Curb parking pricing method based on parking choice behavior

In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.

Parking choice behaviour analysis of rural residents based on the latent variable random forest model

The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution,which has attracted the attention of many scholars as well as policymakers.However,most of the current research on parking choice focuses on urban business and residential areas rather than on rural parking choice behaviour,and focuses on the analysis of observable factors,ignoring the internal relationship with potential variables.This study considers the heterogeneity of individuals and uses the random forest(RF)algorithm to construct a model of rural residents’willingness to choose parking with both latent and explicit variables,to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’parking choices,and to explore parking planning programmes and strategies that are truly applicable to rural areas.The results of the study suggest that the safety and convenience of the parking environment are key factors influencing the parking choice behaviour of rural residents,and can greatly improve the predictive accuracy of the parking willingness model.Upon comparison,it is found that the application of the RF algorithm is also significantly better than the logit model in terms of prediction effect,indicating that there is a nonlinear effect among the factors influencing the parking choice behaviour of rural residents and that the RF model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behaviour of rural residents.

Evolutionary game analysis of shared parking market diffusion under government management

The imbalance between supply and demand in urban settings poses a significant barrier to the sustainable advancement of urban transportation.Shared parking serves as a viable solution to mitigate these challenges.Nevertheless,for its sustained growth,a regulatory mechanism enforced by the government is imperative.To promote shared parking market diffusion,we construct an evolutionary game model that incorporates the government,enterprises and parking demanders.It explores stabilization strategies for these stakeholders and identifies multiple equilibrium states under different parameter conditions.The results show that the rate and stability of these evolutionary strategies are constrained by the mutual benefits derived by the three parties.Furthermore,such stakeholders are reciprocally influenced by their willingness to engage in shared parking to varying degrees.Government subsidies serve as a determining factor for the strategic choices made by both enterprises and demanders,albeit at different evolutionary rates.Demanders who have a higher value of time tend to park on-street,thereby influencing enterprise strategies.To foster the long-term growth of the shared parking market,the government must enact appropriate subsidy policies,maintain consistent regulations and advocate for increased subsidies for parking demanders to reduce the effect of temporal heterogeneity on parking behavioural choices.