A Method for Bus OD Matrix Estimation Using Multisource Data

Abstract

The automated fare collection (AFC) system has gained increasing popularity among transit systems worldwide. The AFC system is usually an entry-only system that only records the serial number of the smart card and the transaction time of each use. Neither the AFC data nor the bus global positioning system (GPS) could reveal the passenger’s alighting information, namely, alighting time and station. Hence, the station-to-station origin-destination (OD) trip information cannot be obtained directly from the available data sources. To address this problem, this paper proposes a methodology that estimates the OD matrix by using smart card and GPS data. In this paper, the characteristics of the basic data sources are first analyzed, based on which the bus arrival time is generated using the density-based clustering algorithm and a time correction strategy, based on which the passenger’s boarding station is identified. The alighting stations are inferred based on the characteristics of bus trip chaining, which could identify over 80% of the alighting stations on average. Finally, the proposed methodology is verified by a comprehensive field survey in Suzhou, China, with 100% sample rate.

Estimating on-board passenger comfort in public transport vehicles using incomplete automatic passenger counting data

“…There are multiple ways of modelling the passenger arrival, boarding, and alighting processes at a station. The very nature of the problem can result in some complex models characterised by several parameters, whose calibration will likely require the availability of vast quantities of data (e.g., Gur and Ben-Shabat, 1997 , Liu et al, 2021 , van Oort et al, 2015 , Wang et al, 2011 , Li et al, 2011 , Ji et al, 2015 , Huang et al, 2020 , Sun et al, 2021 , Tao and Tang, 2019 ). Here, simplified models are developed and employed.…”

“…A related problem that has also received considerable attention has been the estimation and prediction of Origin-Destination (OD) flows and matrices for public transport, usually on the basis of passenger counts or Automatic Fare Collection (AFC) systems. Methods adopted include optimisation ( Gur and Ben-Shabat, 1997 , Liu et al, 2021 ), elasticity ( van Oort et al, 2015 ), trip chaining ( Wang et al, 2011 , Li et al, 2011 ), Iterative Proportional Fitting (IPF) ( Ji et al, 2015 ), clustering ( Huang et al, 2020 ), Bayesian inference ( Sun et al, 2021 ), as well as data fusion and Kalman filtering ( Tao and Tang, 2019 ). Some research has also explored the nature and patterns of prediction and forecasting errors and has created inferential statistics models aiming to address them ( Jung and Casello, 2020 ).…”

A Survey of Traditional and Customized Bus Transportation Planning Methodologies

“…al. [10] introduced a framework for obtaining a stop-to-stop O-D trip information. This framework made collective use of automatic fare collection systems (AFC's) and GPS data of running buses to create an O-D matrix.…”

Scaling Time-Dependent Origin-Destination Matrix Using Growth Factor Model

“…The alighting stations are estimated through a process. In general the alighting station for each passenger is estimated using trip chain model created from the sequence of validations [5], [6], [7], [8]. Along with OD matrix estimation approaches, there are studies in which supplementary source of data is used to calibrate or improve the quality of the initial OD matrix.…”