Route choice behaviour and travel information in a congested network: Static and dynamic recursive models

Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time...

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Main Authors: RAMOS, Giselle de Moraes, MAI, Tien, DAAMEN, Winnie, FREJINGER, Emma
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5282
https://ink.library.smu.edu.sg/context/sis_research/article/6285/viewcontent/1_s2.0_S0968090X19309027_main.pdf
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spelling sg-smu-ink.sis_research-62852022-02-23T08:00:14Z Route choice behaviour and travel information in a congested network: Static and dynamic recursive models RAMOS, Giselle de Moraes MAI, Tien DAAMEN, Winnie FREJINGER, Emma Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy. To this end, we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers, travel diaries and link travel times on the overall network. Though such combined data sets exist, these have not yet been used to investigate route choice behaviour. A dynamic network in which travel times change over time has been used for the estimation of both recursive logit and nested models. Prediction and estimation results are compared to those obtained for a static network. The interpretation of parameter estimates and prediction accuracy differ substantially between dynamic and static networks as well as between models with correlated and uncorrelated utilities. Contrary to the static results, for the dynamic, where travel times are modelled more accurately, travel information does not have a significant impact on route choice behaviour. However, having travel information increases the travel comfort, as interviews with participants have shown. 2020-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5282 info:doi/10.1016/j.trc.2020.02.014 https://ink.library.smu.edu.sg/context/sis_research/article/6285/viewcontent/1_s2.0_S0968090X19309027_main.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 Dynamic and static networks Route choice behaviour Recursive models Travel information Revealed preference data Databases and Information Systems Operations Research, Systems Engineering and Industrial Engineering Transportation
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Dynamic and static networks
Route choice behaviour
Recursive models
Travel information
Revealed preference data
Databases and Information Systems
Operations Research, Systems Engineering and Industrial Engineering
Transportation
spellingShingle Dynamic and static networks
Route choice behaviour
Recursive models
Travel information
Revealed preference data
Databases and Information Systems
Operations Research, Systems Engineering and Industrial Engineering
Transportation
RAMOS, Giselle de Moraes
MAI, Tien
DAAMEN, Winnie
FREJINGER, Emma
Route choice behaviour and travel information in a congested network: Static and dynamic recursive models
description Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy. To this end, we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers, travel diaries and link travel times on the overall network. Though such combined data sets exist, these have not yet been used to investigate route choice behaviour. A dynamic network in which travel times change over time has been used for the estimation of both recursive logit and nested models. Prediction and estimation results are compared to those obtained for a static network. The interpretation of parameter estimates and prediction accuracy differ substantially between dynamic and static networks as well as between models with correlated and uncorrelated utilities. Contrary to the static results, for the dynamic, where travel times are modelled more accurately, travel information does not have a significant impact on route choice behaviour. However, having travel information increases the travel comfort, as interviews with participants have shown.
format text
author RAMOS, Giselle de Moraes
MAI, Tien
DAAMEN, Winnie
FREJINGER, Emma
author_facet RAMOS, Giselle de Moraes
MAI, Tien
DAAMEN, Winnie
FREJINGER, Emma
author_sort RAMOS, Giselle de Moraes
title Route choice behaviour and travel information in a congested network: Static and dynamic recursive models
title_short Route choice behaviour and travel information in a congested network: Static and dynamic recursive models
title_full Route choice behaviour and travel information in a congested network: Static and dynamic recursive models
title_fullStr Route choice behaviour and travel information in a congested network: Static and dynamic recursive models
title_full_unstemmed Route choice behaviour and travel information in a congested network: Static and dynamic recursive models
title_sort route choice behaviour and travel information in a congested network: static and dynamic recursive models
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5282
https://ink.library.smu.edu.sg/context/sis_research/article/6285/viewcontent/1_s2.0_S0968090X19309027_main.pdf
_version_ 1770575370081271808