Short-term prediction of holiday travel demand is a complex but key issue to the planning and management of tour transportation system in big cities. This paper develops an improved spatial and temporal correlation long short-term memory model (STC-LSTM) to forecast short-term holiday travel demand based on deep learning approach. Analysis results show six kinds of tourist flow correlations appears in different sets of tourist attractions, and 27.94 percent of the tourist attractions have mid- or high- positive tourist flow correlation with others, meaning that a positive synchronization mechanism exists between suburban tourist attractions in Beijing. The proposed model predicts the holiday travel demand on the basis of the historical data of the spatial and temporal related tourist flows, and the auxiliary data including meteorological data, temporal data, and Internet search index. Based on actual case study with tourist flow data of the suburban tourist attraction in Beijing, the proposed STC-LSTM is carried out to compared with other conventional prediction approaches. Results show that the proposed approach can improve the prediction accuracy and well capture the different spatial and temporal correlations of tourist flows.

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.

The implementation of shared parking program can effectively increase the utilization rate of existing parking space resources. At present, shared parking program has not been widely practiced in China, and the prerequisite for this prospect to be implemented is whether the private parking space owner group can quickly and widely accept shared parking program. In this study, considering the differences in the economic development, urban planning, and parking pressure in cities of different levels, the theory of planned behavior and the benefit-risk perception model (C-TPB-BRA) are combined as the theoretical framework to explore the intention to share parking space from the perspective of the owners of private parking spaces in cities of different levels. Based on China’s empirical data, structural equation models are built to verify the hypotheses proposed. Our results show that (a) the intention of private parking space owners in different levels of cities to participate in shared parking and the mechanism of action of the psychological factors are different, and not all psychological factors have a direct impact on the intention to share. In first-tier, second-tier, and third-tier cities, Subjective Norm (SN) and Perceived Behavioral Control (PBC) indirectly affect Behavior Intention (BI) through Attitude (ATT), Perceived Benefit (PB), and Perceived Risk (PR). In the fourth-tier cities, SN and PBC directly affect BI. Except for BI, other psychological factors influence each other significantly; (b) the psychological factors affecting the intention to supply shared parking spaces in first-tier, second-tier, third-tier, and fourth-tier cities, respectively, are PB > ATT > PR, PB > PR > ATT, PB > PR > ATT, and PB > SN > PBC > ATT > PR. Our research results could help determine the internal factors that affect the intention of parking space suppliers and their mechanisms of action to participate in shared parking, and on that basis, our findings could also help governments and platform operators to promote shared parking development plans.

The shared parking scheme improves the utilization rate of existing parking resources and contributes to the sustainable development of cities, but many private parking spaces that are not included in the shared parking scheme have a low utilization rate in China. In order to better promote the shared parking scheme, it is necessary to study the intention of the owners of private parking spaces to share their parking spaces. Therefore, this paper used the Unified Theory of Acceptance and Use of Technology (UTAUT) and Benefit-Risk Analysis Model (BRA) as the combined theoretical framework (C-UTAUT-BRA). Hypothesis testing using the Multiple Indicators and Multiple Causes (MIMIC) model was performed using an empirical assessment of the shared parking scheme in China. The results show that (1) the sharing behavioral intention (BI) is directly affected by perceived benefit (PB), perceived risk (PR), social influence (SI), and facilitating condition (FC) and indirectly affected by effort expectancy (EE), of which the total effect of PB is the largest; (2) exogenous variables have an indirect effect on BI through other psychological latent variables; among them, different sociodemographic and economic characteristics have a significant influence on different latent variables, while the built environment has no significant effect on latent variables. This research contributes to theory building in shared parking participation intention and informs business and government leaders on how to promote the shared parking scheme through the action mechanism of influencing factors on BI.

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.

Shared parking has become the most effective way to utilize existing parking resources. Little attention has been focused on drivers’ intention to use shared parking spaces in residential areas considering individual heterogeneity. To fill this gap, this paper explores the influencing factors and mechanism of shared parking use intention (SPUI) and further studies the preferences for the shared parking of different types of drivers. Firstly, based on the extended unified theory of acceptance and use of technology that includes psychological factors, personal attributes, and travel characteristics, the multiple indicator multiple cause (MIMIC) model was employed for parameter estimation and model assessment. Secondly, using MIMIC’s output results as input variables, the segmentation method of the latent class model (LCM) was adopted to explore drivers’ preferences regarding SPUI. Finally, a quantitative study was carried out through questionnaire data. The empirical results show that: (a) the extended unified theory of acceptance and use of technology has good explanatory power for SPUI. SPUI is directly affected by perceived risk (PR), behavioral habit (BH), social influence (SI), facilitating conditions (FCs), and effort expectancy (EE), while performance expectancy (PE) have no significant effect on SPUI. In addition, some factors of personal attributes and travel characteristics affect SPUI through psychological factors. (b) According to individual heterogeneity, the surveyed driver groups are divided into four segments: sensitive type (36%), conservative type (29.6%), neutral type (24.5%), and approved type (9.9%), respectively. There are significant differences in psychological observation variables such as EE, PE, FC, and SI among the four segments of drivers. According to the influence mechanism of psychological factors and preferences analysis of different types of drivers, the shared parking promotion strategy can be formulated from the aspects of management, operation, and technology.