This study develops a cell-based two-stage stochastic program to address the dynamic, spatial and stochastic characteristics of traffic flow for arterial adaptive signal control. To capture demand uncertainty, we formulate the adaptive coordinated traffic signal control as a two-stage stochastic program. To capture dynamic and spatial features of traffic flow, Cell Transmission Model (CTM) is embedded in the two-stage formulation. We incorporate the concept of Phase Clearance Reliability (PCR) to decompose the original two-stage stochastic formulation into separable sub-problems, which greatly enhances solution efficiency. A gradient-based solution algorithm is developed to solve the problem. Numerical examples are constructed to investigate the importance of capturing (or ignoring) each of the dynamic, spatial and stochastic features for traffic control. The results show that failure to account for any of these three traffic flow features will incur a certain extent of delay performance degradation, especially for heavy traffic. Finally, this study validates the findings through VISSIM, with promising results for the newly developed stochastic formulation.

Coordinated ramp metering (CRM) is one effective measure to alleviate urban expressway congestion. Traditional model-based methods generally concentrate on single-bottleneck scenarios, while ignoring the case of multiple bottlenecks. In addition, the fixed-sensor fails to fully capture the dynamic traffic characteristics. The rapid development of traffic detection technology has made available a large amount of automatic vehicle identification (AVI) data, which can record detailed individual trajectories. Taking advantage of the AVI data, CRM can be improved. Besides, multi-agent deep reinforcement learning (MADRL) and game theory have been proven to be effective for traffic signal control. These methods can address the challenges faced by CRM, such as solving nonlinear and high-dimensional optimization problems. This paper proposes a distributed CRM strategy with multi-bottleneck to minimize the total travel time and balance the multiple on-ramps equity, using the individual trajectory information from AVI data. Firstly, the paper defines road segment units, road segment groups, and bottlenecks. Next, the problem is formulated as a potential game that captures the interaction among multiple bottlenecks. The controllers utilize the MADDPG algorithm to determine the green duration of the on-ramps. Finally, the proposed strategy is tested on a real-world urban expressway in a microsimulation platform SUMO. Experimental results demonstrate that the proposed strategy performs better than the baseline methods in eliminating mainline congestion and improving the multiple on-ramps equity. Compared to the no-control scenario, the proposed strategy has improved the performance of the system throughput, average travel time, and average mainline speed by 1.31%, 44.36%, and 115.23%.

Public bus is one of the major green transport modes in most cities, and a proper analysis of public bus can enhance its sustainable development. Hence, the analysis of public bus is always a hot research topic. Generally, to consider the heterogeneity in the studied area and reduce the computation difficulties, the study area is divided into multiple zones and then various analytical methods are applied. However, an improper zone division could lower the quality of the analysis results and then probably mislead the improvement strategy design. This study examines the influence of the two commonly used zone division methods (the grid‐based method and the natural‐boundary‐based method) on the spatial analysis of public bus service using the analytical example. The result shows that the impact of zone division exists. Therefore, this study aims to identify the possible approach to reduce the negative impact and suggests a new zone division method, the bus‐service‐based (BS‐based) method, for the spatial analysis of public bus service. The BS‐based method is compared with the commonly used zone division methods using the real‐world data, and the comparison results demonstrate that the BS‐based method can be more efficient to support the spatial analysis of bus service.