Forecasting Passenger Flow Distribution on Holidays for Urban Rail Transit Based on Destination Choice Behavior Analysis
Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9922660 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers’ destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers’ travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes. |
|---|---|
| ISSN: | 0197-6729 2042-3195 |