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Journal ArticleDOI

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

TLDR
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.
Abstract
A high-quality Origin–Destination (OD) matrix is a fundamental prerequisite for any serious transport system analysis However, it is not always easy to obtain it because OD surveys are expensive and difficult to implement This is particularly relevant in large cities with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection In this paper, we present a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile The proposed method is applied to two 1-week datasets obtained for different time periods From the data available, we obtain detailed information about the time and position of boarding public transportation and generate an estimation of time and position of alighting for over 80% of the boarding transactions The results are available at any desired time–space disaggregation After some post-processing and after incorporating expansion factors to account for unobserved trips, we build public transport OD matrices

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Citations
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Journal ArticleDOI

Big Data Analytics in Intelligent Transportation Systems: A Survey

TL;DR: Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced.
Journal ArticleDOI

Mining smart card data for transit riders’ travel patterns

TL;DR: Wang et al. as mentioned in this paper proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China and identified trip chains based on the temporal and spatial characteristics of their smart card transaction data.
Journal ArticleDOI

Detecting the dynamics of urban structure through spatial network analysis

TL;DR: Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city’s master plan.
Journal ArticleDOI

Demand-driven timetable design for metro services

TL;DR: Three optimization models to design demand-sensitive timetables are formulated and the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration.
Journal ArticleDOI

A survey of data fusion in smart city applications

TL;DR: In this article, a multi-perspectives classification of the data fusion to evaluate the smart city applications is presented, where the proposed classification is applied to evaluate selected applications in each domain of smart city and the potential future direction and challenges of data fusion integration are discussed.
References
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Journal ArticleDOI

A note on two problems in connexion with graphs

TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
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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

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.
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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.
Journal ArticleDOI

Common Bus Lines

TL;DR: In this article, the problem of common bus lines is formulated as an optimization problem within a probabilistic context, and the optimal subset of routes "to be selected" is obtained.
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