Qiang Bai

Traditional road asset management often operates in isolation, leading to suboptimal coordination. While integrated asset management enables multiasset maintenance and rehabilitation (M&R) decisions on a unified platform, existing approaches typically do not systematically account for traffic redistribution effects. This study proposes a road M&R planning method based on bilevel multiobjective optimization (MOO) that explicitly integrates user and environmental considerations with economic and performance objectives. The upper level optimizes network-level multiyear agency cost, network condition, user cost, and greenhouse gas (GHG) emissions. The lower level employs a traffic assignment model to address traffic dynamics caused by reduced link capacity during M&R operations. The bilevel MOO model is solved using the Nondominated Sorting Genetic Algorithm III and Self-Regulated Method of Successful Averages to generate Pareto solutions, with an analytic hierarchy process-based weighted-sum method determining the final solution. A five-year case study on a road network in Liaoning Province, China, demonstrates the method’s effectiveness for pavement and bridge M&R planning: 19.11% improvement in network condition, 9.07% reduction in GHG emissions, and 4.64% reduction in user costs, proving the method’s effectiveness for achieving cost-effective and sustainable M&R decisions. Comparative analysis against a static traffic baseline reveals that explicitly modeling traffic redistribution reduces user costs by 99.39% and GHG emissions by 61.17%, demonstrating traffic dynamics alter optimal M&R decisions. The methodology is validated for a regional network with asphalt pavements, reinforced concrete T-beam bridges, and passenger vehicle traffic under deterministic demand; extensions to heterogeneous vehicle types, elastic demand, and other infrastructures represent directions for future studies.

Connected and automated vehicles (CAVs) indicate the future of human transportation. There will be a mix of conventional vehicles and CAVs sharing the roads for an extended period before CAVs completely replace all human-driven vehicles (HDVs). It is crucial to investigate how to optimize traffic efficiency across the entire network during this transitional phase. Introducing dedicated lanes (DLs) can be an effective approach. This study aims to provide a comprehensive review of existing studies on the deployment of dedicated CAV lanes. An analysis of the literature shows that there are three main groups of studies related to DLs for CAVs: one group focuses on the deployment of dedicated CAV lanes, the second explores the control policies for DLs for CAVs, and the third examines the impacts of implementing dedicated CAV lanes. The main findings of the analysis indicate that the deployment of DLs for CAVs can enhance traffic performance within a certain range of market penetration rates (MPRs), but this range varies slightly across different studies. It is also found that the MPR of CAVs plays a significant role in the deployment of DLs. It is not recommended to deploy DLs for CAVs if the MPRs are either too low or too high. In addition, the deployment plan of dedicated CAV lanes typically considers the influencing factors such as system cost, safety, environmental factors, and total travel time. This review provides an in-depth examination of DL deployment methodologies, evaluates their impact on traffic systems, and identifies key areas for future research.

Passenger emergency evacuation is a critical aspect of operations at multimodal transportation hubs. When a large number of passengers gather within the hub, it is necessary to evacuate them efficiently from multimodal transfer points to ensure their safe departure and to prevent unforeseen incidents such as stampedes. To improve the efficiency of emergency evacuation under limited evacuation capacity conditions, passenger redistribution can be adopted as a strategy to optimize the evacuation process. This study proposes a passenger redistribution model based on a rolling horizon optimization framework for the emergency evacuation of multimodal transportation hubs. The model is formulated as a multiobjective optimization problem with two objectives: (1) minimizing the total evacuation time to maximize the overall evacuation efficiency and (2) minimizing differences in waiting times among different travel modes to reduce imbalance across them. The problem is solved using the NSGA‐II algorithm. A case study based on Terminal 3 of Beijing Capital International Airport is conducted to demonstrate the effectiveness of the proposed passenger redistribution model. The results show that the proposed model can generate effective passenger redistribution plans for emergency evacuation of the transportation hubs. Furthermore, the findings indicate that passenger redistribution is particularly suitable when there is a significant mismatch between evacuation capacity and evacuation demand. However, its effectiveness is limited when a large number of passengers are initially stranded, while it proves to be more effective when the initial number of stranded passengers is relatively small.