooperative game are also found in Bell and Cassir (2002), Bell (2003Bell ( , 2004Bell ( , 2006Bell ( , 2007, Szeto (2013) and Szeto et al. (2017). ...
Having reliable interdependent infrastructure networks is vital for well‐being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision‐maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision‐maker's interest may not converge with the others. Subsequently, this results in each decision‐maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision‐makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.
This study is a driving analysis of the transfer data of container terminals based on simulation interactive modeling technology. In the context of a container yard, a model was established to analyze and predict the arrival time and influencing factors of container transportation through the data from the control center of the yard. The economic benefit index in the index system was determined through expert consultation, the automatic terminal can be obtained by acquiring the actual operating parameters of the terminal, and the terminal to be built can be acquired mainly through simulation modeling. Therefore, when determining the design scheme before constructing the automated container terminal, a terminal simulation model needs to be established that meets the requirements of loading and unloading operations and terminal production operations. In addition, an automated container terminal simulation model needs to be implemented to verify the feasibility of the evaluation model. The results reveal that the accuracy of the current prediction model is still limited—the highest accuracy is only 72%, whether there are continuous or discrete variables, traffic or weather variables. Moreover, the study denotes that the relationship between weather and specific time factors and the arrival time of containers is weak, even negligible. This study provides guidance and decision-making support for the construction of automated terminals.
The unavoidable travel time variability in transportation networks, resulted from the widespread supply-side and demand-side uncertainties, makes travel time reliability (TTR) be a common and core interest of all of the stakeholders in transportation systems, including planners, travelers, service providers, and managers. This common and core interest stimulates extensive studies on modeling travel time reliability. Researchers have developed a range of theories and models of TTR, many of which have been incorporated into transportation models, transport policies, and project appraisals. Adopting the network perspective, this paper aims to provide an integrated framework for summarizing the methodological developments of modeling TTR in transportation networks, including its characterization, evaluation and valuation, and traffic assignment. Specifically , the TTR characterization provides a whole picture of travel time distribution in transportation networks; TTR evaluation and TTR valuation (known as the value of reliability, VOR) interpret abstract characterized TTR in a simple and intuitive way in order to be well understood by different stakeholders of transportation systems; and lastly TTR-based traffic assignment investigates the effects of TTR on the individual users' travel behavior and consequently the collective network flow pattern. As the above three topics are mainly separately studied in different disciplines and research areas, the integrated framework allows to better understand their relationships and may contribute to developing more possible combinations of TTR modeling philosophy. Also, the network perspective enables to pay more attention to some common challenges of modeling TTR in transportation networks, especially the uncertainty propagation from the uncertainty sources to the TTR at various spatial levels including link, route, and the entire network. Some potential directions for future research are discussed in the era of new data environment, applications, and emerging technologies.
Emergency road networks (ERNs), an important part of local disaster prevention systems, can provide security to residents and their property. Exploring the ERNs structure is of great significance in terms of promoting disaster prevention and establishing road safety in dangerous mountainous areas. This study considered the ERNs of the Kangding section of the Dadu River Basin as the area for a case study. Complex Network Analysis was used to examine the relationship between the four characteristic indicators of mountain roads and the degree of earthquake impacts under the Lushan, Wenchuan, and Kangding Earthquake scenarios. Based on the analysis results, the southwest mountain road network was evaluated; then, computer simulations were used to evaluate the structural changes in the road network after index changes. The network was optimized, and the corresponding emergency avoidance network was proposed to provide a reference for the establishment of the mountainous ERN. The results show that the overall completeness of the mountainous ERNs in Southwest China is poor and prone to traffic accidents. Moreover, the local stability is poor, and the network is susceptible to natural hazards. The overall structure of the road network is balanced, but that of certain road sections is not. Road sections with different attributes present a “gathering-scattering” spatial distribution, i.e, some sections are clustered together while others are far apart. Accordingly, a planning optimization strategy is proposed to better understand the complexity and systematic nature of the mountainous ERN as a whole and to provide a reference for disaster prevention and mitigation planning in mountainous regions in Southwest China.
