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.

With the development of Internet technology, online car-hailing is booming in China, which has profoundly affected people’s travel structures. In order to seek the sustainable development of taxi and online car-hailing services from the perspective of passenger mode choice behavior, the mechanism of passengers’ decision-making procedures and their travel mode choice behaviors were analyzed. To study the influence of latent variable factors on passenger choice behavior, this paper firstly designed a questionnaire, and a structural equation model (SEM) was established for the preliminary study of the relationship between the latent variables and the behavioral intentions using the online survey data. Then, the latent variables were introduced into the Logit model, setting up the SEM-Logit model to explore the mode choice patterns between taxis and online car services. The results showed that the SEM-Logit model with the latent variables is better than a general Logit model in terms of the model precision and hit ratio. Meanwhile, after introducing the latent variables, it was found that convenience, comfort, and economy factors have a significant influence on the model, and the explanatory power of the model increases accordingly.

Releaving traffic congestion by developing public transport as an alternative mode of travel is a common practice all over the world. However, the increasing public transport subsidies have created a financial burden for governments. Encouragingly, private capital supplies an opportunity for public transport in sustainable finance. Previous research mainly focuses on qualitative analysis and money-for-value (MFV) analysis. In this paper, a new investment model is proposed based on the concept ‘passenger value’, and a bi-level programming model (BLPM) is constructed as a quantitative analysis tool. The upper target of BLPM is the total surplus (including the value of time (VOT) of passengers) of the public transport system and the upper constraint is the ticket price. The lower target of BLPM is passenger’s surplus, the lower constraints are service capability and the lowest return rate of the private sector. The public transport of Jinan City, China is taken as a case to quantify the impacts of private capital investment in public transport. Results show that the proposed investment model considering passenger value is superior to the traditional one, and effective private capital investment could increase the total societal benefit of the transportation system. The proposed investment strategy satisfies economic viability and is a financially sustainability strategy. Additionally, travelers should be encouraged to use public transport through improving the service quality and passenger returns. Only in this way can the success rate of the private sector investment in public transport be improved efficiently.

An effective way to optimize traffic structures is by changing travel costs, thereby moving travelers from private transportation to public transportation. However, according to the existing studies, the traveler will not transfer from one mode to another unless the change in travel utility is greater than the indifference threshold. Therefore, the “indifference threshold” is one of the most important factors influencing a traveler’s choice of behavior. This study defines the “indifference threshold” as the traveler’s sensitivity to changes in travel utilities. In the framework of the theory of planned behavior (TPB), a structural equation model (SEM) considering the indifference threshold is established to analyze a traveler’s mode choice behavior. The analysis results showed that a travelers’ sensitivity to changes in travel utilities has the greatest impact on mode-choice behavior intentions and mode choice behavior. Perceptual behavior control has the strongest influence on travel choice behavior. In addition, in order to further explore the heterogeneity of a traveler’s behavior, the travelers were subdivided into four types, by establishing a latent class model (LCM) considering the indifference threshold. Finally, different traffic management suggestions are proposed for different types of travelers.

Tourists are confronted with congestion caused by concentrated travel during public holidays. In order to guide tourists to make voluntary changes regarding their travel times during holidays, this paper focuses on exploring holiday rush-hour avoidance travel behavior (HRATB) considering psychological factors. First, based on the theory of planned behavior, the effects of psychological factors including attitude, subjective norm, and perceived behavior control on holiday avoidance travel intention and behavior were quantitatively analyzed by the structural equation model. Second, according to those three subjective psychological factors and the three objective factors of age, monthly income, and tourist group, the segmentation method of the latent class model was adopted to explore tourists’ preferences with regard to HRATB. Finally, an empirical analysis was carried out through questionnaire data. The results show that attitude, subjective norm, and perceived behavior control have significant impacts on intention and behavior with regard to holiday avoidance travel. There are significant differences in psychological observation variables such as rush-hour avoidance travel intention, attitude and subjective norm among the four segments of tourists, and cost sensitivity. In addition, this paper puts forward some countermeasures and suggestions for the four types of tourists. Conclusions provide a theoretical basis for formulating travel measures to attract different types of tourists.