To alleviate the problem between parking demand and supply, as well as improving urban traffic environment, shared parking has attracted great interest among researchers, policymakers, and entrepreneurs. Naturally, it is a prerequisite of sharing private parking spaces with public users (P-users) that all of the owners (O-users) providing parking spaces have space to park whenever they come back, with a unified management of all parking resources. However, it remains a challenge both in theory and practice. To solve this problem, firstly, we introduced a management framework of shared parking resource in terms of time and spatial dimension. Under this framework, to control the access to parking spaces of P-users, four phases (preparatory phase, open phase, releasing phase, and reconstructive phase) are divided in time dimension, and two types of parking spaces (the prestored parking spaces, and the shared parking spaces) are classified in spatial dimension. Then, based on the proposed management framework, an intelligent parking management system (IPMS) was developed to simulate the operation of shared parking considering the uncertainties of P-users’ and O-users’ arrival and departure. Furthermore, detailed sensitivity analysis, based on real-world data and simulations, evaluated the proposed framework and the developed IPMS in a case study concerning parking lots in Beijing, China. The results show that the IPMS can not only realize that there will always be enough available parking spaces satisfying O-users’ parking demand, but also bring about vast improvements in both utilization and turnover rate of parking spaces, comparing with the non-shared management strategy.

With the help of accurate parking navigation systems, sharing vacant private parking spaces with public travelers may have the potential of reducing the number of cruising vehicles and contribute to traffic emission reduction. However, the quantitative effects of such a measure are still relatively unknown in traffic management. In this paper, we firstly established an optimal allocation model of shared parking spaces, which is a pure integer linear programming model of maximizing the number of served public vehicles to avoid cruising for parking. Further, the parking space allocation model was expanded to an estimation model that quantifies the effect of emission reduction. Secondly, a branch-and-cut algorithm was introduced as the core algorithm to solve the proposed models. Finally, detailed sensitivity analysis, based on empirical data collected by electronic parking toll collections and questionnaire surveys in Beijing, China, evaluated the proposed model and algorithm. The results indicate that shared parking can both effectively reduce the cruising time and the number of vehicles, but also has significantly a positive effect on emission reduction. This research is helpful to provide theoretical support for alleviating parking pressure and environmental problems.

In order to solve the problem of cross-regional customized bus (CB) route planning during the COVID-19, we develop a CB route planning method based on an improved Q-learning algorithm. First, we design a sub-regional route planning approach considering commuters’ time windows of pick-up stops and drop-off stops. Second, for the CB route with the optimal social total travel cost, we improve the traditional Q-learning algorithm, including state-action pair, reward function and update rule of Q value table. Then, a setup method of CB stops is designed and the path impedance function is constructed to obtain the optimal operating path between each of the two stops. Finally, we take three CB lines in Beijing as examples for numerical experiment, the theoretical and numerical results show that (i) compared with the current situation, although the actual operating cost of optimized route increases slightly, it is covered by the reduction of travel cost of passengers and the transmission risk of COVID-19 has also dropped significantly; (ii) the improved Q-learning algorithm can solve the problem of data transmission lag effectively and reduce the social total travel cost obviously.

This study aims to analyze the cascading failure process of traffic network in tourist attraction under unexpected emergency events, and discuss the measures to improve traffic network invulnerability. For that purpose, taking tourist attraction as the research object, the topology model of traffic network in tourist attraction is established based on Space L method. After different types of emergency events are simulated as different attack strategies, we discuss the spatial and temporal distribution characteristics and evolution process of tourism emergency events. The cascading failure model of traffic network based on load-capacity is constructed, then the main factors affecting the scale of dynamic cascading failures are given and their sensitivity are analyzed. Taking the Summer Palace in Beijing as an example, the invulnerability analysis of the traffic network is carried out and the node protection strategies with different network load are proposed. The study reveals that (a) the traffic network of the Summer Palace has a typical network characteristics of small world; (b) network load, node capacity, node attack strategy, adjacent nodes relationship, load distribution rule significantly affect the scale of cascading failure of the traffic network in tourist attraction, (c) when the network load coefficient δ <; 0.7, the cascading failure rate (CFR) can be effectively controlled within 0.2 to avoid large-scale cascading failure; (d) the scale of cascading failure can be effectively reduced by 22.14%, 40.91%, and 63.66%, after increasing the capacity of the nodes which are the top 5%, 10%, and 20% in the ranking of CFR, respectively.

This paper proposes an optimal dynamic supply of parking permits under uncertainties in the mixed traffic environment of regular vehicles (RVs) and autonomous vehicles (AVs). The uncertainties from queuing delay and cruising-for-parking time, respectively, are derived from parking permit holders’ unascertainable early arrival/late departure and the randomness of vehicle speed for cruising-for-parking users. We formulate the dynamic system optimum as a stochastic optimal control problem, aiming to minimize the total time lost for all parking users. On this basis, we express the optimality condition as Hamilton-Jacobi-Bellman equation and propose an optimal control strategy for dynamic supply of parking permits (called optimal parking supply scheme). The proposed scheme is determined by two observable and measurable variables, i.e., queuing time and cruising-for-parking time. We find that the scheme can significantly reduce the total system time lost, especially when the uncertainty from parking permit holders is at a high level or the percentage of AVs is small. Finally, a case study of a selected sample area in Beijing is conducted, which verifies the efficiency of the proposed scheme numerically.

Bus dwelling time at stops is affected by the number of boarding and alighting passengers. Due to the uncertainty of traffic state, dwelling time will become unstable, resulting in unreliable bus operations. A short-turn strategy shares passenger demand in certain segments of the full-length vehicles; therefore, this strategy can reduce the dwell time of the full-length vehicles and have potential to improve reliability of bus operations. Firstly, this paper builds a bus operation state model considering short-turn vehicles. Secondly, the process of bus operation is analyzed to investigate the impact of passenger demand on reliability. Then, we propose a short-turn dispatching strategy, which can accurately determine the operation area and departure time. Finally, a numerical simulation is used to evaluate different strategies. As the result, the analysis reveals the following: (1) the bus operational reliability of full-length vehicles under high passenger demand could be improved by a well-designed short-turn strategy; (2) it is beneficial to improve the reliability of bus operation when short-turn vehicles departure at the change point; and (3) an undesigned short-turn dispatching strategy has a negative effect on full-length vehicles operational reliability. This study can help design a short-turn strategy and make valuable contributions to the bus management system.