In this paper, the problem of decision making for autonomous vehicles changing lanes is addressed by formulating multiple games in normal form for pairs of agents. This formulation generates the optimal action for the Ego vehicle at a given state and does not consider global optimality for all agents. The payoff matrices of the games are designed based on a user-defined set of rules. The constant parameters of these payoffs are then adjusted using neural learning to generate optimal behavior among the vehicles. An algorithm integrating deep reinforcement learning and game theory, regarded as Nash Q-learning, is included in the decision-making scheme. The applicability of the proposed method in a lane-changing scenario is tested via simulation.
Road networks are vulnerable to disruptions caused by both man-made and natural events. Traditionally, assessing the vulnerability of a real-size road network has been a computationally intensive operation. The purpose of this paper is to propose a heuristic algorithm that utilizes a game theory framework and three distinct link selection methods to identify the most critical combination of vulnerable links under conditions of varying disruption efficiency. This paper uses the Broward County’s road network in the state of Florida as a case study. Broward County was chosen due to its importance in the state of Florida's freight movement. The case study results indicated that critical sets of links are highly dependent on the adversary's intelligence, the attack's link selection, and the disruption scenario defined in terms of partial or complete link closure.
This article aims to develop a new link criticality indictor based on the network capacity concept of a multimodal transportation network. Model formulation and solution algorithm are developed for this multimodal transportation network capacity problem. To improve the efficiency, a sensitivity-based approximation approach is also developed to avoid the need to repeatedly solve the bi-level network capacity problem for each disrupted link as in the typical network scanning approach. Numerical experiments are conducted and compared to the traditional efficiency-based criticality indicator. The findings reveal the proposed network capacity-based indicator indeed is more useful than the efficiency-based indicator when the link capacity limit is violated in the degraded network.
Traffic congestion is a major problem in the road network. Traffic in the form of Platoons solves this problem. However, when creating a Platoon, we have to follow the speed of the leading vehicle even if it does not reach the maximum speed of the road due to physical constraints, and there are one or more followers that can reach it. Therefore, the traffic in the form of Platoons in its current state does not allow to increase the road traffic and to minimize the road congestion. To efficiently solve this problem we proposed an algorithm to classify the vehicles in a Platoon according to the traffic speed.
Due to insufficient funds to implement all candidate road infrastructure projects, there is a need to efficiently utilize available funds and select candidate projects that maximize performance criteria decision-makers. This paper proposes an incremental benefit–cost analysis (IBCA) framework to prioritize low-volume road (LVR) projects that maximize road network accessibility considering project cost and network accessibility requirements. The study results show that the accessibility benefits of road projects depend not only on their cost requirements but also on their spatial locations in the network that affect their network-level accessibility benefits per unit cost of investment. Additionally, the number of disrupted LVR links cannot fully determine the degree of change in network accessibility. The framework enables decision-makers to consider project cost requirements and the accessibility-related impacts of LVR projects, maximize economic benefits, and ensure the sustainability of the LVR network performance.
Two novel centrality indices, PathRank and Icentr, are defined. PathRank is a generalization of the PageRank algorithm, suitable to rank nodes of undirected graphs according to number and weight of paths in the graph. Icentr ranks the nodes of the graph by means of a combination of the weights of nodes and edges, scaled according to the distance from each node, one at a time. We apply the two novel indices to underground transportation networks, since these networks represent an infrastructural backbone for the transportation system of most big cities over the world. The characterization of the most important components of those networks and the simulation of their responses when they stop working properly, are vital for maintaining the mobility service at a desirable level. Since there are different ways to associate a graph to an underground network according to the degree of detail and aims of the study, we describe the methodology we adopted to associate a graph to such a network. The methodology was applied to 34 underground networks of worldwide cities, and the resulting graphs constitute the reference dataset. A detailed study of both Boston network and the dataset is proposed as prototypal for either a graph alone or all graphs in a dataset. Results show how different features of graphs are revealed by the two novel indices.
