Abstract

The crowding management of urban rail platforms is important to improve level of service, such as train delay prevention and passenger safety. Measuring accurate crowding level should precede effective crowding improvement policies. However, the existing crowding level is measured only at specific locations and times every one or two years, resulting in low accuracy. In recent years, the smart card data has been effectively collected thanks to the development of the AFC (Automated Care Collection) technology and ICT (Information Communication Technology). The smart card data is collected in real time every day and can be used to measure the platform crowding by year, month, day and time.

This study suggests a method to estimate the time dependent crowding level of the urban railway platform using the smart card data. This study analyzes the crowding level at the urban railway network level, since crowding is the concentration of passenger moving through the network. Crowding is measured in two stages. First, it identifies the trajectory of all urban railway passengers recorded in the smart card data, and estimates the concentration and dispersed demand on the platform at every one minute interval. For this, a passenger’s optimal route choice model is constructed to calculate the boarding, alighting and railway travel passengers of individual stations. The model was validated using the transfer information of the private lines recorded for the fare settlement. Second, the crowding level is estimated by dividing the practical waiting area of each station platform into platform demand. The practical waiting area varies depending on the platform structure. For example, in the island-type platforms, all passengers of the two-way route use one platform, while in the side-type platform, passengers use separate platforms for each direction.

This study also proposed a strategy to mitigate crowding that would suit the characteristics of individual stations by utilizing estimated real-time crowding data. In addition, the effectiveness of the Early Bird policy, which is a representative example of demand management policy, was also assessed.

From this study, first it is possible to solve the temporal and spatial constraints of crowding estimation and to perform long-term time dependent analysis. In other words, it is possible to conduct crowding analysis at all times from the start to the end of operation across the railway network. Second, it provides real-time and future crowding information to passengers. Third, it can be used to design and change the platform structure and to adjust the train schedule.

Contents

01 Introduction

1_Background and purpose

2_Research methods

02 Estimation of time dependent crowding of urban railway platform

1_Data

2_Model for estimation of time dependent crowding

3_Method of estimation of time dependent crowding

03 Analysis of passenger flows in urban railway station

1_Demand of Seoul metropolitan area railway system

2_Demand by railway lines in Seoul metropolitan area

04 Case study

1_Station with a high percentage of boarding and alighting passengers

2_Station with a high percentage of transfer passengers

05 Improvement of time dependent crowding of urban railway platform

1_Policies for the mitigation of urban railway crowding

2_Strategies to improve platform crowding

3_Application of Smart Card-based Demand Analysis Model

4_Importance and improvement direction of Smart Card-based Demand Analysis Model

References

Appendices