Smartphone sensing meets transport data: A collaborative framework for transportation service analytics

We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type i...

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Main Authors: LU, Yu, MISRA, Archan, SUN, Wen, WU, Huayu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/3787
https://ink.library.smu.edu.sg/context/sis_research/article/4789/viewcontent/08015165__1_.pdf
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spelling sg-smu-ink.sis_research-47892020-03-27T01:06:35Z Smartphone sensing meets transport data: A collaborative framework for transportation service analytics LU, Yu MISRA, Archan SUN, Wen WU, Huayu We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack of appropriate sensing triggers. TRANSense demonstrates how a judicious fusion of such disparate data sources can overcome these challenges and offer novel insights. We detail two examples: (a) Taxi Service Analyzer that provides accurate detection of commuter queuing for taxis and estimates their wait time, by using taxi trip records to identify potential taxi locations with high demand and subsequently selectively triggering mobile sensing-based queuing analytics on nearby commuters; and (b) Subway Boarding Analyzer that identifies instances when passengers fail to board arriving trains, by first estimating train arrivals from temporal patterns of passenger egress at station gantries, and then using mobile sensing-based analysis of commuter movement behavior on platforms. Experiments with real-world datasets (from over 20,000 taxis and 1.7 million commuters in Singapore) show the power of this approach: the taxi service analyzer detects commuter queuing with over 90% accuracy with negligible energy overhead and estimates wait times with error margins below 15%, whereas the subway boarding analyzer can detect failed boarding events with a precision of over 90% (more than thrice what is achievable through purely mobile sensing). IEEE 2017-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3787 info:doi/10.1109/TMC.2017.2743176 https://ink.library.smu.edu.sg/context/sis_research/article/4789/viewcontent/08015165__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Crowdsourcing Data analysis Data integration Pervasive computing Public transportation Databases and Information Systems Data Storage Systems Transportation
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Crowdsourcing
Data analysis
Data integration
Pervasive computing
Public transportation
Databases and Information Systems
Data Storage Systems
Transportation
spellingShingle Crowdsourcing
Data analysis
Data integration
Pervasive computing
Public transportation
Databases and Information Systems
Data Storage Systems
Transportation
LU, Yu
MISRA, Archan
SUN, Wen
WU, Huayu
Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
description We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack of appropriate sensing triggers. TRANSense demonstrates how a judicious fusion of such disparate data sources can overcome these challenges and offer novel insights. We detail two examples: (a) Taxi Service Analyzer that provides accurate detection of commuter queuing for taxis and estimates their wait time, by using taxi trip records to identify potential taxi locations with high demand and subsequently selectively triggering mobile sensing-based queuing analytics on nearby commuters; and (b) Subway Boarding Analyzer that identifies instances when passengers fail to board arriving trains, by first estimating train arrivals from temporal patterns of passenger egress at station gantries, and then using mobile sensing-based analysis of commuter movement behavior on platforms. Experiments with real-world datasets (from over 20,000 taxis and 1.7 million commuters in Singapore) show the power of this approach: the taxi service analyzer detects commuter queuing with over 90% accuracy with negligible energy overhead and estimates wait times with error margins below 15%, whereas the subway boarding analyzer can detect failed boarding events with a precision of over 90% (more than thrice what is achievable through purely mobile sensing). IEEE
format text
author LU, Yu
MISRA, Archan
SUN, Wen
WU, Huayu
author_facet LU, Yu
MISRA, Archan
SUN, Wen
WU, Huayu
author_sort LU, Yu
title Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
title_short Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
title_full Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
title_fullStr Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
title_full_unstemmed Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
title_sort smartphone sensing meets transport data: a collaborative framework for transportation service analytics
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/3787
https://ink.library.smu.edu.sg/context/sis_research/article/4789/viewcontent/08015165__1_.pdf
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