Pub Date : 2026-04-01Epub Date: 2026-02-14DOI: 10.1016/j.trb.2026.103423
Wen-Jing Liu , Zhi-Chun Li , Xiaowen Fu , Kun Wang
This paper investigates the issues of airport regulation and terminal capacity expansion in the presence of terminal congestion. An analytical bottleneck model is first proposed for simulating the passenger arrival behavior at the terminal, which captures the dynamic formation and dissipation of passenger queues there. Using the proposed model, the interactions among passenger arrival distribution, terminal congestion, non-aeronautical services, and terminal capacity are revealed. A vertical-structure game-theoretical model is then developed to determine the optimal airport charge and the optimal terminal capacity under different scenarios, including profit maximization without regulation, single-till regulation, dual-till regulation, and welfare maximization. Our findings show that the single-till regulation leads to a higher social welfare compared to the dual-till regulation. When the marginal benefit from non-aeronautical services is relatively high, both welfare-maximizing and profit-maximizing airports under-invest in the terminal capacity, aiming to prolong passenger dwell time and thus increase non-aeronautical profit. Particularly, the welfare-maximizing airport suffers a longer total queuing time than the profit-maximizing airport, reflecting heavier underinvestment in its terminal capacity. The airport regulation would distort the terminal capacity investment, and the profit-maximizing airport under the dual-till regulation always under-invests in the terminal capacity, causing terminal congestion delay.
{"title":"Airport regulation, terminal congestion, and capacity expansion","authors":"Wen-Jing Liu , Zhi-Chun Li , Xiaowen Fu , Kun Wang","doi":"10.1016/j.trb.2026.103423","DOIUrl":"10.1016/j.trb.2026.103423","url":null,"abstract":"<div><div>This paper investigates the issues of airport regulation and terminal capacity expansion in the presence of terminal congestion. An analytical bottleneck model is first proposed for simulating the passenger arrival behavior at the terminal, which captures the dynamic formation and dissipation of passenger queues there. Using the proposed model, the interactions among passenger arrival distribution, terminal congestion, non-aeronautical services, and terminal capacity are revealed. A vertical-structure game-theoretical model is then developed to determine the optimal airport charge and the optimal terminal capacity under different scenarios, including profit maximization without regulation, single-till regulation, dual-till regulation, and welfare maximization. Our findings show that the single-till regulation leads to a higher social welfare compared to the dual-till regulation. When the marginal benefit from non-aeronautical services is relatively high, both welfare-maximizing and profit-maximizing airports under-invest in the terminal capacity, aiming to prolong passenger dwell time and thus increase non-aeronautical profit. Particularly, the welfare-maximizing airport suffers a longer total queuing time than the profit-maximizing airport, reflecting heavier underinvestment in its terminal capacity. The airport regulation would distort the terminal capacity investment, and the profit-maximizing airport under the dual-till regulation always under-invests in the terminal capacity, causing terminal congestion delay.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103423"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-04DOI: 10.1016/j.trb.2026.103410
Mengtong Wang , Shukai Chen , Qiang Meng
This study investigates the emerging application of unmanned aerial vehicles (UAVs), or drones, for shore-to-ship delivery services between onshore and offshore locations. However, deploying drones for shore-to-ship delivery can encounter unique operational challenges, including constantly moving target vessels and non-linear drone energy consumption. To address these issues, we propose a novel and practical drone routing problem for shore-to-ship delivery services (DRP-SSDS) considering the non-linear energy consumption related to payload, flight phase, and flight time. The proposed DRP-SSDS is formulated as a mixed-integer second-order cone programming (MISOCP) model that integrates continuous decisions on both time and location to realistically capture vessel movements within port waters. We then develop a tailored branch-and-price algorithm that can solve DRP-SSDS exactly and efficiently for medium-scale instances. Additionally, we design an effective heuristic method that can provide high-quality solutions in a reasonable time limit for large-scale instances. Extensive numerical experiments demonstrate the superiority of the proposed solution methods over the off-the-shelf optimization solver and a benchmark method across all tested instances.
