Analysis on the Impact Factors of Park-and-Ride Behaviors in. Beijing

Transcription

1 Analysis on the Impact Factors of Park-and-Ride Behaviors in Beijing CHEN PEI-HONG 1, SHI MING-XIN 2 1 School of Economics and Management, Beijing Jiaotong University, Beijing , China 2 School of Economics and Management, Beijing Jiaotong University, Beijing , China Abstract: The research on the Park-and-Ride behaviors can provide useful information for the authorities to formulate and implement the policy of Park-and-Ride. Some private car owners in Beijing have been investigated and a Stated Preference survey has been carried out. After collecting and analyzing the data from the survey, a binary logit regression model based on the theory of utility maximization was set up. The results of the model show there are nine factors influencing the choice of the Park-and-Ride modes. In addition, through the orthogonal experimental design method, we select six hypothetical scenarios which associated with quality of Park-and-Ride facilities. Through a chi-square analysis model, we find the order of sensitivity of relevant factors with the quality of Park-and-Ride facilities. Finally, we propose suggestions for the Beijing municipal government about quantities, locations and designs of Park-and-Ride facilities, charges and promotions of Park-and-Ride mode, and relevant transport policies. Key words: Park-and-Ride; Stated Preference; Binary Logit regression model In recent years, with the acceleration of economic development and urbanization process, the Beijing urban area is expanding. There are more people driving from the Peri-urban areas to downtown, with more congestion in the city. As a kind of travelling mode, Park-and-Ride (P&R) refers to private car travelers parking near the transit station outside the downtown and take bus or subway to the city center. This mode can decrease the vehicle traffic to the downtown and alleviate the traffic pressure of urban area. The P&R mode was introduced in 2003 by Beijing municipal government. The first P&R lot was built in Tiantongyuan, the terminal of metro line 5 in Until August 2016, Beijing has built 45 P&R lots, which providing more than 10,000 parking spaces. However, there are still some problems in the progress of implementing the P&R policy: (1) The total amount of P&R facilities is still insufficient. (2) Not all the P&R lots are located properly. (3) The supporting facilities are incomplete, lacking of gas stations, electric vehicle charging piles and so on. (4) The promotion of the policy is not sufficient. These problems affect the effectiveness of the P&R policy. Therefore, in order to attract more private car travelers to choose P&R mode and make better use of P&R facilities, the study on the impact factors of P&R behavior in Beijing is necessary. Based on the theory of utility maximization, this paper studies the impact factors that affect the P&R behavior of private car owners in Beijing. First, through the method of literature review, we summarize six categories of factors influencing the choice behavior of P&R. Then, through in-depth interview, we select the factors in line with the traffic situation in Beijing. What`s more, a questionnaire survey has been carried out, and binary logit regression model was set up according to the survey results, which analyze the influence of different factors. In addition, through the

2 orthogonal experimental design method, we select six hypothetical scenarios which associated with quality of P&R facilities, and obtain the order of sensitivity of P&R facilities variables through the model analysis. Finally, based on the results of empirical analysis, this paper puts forward to the specific suggestions for the planning and management of P&R facilities. 1 Literature review In most literature, factors affecting P&R choice behavior are divided into six categories: socio-demographic characteristics, trip characteristics, traffic and parking conditions in urban center, quality characteristics of P&R facilities, service level of transit, and policies affecting choice of travel mode. Among them, the research of Clayton W.(2014) shows that personal and trip information such as age, gender, travel distance and family income are closely related to the choice of P&R. Anna Lena(2003) argues that, compared with other factors, factors related to quality of P&R facility such as space availability, security, fares of P&R facilities and the walking distance between interchange station and P&R facilities are the main reasons for choice. In general, the studies on P&R behavior are relatively mature in western countries, and now the studies focus on security, such as the safety of the P&R facilities, the age beginning to driving a car and so on. Yun MeiPing(2012) mainly analyzes the effects of traffic and parking condition in urban center, which find that private car travelers pay more attention to the travel cost when traffic is not blocked in urban center, however, they pay more attention to the travel time when traffic congestion is serious. While Chinese researchers pay more attention to how much travel time and cost can be saved through P&R facilities, and ignore the factors of quality of P&R facilities and public transport service level. What`s more, the existing studies are lack of analysis according to the existing traffic situation in most cities in China. For example, with the development of new energy vehicles, car owners need more electric vehicle charging pile at parking lots, etc. In aspect of research methods of P&R behavior, qualitative and quantitative methods are used in most of the existing literature. Qualitative research methods mainly include focus group and in-depth interview, and quantitative methods include the RP(revealed preference)survey and the SP(stated preference)survey. Some literatures only use one method to investigate. For example, Shirgaokar M. (2005) uses focus group investigation to study the choice behavior of P&R. Hole (2004) uses the SP survey to establish binomial logit model. Bos L. (2005) takes Nijmegen as example, uses the SP survey to establish a model based on three modes of transportation, namely P&R, driving all the way and taking bus all the way. Some literatures obtain data though two or more methods. For example, the study of Islam S.T. (2015) uses RP and SP survey at the same time, which using the RP data to obtain the impact factors of travel behavior, and using the SP data to establish the empirical model of multinomial logistic regression. Qin Huanmei(2004) takes private car travelers in Beijing as the research objects in RP and SP survey. And in her survey, different degree of change of the travel time and travel cost are combined to simulate different scenarios, and it can obtains the willing to choose P&R under different hypothesis. Anna Lena(2003)collected and analyzed data in a combination of focus group and SP survey, and finally established a binary logit regression model based on utility maximization theory according to the survey results.

