Evaluating Citywide Bus Service Reliability Using Noisy GPS Data

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Title: Evaluating Citywide Bus Service Reliability Using Noisy GPS Data
Authors: Wang, Shen
MacNamee, Brian
Permanent link: http://hdl.handle.net/10197/9113
Date: 17-Sep-2017
Abstract: AbstractAn increasing number of people use smartphoneapplications to plan their trips. Unfortunately, for variousreasons, bus trips suggested by such applications are not asreliable as other trip types (e.g. by car, on foot, or by bicycle),which can result in excessive waiting time, or even the needto revise a planned trip. Traditional punctuality-based busservice reliability metrics do not capture route deviations, whichare especially frequent in rapid changing urban environmentsdue to rapidly changing road conditions caused by trafficcongestion, road maintenance, etc. The prevalence of GPS dataallows buses to be tracked and route deviations to be captured.We use such data to propose and calculate a novel reliabilityscore for bus trips. This score is a linear weighted combinationof distance, time, and speed deviations from an expected, predefinedbus trip. GPS trajectory data is large and noisy whichmakes it challenging to process. This paper also presents anefficient framework that can de-noise and semantically splitraw GPS data by pre-defined bus trips in citywide. Finally,the paper presents a comparative case study that applies theproposed reliability score to publicly available open bus datafrom Rio de Janeiro in Brazil and Dublin in Ireland.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2017 IEE
Keywords: Machine learning;Statistics
DOI: 10.1109/ISC2.2017.8090843
Language: en
Status of Item: Peer reviewed
Is part of: Proceedings of the 2017 International Smart Cities Conference (ISC2)
Conference Details: International Smart Cities Conference (ISC2), Wuxi, China, 14 Sep - 17 Sep 2017
Appears in Collections:Insight Research Collection

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