Abstract
The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers.
Highlights
Taxi is an important transport mode in urban transportation system
Zhou developed a model of taxi path selection as well as rate optimization based on the fairness principle, which takes passengers’ minimum travel time and cost as the objective function and guarantees drivers’ reasonable income as constraints, considering the interests of drivers and passengers
The results show that the improved multi-objective genetic algorithm designed in this paper can obtain a more satisfactory solution, and has faster convergence speed compared with the traditional genetic algorithm
Summary
Taxi is an important transport mode in urban transportation system. Encouraging passengers sharing taxies could increase the transportation efficiency, shorten the total distance and cutting off idling time, alleviate road congestion, and reduce emissions [1]. Zhou developed a model of taxi path selection as well as rate optimization based on the fairness principle, which takes passengers’ minimum travel time and cost as the objective function and guarantees drivers’ reasonable income as constraints, considering the interests of drivers and passengers. Wang discussed the static and dynamic carpooling modes, dividing carpooling mode into several types, such as one to one, one to many and many to many modes He developed the path selection method and rate optimization model [8]. This paper constructs a taxi path optimization model considering the two aspects, which takes minimize taxi traveling distance and time as the objective function, takes taxi detour distance, passenger satisfaction, passenger fees and taxi driver income as constraints, and the improved genetic algorithm is developed to solve the model. The taxi driver chooses the shortest path under the condition of single riding taxi
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