Vulnerability analysis of transportation networks has rapidly become important in recent decades given the increasing numbers of transportation disasters. In this paper, we describe a method for calculating the minimum cuts between all pairs of nodes in a transportation network and develop two indices for analyzing vulnerability; these are derived from topology-based vulnerability/demand-accountable analyses and enable evaluation of cuts without assuming route-choice behaviors. We show that such analyses can be performed using a Gomory–Hu tree to reduce the computational load. This method is efficient, requiring only N − 1 calculations of the maximum flow problem even though the number of node pairs is N², where N is the number of nodes. In addition, we show that the total demand passing each minimum cut, which is necessary for demand-accountable analysis, can be obtained by network loading onto the tree using the Gomory–Hu tree features. We apply the proposed method to the central region of Japan to illustrate the applicability of the two indices for identifying vulnerable links in the road network.
The deployment of connected, automated vehicles (CAVs) provides the opportunity to enhance the safety and efficiency of transportation systems. However, despite the rapid development of this technology, human-driven vehicles are predicted to predominate the vehicle fleet, compelling CAVs to be able to operate in a mixed traffic environment. The key to achieving a reliable and safe human-CAV collaboration in such environments is to characterize the interactions between the actors and incorporate the underlying decision-making mechanism of human drivers into CAVs' motion planning algorithms. Towards this goal and extending a previously developed game theoretical model, the present study proposes a decision-making dynamic to achieve more realistic models of human behavior when making conflicting maneuvers at intersections. A novel field test is conducted to extract the required modeling data directly from CAVs' perception system, facilitating the incorporation of the model into CAV navigation algorithms. Model validation and sensitivity analysis provided invaluable insights into the nature of human decisions and indicated that the proposed structure is robust to environmental uncertainties and can well capture the real-world behavior of human drivers in unprotected left-turn maneuvers. The derived knowledge can be directly used in CAV motion planning algorithms to provide the vehicle with more accurate predictions of human actions when operating in mixed traffic environments.
City logistics involves movements of goods in urban areas respecting the municipal and administrative guidelines. The importance of city logistics is growing over the years especially with its role in minimizing traffic congestion and freeing up of public space for city residents. Collaboration is key to managing city logistics operations efficiently. Collaboration can take place in the form of goods consolidation, sharing of resources, information sharing, etc. We investigate the problems of collaboration planning of stakeholders to achieve sustainable city logistics operations. Two categories of models are proposed to evaluate the collaboration strategies. At the macro level, we have the simplified collaboration square model and advance collaboration square model and at the micro level we have the operational level model. These collaboration decision making models, with their mathematical elaborations on business-to-business, business-to-customer, customer-to-business, and customer-to-customer provide roadmaps for evaluating the collaboration strategies of stakeholders for achieving sustainable city logistics operations attainable under non-chaotic situation and presumptions of human levity tendency. City logistics stakeholders can strive to achieve effective collaboration strategies for sustainable city logistics operations by mitigating the uncertainty effect and understanding the theories behind the moving nature of the individual complexities of a city. To investigate system complexity, we propose axioms of uncertainty and use spider networks and system dynamics modeling to investigate system elements and their behavior over time.
Sustainable transport systems require expedient traffic management strategies and transport policies. To evaluate such strategies and policies, efficient quality monitoring schemes need to be readily available. The backbone of sustainable transport systems remains public transportation (PT). In this work, we introduce a network reliability performance indicator which allows us to analyze the spatial impacts of delays in a PT system. The proposed methodology is based on the well-established notion of accessibility, flexible and transferable. In a case study, we successfully provide a proof of concept for Hof, Germany. We effectively demonstrate the technical feasibility of our methodology, show that the results can be interpreted intuitively, and highlight their potential for further spatial analyses. We conclude that the proposed indicator contributes to efficient PT system monitoring, and thus supports the development of sustainable transport systems.