{"title":"Drone routing problem for shore-to-ship delivery services considering non-linear energy consumption","authors":"Mengtong Wang , Shukai Chen , Qiang Meng","doi":"10.1016/j.trb.2026.103410","DOIUrl":"10.1016/j.trb.2026.103410","url":null,"abstract":"<div><div>This study investigates the emerging application of unmanned aerial vehicles (UAVs), or drones, for shore-to-ship delivery services between onshore and offshore locations. However, deploying drones for shore-to-ship delivery can encounter unique operational challenges, including constantly moving target vessels and non-linear drone energy consumption. To address these issues, we propose a novel and practical drone routing problem for shore-to-ship delivery services (DRP-SSDS) considering the non-linear energy consumption related to payload, flight phase, and flight time. The proposed DRP-SSDS is formulated as a mixed-integer second-order cone programming (MISOCP) model that integrates continuous decisions on both time and location to realistically capture vessel movements within port waters. We then develop a tailored branch-and-price algorithm that can solve DRP-SSDS exactly and efficiently for medium-scale instances. Additionally, we design an effective heuristic method that can provide high-quality solutions in a reasonable time limit for large-scale instances. Extensive numerical experiments demonstrate the superiority of the proposed solution methods over the off-the-shelf optimization solver and a benchmark method across all tested instances.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103410"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-13DOI: 10.1016/j.trb.2026.103425
Jisoon Lim, Neda Masoud
The evolving transportation system has intensified the demand for diverse uses of curb space in urban areas, emphasizing the critical need for effective curb space management. In this paper, we discuss a spatiotemporal pricing strategy for curb infrastructure designed to enhance the utility for both curb space operator and user groups. We introduce a Stackelberg game model for curb space stakeholders, illustrating how curb space operations can accommodate varying curb space activities and their demand levels across different zones through spatiotemporal pricing. We formulate this game as a mathematical program with complementarity constraints (MPCC) and solve it using Lagrangian relaxation. Numerical experiments demonstrate the effectiveness of spatiotemporal pricing schemes in improving the game equilibrium. We further explore incorporating practical considerations into the game model to capture the intricacies of curb space user characteristics and to ensure that spatiotemporal pricing schemes can effectively incentivize all curb space stakeholders.
{"title":"Spatiotemporal pricing of curb space for improving operator and user utilities","authors":"Jisoon Lim, Neda Masoud","doi":"10.1016/j.trb.2026.103425","DOIUrl":"10.1016/j.trb.2026.103425","url":null,"abstract":"<div><div>The evolving transportation system has intensified the demand for diverse uses of curb space in urban areas, emphasizing the critical need for effective curb space management. In this paper, we discuss a spatiotemporal pricing strategy for curb infrastructure designed to enhance the utility for both curb space operator and user groups. We introduce a Stackelberg game model for curb space stakeholders, illustrating how curb space operations can accommodate varying curb space activities and their demand levels across different zones through spatiotemporal pricing. We formulate this game as a mathematical program with complementarity constraints (MPCC) and solve it using Lagrangian relaxation. Numerical experiments demonstrate the effectiveness of spatiotemporal pricing schemes in improving the game equilibrium. We further explore incorporating practical considerations into the game model to capture the intricacies of curb space user characteristics and to ensure that spatiotemporal pricing schemes can effectively incentivize all curb space stakeholders.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103425"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-05DOI: 10.1016/j.trb.2026.103406
Gunnar Flötteröd
A dynamic traffic assignment problem is considered where travelers are modeled as integral decision makers and network flow is composed of integral vehicles. As travel behavior affects network conditions and network conditions affect travel behavior, a complex model system results. The versatility of the considered model class has led to increasing practical interest (“agent-based simulation”) but also complicates the development of solvers for mutually consistent travel behavior and network conditions that represent possible long-term states of a transport system. Continuum flow assignment techniques are not applicable to this model class. This work starts out from a Nikaido-Isoda gap function for the traveler- and vehicle-discrete dynamic traffic assignment problem. A tractable but rather uninformative upper bound on this gap function is derived. A reformulation is presented that violates this bound as little as possible while ensuring that the reformulated bound carries relevant information for the subsequently developed new assignment heuristic. The proposed approach is formally related to and experimentally compared with relevant methods from the literature. It is found to exhibit superior performance in nontrivial case studies for Stockholm (Sweden), Oslo (Norway), and Berlin (Germany).
