This paper proposes a two-stage multi-lane unsignalized intersection cooperative control strategy based on mixed platoons (MICS-PF) in the mixed traffic environment of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). In the first stage, we propose a 0-1 integer programming model based on vehicles’ intersection movements and potential car-following costs to organize vehicles into mixed platoons led by CAVs. Meanwhile, we design a rule-based algorithm to coordinate the relative distribution of platoons. Then, the lane-changing and car-following models are integrated to ensure that the platoons organization plan can be completed in the limited functional zone. In the second stage, we design an improved minimum covering clique algorithm to generate a conflict-free passing order of platoons by considering the platoon-level conflict relation and the potential yield delay. The proposed strategy couples the process of organizing vehicles into mixed platoons to pass through the intersection. The optimal platoon organization enables CHVs to receive accurate guidance when entering the intersection and avoids the disorderly lane change at the upstream road sections. The mixed platoon control also allows vehicles to avoid stop-and-go, improve the capacity of approach roads, and prevent the intersection capacity from being restricted by the inefficient traffic at the approach road. The conflict-free coordination method of mixed platoons optimizes the throughput of intersections and broadens the usage conditions of unsignalized intersections. A series of simulation experiments verify the effectiveness of the strategy, and the applicable boundary conditions are obtained.

With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.

Mobile data are a feasible way for us to understand and reveal the feature of human mobility. However, it is extremely hard to have a fine-grained picture of large-scale mobility data, in particular at an urban scale. Here, we present a large-scale dataset of 2-million mobile phone users with time-varying locations, denoted as the temporal network of individuals, conducted by an open-data program in Changchun Municipality. To reveal human mobility across locations, we further construct the aggregated mobility network for each day by taking cellular base stations as nodes coupled by edges weighted by the total number of users’ movements between pairs of nodes. The resulting temporal network of mobile phone users and the dynamic, weighted and directed mobility network are released in simple formats for easy access to motivating research using this new and extensive data of human mobility.

The vehicle trajectory data is a feasible way for us to understand and reveal urban traffic conditions and human mobility. Therefore, it is extremely valuable to have a fine-grained picture of large-scale vehicle trajectory data, particularly in two different modes, taxis and buses, over the same period at an urban scale. This paper integrates the trajectory data of approximately 7,000 taxis and 1,500 buses in Changchun City, China and accesses the temporal geographically-explicit network of public transport via sequential snapshots of vehicle trajectory data every 30 seconds of the first week in March 2018. In order to reveal urban traffic conditions and human mobility, we construct two-layer urban traffic network (UTN) between these two different transport modes, take crossings as nodes and roads as edges weighted by the volume or average speed of vehicles in each hour. We released this temporal geographically-explicit network of public transport and the dynamics, weighted and directed UTN in simple formats for easy access.