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Validating travel behavior estimated from smartcard data

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TLDR
In this paper, a validation of public transport origin-destination (OD) matrices obtained from smartcard and GPS data is presented, where the results are very positive, as the percentages of correct estimation are approximately 90% in all cases.
Abstract
In this paper, we present a validation of public transport origin–destination (OD) matrices obtained from smartcard and GPS data. These matrices are very valuable for management and planning but have not been validated until now. In this work, we verify the assumptions and results of the method using three sources of information: the same database used to make the estimations, a Metro OD survey in which the card numbers are registered for a group of users, and a sample of volunteers. The results are very positive, as the percentages of correct estimation are approximately 90% in all cases.

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Citations
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Journal ArticleDOI

Analyzing year-to-year changes in public transport passenger behaviour using smart card data

TL;DR: A two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage to demonstrate the efficiency of this approach in identifying a reduced set of passenger clusters linked to their fare types.
Journal ArticleDOI

Validating and improving public transport origin–destination estimation algorithm using smart card fare data ☆

TL;DR: The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.
Journal ArticleDOI

Crowding valuation in urban tram and bus transportation based on smart card data

TL;DR: In this article, the authors investigated the impact of crowding in public transport and concluded that crowding plays a significant role in passengers' route choice in public transportation, which can support the decision-making process of policy-makers, by quantifying the benefits of measures aiming to reduce crowding levels.
Journal ArticleDOI

Activity detection and transfer identification for public transit fare card data

TL;DR: A new heuristic is proposed to estimate the stop-level origins and destinations by detecting the traveller activities in the observed transactions in a fare card dataset, based on the proposed concept of off-optimality for a more accurate identification of short/hidden activities within the labelled transfers.
Journal ArticleDOI

Use of Smart Card Fare Data to Estimate Public Transport Origin–Destination Matrix

TL;DR: Unique smart card fare data from Brisbane, Queensland, Australia, offered an opportunity to assess previous methods and their assumptions and to study the effects of different assumptions on estimated O-D matrices.
References
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Journal ArticleDOI

Smart card data use in public transit: A literature review

TL;DR: The most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.
Journal ArticleDOI

Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile

TL;DR: This paper presents a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile and generates an estimation of time and position of alighting for over 80% of the boarding transactions.
Journal ArticleDOI

The Potential of Public Transport Smart Card Data

TL;DR: There are limitations, mainly that trip length is not recorded on systems based on validating cards only on entry to a bus, and that certain types of data still require direct survey methods for their collection (such as journey purpose).
Journal ArticleDOI

Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System

TL;DR: A model to estimate the destination location for each individual boarding a bus with a smart card, with a success rate of 66% for destination estimation and reaching about 80% at peak hours is presented.
Journal ArticleDOI

Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems

TL;DR: This research presents a case study of the automatic fare collection system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin‐destination matrices from an origin‐only AFC system to replace expensive passenger OD surveys.
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