{"title":"A simulation heuristic for traveler- and vehicle-discrete dynamic traffic assignment","authors":"Gunnar Flötteröd","doi":"10.1016/j.trb.2026.103406","DOIUrl":"10.1016/j.trb.2026.103406","url":null,"abstract":"<div><div>A dynamic traffic assignment problem is considered where travelers are modeled as integral decision makers and network flow is composed of integral vehicles. As travel behavior affects network conditions and network conditions affect travel behavior, a complex model system results. The versatility of the considered model class has led to increasing practical interest (“agent-based simulation”) but also complicates the development of solvers for mutually consistent travel behavior and network conditions that represent possible long-term states of a transport system. Continuum flow assignment techniques are not applicable to this model class. This work starts out from a Nikaido-Isoda gap function for the traveler- and vehicle-discrete dynamic traffic assignment problem. A tractable but rather uninformative upper bound on this gap function is derived. A reformulation is presented that violates this bound as little as possible while ensuring that the reformulated bound carries relevant information for the subsequently developed new assignment heuristic. The proposed approach is formally related to and experimentally compared with relevant methods from the literature. It is found to exhibit superior performance in nontrivial case studies for Stockholm (Sweden), Oslo (Norway), and Berlin (Germany).</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103406"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-10DOI: 10.1016/j.trb.2026.103413
Shaocheng JIA , S.C. WONG , Wai WONG
The increasing adoption of connected vehicles (CVs) has facilitated the development of CV-based traffic signal control. However, most methods rely on deterministic control models that do not account for uncertainty in input traffic states. This can lead to suboptimal signal timing and even control failure, particularly in highly dynamic, nonlinear transportation systems. Additionally, while adaptive signal control for isolated intersections and corridors has been extensively studied, relatively little attention has been given to the more complex challenge of network-wide signal coordination in common grid networks. This paper addresses these gaps by developing a cycle-by-cycle adaptive and stochastic signal control for grid networks. To this end, a CV-based traffic pattern model is first proposed for estimating various correlated traffic patterns across all network lanes using estimated vehicle locations. A CV-based coordinated signal control framework is then formulated, incorporating a queue pattern-based delay model, a set of signal optimization constraints, and two vehicle location control strategies: deterministic vehicle location control (DVLC) and stochastic vehicle location control (SVLC). Unlike DVLC, SVLC explicitly and realistically considers uncertainty in vehicle locations arising from uncertain CV penetration rates. To efficiently solve the high-dimensional, non-convex, non-analytical integer optimization problems, a hierarchical max-green optimization algorithm is developed, which decomposes the original problem into a series of integer linear programming subproblems. Extensive VISSIM simulations demonstrate the effectiveness of the proposed model, highlighting its ability to enhance network-wide traffic performance and the importance of incorporating uncertainty in traffic state estimation for signal optimization.
