Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data

Seaborn, Catherine; Attanucci, John; Wilson, Nigel H. M.

doi:10.3141/2121-06

Cited by 148 publications

(104 citation statements)

References 7 publications

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“…They are designed to gather rich information about travel choices but are expensive to distribute over the entire population, covering typically, only a very limited time span and number of individuals. As an alternative, rich amount of information containing individual's coordinates is automatically recorded each time a person makes a call, sends an email, uses a credit card, or travels using a public transport smart card [16,17,18]. This traces of coordinates are recorded over months or even years.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Urban Human Mobility

et al. 2012

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Abstract. The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples' visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Urban Human Mobility

et al. 2012

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“…In recent years, automatic fare collection (AFC) data such as smart card data have been used by transit service providers to analyze passenger demand and system performance. These data have been used for O-D matrices estimation [8,9], demand analysis [10,11], travel behavior analysis [12], operational management, and public transit planning [13][14][15], and so forth. In particular, there are emerging studies dealing with AFC data of URT systems.…”

Section: Introductionmentioning

confidence: 99%

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Zhu

Wang

Huang

2017

Journal of Advanced Transportation

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An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger's travel. Thus, it is the key to estimate passengers' train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger's train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski's paradigm on modelling choice. Then, an integrated framework for estimating individual passenger's train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.

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“…Typical assumptions of trip chaining are made in this study as well as several other studies (3,4,9). For instance, it is assumed that travelers who use the transit system do not use any other modes within the given sequence of daily transit trips.…”

Section: Trip-chaining Model Assumptionsmentioning

confidence: 99%

Transit Stop-Level Origin–Destination Estimation through Use of Transit Schedule and Automated Data Collection System

Nassir

Khani

Lee

et al. 2011

Transportation Research Record: Journal of the Transportation R

115

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Transit automated data collection (ADC) systems have allowed estimation of valuable behavioral patterns, especially for multimodal transit and with consideration of the sequence of passenger trips. Mainly, the interplay with data from the ADC systems-automatic fare collection (AFC), automated vehicle location (AVL), and other geographic information systems-provides more access to individual passenger's trip chain beyond that imagined at a more-aggregate level. Studies to identify passengers' trip sequences have been expanded to include multimodal transit networks as well as much larger networks

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Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data

Cited by 148 publications

References 7 publications

Spatiotemporal Patterns of Urban Human Mobility

Spatiotemporal Patterns of Urban Human Mobility

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Transit Stop-Level Origin–Destination Estimation through Use of Transit Schedule and Automated Data Collection System

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