For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that the
Holiday travel demand has obvious characteristics and regularity which is different from weekdays. As passenger flow increases greatly and peak hours are extended during holidays, traffic congestion problems in many cities have become more and more serious. Urban rail transit, as an important component of urban integrated transport system, undertakes more and more person trips. To organize transportation and adjust operation plan effectively, it is necessary to master the origin-destination (OD) flow distribution firstly, especially when subway network changes and operation plan is adjusted. Since OD flow distribution is just the aggregated expression of individual’s destination choice result, study on holiday destination choice behavior for urban rail transit does great help to capture characteristics of holiday trips and provides theoretic support to holiday travel demand analysis.
Holiday-related decision making and behavior are important study topics in the fields of transportation and tourism. The literature focusing on holiday destination choice decisions generally employ the multinomial logit (MNL) or nested logit (NL) model based on the random utility. Hong et al. [
Apart from holiday destination choice decisions, destination choice on weekdays is also widely studied [
In the theoretical level, explaining destination choice behavior with the disaggregate model is better than the traditional four-stage method, for that destinations are discrete alternatives. However, there are still some problems existing in those above researches. First, most researches focus on the whole network of integrated transport system or road network, while they rarely study holiday destination choice decisions for urban rail transit network. Second, current destination choice methods are mostly based on the MNL model or NL model which takes travel destination and mode choice into consideration. However, each OD has several feasible routes and route choice is usually neglected. Besides, plenty of disaggregate data which requires special questionnaire survey is needed to estimate the models. Third, most studies typically focus on factors such as personal attributes [
The paper is organized as follows. In the first section, the background and significance of holiday destination choice behavior analysis are provided, along with a review of previous work on destination choice decisions. The second section analyzes the characteristics of holiday trips and destination choice preference which are different from weekdays. In the third section, holiday destination choice models are established for each New Year’s day, respectively, and some novel explanatory variables (such as attractiveness of destination) are introduced in the proposed models. In the fourth section, based on the data collected from Guangzhou Metro, the models are calibrated and compared with base models (in which attractiveness of the destination is not considered). The conclusions are then given in the final section.
To analyze characteristics of holiday trips and establish holiday destination choice model, OD flow around New Year’s days was collected by AFC system at Guangzhou Metro stations. Guangzhou metro system has seven lines and 123 stations, 12 of them are transfer stations.
Travel demand during holidays has presented different characteristics compared with weekdays. Viewed from the temporal distribution of OD flow firstly, OD flow of urban rail transit network changes greatly around the holiday, as presented in Figure
OD flow of Guangzhou Metro around New Year’s days.
As shown in Figure
Then, from the spatial distribution of OD flow, OD flow is distributed more widely during holidays than weekdays. In order to express the spatial distribution dispersion, TDD (trip distribution dispersion) is defined as follows:
TDD values for New Year’s days.
January 1 | January 2 | January 3 | Workday | Weekend | |
---|---|---|---|---|---|
|
195 | 194 | 173 | 193 | 191 |
|
189 | 158 | 143 | 155 | 156 |
TDD | 0.970 | 0.815 | 0.827 | 0.803 | 0.817 |
As shown in Table
Apart from the characteristics of holiday trips mentioned above, holiday destination choice preference has also greatly changed. For most people prefer to go to tourist attractions and shopping centers for entertainment, only fewer people take office areas as their destination. In order to illustrate the destination choice preference, Kengkou Station which is in the residential area is taken as the origin station and then some typical OD pairs are selected, as shown in Figure
Change Rate of OD passenger flow compared with workdays.
As presented in Figure
In addition, depending on the nature of the destination, the rule of passenger flow will vary, as shown in Figure
Passenger flow’s temporal distribution of typical destinations on January 1.
Holiday destination choice decisions are generally analyzed based on disaggregate model, such as MNL or NL. However, the disaggregate data for disaggregate modeling usually needs a special questionnaire survey, which calls for a lot of time and manpower. On the other hand, there is usually a large amount of aggregate data such as OD flow and operation information collected automatically by AFC system for urban rail transit. Therefore, based on the concept of the representative individual, in which passengers with the same origin and destination are recognized to make the same choice on destination and treated as a group of persons with homogeneous personalized attributes, the aggregate OD flow data is transformed to disaggregate data and applied to the disaggregate model’s estimation in this paper.
Holiday destination choice models are generally derived from random utility theory. According to random utility theory, passengers try to maximize the utility
Destination choice decision generally includes destination choice and route choice, and route choice is made conditional on the selection of a specific destination. Through the stated preference (SP) survey data, above 50% passengers usually would chose the shortest time route. Therefore, the level of service (LOS) of the shortest time route for a specified OD pair is used as LOS variables of the OD for simplicity, and then the destination choice model based on MNL structure is established as follows:
To estimate the destination choice model, as mentioned above, the aggregate OD flow data need to be transformed into a disaggregate form because of the inefficiency of aggregate data in the Berkson and Theil’s method [
Moreover, weighted exogenous sample maximum likelihood (WESML) [
Although personal characteristics influence destination choice decision as well, for it cannot be obtained from AFC system, three types of explanatory variables, that is, accessibility of the destination, matching degree of each OD pair, and attractiveness of the destination, are considered in the proposed model.