{"title":"Network-wide adaptive signal control with partial connectivity: A stochastic optimization model for uncertain vehicle locations","authors":"Shaocheng JIA , S.C. WONG , Wai WONG","doi":"10.1016/j.trb.2026.103413","DOIUrl":"10.1016/j.trb.2026.103413","url":null,"abstract":"<div><div>The increasing adoption of connected vehicles (CVs) has facilitated the development of CV-based traffic signal control. However, most methods rely on deterministic control models that do not account for uncertainty in input traffic states. This can lead to suboptimal signal timing and even control failure, particularly in highly dynamic, nonlinear transportation systems. Additionally, while adaptive signal control for isolated intersections and corridors has been extensively studied, relatively little attention has been given to the more complex challenge of network-wide signal coordination in common grid networks. This paper addresses these gaps by developing a cycle-by-cycle adaptive and stochastic signal control for grid networks. To this end, a CV-based traffic pattern model is first proposed for estimating various correlated traffic patterns across all network lanes using estimated vehicle locations. A CV-based coordinated signal control framework is then formulated, incorporating a queue pattern-based delay model, a set of signal optimization constraints, and two vehicle location control strategies: deterministic vehicle location control (DVLC) and stochastic vehicle location control (SVLC). Unlike DVLC, SVLC explicitly and realistically considers uncertainty in vehicle locations arising from uncertain CV penetration rates. To efficiently solve the high-dimensional, non-convex, non-analytical integer optimization problems, a hierarchical max-green optimization algorithm is developed, which decomposes the original problem into a series of integer linear programming subproblems. Extensive VISSIM simulations demonstrate the effectiveness of the proposed model, highlighting its ability to enhance network-wide traffic performance and the importance of incorporating uncertainty in traffic state estimation for signal optimization.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103413"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-05DOI: 10.1016/j.trb.2026.103411
Yuhang Guo , Zicheng Su , Hai Yang , Enming Liang , Chen Zhong , Wanjing Ma
Matching the imbalanced supply and demand through vehicle rebalancing is critical for enhancing the operational efficiency of ride-hailing platforms. However, the traditional two-stage Predict-then-Optimize (PO) framework suffers from a mismatch between the loss function of the upstream prediction model and the objective function used in downstream decision-making. To tackle this challenge, we propose a Smart Predict-then-Optimize (SPO) framework, in which the prediction model is trained to directly minimize decision loss. Firstly, we formulate the regional-level vehicle rebalancing problem as a mixed integer linear programming (MILP) model, aiming to maximize the Gross Merchandise Volume (GMV) of the ride-hailing platform. After that, the Spatial and Temporal Identity (STID) model is employed to predict future demand and supply. Instead of training the prediction model by minimizing fitting error, we adopt a decision-focused loss function determined by the solution of the optimization model. Considering that uncertain parameters appear in the constraints, we develop a penalty-augmented loss function along with a corresponding solution adjustment method. Moreover, we propose a perturbation-based method to address the challenge of gradient backpropagation through the non-differentiable optimization layer, which enables the gradients of the decision loss to be obtained via zeroth-order approximation. The theoretical properties are checked, showing that the method can yield an unbiased approximation of the gradient. We conduct extensive experiments on a real-world dataset from Didi Chuxing, including both numerical studies and simulation experiments. The results show that the proposed SPO framework improves the average GMV by 2.19% compared to rule-based rebalancing and by 0.28% compared to the PO strategy. In particular, the prediction model trained by the SPO method can learn the utility of each region, enabling more effective vehicle rebalancing by dispatching drivers from low-utility origins to high-utility destinations.
