A Study On The Siting Of P+R Facilities In Zhengzhou For Commuter Travel Demand

In recent years,with the rapid development of the economy and the acceleration of urbanization,the siphon effect of cities with advantageous positions has become increasingly strong.A large number of people have begun to gather from the surrounding areas to cities,and the number of private cars in the city has grown rapidly.However,urban space is limited,and the growth of urban traffic resources is constrained by space.There is a significant gap between the growth rate of road resources and private cars,and urban traffic contradictions are becoming increasingly prominent.To alleviate the contradiction between urban traffic supply and demand,many countries have started to set up Park and Ride(P+R)systems near public transportation corridors outside the central urban area to block private cars from entering the central area and ease traffic congestion.P+R has been widely used in Europe and,based on the experience of "learning from the past," park and ride can effectively regulate traffic demand.A reasonably arranged transfer facility can indeed guide people from private cars to public transportation,improve the share rate of public transportation,and alleviate the contradiction between urban traffic supply and demand.Therefore,the location of park and ride facilities is an essential part of urban planning.This article takes the location of Park and Ride facilities connected with rail transit in Zhengzhou as an example,mainly involving demand forecasting and facility location.The demand forecasting aspect summarizes and generalizes the factors affecting transfer behavior,focusing on the analysis of the personal attributes of commuters and the attributes of the plan,designing RP+SP survey questionnaires based on this,conducting field surveys on private car commuters in Zhengzhou,and analyzing the characteristics of different commuting groups.With the help of SPSS software,the obtained variables are subjected to collinearity test,independence test,and significance test,and variables with poor correlation and significance are eliminated.Finally,the likelihood ratio test,model accuracy test,and goodness of fit test are performed on each independent variable of the model,and the binary Logit model is used to calculate the mode share rate of transfer trips;the facility location aspect constructs a preliminary location model based on the suitability evaluation of influencing factors,and obtains the set of alternative facility locations based on the comprehensive suitability level.Considering the characteristics of urban and suburban demands and facility coverage differences,a multi-objective suburban P+R location model based on the maximization of coverage potential demand and a multi-objective suburban P+R location model based on the maximization of interception are constructed separately,and the genetic algorithm and entropy weight TOPSIS model are used to solve the objective function to obtain the final set of locations.The main conclusions are as follows:(1)In the analysis of the influencing factors of transfer,the results show that the sig.values of independent variables such as the number of private cars,the total commuting time of private cars,the time of commuting by subway,the maximum number of transfers that can be tolerated,the maximum travel time that can be accepted,and the maximum travel cost that can be accepted are greater than 0.05,indicating that there is little correlation between them and the dependent variable.Among the factors with significant sig.,gender,age,family annual income,time required to reach the nearest subway station,and the number of transfers have EXP(B)values less than 1,indicating a significant negative impact relationship with the dependent variable.Similarly,road congestion levels,parking space supply,private car destination parking fees,the maximum tolerable parking transfer time,and acceptable P+R parking fees have EXP(B)values greater than 1,indicating a positive impact relationship with the dependent variable.The model test results show that the model has a good explanatory power for the relationship between independent variables and dependent variables,and the model fitting results are good.The binary Logit model constructed based on the principle of utility maximization shows that the mode share rate of transfer travel in Zhengzhou is 10.35%.(2)Single-factor and multi-factor evaluations of all subway stations are conducted,resulting in a set of 147 alternative locations.The results of the suitability evaluation of influencing factors show that the areas with the highest suitability level in Zhengzhou basically coincide with class I areas,and the areas with a lower suitability evaluation level are obviously marginalized due to population and land use type restrictions.Although the southern part of the class I area has a relatively high comprehensive evaluation suitability level,the overall number of alternative locations is sparse due to the density of subway stations,which is significantly different from other areas.(3)Based on the preliminary location and demand forecasting results,combined with the urban and suburban final location models,a total of 30 facility locations are obtained for urban and suburban areas.The location results show that the uneven distribution of population and public transportation resources in Zhengzhou has led to a much higher density of facilities in the northwest region of the city than in the southeast region.