Travel Behavior and Congestion Tolling Strategies in a Bi-Modal Bottleneck Model with Autonomous and Human-Driven Vehicles under Linear Scheduling Preferences

TRAVEL AS PRODUCTION : ENDOGENOUS IN - TRIP OUTPUT , SCHEDULING , AND THE BOTTLENECK

Mitigating Regional Traffic Congestion via School Start Time Scheduling: A Bilevel Alternating Optimization Approach

This paper addresses morning commute congestion caused by concentrated school-related trips in urban networks. We propose a bi-level optimization framework for regulating school start times in a multi-region urban network characterized by Macroscopic Fundamental Diagrams (MFDs), explicitly coupling system-level regulation with multi-class user-equilibrium-based departure-time choices. The Upper-Level problem jointly minimizes total time spent and deviations from current school schedules, while the Lower-Level problem models commuter behavior through a deterministic dynamic multi-class user equilibrium formulation incorporating alpha-beta-gamma preferences for travel time, earliness, and lateness costs. To address the computational challenges arising from the bilevel structure, non-convex traffic dynamics, and endogenous demand responses, an iterative algorithm alternating between the Upper- and Lower-Level problems is developed. The Upper-Level problem is approximated through a formulation solvable with standard mathematical programming solvers, while an iterative algorithm provides an approximate solution to the Lower-Level equilibrium problem. Numerical results demonstrate substantial congestion reductions and characterize the trade-off between school start-time flexibility and traffic efficiency. Sensitivity analyses further examine the effects of MFD uncertainty and scheduling preferences.

Behavioral Stability of Multiple Vehicle Platoons with User Preference via a Competitive Migration Game

Highway-dominated urban societies face many challenges in civilian and commercial transportation. Highways have low passenger density and higher rates of accidents and fatalities than bus- and locomotive-dominated urban areas. Autonomous Vehicles (AVs) promise to reduce driver-related highway accidents by shifting responsibility; however, the problem of low passenger density on highways persists. Vehicle Platooning (VP) increases passenger density on highways by coordinating many AVs to follow each other closely, allowing more vehicles to fit within a given length and reducing aerodynamic drag, thereby increasing energy efficiency. This proof-of-concept study introduces a Competitive Migration Game (CMG) applied to the real-time formation of multiple VPs to ensure the behavioral stability of platoon members via Nash Equilibrium (NE) and control the maximum platoon size to maintain highway safety. The CMG also respects user preferences for which platoon to join, whether based on platoon driving characteristics or fleet brand. Simulation results indicate behavioral stability in the formation and management of multiple platoons via NE.

Dynamic User Equilibrium for Electric Vehicle Departure Time and Path–Charging Choices with Wireless and Fast Charging Services

This study investigates how coordinated wireless and fast charging services reshape electric vehicle departure time and path–charging choices when a trip-level charging requirement must be completed before arrival. A multi-class dynamic user equilibrium model is formulated for road networks containing wireless charging lanes and fast charging stations. An energy-aware dynamic network loading model propagates traffic and battery states, transfers upstream wireless energy into the residual station workload, and determines endogenous waiting. The equilibrium is expressed as a finite-dimensional variational inequality and solved by an energy-aware inertial fixed-point framework with safeguarded route swapping and independent verification. Experiments on the Nguyen–Dupuis and Sioux Falls networks show that low-state-of-charge users depart 6.91 min earlier on average, while exposure-informed wireless-charging placement can substantially reduce downstream station waiting and exhibits saturation once all behaviorally exposed links are active. Under compound demand and low-state-of-charge pressure, roadway queues activate more sharply than station waiting. In a common Sioux Falls algorithm benchmark, the inertial method reaches stable acceptance in 776.2 s compared with 1562.9 s for its non-inertial counterpart. The method of successive averages crosses the practical gap threshold earlier but does not satisfy the common flow-stability criterion within 3000 updates and 9018.1 s. Across 30 final Sioux Falls scenarios, all solutions satisfy the practical verified gap and physical feasibility gates, with 11 difficult cases requiring explicit route-swap continuation. The results clarify the complementary operational roles of corridor and station charging while delimiting the numerical and behavioral assumptions of the framework.

Optimization of Differentiated Pricing Strategies for Freeways Considering Vehicle Collision Probability

In response to the limitations of the conventional fixed-rate pricing model for freeways in addressing spatiotemporal imbalances in traffic flow distribution and low road network resource utilization, as well as the neglect of Vehicle Collision probability in existing differentiated pricing frameworks, this study analyzes the underlying relationship between road traffic saturation and collision probability. On this basis, a bi-level programming model for differentiated freeway pricing that explicitly accounts for Vehicle Collision probability is developed. The upper-level model maximizes the economic benefit of freeway operations while incorporating vehicle-collision-probability-related costs, whereas the lower-level model minimizes vehicle travel impedance, including both travel cost and time delays induced by collisions. A hybrid algorithm combining particle swarm optimization (PSO) and pattern search (PS) is employed to solve the optimal pricing scheme across different vehicle types (passenger cars and trucks), road segments, and time periods. A nested Logit model is introduced to characterize travelers’ route choice behavior between alternative routes, thereby capturing the traffic redistribution effect induced by pricing rate adjustments. Empirical data from a section of the Shenyang–Haikou Freeway corridor in China for the year 2024 are used for model calibration and validation. The results indicate that following the optimized pricing scheme, the freeway’s monthly net revenue increased by 9.44%, truck and passenger car traffic volumes rose by 8.59% and 6.12%, respectively, and total segment traffic volume increased by 7.18%. The saturation of the freeway increased from approximately 0.18 to about 0.32, while that of the parallel arterial decreased from approximately 0.9 to about 0.67, indicating a more balanced traffic flow distribution across the network and a substantial reduction in Vehicle Collision probability. These findings demonstrate that the proposed method can effectively balance the trade-offs among operational revenue, traffic efficiency, and driving safety, providing both theoretical support and a quantitative tool for the scientific formulation of differentiated freeway pricing schemes.

Behavior-Aware Multi-Modal Trip Planner: Combining GTFS, OSM, and Reinforcement Learning for User-Centric Routing

Urban public transportation systems are widely used, yet most route planning tools primarily optimize objective factors such as travel time or distance, often overlooking subjective aspects of the travel experience such as comfort, crowding, and walking tolerance. As a result, routes that are optimal by technical criteria may feel stressful or impractical to users, limiting the effectiveness of transit systems. This paper proposes a behavior trait-aware multimodal trip planning framework that takes into consideration the personal characteristics of each user. In this regard, the proposed system integrates scheduled transit data, street-level network data, and ride-hailing connectivity within a unified network. In addition, user traits such as urgency, crowding, and willingness to walk are considered within the routing process, affecting both perceived travel cost and routing strategy. The problem of routing is modeled as a reinforcement learning problem using a policy-based optimization approach (Proximal Policy Optimization, PPO), in which the agent is aware of its location, the behavioral characteristics of the user, and the available travel options, in addition to receiving feedback based on travel efficiency and preference alignment.