{"title":"A smart predict-then-optimize framework for vehicle rebalancing problem","authors":"Yuhang Guo , Zicheng Su , Hai Yang , Enming Liang , Chen Zhong , Wanjing Ma","doi":"10.1016/j.trb.2026.103411","DOIUrl":"10.1016/j.trb.2026.103411","url":null,"abstract":"<div><div>Matching the imbalanced supply and demand through vehicle rebalancing is critical for enhancing the operational efficiency of ride-hailing platforms. However, the traditional two-stage Predict-then-Optimize (PO) framework suffers from a mismatch between the loss function of the upstream prediction model and the objective function used in downstream decision-making. To tackle this challenge, we propose a Smart Predict-then-Optimize (SPO) framework, in which the prediction model is trained to directly minimize decision loss. Firstly, we formulate the regional-level vehicle rebalancing problem as a mixed integer linear programming (MILP) model, aiming to maximize the Gross Merchandise Volume (GMV) of the ride-hailing platform. After that, the Spatial and Temporal Identity (STID) model is employed to predict future demand and supply. Instead of training the prediction model by minimizing fitting error, we adopt a decision-focused loss function determined by the solution of the optimization model. Considering that uncertain parameters appear in the constraints, we develop a penalty-augmented loss function along with a corresponding solution adjustment method. Moreover, we propose a perturbation-based method to address the challenge of gradient backpropagation through the non-differentiable optimization layer, which enables the gradients of the decision loss to be obtained via zeroth-order approximation. The theoretical properties are checked, showing that the method can yield an unbiased approximation of the gradient. We conduct extensive experiments on a real-world dataset from Didi Chuxing, including both numerical studies and simulation experiments. The results show that the proposed SPO framework improves the average GMV by 2.19% compared to rule-based rebalancing and by 0.28% compared to the PO strategy. In particular, the prediction model trained by the SPO method can learn the utility of each region, enabling more effective vehicle rebalancing by dispatching drivers from low-utility origins to high-utility destinations.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103411"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-03DOI: 10.1016/j.trb.2026.103407
Meiqi Liu , Xinwei Wang , Meng Wang
We put forward a hybrid optimal control approach for joint optimization of cooperative (automated) vehicle trajectories and traffic signals for an intersection configured with different turning movements on multiple arms. The ride comfort, travel time, and throughput involving all vehicles are optimized by continuous vehicle acceleration and discrete traffic signal switch decisions subject to vehicle motion constraints on following gaps, speeds, accelerations, and upper bounds on the maximal signal stage lengths. The red time is designed as a concise vehicle position constraint to enable simultaneous evaluation of traffic-level and vehicle-level decisions. To decrease the computational burden of the mixed integer nonlinear program, the joint control formulation of an intersection is linearized and then decomposed using the Benders decomposition algorithm, generating a sequence of independent slave sub-problems on a lane level that can be solved in a decentralized manner. The control performance is verified via simulation at a four-arm signalized intersection. The simulation results show the joint control approach is flexible in incorporating multiple signal settings (such as cycle lengths and dual-ring design) and turning movements under different traffic demand levels and vehicle arrival rates. Furthermore, the benefits of the proposed control approach and computationally scalable algorithm in mean runtimes and performance metrics of travel delay, throughput, fuel consumption, and emission are revealed by comparison with three baselines.
{"title":"Hybrid optimal control of cooperative vehicle trajectories and traffic signals at intersections: A Benders decomposition-based algorithm","authors":"Meiqi Liu , Xinwei Wang , Meng Wang","doi":"10.1016/j.trb.2026.103407","DOIUrl":"10.1016/j.trb.2026.103407","url":null,"abstract":"<div><div>We put forward a hybrid optimal control approach for joint optimization of cooperative (automated) vehicle trajectories and traffic signals for an intersection configured with different turning movements on multiple arms. The ride comfort, travel time, and throughput involving all vehicles are optimized by continuous vehicle acceleration and discrete traffic signal switch decisions subject to vehicle motion constraints on following gaps, speeds, accelerations, and upper bounds on the maximal signal stage lengths. The red time is designed as a concise vehicle position constraint