Multimodal arterial signal coordination for buses and passenger vehicles can improve arterial travel smoothness and efficiency. However, uncertainty in bus operations requires signal priority at intersections, which impacts coordination and increases stop times for other traffic types. Therefore, this study proposes a stochastic priority-integrated signal coordination (SPIC) method. It includes an offline stochastic programme to determine the arterial signal coordination, i.e. cycle length and offsets, considering the stochastic signal priority, and an online mixed-integer nonlinear programme to determine the signal priority together with the bus arrival and departure times at and from stops and intersections in a connected vehicle environment. A scenario-based heuristic algorithm is proposed to solve the SPIC efficiently. Numerical studies have validated that SPIC can improve the efficiency of buses and passenger vehicles. Sensitivity analyses show that the SPIC effectively reduces delays with fluctuations in the bus travel time, dwell time, and passenger vehicle demands.@en

With the development of internet of vehicles and automated driving, individual-based trajectory control at intersections becomes possible. Trajectory planning and coordination for connected and automated vehicles (CAVs) have been studied at isolated “signal-free” intersections and in “signal-free” corridors under the fully CAV environment in the literature. Most existing studies are based on the definition of approaching and exit lanes. The route a vehicle takes to pass through an intersection is determined by its movement. That is, only the origin and destination arms are included. This study proposes a mixed-integer linear programming (MILP) model to optimize vehicle trajectories at an isolated “signal-free” intersection without lane allocation, denoted as “lane-allocation-free” (LAF) control. Each lane can be used as both approaching and exit lanes for all vehicle movements including left-turn, through, and right-turn. A vehicle can take a flexible route by way of multiple arms to pass through the intersection. In this way, the spatial–temporal resources are expected to be fully utilized. The interactions between vehicle trajectories are modeled explicitly at the microscopic level. Vehicle routes and trajectories (i.e., car-following and lane-changing behaviors) at the intersection are optimized in one unified framework for system optimality in terms of total vehicle delay. Considering varying traffic conditions, the planning horizon is adaptively adjusted in the implementation of the proposed model to make a balance between solution feasibility and computational burden. Numerical studies validate the advantages of the LAF control in terms of both vehicle delay and throughput with different demand structures and temporal safety gaps.@en

Due to the manoeuvre complexity, models describing the platoon formation process on urban roads are lacking in the literature. Inspired by flocking behaviours in nature, we proposed a two-dimensional model to describe connected automated vehicle (CAV) group dynamics based on the potential theory, which is composed of the elastic potential energy for the inter-vehicle spring-mass system and the cross-section artificial potential field. The inter-vehicle elastic potential energy enables CAVs to attract each other at long distances, and repel each other otherwise. It also generates incentives for lane changes. The cross-section artificial potential field is able to mimic the lane-keeping behaviour and creates resistance to avoid unnecessary lane changes at very low incentives. These modelling principles can also be applied to human-driven vehicles in a mixed traffic environment. The behavioural plausibility of the model is demonstrated analytically and further verified in a simulation of typical driving scenarios.@en

This paper studies the demand-supply imbalance problem for one-way carsharing systems under a combination of pricing strategy, relocations and access trips considering stochastic demand. A novel concept of a virtual zone is utilized to capture vehicle relocation range and client walking or biking distance constraints in one-way carsharing systems. The vehicle imbalance problem is further addressed by combining a long-term pricing strategy and real-time vehicle relocations in a two-stage stochastic programming model. In the first stage, the tactical decisions including fleet size and trip price are optimized, while anticipating the operational costs from the second stage. The second stage optimizes operational decisions under uncertain demand including vehicle relocations conditional on the tactical decisions in stage one. The model aims to maximize the profit of a carsharing company considering the fleet costs calculated in stage one and the expected operational costs and revenue obtained in stage two. A dedicated gradient search algorithm is developed to solve the two-stage stochastic programming and results are compared to a genetic algorithm and an iterated local search algorithm. The proposed model and corresponding solution approach are applied to a large-scale network with 50 zones and over 1000 vehicles in Suzhou, China. The application allows us to attain additional operational insight. Results suggest that increased prices for high demand stations during peak hours reduce demand while maintaining profitability of the system. It is also found that the real-time vehicle relocations and flexibility of clients to pick up vehicles at farther stations can increase demand service rate by as much as 10%.@en