Accessibility of the destination is represented by the attributes of the feasible route between the OD pair, and the attributes such as in-vehicle travel time, transfer time, and the number of transfers are taken into account. It can be expressed as follows:
Matching degree of an OD pair expresses common influence of the origin and destination attribute on destination choice behavior, including the land use type, the land use intensity, and the same line indicator (which indicates if the origin and destination stations locate in the same line). As for the land use type variable, all stations are classified into different types firstly using fuzzy clustering method. The three factors are all treated as dummy variables as follows:
As regards attractiveness of destination, correlation degree is defined firstly. Correlation degree is expressed by the number of the origin stations whose passenger flow to the destination is larger than 50 person times a day. The greater the correlation degree is, the more attractive the destination will be. In order to get correlation degree of new subway stations, the relationship among the correlation degree, trip generation, and trip attraction of the stations is analyzed. There is a significant correlation between correlation degree and trip generation, as presented in Table
The correlation between
January 1 | January 2 | January 3 | |
---|---|---|---|
Pearson correlation | 0.715 | 0.736 | 0.755 |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 |
|
123 | 123 | 123 |
Therefore, correlation degree of new stations can be obtained through trip generation of these stations. In addition, trip attraction of a destination also represents the attractiveness of the destination to some extent. Passengers tend to choose the destinations which are heavily chosen by other passengers. Obviously, the attractiveness of destination is closely related to land use type and intensity around stations. So attractiveness of a destination can be expressed as follows:
To sum up, considering the above factors, the generalized cost function can be expressed as follows:
Finally, the results of multicollinearity test for those variables are summarized in Table
Multicollinearity test results.
Variable | Tolerance | VIF |
---|---|---|
In-vehicle travel time | 0.479 | 2.088 |
Transfer time | 0.179 | 5.586 |
Number of transfers | 0.172 | 5.813 |
Land use type | 0.840 | 1.191 |
Land use intensity | 0.586 | 1.706 |
Same line indicator | 0.595 | 1.680 |
Correlation degree | 0.444 | 2.253 |
Trip attraction | 0.377 | 2.655 |
As shown in Table
Based on the data collected by AFC system in Guangzhou Metro, using the WESML method, the destination choice models for the New Year’s days (i.e., January 1, January 2, and January 3) are estimated, respectively. The results of models estimation are summarized in Table
Estimation results of holiday destination choice models.
Variable | January 1 | January 2 | January 3 |
---|---|---|---|
Coefficients ( |
Coefficients ( |
Coefficients ( |
|
In-vehicle travel time | −0.931 (−13.130) | −0.997 (−12.950) | −1.021 (−12.689) |
Transfer time | −4.357 (−4.505) | −3.235 (−3.119) | −2.938 (−2.736) |
Number of transfers | −0.115 (−2.802) | −0.304 (−4.415) | −0.362 (−5.039) |
Land use type | 0.067 (2.094) | 0.011 (2.010) | 0.077 (2.168) |
Land use intensity | 0.205 (4.828) | 0.189 (4.534) | 0.172 (3.944) |
Same line indicator | 0.484 (11.725) | 0.350 (8.057) | 0.368 (8.161) |
Correlation degree | 2.209 (24.537) | 2.243 (23.714) | 2.319 (23.468) |
Trip attraction | 0.069 (18.146) | 0.062 (10.607) | 0.045 (5.676) |
Sample size | 9313 | 8605 | 8093 |
|
0.226 | 0.226 | 0.227 |
According to Table
Also observed from the results in Table
In order to indicate the improvement in goodness of fit brought by attractiveness of destination factors, the base destination choice models, in which correlation degree and trip attraction variables are not considered, are established for comparison. The results of parameters calibration for the base destination choice models are shown in Table
Estimation results of the base destination choice models.
Variable | January 1 | January 2 | January 3 |
---|---|---|---|
Coefficients ( |
Coefficients ( |
Coefficients ( |
|
In-vehicle travel time | −1.264 (−19.103) | −1.429 (−19.804) | −1.500 (−19.941) |
Transfer time | −5.509 (−5.876) | −4.751 (−4.718) | −3.458 (−3.297) |
Number of transfers | −0.128 (−2.070) | −0.257 (−3.850) | −0.344 (−4.887) |
Land use type | 0.098 (2.383) | 0.105 (2.427) | 0.039 (2.507) |
Land use intensity | 1.380 (39.064) | 1.252 (37.946) | 1.173 (34.764) |
Same line indicator | 0.420 (10.282) | 0.257 (5.931) | 0.288 (6.407) |
Sample size | 9313 | 8605 | 8093 |
|
0.166 | 0.181 | 0.187 |
As shown in Table
Through analyzing destination choice behavior for urban rail transit passengers on holiday, it is clear that people prefer to go to tourist attractions and shopping centers for entertainment rather than office areas, which is obviously different from the situation of weekdays. The holiday destination choice models for urban railway transit are proposed, in which, route choice, accessibility, matching degree of each OD pair, and attractiveness of the destination are comprehensively taken into consideration. Based on the assumption of representative individual, the aggregate OD flow data collected by AFC system on New Year’s days in Guangzhou Metro is transformed to disaggregate data and applied successfully to the disaggregate model’s estimation. Furthermore, choice-based sampling method is used to establish the destination alternative set, and then WESML estimation is applied to calibrate model parameters. The results demonstrate that the attractiveness of the destination has an obvious influence on individual destination decision making, on account that, with the introduction of attractiveness of destination variable, the
As mentioned before, the attractiveness of destination is closely related to land use type and intensity around stations. People prefer to go to tourist attractions and recreational places for entertainment on holidays. Therefore, with regard to metro operation organization, operational departments should pay great attention to the stations near tourist attractions or recreational places and take necessary measures to evacuate passenger flow on holidays. Besides, as for urban planning, the capacity of metro stations should be taken into account when making a layout plan of recreational places, in case of overcrowding in some stations.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research is supported by National 973 Program of China (no. 2012CB725403).