Oversaturation has become a serious issue for urban intersections worldwide due to the rapid increase in population and traffic demands. The emergence of connected and automated vehicle (CAV) technologies demonstrates the potential to improve oversaturated arterial traffic. Integrating vehicular control and intersection controller optimization into a single process based on CAV technologies can optimize the performance of mixed traffic flow scenarios with various levels of CAV market penetration. This paper proposes an efficient reservation-based cooperative ecodriving model (RCEM) for an isolated intersection under partial and complete CAV market penetration, which can simultaneously optimize the CAV trajectories and intersection controller. CAVs are utilized to precluster manual vehicles into a platoon to improve vehicle passage efficiency. Then, a heuristic-based algorithm is developed to effectively obtain an optimal solution. The proposed RCEM scheme is compared with fixed signal control and actuated signal control in a Simulation of Urban MObility (SUMO)-based platform. Experimental results prove that the RCEM scheme outperforms the fixed signal control and actuated signal control in terms of stop delay, fuel consumption, and emissions under the condition of low levels of CAV penetration. Sensitivity analysis indicates that the system performance further improves as the CAV penetration rate increases, and the stop delay is almost eliminated when the CAV market penetration reaches 100%. Furthermore, the vehicle delay fluctuation under left-turning rates ranging from 5%-75% is 4.4 sec, which is far better than the vehicle delay fluctuation of the fixed signal control (176 sec) and actuated control (65.6 sec).

Since emergency vehicles (EMVs) in urban transit systems play a crucial role in responding to time-critical events, the quick response of EMVs is essential for improving the success rate of rescue operations and minimizing property loss. Booming connected vehicle (CV) technology provides a new perspective to further enhance the effectiveness of EMV priority. Based on this CV technology, we propose a cooperative multiple EMV priority model in which the speed, acceleration, and lane changing actions of both the EMVs and surrounding ordinary vehicles (OVs) are set as decision variables. This proposed model is rigorously formulated in integer linear programming to maximize the EMV traffic efficiency and find a trade-off between the interference with normal traffic flows and the smoothness of the EMV driving trajectories. Two customized algorithms are developed to reduce the number of decision variables and constraints to obtain the better feasible solution in an acceptable computational time. A numerical experiment based on real-world data is proposed to further verify the utility and effectiveness of the aforementioned mathematical model. The customized algorithms achieve near-exact solutions with significantly faster computation compared to the benchmark solver. The robustness of the proposed model is tested with different parameter settings in the sensitivity analysis.

This study proposes an alternative flexible transit model with two levels of bus stops, A level and B level. A‐level bus stops are fixed, while B‐level bus stops are flexible and provide service only when passengers indicate a strong willingness to pay (WTP). This fare structure encourages passengers to choose bus stops with their mobile phones or computers. An optimisation model of 0–1 integer‐programming is formulated based on whether certain B‐level stops can be serviced. With a numerical example, the authors compare the performance of the proposed traversing method and a tabu search algorithm, both of which are adapted to solve the model. Finally, a real case is provided to evaluate the proposed transit system against comparable systems (e.g. a fixed‐route transit system and a taxi service), and the result shows that the flexible transit routing model will help both passengers and bus companies, thus creating a win–win situation.

The longest cross-sea bridge worldwide, i.e., the Hong Kong-Zhuhai-Macao Bridge (HZMB), opened in October 2018, integrating nine cities and two special administrative regions of China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The GBA is one of the regions with the most active economic vitality in China and has an important strategic position in the overall national development. Using full-sample freeway toll data of the GBA, this paper proposed a new structured evaluation methodology that combines the node degree, traffic volume, and topological and flow field-theory (e.g., agglomeration and distribution) to explore the hub city ranking and evolution in this area. The data period we selected to analyze is immediately before and after the opening of the HZMB, and we assess the counties' centrality changes in the GBA by analyzing these data. The findings reported in this paper can reflect the traffic hub evaluation process and provide more macroscopic intercity transportation views to the government.

In densely populated and traffic-congested major cities, medical emergency rescue incidents occur frequently, making the use of drones for emergency medical supplies delivery a new emergency distribution method. However, establishing drone transportation networks in urban areas requires balancing spatiotemporal fluctuations in emergency needs, meeting hospitals’ mandatory constraints on response time, and addressing factors like airspace restrictions and weather impacts. By analyzing the spatiotemporal distribution characteristics of medical emergency logistics in large cities, this study constructs a drone base station location optimization model integrating dynamic and static factors. The model combines multi-source data including emergency needs, geographic information, and airspace limitations. It employs kernel density estimation to identify hotspot areas, uses DBSCAN clustering to detect long-term stable demand hotspots, and applies LSTM methods to predict short-term and sudden demand fluctuations. The model optimizes coverage rate, response time, and cost budget control for drone transportation networks through a multi-objective genetic algorithm. Using Guangzhou as a case study, the results demonstrate that through “dynamic-static” collaborative deployment and multi-model drone coordination, the network achieves 96.18% demand coverage with an average response time of 673.38 s, significantly outperforming traditional vehicle transportation. Sensitivity analysis and robustness testing further validate the model’s effectiveness in handling demand fluctuations, weather changes, and airspace restrictions. This research provides theoretical support and decision-making basis for scientific planning of urban medical emergency drone transportation networks, offering practical significance for enhancing urban emergency rescue capabilities.