Highlights We propose a cell-based logit-opportunity taxi customer-search model. It combines the modeling principles of the logit and intervening opportunity models. We calibrate the model using taxi GPS data and the proposed bi-level approach. Vacant taxi drivers do not search their customers under the random walk principle. They consider the cumulative probability of success of being hired in customer-search.
Abstract This paper proposes a cell-based model to predict local customer-search movements of vacant taxi drivers, which incorporates the modeling principles of the logit-based search model and the intervening opportunity model. The local customer-search movements were extracted from the global positioning system data of 460 Hong Kong urban taxis and inputted into a cell-based taxi operating network to calibrate the model and validate the modeling concepts. The model results reveal that the taxi drivers’ local search decisions are significantly affected by the (cumulative) probability of successfully picking up a customer along the search route, and that the drivers do not search their customers under the random walk principle. The proposed model helps predict the effects of the implementation of the policies in adjusting the taxi fleet size and the changes in passenger demand on the customer-search distance and time of taxi drivers.
A cell-based logit-opportunity taxi customer-search model
2014-08-14
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A cell-based logit-opportunity taxi customer-search model
Online Contents | 2014
|Empirical evidence for taxi customer-search model
Online Contents | 2010
|Behavior of taxi customers in hailing vacant taxis: a nested logit model for policy analysis
Online Contents | 2015
|