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contributor authorIrfan Muhammad;Khurshid Ahmed N.;Khurshid Muhammad B.;Ali Yasir;Khattak Aemal
date accessioned2019-02-26T07:37:31Z
date available2019-02-26T07:37:31Z
date issued2018
identifier otherJTEPBS.0000158.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4248348
description abstractTransportation agencies seek comprehensive policies and planning to overcome urban congestion and manage transportation demand with due cognizance of traveler behavior. This research develops a travel behavioral model for work-trip mode using revealed and stated choice data collected through a survey questionnaire in the city of Rawalpindi, Pakistan. The multinomial logit model specification is found best suited to develop a modal-split model and estimate travelers’ perceived expectation utility functions. The model is used to calculate elasticities and demand response to the policies of improvement in transit/bus rapid transit (BRT) and implement congestion pricing on major arterials of an urban road network. Travel demand is found elastic with respect to congestion pricing and out-of-vehicle travel time. It is concluded that improvement in transit services by introducing BRT alone does not induce a major change in the share proportion of automobile demand; however, congestion pricing has a significant effect on the reduction of automobile demand. Furthermore, the combination of two policies induces more modal-split than does congestion pricing alone. This research highlights traffic congestion pricing as one of the means of traffic demand management by demonstrating its contribution to improving urban traffic congestion.
publisherAmerican Society of Civil Engineers
titlePolicy Implications of Work-Trip Mode Choice Using Econometric Modeling
typeJournal Paper
journal volume144
journal issue8
journal titleJournal of Transportation Engineering, Part A: Systems
identifier doi10.1061/JTEPBS.0000158
page4018035
treeJournal of Transportation Engineering, Part A: Systems:;2018:;Volume ( 144 ):;issue: 008
contenttypeFulltext


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