Existing dynamic bus lanes (DBLs) typically use a fixed and long blocking space to guarantee bus priority, leading to a conflict between bus priority and traffic capacity. This study proposes an innovative approach in a connected-vehicle (CV) environment, called dynamic bus lane with moving block (DBLMB). The length of the moving block can be adjusted with the bus speed in real time, aiming to ensure bus priority with fewer road resources, thus improving traffic capacity. A three-lane cellular automata model is established to evaluate DBLMB in comparison with two other strategies, bus lane without priority and bus lane with intermittent priority (BLIP). First, the benefits of DBLMB over the established DBLs are analyzed qualitatively by the macroscopic fundamental diagram. Next, simulation experiments are conducted to compare the coordination between bus priority and capacity under different strategies. Then, the impact of DBLMB on microscopic traffic flow is investigated through evaluation indicators including lane density, lane speed, lane-changing frequency, and travel speed. Finally, the sensitivity of bus delay and traffic capacity to CV penetration is discussed. The results show that: (1) the capacity of a three-lane road adopting DBLMB strategy can be stabilized above 5200 passenger car units per hour (pcu/h) when the bus departure interval is higher than 60 s and the expected level of service is below B. (2) With 100%, 80%, 60%, 40%, and 20% of CV penetration, DBLMB improves the average capacity of the road by 523 pcu/h compared with BLIP, with only an incremental bus delay of 6.43 s per vehicle.

The parking cruising problem can be attributed to the unbalanced spatial parking space occupancy in the parking lot with multiple trip purposes and high traffic volumes, and it has already become an obstacle to improving travel efficiency. This study aims to investigate the reasons for the unbalanced occupancy of parking spaces and develop strategies to achieve spatial equilibrium by reducing parking cruising time. A field survey is first conducted to analyze the factors influencing parking space choice behavior, such as searching distances, walking distances, parking price, and parkers’ attributes. Based on the survey, a mixed logit model is calibrated to reveal parking space choice patterns of different parkers. Afterwards, the parking lot is divided into three zones based on searching distances and walking distances, using the K-means algorithm. According to the results, a simulation-based optimization model is then established, for minimizing the variance of average occupancy. An optimal pricing strategy is obtained at last. A case study in Nanjing, China, is conducted to verify the feasibility and rationality of the proposed method. The difference between weekdays and weekends is explored. The results show that, with the optimal strategies, the standard deviation of average occupancy in each zone decreases by 39.52% during weekends and 42.29% during weekdays. Finally, political implications and recommendations on the layout design of facilities and the adoption of differential parking pricing strategies are provided.

Mitigating negative user experiences in electric vehicle adoption is a crucial strategy for advancing widespread transportation electrification. To reduce the unavoidable charging wait times faced by private electric cars (PECs) during intercity long-distance travel, caused by their limited driving range and slow charging speeds, this article explores an optimization model that leverages backhaul empty electric trucks (BEETs) as temporary mobile chargers to increase the service capacity of fixed charging stations. To address the optimization of BEETs serving PECs, we first construct a model in which all BEETs are guaranteed profits, while jointly maximizing their profits and overall social benefits. Due to the inclusion of nonlinear terms, the model falls under the category of mixed-integer nonlinear programming (MINLP). To enable efficient computation, we propose a model transformation method based on time discretization, converting the original MINLP into a mixed-integer linear programming (MILP) formulation. Case study results from Sichuan, China, demonstrate that BEETs, by proactively detouring to offer charging services, can significantly reduce PECs’ cumulative waiting time. The system delivers a high social benefit-to-cost ratio, underscoring the dual role of BEETs as both profit-generating assets and effective delay-reduction tools without requiring additional infrastructure. Sensitivity analyses offer systematic managerial insights for addressing future development trends.