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Optimization Model of Taxi Fleet Size Based on GPS Tracking Data
A reasonable taxi fleet size has a significant impact on the satisfaction of urban traffic demand, the alleviation of urban traffic congestion, and the stability of taxi business groups. Most existing studies measure the overall scale by using macro indices, and few studies are from the micro level. To meet the transportation demand for taxis, mitigating the mismatch between taxi supply and demand, this research proposes an urban taxi fleet size calculating model based on GPS tracking data. Firstly, on the basis of road network segmentation, the probability model of a passenger taxi-taking a road section as a unit is built to evaluate the difficulty of taxi-taking on a road section. Furthermore, a user queuing model is built for the “difficult to take a taxi„ road section in the peak period, and the service mileage required by potential taxi users is calculated. After that, a transportation capacity measurement model is built to estimate the number of taxis required in different time periods, Finally, the income constraint model is used to explain the impact of different vehicle fleet sizes on the income of taxi groups, so as to provide a reference for the determination of the final fleet size. The model is applied to data from Xi’an. The calculation results are based on data from May 2014, and show that the scale of taxi demand is about 654⁻2237, and after considering the impact of different fleet size increases on income, when the income variation index is limited to 0.10, i.e., the decrease of drivers’ income will not exceed 10%, an increase of 1286 taxis will be able to meet 66% of the unmet demand in the peak period. The conclusion indicates that the model can effectively calculate the required fleet size and formulate the constraint solutions. This method provided can be considered as a support for formulating the regulation strategy of an urban taxi fleet size.
Optimization Model of Taxi Fleet Size Based on GPS Tracking Data
A reasonable taxi fleet size has a significant impact on the satisfaction of urban traffic demand, the alleviation of urban traffic congestion, and the stability of taxi business groups. Most existing studies measure the overall scale by using macro indices, and few studies are from the micro level. To meet the transportation demand for taxis, mitigating the mismatch between taxi supply and demand, this research proposes an urban taxi fleet size calculating model based on GPS tracking data. Firstly, on the basis of road network segmentation, the probability model of a passenger taxi-taking a road section as a unit is built to evaluate the difficulty of taxi-taking on a road section. Furthermore, a user queuing model is built for the “difficult to take a taxi„ road section in the peak period, and the service mileage required by potential taxi users is calculated. After that, a transportation capacity measurement model is built to estimate the number of taxis required in different time periods, Finally, the income constraint model is used to explain the impact of different vehicle fleet sizes on the income of taxi groups, so as to provide a reference for the determination of the final fleet size. The model is applied to data from Xi’an. The calculation results are based on data from May 2014, and show that the scale of taxi demand is about 654⁻2237, and after considering the impact of different fleet size increases on income, when the income variation index is limited to 0.10, i.e., the decrease of drivers’ income will not exceed 10%, an increase of 1286 taxis will be able to meet 66% of the unmet demand in the peak period. The conclusion indicates that the model can effectively calculate the required fleet size and formulate the constraint solutions. This method provided can be considered as a support for formulating the regulation strategy of an urban taxi fleet size.
Optimization Model of Taxi Fleet Size Based on GPS Tracking Data
Yang Yang (author) / Zhenzhou Yuan (author) / Xin Fu (author) / Yinhai Wang (author) / Dongye Sun (author)
2019
Article (Journal)
Electronic Resource
Unknown
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