to enable simultaneous evaluation of traffic-level and vehicle-level decisions. To decrease the computational burden of the mixed integer nonlinear program, the joint control formulation of an intersection is linearized and then decomposed using the Benders decomposition algorithm, generating a sequence of independent slave sub-problems on a lane level that can be solved in a decentralized manner. The control performance is verified via simulation at a four-arm signalized intersection. The simulation results show the joint control approach is flexible in incorporating multiple signal settings (such as cycle lengths and dual-ring design) and turning movements under different traffic demand levels and vehicle arrival rates. Furthermore, the benefits of the proposed control approach and computationally scalable algorithm in mean runtimes and performance metrics of travel delay, throughput, fuel consumption, and emission are revealed by comparison with three baselines.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"206 ","pages":"Article 103407"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1016/j.trb.2026.103447
Tongfei Li, Hongfei Zhu, Min Xu, Huijun Sun
{"title":"Unveiling traffic capacity in the mixed HV and CAV environment: A theoretical approach with CAV clustering intensity","authors":"Tongfei Li, Hongfei Zhu, Min Xu, Huijun Sun","doi":"10.1016/j.trb.2026.103447","DOIUrl":"https://doi.org/10.1016/j.trb.2026.103447","url":null,"abstract":"","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In mixed-autonomy traffic systems, high-dimensional interactions, partial observability, and human behavioral uncertainty jointly pose fundamental challenges to ensuring individual vehicle safety while maintaining overall system efficiency. To address these issues, this study proposes a bilevel, trust-aware multi-agent coordination framework that integrates global risk awareness with local real-time safety control. At the macro level, predicted multi-agent trajectories are used to quantify spatiotemporal risk via Conditional Value-at-Risk (CVaR) modeling, and an optimization-based trust modulation vector guides cooperative behavior at the system scale. At the micro level, each autonomous vehicle dynamically refines its policy through a two-tier safety mechanism: (i) a real-time hard-constraint module based on differentiable Control Barrier Functions (CBF); and (ii) a proactive risk-triggering mechanism that leverages Forward-Reachable Sets (FRS) and Time-To-Collision (TTC) to switch to safety-prioritized policies before hazards escalate. This study demonstrates that, under the trust modulation mechanism, the proposed Bellman operator constitutes a γ-contraction, thereby guaranteeing that the value iteration process converges to a unique optimal policy function. Simulation results further indicate that the framework significantly outperforms state-of-the-art (SOTA) multi-agent reinforcement learning (MARL) baselines across varying traffic densities, reducing collision rates by 1.49%, improving traffic efficiency by 3.86%, and enhancing ride comfort by 4.08%. The overall framework exhibits strong scalability, socially adaptive coordination, and formally verifiable safety guarantees, providing a robust foundation for intelligent cooperation in dynamic and uncertain traffic environments.
{"title":"Macro-Micro Synergistic Safety Coordination for Mixed-autonomy Traffic: A Trust and Risk-aware Multi-agent Framework","authors":"Haitao Li, Yongneng Xu, Tao Peng, Qinyuan Fan, Ningguo Qiao, Ying Zhang","doi":"10.1016/j.trb.2026.103445","DOIUrl":"https://doi.org/10.1016/j.trb.2026.103445","url":null,"abstract":"In mixed-autonomy traffic systems, high-dimensional interactions, partial observability, and human behavioral uncertainty jointly pose fundamental challenges to ensuring individual vehicle safety while maintaining overall system efficiency. To address these issues, this study proposes a bilevel, trust-aware multi-agent coordination framework that integrates global risk awareness with local real-time safety control. At the macro level, predicted multi-agent trajectories are used to quantify spatiotemporal risk via Conditional Value-at-Risk (CVaR) modeling, and an optimization-based trust modulation vector guides cooperative behavior at the system scale. At the micro level, each autonomous vehicle dynamically refines its policy through a two-tier safety mechanism: (i) a real-time hard-constraint module based on differentiable Control Barrier Functions (CBF); and (ii) a proactive risk-triggering mechanism that leverages Forward-Reachable Sets (FRS) and Time-To-Collision (TTC) to switch to safety-prioritized policies before hazards escalate. This study demonstrates that, under the trust modulation mechanism, the proposed Bellman operator constitutes a <ce:italic>γ</ce:italic>-contraction, thereby guaranteeing that the value iteration process converges to a unique optimal policy function. Simulation results further indicate that the framework significantly outperforms state-of-the-art (SOTA) multi-agent reinforcement learning (MARL) baselines across varying traffic densities, reducing collision rates by 1.49%, improving traffic efficiency by 3.86%, and enhancing ride comfort by 4.08%. The overall framework exhibits strong scalability, socially adaptive coordination, and formally verifiable safety guarantees, providing a robust foundation for intelligent cooperation in dynamic and uncertain traffic environments.","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"5 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-16DOI: 10.1016/j.trb.2026.103433