With the development of integrated and intelligent transportation systems, the stability and security of system performance are highly emphasized. Resilience and vulnerability are representative indicators in the performance analysis of transportation systems. A large number of related studies have emerged in recent years. Therefore, this paper reviews the recent progress in the study of vulnerability and resilience. Specific definitions of resilience and vulnerability are first given from the perspective of transportation system's supply and demand. Other related concepts of transportation system performance(TSP) are also discussed including reliability , robustness, survivability and risk. The existing studies can be divided into two aspects, i.e., the traditional topological structure and system structure analysis. The study of topology structure mainly revolves around graph theory, which is also the cornerstone of TSP research. In recent years, advances in data analysis and model simulation technology have led to an increasing number of studies considering the overall transportation system structure. The related metrics and research methods are carefully analyzed and summarized from qualitative and quantitative perspectives. Research challenges are discussed, and future directions are presented.
The implementation of transportation infrastructure projects plays a vital role in maximizing network accessibility and optimizing system users’ mobility. In this study, a network-accessibility-based project prioritization framework was developed. Candidate infrastructure projects were prioritized based on their importance to network accessibility considering stakeholders’ preferences for candidate road projects. The case study results showed that the spatial locations of LVR projects in the network affect their contribution to the overall network accessibility. The projects’ ranking was affected by the degree of preferences of transportation stakeholders to the candidate projects. Therefore, infrastructure investment decision-makers should consider all transportation stakeholders and their preferences for candidate projects in the investment decision-making process. The simulation results also showed that network accessibility could increase after network disruption events due to the implementation of LVR roadway projects. Therefore, the proposed framework could help transportation planners reduce the network accessibility impacts of natural and human-made network-disruption events.
The transportation of hazardous materials (hazmat) is a challenging problem that often requires a trade‐off between conflicting objectives. In practice, the complexity of the problem is exacerbated due to the lack of sufficient and reliable historical data. In this research, a stochastic multi‐objective optimization model for hazardous materials (hazmat) vehicle routing and scheduling problem is developed. The goal is to find optimal links and routes to obtain a trade‐off between the safe and fast distribution of hazmat through a transport network under customers' demand and service time uncertainty. We utilized a hybrid game theory based compromise programming to develop a solution algorithm to determine the Pareto‐optimal solutions, which are based on the total travel distance and total risk imposed on the transportation process. Computational results of a realistic numerical case study demonstrate the effectiveness of the proposed model and the solution algorithm in obtaining Pareto‐optimal solutions.
A number of methods for obtaining approximate terminal reliability between given two nodes in a road network have been proposed so far. In this paper, the following four methods are compared. Two of them are based on Reliability Graph Analysis with partial minimal path sets and cut sets developed by authors; One method determines upper and lower bounds with Boolean algebraic absorption and the other evaluates an approximate value without Boolean absorption. The remainder are Monte Carlo methods; One is a traditional Monte Carlo method with direct sampling and the other is an improved Monte Carlo method with restricted sampling by a variance reducing technique. Numerical examples for model networks and a real network are executed and the merits and demerits of those methods are discussed.
This paper describes sensitivity analysis for a degradable transportation system, based on an integrated equilibrium model with elastic travel demand, to identify critical components and assist efforts to improve system reliability. A reliability model, involving practical measures of reliability, is also described. Algorithms for solving the reliability model are discussed.
An approximation method of terminal reliability of road network using partial minimal path and cut set
- Y Iida
- H Wakabayashi
Iida, Y., Wakabayashi, H., 1989. An approximation method of terminal reliability of road network using partial minimal path and cut set. In: Proceedings of the Fifth World Conference, vol. IV. Yokohama, Japan, pp. 367±380. M.G.H. Bell / Transportation Research Part B 34 (2000) 533±545
A sensitivity based approach to network reliability assessment
- M G H Bell
- C Cassir
- Y Iida
- W H K Lam
Bell, M.G.H., Cassir, C., Iida, Y., Lam, W.H.K., 1999. A sensitivity based approach to network reliability assessment. Proceedings of the 14th International Symposium on Transportation and Trac Theory (A. Ceder ed.), Pergamon, Oxford, pp. 283±300.
Traffic assignment in a road network with degraded links by natural disasters
- Asakura