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The combination of self-driving technology and taxis has a considerable potential in improving service quality and economic efficiency that can completely change traditional taxi operation. This study accordingly focuses on the autonomous taxi (aTaxi) dispatching problem in hybrid request mode where travelers can either request immediate rides or reserve taxi services ahead of time. In this study, a centralized dispatcher and decentralized autonomous dispatchers are designed to plan short-term and long-term routes for aTaxis in real time, respectively. The centralized dispatcher integrates vehicle-to-passenger assignment with empty vehicle rebalance to guarantee solution quality. Decentralized autonomous dispatchers distribute a portion of the calculation to aTaxis, thus reducing the centralized dispatcher’s workload. The dispatching strategy ensures that the response to traveler requests can be made immediately, and if such requests are accepted, the travelers will receive the expected services. Finally, experiments are conducted based on the actual road network and the trip data of Manhattan to investigate the dispatching strategy. The results confirm the high performance of the proposed dispatching strategies in terms of service quality and economic efficiency. It is also found that the increase in reservation rates can enhance the robustness of the method with respect to errors in predicted requests. Moreover, when the number of vehicles is adequate and reservations are made longer ahead of time, the completion rate of requests and the revenue improve.

The railway container transportation is attracting more and more attention in China. In order to improve the service quality, a novel concept of passenger-like container train is proposed, which can reduce the accumulation time of containers at the origin station and increase the train frequency compared with the traditional container through train. With the aim of generating optimal operation strategies for passenger-like container trains, this paper establishes an optimization model for the train stop plan problem, in which the objective is to minimize the total number of stops. In addition, the specific container-to-train distribution and the utilization rate of each individual train are considered. The proposed model is a mixed-integer linear programming one, which can be solved by using the CPLEX solver. Finally, the numerical experiments are performed to test the effectiveness of our model by using a simple railway line and the China Railway Express corridor as examples. The results prove the advantages of our method.

Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions. (C) 2017 Elsevier Ltd. All rights reserved.