Jianxiu Xiao, Changmin Jiang, Hangjun Yang, Xiaoqian Sun
Ride-hailing services (e.g., Uber and DiDi) have significantly transformed airport ground transportation, thereby impacting airport revenues. In the past, airports primarily relied on parking and car rental services to generate stable non-aeronautical revenue. However, as a growing number of passengers have shifted to ride-hailing, these revenue streams have shown a marked decline. In response to this pressure, airports must reconsider their ground transportation pricing strategies. One approach is to directly impose charges on ride-hailing companies to compensate for revenue losses, though such a strategy may suppress related travel demand. Another approach involves making strategic investments to enhance ride-hailing operational efficiency and subsequently imposing reasonable access fees, thereby achieving revenue growth. To systematically evaluate the impacts of different strategies on airport revenues and social welfare, this study develops a two-stage game-theoretic model involving the airport and the ride-hailing company (RHC). The model is used to identify the airport’s optimal strategies and to propose corresponding government regulatory policies. The results indicate that when the cost of improving ride-hailing operational efficiency is relatively low, the airport can achieve a "win-win-win" situation for the airport, the RHC, and passengers by enhancing ride-hailing operational efficiency while charging fees. Conversely, when the cost of operational efficiency improvement is high, the airport tends to adopt direct charging strategies to secure revenue streams. In this case, although airport profitability is maintained, overall social welfare may decline. If the government aims to maximize social welfare, fiscal subsidies may be necessary to incentivize airports to adopt the strategy of efficiency improvement combined with reasonable charges.
{"title":"Airport pricing strategies for ride-hailing services: A game-theoretic analysis","authors":"Jianxiu Xiao, Changmin Jiang, Hangjun Yang, Xiaoqian Sun","doi":"10.1016/j.trb.2026.103433","DOIUrl":"https://doi.org/10.1016/j.trb.2026.103433","url":null,"abstract":"Ride-hailing services (e.g., Uber and DiDi) have significantly transformed airport ground transportation, thereby impacting airport revenues. In the past, airports primarily relied on parking and car rental services to generate stable non-aeronautical revenue. However, as a growing number of passengers have shifted to ride-hailing, these revenue streams have shown a marked decline. In response to this pressure, airports must reconsider their ground transportation pricing strategies. One approach is to directly impose charges on ride-hailing companies to compensate for revenue losses, though such a strategy may suppress related travel demand. Another approach involves making strategic investments to enhance ride-hailing operational efficiency and subsequently imposing reasonable access fees, thereby achieving revenue growth. To systematically evaluate the impacts of different strategies on airport revenues and social welfare, this study develops a two-stage game-theoretic model involving the airport and the ride-hailing company (RHC). The model is used to identify the airport’s optimal strategies and to propose corresponding government regulatory policies. The results indicate that when the cost of improving ride-hailing operational efficiency is relatively low, the airport can achieve a \"win-win-win\" situation for the airport, the RHC, and passengers by enhancing ride-hailing operational efficiency while charging fees. Conversely, when the cost of operational efficiency improvement is high, the airport tends to adopt direct charging strategies to secure revenue streams. In this case, although airport profitability is maintained, overall social welfare may decline. If the government aims to maximize social welfare, fiscal subsidies may be necessary to incentivize airports to adopt the strategy of efficiency improvement combined with reasonable charges.","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"57 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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