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A cell-based logit-opportunity taxi customer-search model |
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| Affiliation: | 1. The Glenn Department of Civil Engineering, Clemson University, 125 Lowry Hall, Clemson, SC 29634, United States;2. 3240R Patrick F. Taylor Hall, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States;3. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China;1. Institute of Geography Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China;2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;3. School of Economy and Management, Chang’an University, Xi’an, Shaanxi, 710064, China;1. School of Traffic and Transportation Engineering, Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha, China;2. School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73, Huang-He Street, 150090, Harbin, China;3. School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China |
| 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. |
| Keywords: | Logit-opportunity model Probability of success Taxi customer-search Cell-based network Global positioning system data |
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