3 Qualitative and quantitative methods are both used in this paper. First at all, the impact factors can be classified into six categories according to the existing literature, and we select impact factors which can be included into the questionnaire through the in-depth interview. Then, the SP survey is used to design questionnaire, and binary logit regression model was set up to analyze how these factors influence the choice of P&R. Due to the domestic research paying less attention to the quality of P&R facilities, the paper takes the quality of P&R facilities as the main variables. 2 SP survey of P&R behavior in Beijing 2.1 Factor classification and index selection According to the analysis on literature, the existing research has not been comprehensive enough to cover all possible aspects of the impact factors of the choice behavior of P&R. This article will take all six categories of factors into consideration, namely socio-demographic characteristic, trip characteristic, traffic and parking condition in urban center, quality characteristic of P&R facility, service level of public transport, and policy affecting choice of travel mode. In combination with the specific traffic situation of Beijing, 20 people as subjects were selected for in-depth interview before the formal investigation. The subjects include seven students, seven ordinary employees of enterprises and institutions, two civil servants, two management personnel of enterprises and institutions, and two freelance. The in-depth interview almost covers different professional. Through the results, we eliminate the factors which affect the choice not obviously under the existing traffic and parking situation in Beijing. Finally, we screen out the factors affecting P&R choice behavior of private car travelers, as shown in table 2-1. Table 2-1 The Factors affecting P&R behavior in Beijing Socio-demographic gender age education employment status characteristic personal habit and preference monthly income children condition trip characteristic travel time travel distance traffic and parking condition in urban center quality characteristic of P&R facility travel mode travel fares when to start road congestion level in urban center parking condition in urban center available parking space parking fares walking time from P&R lot to station platform level of additional service level of information sharing service level of public transport punctuality comfort policy affecting choice of travel mode congestion charging restriction of time and traffic area more parking fares in urban center preferential to P&R lot

4 2.2 The implementation of SP survey We design the questionnaire according to the above information. The questionnaire mainly includes four parts: basic information, travel information, selection information and hypothetical scenarios. Owing to the target subjects of this research are the private car owners, this article selects three typical P&R lots in Beijing (Huoying P&R lot, Anheqiao North P&R lot, and Tongzhoubeiyuan P&R lot) for investigation. 502 questionnaires were collected in this survey. After eliminating 34 invalid questionnaires, 468 valid questionnaires were recycled. The valid recovery rate is 90.84%. The valid questionnaires can cover all levels of the impact factors evenly, which with good randomness, comprehensiveness and effectiveness. Among the valid questionnaires, the respondents of 12 questionnaires have a preference on driving all the way. For these people, no matter how to improve the conditions of P&R,they will only choose drive all the way, and it`s impossible to attract them to select P&R through changing policies. Therefore, only the remaining 456 questionnaires were analyzed in the following study. 2.3 The descriptive analysis of SP survey results Basic information In terms of gender: the proportion of men in the survey results is 55.3% and the proportion of women is 44.7%.In terms of age: the age of most respondents are between 19 and 45, which accounting for 76.3% of the total sample. In aspect of education: the distributions mainly concentrate in college and above, which accounting for 94.7%. In terms of employment status: the distribution of respondents mainly focuses on the ordinary employees and management personnel of enterprises and institutions, which accounting for 46.5 % and 28.5% respectively. In terms of monthly income: the proportion of the average monthly income between 5000 and is 59.6%.In terms of children condition:53.1% of the respondents have no children. Among families which have children, the proportion of kindergarten and below has the largest percentage of 27.6%. Table 2-2 The SP survey results of socio-demographic characteristic frequency percentage gender men women age between years between31-45 years between46-55 years and older education high school and below college or undergraduate graduate and above employment student status ordinary employees of enterprises and institutions management personnel of

