Abstract
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.
Highlights
With the development of the economic level, the travel activities and frequencies of urban residents continue to increase, which leads to the rapid growth of urban residents’ demand for urban public transport
New stations divert the passenger flow of old stations, and it is not easy to obtain the development data of all O-D pairs in time series, especially for newly added stations. erefore, based on the above analysis of passenger flow’s features, this paper constructs a destination choice model to describe the characteristics of passengers. en, a forecasting model of holiday passenger flow distribution is developed, which is suitable for the structural change of the network and does not depend on long-term data collection
Conclusions is paper utilizes Automatic Fare Collection (AFC) data to propose a forecasting model for passenger flow distribution for urban rail transit, which is suitable for network structure and the unique characteristics of holidays. e weighted exogenous sampling maximum likelihood (WESML) estimation method is used to calibrate the parameters. e aggregate data extracted from AFC are transformed into the disaggregate form, which realizes the valid calibration of the parameters
Summary
With the development of the economic level, the travel activities and frequencies of urban residents continue to increase, which leads to the rapid growth of urban residents’ demand for urban public transport. A large number of new lines have opened and connected to the metro network, making the network operation effect of many cities evident, significantly affecting regional accessibility and passenger flow distribution in the metro network. In regard to holidays, because of the exceptional flexibility of departure time and the diversity of destinations, the passenger travel characteristics are quite distinct from normal days, and the spatiotemporal distribution of holiday travel demand presents complex characteristics [2, 3]. E particularity of holidays aggravates travel demand’s complexity, which poses a significant challenge to the metro system. Erefore, to effectively organize the large passenger flow and alleviate traffic congestion during holidays, it is essential to accurately predict the distribution of holiday passenger flow, which is the basis of a reasonable train operation plan-making and the development of passenger flow induction strategy The same holiday only occurs once a year, which is not conducive to study the characteristics in terms of lacking data sources. erefore, to effectively organize the large passenger flow and alleviate traffic congestion during holidays, it is essential to accurately predict the distribution of holiday passenger flow, which is the basis of a reasonable train operation plan-making and the development of passenger flow induction strategy
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