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Article: Guiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications

TitleGuiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications
Authors
KeywordsObservational survey
Sequential binary logistic regression model
Simulation model
Taxi customer-search
Taxi-calling signal
Issue Date2019
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/tranpol
Citation
Transport Policy, 2019, v. 76, p. 100-110 How to Cite?
AbstractTaxi-calling signals (TCSs) have appeared in many cities to reveal passenger demand at locations away from the roadside to cruising vacant taxis to reduce search times for both vacant taxi drivers and customers. This study aims to find out the factors influencing vacant taxi drivers' customer-search decisions on whether to enter or bypass recommended areas while the drivers are cruising along a road with a series of TCSs. Observational survey data were collected and analyzed to understand the travel behavior of vacant taxi drivers. A sequential binary logistic regression (SBLR) model is first proposed to examine the dynamic decision-making process of vacant taxi drivers. A simulation model and a solution procedure are then developed by adopting the intervening opportunity modeling concept to validate the SBLR model. A sensitivity analysis is consequently conducted to show that the installation of TCSs can effectively increase the number of vacant taxis entering off-road locations for picking up customers. Potential policy implications are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/258563
ISSN
2023 Impact Factor: 6.3
2023 SCImago Journal Rankings: 1.742
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSzeto, WY-
dc.contributor.authorWong, CPR-
dc.contributor.authorYang, W-
dc.date.accessioned2018-08-22T01:40:30Z-
dc.date.available2018-08-22T01:40:30Z-
dc.date.issued2019-
dc.identifier.citationTransport Policy, 2019, v. 76, p. 100-110-
dc.identifier.issn0967-070X-
dc.identifier.urihttp://hdl.handle.net/10722/258563-
dc.description.abstractTaxi-calling signals (TCSs) have appeared in many cities to reveal passenger demand at locations away from the roadside to cruising vacant taxis to reduce search times for both vacant taxi drivers and customers. This study aims to find out the factors influencing vacant taxi drivers' customer-search decisions on whether to enter or bypass recommended areas while the drivers are cruising along a road with a series of TCSs. Observational survey data were collected and analyzed to understand the travel behavior of vacant taxi drivers. A sequential binary logistic regression (SBLR) model is first proposed to examine the dynamic decision-making process of vacant taxi drivers. A simulation model and a solution procedure are then developed by adopting the intervening opportunity modeling concept to validate the SBLR model. A sensitivity analysis is consequently conducted to show that the installation of TCSs can effectively increase the number of vacant taxis entering off-road locations for picking up customers. Potential policy implications are discussed.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/tranpol-
dc.relation.ispartofTransport Policy-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectObservational survey-
dc.subjectSequential binary logistic regression model-
dc.subjectSimulation model-
dc.subjectTaxi customer-search-
dc.subjectTaxi-calling signal-
dc.titleGuiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications-
dc.typeArticle-
dc.identifier.emailSzeto, WY: ceszeto@hku.hk-
dc.identifier.emailWong, CPR: cpwryan@hku.hk-
dc.identifier.authoritySzeto, WY=rp01377-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.tranpol.2018.06.009-
dc.identifier.scopuseid_2-s2.0-85049355890-
dc.identifier.hkuros286526-
dc.identifier.volume76-
dc.identifier.spage100-
dc.identifier.epage110-
dc.identifier.isiWOS:000463127600011-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0967-070X-

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