5 monthly income (yuan) children condition enterprises and institutions business/freelance less than between between more than none kindergarten and below primary school high school university and above Travel information Among the respondents, the travel distance is mainly between 5-15km and 15-25km, which accounting for 39.5% and 33.3% respectively. The travel time is mainly concentrated in 30-60min, which accounting for a half, and the followed are 60-90min and less than 30min,. The proportion of travel time is only 7.5% for those who spend more than 90min. In terms of travel modes, the respondents travelling through private cars accounted for 52.6%, followed by travelling through public vehicles, which accounting for 35.5%. Since the investigation sites are P&R lots, the results are in line with the actual situation. Table 2-3 The SP survey results of trip characteristic frequency percentage travel distance less than 5km between 5-15km between 15-25km more than 25km travel time less than 30min between 30-60min between 60-90min more than90min travel mode private car public transport (bus/subway) taxi others Selection information In order to understand the respondents` subjective feeling on other four categories of characteristic variables excluding socio-demographic characteristics and trip characteristics, we add the multiple choice question-the main factors you will consider when choose the P&R-in the questionnaire. The survey results conclude that the order of importance of the impact factors is: road congestion level in urban center(70.2%), parking condition and fares in urban center(51.8%), parking space availability of P&R lot(48.2%), parking fares of P&R lot(38.6%),walking time from

6 P&R lot to station platform(37.3%), frequency and punctuality of public transport(32.5%), level of additional service and information sharing of P&R lot(18.9%), restrictions on motor vehicles(18.9%), and congestion charging(16.2%). According to the results, except socio-demographic characteristics and trip characteristics, the order of other four categories of variables from high to low is: traffic and parking condition in urban center, quality characteristic of P&R facility, service level of public transport and policy affecting choice of travel mode. Figure 2-1 The chart of the factors influencing the P&R 3 Binary logit regression model of the choice behavior of P&R in Beijing 3.1 Variable selection According to the traffic situation in Beijing, this paper selects 17 characteristic variables based on the survey results of the P&R choice behavior. The details are as follows: Table 3-1 Characteristic variables and their segmentation attribution Socio-demographi c characteristic characteristic variable gender age education employment status segmentation men=1;women=2 between19-30 years=1; between31-45 years=2; between46-55 years=3; 55 and older=4 high school and below=1; college or undergraduate=2; graduate and above=3 student=1; ordinary employees of enterprises and institutions=2; management personnel of enterprises and institutions=3; business/freelance=4

7 trip characteristic traffic and parking condition in urban center quality characteristic of P&R facility service level of public transport monthly income (yuan) children condition travel distance travel time travel mode road congestion level of urban center time to look for parking space in urban center parking fares in urban center (yuan/day) walking time from P&R lot to station platform parking fares of P&R lot (yuan/day) space availability of P&R lot full capacity of public transport less than 5000=1; between =2; between =3; more than 40000=4 none=1; kindergarten stage and below=2; primary school=3; high school=4; university stage and above=5 less than 5km=1; between 5-15km=2; between 15-25km=3; more than 25km=4 less than 30min=1; between 30-60min=2; between 60-90min=3; more than90min=4 private car=1 public transport(bus/subway)=2 taxi=3 others=4 10km/h=1;20km/h=2 ;30km/h=3 ;40km/h=4 less than 5min=1; between 5-10min=2; between 10-15min=3; more than 15min=4 between 0-15=1; between 15-30=2; between 30-45=3; more than 45=4 less than 8min=1; between 8-16min=2; more than 16min=3 between 0-5=1; between 5-10=2; more than 10=3 find parking space anytime=1; can`t find parking space 1 or 2 times per week=2; can`t find parking space more than 2 times per week=3 have seats=1; 1/4 passengers stand=2; 1/2 passengers stand=3; have no seats=4 3.2 Variable Filtering Chi-square test for independence First of all, we conduct the Chi-square test for independence. The analysis results are as follows:

