With the expansion of the sharing economy, growing urban traffic, and increasing environmental pollution, bike-sharing systems (BSSs) are developing rapidly all over the world. A major operational issue in BSS is to reposition the bikes over time such that enough bikes and open parking slots are available to users. Especially during peak hours, it is essential to stabilize BSS in use. To cope with the issue, this article proposes a new approach integrating multiobjective optimization and a weighting factor based on the shortage event types of each station. In addition, the multiobjective artificial bee colony algorithm is modified according to the features of this work to find optimal solutions. The proposed approach is applied to the real-life repositioning of a BSS during peak hours to verify its feasibility and effectiveness. Also, the algorithm is compared with other frequently used multiobjective algorithms. For the comparative study, convergence metric and spacing are adopted to further measure the algorithm performance. The scalability of the proposed approach in addressing the multiobjective repositioning problems during peak hours is also verified by multiple trials.

In this paper, we investigate the effect of emergency signs on evacuation dynamics under smoke conditions. We assume that in a smoky hall the visual field of pedestrians is limited to a certain range, and they do not know the exact location of the exit. In this kind of evacuation process, we analyze the influence of emergency signs on movement direction and speed, and the herd behavior of pedestrians. In the analysis, we divide the emergency signs into two types: the wall signs (WS) and the ground signs (GS). Then, we analyze the variation of pedestrian behavior when they encounter the WS, the GS, and the exit in the evacuation process. Combined with the analysis results, we build our improved model based on the social force model. In the simulation, we study the evacuation process in the case of WS and GS. According to the result of the simulation, we consider that the effect of the emergency signs on herd behavior and the desired speed is an important factor to improve evacuation efficiency. We find that, from the perspective of evacuation time, the evacuation in the case of WS is more efficient, but from the perspective of the interaction between pedestrians, the evacuation in the case of GS presents less security risk. Finally, we explore how to design a mixed layout scheme of WS and GS.

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.

In this paper, we seek to identify the different impacts of external and internal information on taxis' cruising behaviors and to find effective methods to enhance taxis' cruising efficiency. Through global positioning system trajectory data collected in Shenzhen, China, we determine the impacts of external factors (land use, traffic conditions, and road grade) and internal factors (previous pick-up experience) on cruising location choice using a zero-inflated negative binomial model. The results indicate that the external factors have a more significant influence than the internal ones. Specifically, traffic conditions, land use, urban expressways, and previous pick-up points are the major factors that influence drivers' cruising decisions. The results also show that drivers follow different patterns during different times of day, i.e., relying on traffic conditions and land use in the morning/ evening peak hours but emphasizing land use and previous pick-up experience during non-peak hours. In addition, high-earning drivers and roaming drivers prefer to cruise in areas with high-density land use and optimal traffic conditions, whereas low-earning drivers and target drivers tend to cruise in areas with more previous pick-up points. These findings uncover the underlying mechanisms of cruising decisions and facilitate the development of strategies to minimize empty cruising time. Based on the study results, the external and internal information that was found to affect cruising decisions can be released to taxi drivers to improve their cruising efficiency. This information is also helpful to managers in deciding the overall layout of taxi service locations.

The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin–destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link’s closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.

The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.