Arterial traffic signal coordination makes traffic flow more efficient and safer. This paper presents a partition-enabled multi-mode band (PM-BAND) model that is designed to solve the signal coordination problem for arterials with multiple modes, i.e., passenger cars and transit vehicles. The proposed method permits the progression bands to be broken if necessary and optimizes system partition and signal coordination in one unified framework. The impacts of traffic demand of passenger cars and transit vehicles as well as the geometry characteristics of the arterials are taken into account. Signal timings and waiting time of transit vehicles at stations are optimized simultaneously. The PM-BAND model is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical example results have demonstrated that the PM-BAND model can significantly reduce the average number of stops and delay compared with the other models, i.e., MAXBAND and MULTIBAND. Moreover, the progression bands generated by the PM-BAND model have a higher reliability and effectiveness.@en

One-way carsharing is an alternative shared-use transportation mode that provides flexible travel accommodations for urban mobility. However, vehicle distributions can be mismatched with demand distributions because users are not required to return to their departure locations. Conventional operator-based vehicle relocation is limited by labor resources, but user-based and demand-controlled approaches can open new avenues for mitigating vehicle imbalance. This paper proposes a method for controlling demand patterns by applying measures of combinatorial monetary incentives and surcharges. A two-level nested logit model is adopted to analyze user decisions regarding the travel process in response to differentiated pricing combinations. A user choice model is aggregated and loaded into a time-space network that reveals the dynamics of the carsharing system. An optimization framework is proposed to determine the incentives and surcharges at different stations and times of day. This paper presents an algorithm for solving the proposed optimization model, as well as an example of parameter calibration and the solving process. Case analysis suggests that the proposed method can increase revenues by 22.5% compared to a scenario without demand control and vehicle relocation policies. Comparisons suggest that the proposed demand-based control policy can achieve higher revenues than operator-based relocation, whereas operator-based relocation could satisfy greater demand.@en

Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident if one is to maximize their potential in enhancing travel efficiency. In this study, we address the problem of jointly scheduling and routing SAVs across an urban network with the possibility of platooning the vehicles at intersections to reduce their travel time. We argue that this is especially useful in large urban areas. We introduce a novel vehicle scheduling and routing method that allows a specific number of SAVs to converge at the intersections of urban corridors within designated time intervals, facilitating the formation of SAV platoons. Dedicated lanes and signal priority control are activated to ensure that these platoons go through the corridors efficiently. Based on the above concept, we propose a linear integer programming model to minimize the total travel time of SAVs and the delays experienced by the conventional vehicles due to SAV priority, thereby optimizing the overall performance of the road network. For large instances, we develop a two-stage heuristic algorithm to solve it faster. In the first stage, leveraging an evaluation index that manifests the compatibility of each vehicle-to-request combination, we allocate passenger requests to a fleet of SAVs. In the second stage, a customized genetic algorithm is designed to coordinate the paths of various SAVs, thus achieving the desired vehicle platooning effect. The optimization method is tested on a real-world road network in Shanghai, China. The results display a remarkable reduction of 15.76 % in the total travel time of the SAVs that formed platoons. The overall performance of the road network could be improved with the total travel time increase of conventional vehicles significantly smaller than the reduction observed in SAVs’ total travel time.@en

Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident if one is to maximize their potential in enhancing travel efficiency. In this study, we address the problem of jointly scheduling and routing SAVs across an urban network with the possibility of platooning the vehicles at intersections to reduce their travel time. We argue that this is especially useful in large urban areas. We introduce a novel vehicle scheduling and routing method that allows a specific number of SAVs to converge at the intersections of urban corridors within designated time intervals, facilitating the formation of SAV platoons. Dedicated lanes and signal priority control are activated to ensure that these platoons go through the corridors efficiently. Based on the above concept, we propose a linear integer programming model to minimize the total travel time of SAVs and the delays experienced by the conventional vehicles due to SAV priority, thereby optimizing the overall performance of the road network. For large instances, we develop a two-stage heuristic algorithm to solve it faster. In the first stage, leveraging an evaluation index that manifests the compatibility of each vehicle-to-request combination, we allocate passenger requests to a fleet of SAVs. In the second stage, a customized genetic algorithm is designed to coordinate the paths of various SAVs, thus achieving the desired vehicle platooning effect. The optimization method is tested on a real-world road network in Shanghai, China. The results display a remarkable reduction of 15.76 % in the total travel time of the SAVs that formed platoons. The overall performance of the road network could be improved with the total travel time increase of conventional vehicles significantly smaller than the reduction observed in SAVs’ total travel time.@en