8 Table 3-2 Pearson chi-square test pearson 2 df Sig. gender age education employment status monthly income (yuan) children condition travel distance travel time travel mode road congestion level in urban center time to look for parking space in urban center parking fares in urban center (yuan/day) walking time from P&R lot to station platform parking fares of P&R lot (yuan/day) space availability of P&R lot full capacity of public transport The table 3-2 shows that there are five independent variables, namely gender, children condition, travel mode, parking fares of P&R lot and the full capacity of public transport, in 95% confidence interval, whose Sig.> It shows that there is no correlation between choice behavior and these variables. So these variables will not be included into the model. However, there are independent variables like age, education, employment status, monthly income, travel distance, travel time, road congestion level of urban center, time to look for parking space of urban center, parking fares of urban center, walking time from P&R lot to station platform, and space availability of P&R lot, in the 95% confidence interval, whose Sig. < 0.05.It suggests that there is significant correlation between choice behavior and these variables. Next, we will conduct collinear test with these variables Collinear test Pearson correlation coefficient is used to verify the correlation between independent variables and independent variables. The results show that there is a correlation between age, monthly income and employment status. And there is a correlation between travel time, travel distance and road congestion level of urban center. Then, we eliminate age and travel time from characteristic variables. After the two ways of filtering, we take the rest impact factors as characteristic variables to conduct the logistic regression. These factors are: education, employment status, monthly income, travel distance, road congestion level in urban center, time to look for parking space in urban center, parking fares in urban center, walking time from

9 P&R lot to station platform and space availability of P&R lot. 3.3 Model establishment Travelers always choose travel mode to maximize their utility. In this paper, respondents will choose the best travel mode for themselves between P&R and driving all the way. So the discrete choice model is called binary choice model. Therefore, this paper establishes a binary logit regression model of the choice behavior of P&R. The model shows how different factors affect the choice of travel mode, how much stronger the influence is and what relationship between these factors. The probability of choosing P&R is: exp( V1 n) P1 n prob( V1 n V2n ) exp( V ) exp( V ) 1n 2n The probability of choosing driving all the way is: exp( V2 n) P2 n 1 P1 n exp( V ) exp( V ) 1n 2n (3-1) (3-2) Through the filtering above, we finally select nine variables:x1 education, X2 employment status, X3 monthly income, X4 travel distance, X5 road congestion level of urban center, X6 time to look for parking space in urban center, X7 parking fares in urban center, X8 walking time from P&R lot to station platform, and X9 space availability of P&R lot, as the independent variables of this model. The utility function of binary logit regression model is: Logit P(Y=1)= 0+ 1*X1+ 2*X2+ 3*X3+ 4*X4+ 5*X5+ 6*X6+ 7*X7+ 8*X8+ 9*X9 (3-3) 0 is constant, 1 to 9 are coefficient of independent variables, P(Y=1) is the probability of choosing P&R, and P(Y=0) is the probability of choosing driving all the way. 3.4 Model results The results of parameters calibration and test are shown in table 3-3.The first column in the table is characteristic variables (independent variables), other columns respectively represent for partial correlation coefficient, standard error, Wald value, degree of freedom, Wald test significance value and Exp (θ). Table 3-3 Parameters calibration and test of the model θ S.E Wals df Sig. Exp (θ) X X X X X X

10 X X X X X X X X X X X X X X X X X X X X X X X X X X X constant Note: X1--1: X stands for education, see formula stands for high school and below, see table 3-1. From the final output results of logistic regression analysis, we can see that in the 95% confidence interval, all the variables` Sig. < 0.05, which suggests that there is significant correlation between these variables and the dependent variable Y, and all can be incorporated into the final equation. Through the analysis on Wald value, Sig., and Exp(θ) in the table above, we can know that the higher the education level, the more tendency to choose P&R, and the P&R choice intention of people who are graduate and above is times of people who are high school and below. Management personnel of enterprises and institutions and business/freelance are less inclined to choose P&R. While ordinary employees of enterprises and institutions are more likely to choose P&R. It is because different jobs have different requirements for punctuality. The higher the monthly income, the less willing to choose P&R, and the willing to choose P&R whose monthly income is above yuan is far below those less than yuan. For example, the willing to choose P&R of people whose monthly income is more than yuan is only times of people whose monthly income

