A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules

Abstract

Precise estimation of passenger spatial-temporal trajectory is the basis for urban rail transit (URT) passenger flow assignment and ticket fare clearing. Inspired by the correlation between passenger tap-in/out time and train schedules, we present a method to estimate URT passenger spatial-temporal trajectory. First, we classify passengers into four types according to the number of their routes and transfers. Subsequently, based on the characteristic that passengers tap-out in batches at each station, the K-means algorithm is used to assign passengers to trains. Then, we acquire passenger access, egress, and transfer time distribution, which are used to give a probability estimation of passenger trajectories. Finally, in a multi-route case of the Beijing Subway, this method presents an estimation result with 91.2% of the passengers choosing the same route in two consecutive days, and the difference of route choice ratio in these two days is 3.8%. Our method has high accuracy and provides a new method for passenger microcosmic behavior research.

Estimating Left Behind Passengers and Train Loads in Congested Urban Rail Transit Networks: A Data-Driven Passenger-to-Train Assignment Approach

“…Using θ and β as prior knowledge, the authors calculated the probability of each route being chosen for an OD with multiple routes. Yang et al [19] presented an unbiased sampling method to obtain reference passengers and estimate their walking time distribution.…”

“…On one hand, they are based on simplifying assumptions that could introduce bias and restrict their applicability. On the other hand, they address specific scenarios, such as no transfers [14]- [17] or the omission of denied boarding [14], [19][20], [26], [28]. These limitations underscore the need for new and more comprehensive approaches.…”

Identification and Classification of Bus and Subway Passenger Travel Patterns in Beijing Using Transit Smart Card Data

“…Zhao et al [9] analyzed the spatial-temporal aspects of passengers' travel patterns and clustered metro passengers into four groups using statistical and unsupervised clusterbased methods. Yang et al [16] classifed passengers into four types according to the number of routes and transfers and proposed a method to estimate passenger spatial-temporal trajectory. To better understand long-term patterns in passenger travel, Kaewkluengklom et al [17] studied changes in the individual travel behavior by using three years of longitudinal smart card data from Shizuoka Prefecture, Japan, and classifed passengers using the K-means method.…”

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing

“…The third category was big data analytics. The rapid development of big data technology provided support for studying passenger travel in rail transit, such as Smart Card Data [ 7 ] and Automatic Fare Collection System (AFCS) [ 8 ]. These technologies could mine spatiotemporal characteristics of passenger travel based on historical and real-time data, predict passenger demand, identify key stations, and optimize station operations.…”