11 is less than yuan. The longer the travel distance, the more willing to choose P&R. The willing to choose P&R of people whose travel distance is more than 25km is times of people whose travel distance is less than 5km. In addition, the more serious the congestion level in the urban center, the longer the time to look for parking space in urban center and the higher the parking fares in the urban center, the more likely to choose P&R. The shorter the walking time from P&R facilities to station platform and the more abundant the supply of P&R facilities, the more willing to choose P&R. 3.5 Model test Model test Table 3-4 Coefficient table of model test Likelihood ratio test Goodness of fit test R 2 test -2 log 2 Sig. 2 Sig. Cox & likelihood Snell R 2 Nagelkerke hit R 2 ratio In this model, Hosmer and Lemeshow test is used to test the goodness of fit. If the statistical test is not significant, the hypothesis cannot be rejected, and it indicates the imitative effect is good. From table 3-4, it can be seen that the test result is 0.164, which is not significant. Therefore, we can conclude the imitative effect of this model is good. R 2 describes the percentage of dependent variables explained by independent variables. Cox & Snell R 2 and Nagelkerke R 2 in this model are and respectively, which indicate the accuracy of model prediction is acceptable. The hit ratio is an indicator of whether the result of actual observation is consistent with the result of prediction. The hit ratio in this model is 72.8%, which indicates the prediction accuracy is relatively good. 4 Study on hypothetical scenarios of quality characteristics of P&R facilities Considering that the factors relative to quality of P&R facility are most likely to be improved in a short time, and it is concluded from the in-depth interview that these factors are quite important on affecting the choice of private car travelers. So, this paper further analyzes these factors. Firstly,this article selects three factors associated with quality of P&R facilities as attributes, and determines the corresponding level. Then, it designs different scenarios for different level, and study the differences between different scenarios. If adopting the overall design method, there are 27(3 3 ) options.orthogonal experiment design method has advantages such as the distribution is uniform and the accuracy is high. So we use the orthogonal experiment design method to design a orthogonal table including 3 attributes and 3 levels, which can be combined for nine alternatives. In order to clearly see the influence degree on the choice of travel mode when different

12 levels change, we select six combinations as hypothetical scenarios. All hypothetical scenarios is shown in table 5-1. Table 4-1 Six different hypothetical scenarios hypothetical scenarios parking fares of P&R lot(yuan/day) walking time from P&R lot to station platform space availability of P&R lot Alternative <8 min find parking space at anytime Alternative <8 min Can`t find parking space more than 2 times per week Alternative >16 min find parking space at anytime Alternative Between 8-16 min Can`t find parking space 1 or 2 times per week Alternative 5 > 10 <8 min find parking space at anytime Alternative 6 > 10 >16 min Can`t find parking space more than 2 times per week Through a chi-square analysis on the results of the choice of travel mode on six different scenarios, we conclude that: in the 95% confidence interval, the choice of respondents have significant difference in different scenarios (P < 0.05), which is shown in table 4-2. As can be seen from the cross tabulation, interviewees are more inclined to P&R in alternative 1, 3 and 5, and prefer to drive all the way in alternative 2, 4 and 6. The order of sensitive degree of the factors of quality of P&R facility from high to low is :space availability of P&R lot, walking time from P&R lot to station platform and parking fares of P&R lot. Table 4-2 The choice of travel mode in different hypothetical scenarios scenario* transfer choice cross tabulation hypothetical scenarios the choice of travel mode P&R Drive all the way Sig. Alternative 1 count percentage 82.5% 17.5% Alternative 2 count percentage 42.1% 57.9% Alternative 3 count percentage 55.7% 44.3% Alternative 4 count percentage 46.5% 53.5% Alternative 5 count percentage 59.2% 40.8% Alternative 6 count percentage 22.8% 77.2% To contrast alternative 3 (55.7%) and 5 (59.2%),we can conclude that when the space availability of P&R lot is at the same level, for travelers, the walking time from P&R lot to station platform has bigger influence on the choice of P&R than the parking fares of P&R lot. To contrast alternative 2 (42.1%) and 4 (46.5%), we can conclude that travelers are more concerned about the space availability of P&R lot, comparing with the walking time from P&R lot to station platform and the parking fares of P&R lot. In

13 alternative 4, even the walking time and the parking fares of P&R lot are both higher than alternative 2, the probability of choosing P&R is still higher than alternative 2. To contrast alternative 1 (82.5%) and 6 (22.8%), we can conclude that when the space availability of P&R lot,the walking time from P&R lot to station platform and the parking fares of P&R lot are all obviously improved, the probability of choosing P&R increase significantly. It means that the quality of P&R facilities, to a great extent, affects the probability of choosing P&R. In the future, government will have large space to improve the possibility of choosing P&R by improving the quality of P&R facilities. 5 Policy suggestions to improve the willing to choose P&R 5.1 Supply of P&R facility This survey results show that the more serious the congestion level is, the longer the time to look for parking space is and the higher the parking fares is in urban center, the more likely to choose P&R. While the shorter the walking time from P&R facilities to station platform is and the more the supply of P&R facilities is, the more incline to choose P&R. Therefore, our government should change the past administering thinking. For example, we should not increase the supply of parking space constantly in urban center, however, we should use the saved money to increase the supply of P&R facilities in urban periphery. The study of hypothesis scenarios shows that the supply of P&R facilities is the most important factor among P&R facility characteristics. So, government should increase the number of P&R lots which are in short supply. What`s more, due to the limited land and road resources in Beijing, it is urgent to plan and construct the three-dimensional P&R lots and expand the original P&R lots, which can make full use of urban space. 5.2 Site selection of P&R facility Abundant demand is the precondition of P&R facilities can be made full use of. The government should follow the principle of maximizing the number of users when select sites for P&R facilities, and choose the area where is densely populated, has large travel demand and prone to congestion during the peak period. We should avoid the low utilization rate caused by unreasonable site selection and waste public resources. P&R facilities should be attached to the mature system of public transport, which near the public traffic station, such as subway,bus terminal station and so on.it is convenient for passengers to stop and transfer to the urban center. 5.3 Design of P&R facility The walking time from P&R lot to station platform is one of the important factors affecting the choice of P&R, and the shorter walking time is more attractive.

14 In the future, the government should plan and layout reasonably, try to reduce the walking time and detour. The government should ensure that P&R facilities are closely linked to public transport platforms and provide convenience for travelers to the utmost. 5.4 Fares of P&R facility The fares of P&R facilities in Beijing is lower than other cities in China. This survey finds that the fares of P&R facilities is not the key factor influencing the travelers` choice. In view of this, we suggest that the Beijing municipal government increase the charging standard of P&R facilities appropriately. We can also use the extra parking fares to build more transportation infrastructures, such as three-dimensional P&R facilities. 5.5 Propaganda promotion of P&R facility The survey results show that the higher the education level is, the more likely to choose P&R, which indirectly indicates that with more understanding from citizens on policies easing traffic congestion, the transport policies will get more supports. In addition, during the investigation, we found that some travelers have no idea about P&R facilities and corresponding preferential. Therefore, our government should strengthen the propaganda promotion of P&R. Propaganda can through the Internet and other new media forms, such as WeChat, Weibo, car radio and advertisement and signs in subway stations and bus stations. We should propaganda P&R from multiple perspectives, such as the advantages of P&R, the enhancement of environmental awareness, the social responsibility and so on. 5.6 Traffic policy The influence degree of traffic policies is lower in the six categories of variables, but traffic policies are easy to change. These policies include the traffic congestion charging, the limitation on travel time and road, the limitation on tail number and so on. In the future, Beijing municipal government should strengthen the means of traffic demand management. On the one hand, we can increase the driving cost in city center to reduce the possibility of driving all the way. On the other hand, we should release policies which are good for P&R and transit trip. Reference [1] Anna Lena. Factors that Influence Choice of Travel Mode in Major Urban Areas.The Attractiveness of Park & Ride[D].TRITA INFRA,2003. [2] Bos L.,Molin E. The Impact of Policy Measures on P&R Choice:Simulations based on a P&R Choice Model[C].TRB 2005 Annual Meeting CD-ROM,2005. [3] Clayton, W., Parkhurst, G. and Ricci, M. Where to park? A behavioural comparison of

15 bus-based park and ride and city centre car park usage in Bath, UK. [J]Journal of Transport Geography, 2014: [4] Hole A.R. Forecasting the Demand for all Employee Park and Ride Service Using Commuters Stated Choices [D].Department of Economics, University of St Andrews, [5] Islam S.T., Liu Zhiyuan, Majid Sarvi. Exploring the Mode Change Behavior of Park-and-Ride Users. Mathematical Problems in Engineering, Hindawi Publishing Corporation, 2015:1-8. [6] Qin Huanmei, Guan Hongzhi.A Preliminary Analysis of the Choice Behavior of P&R System in Big Cities-Taking Beijing as an Example[J].Journal of Transportation Engineering and Information,2004,04: [7] Shirgaokar, M. and Deakin, E.A s tudy of park-and-ride facilities and their use in the San Francisco bay area of California. Transportation Research Record, 2005: [8] Yun Meiping, etc. Analysis and Modeling of P&R Choice Behavior in Commuting [J].Journal of Tongji University (Natural Science Edition), 2012,12: