Research on Taxi Pricing Model and Optimization for Carpooling Detour Problem

Hindawi Journal of Advanced Transportation Volume 2019, Article ID 3867874, 11 pages https://doi.org/10.1155/2019/3867874 Research Article Research on Taxi Pricing Model and Optimization for Carpooling Detour Problem Wei Zhang 1 ,1 Ruichun He ,1 Yong Chen,2 Mingxia Gao ,1 and Changxi Ma 1 School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China 2 Correspondence should be addressed to Ruichun He; Received 27 December 2018; Revised 8 March 2019; Accepted 31 March 2019; Published 14 April 2019 Academic Editor: Stefano de Luca Copyright © 2019 Wei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper builds a multiobjective optimization model for solving the taxi carpooling with detour problem and designs a genetic algorithm to determine a fair pricing scheme for riders and drivers. The researches show that it is feasible to share a taxi with detour. It is the key to determine appropriate carpooling payment ratio and detour carpooling payment ratio. The ratio of detour distance to travel distance has an important influence on detour carpooling. It should be limited to less than certain values. Payment ratios and the maximum value of the ratio of detour distance to travel distance are determined by the method proposed in this paper. The method can ensure benefits of passengers and drivers, which makes detour carpooling a reality. These conclusions and the method have a certain guiding significance for formulating taxi policy. 1. Introduction Taxi carpooling mode has become a common travelling mode. The mode permits several passengers to share the same taxi. Taxi carpooling can effectively solve the problem of having difficulty getting a taxi at peak time. It does not only ease the traffic pressure and improve the transportation efficiency [1, 2], but also reduce energy consumption [3–6]. It is an effective solution to solve the urban traffic problem. Many scholars studied the problem of carpooling [7–12]. This research work focuses on the following two aspects: on the one hand, the characteristics of carpooling behavior, the influence factors of carpooling, and the effects of carpooling are studied [13–18]. Shaheen analyzed carpooling situation in the San Francisco Bay Area and studied passenger characteristics, behaviors, and motivation [19]. Delhomme researched the main determinants of the practice of carpooling by investigating the factual data and gave some strategies for increasing the number of carpoolers and the frequency of carpooling [20]. Malodia studied the characteristics of Indian residents preference for passenger sharing based on the data from the internet survey and found that cognitive attitudes have important effects on passenger sharing behaviour [21]. Tahmasseby found that distance, time, cost, gender, occupation, age, weather conditions, and other factors can affect the choice of sharing [22]. Javid studied the sharing characteristics of 50 states in the United States and the District of Columbia and analyzed the impact of the carpooling policy on the environment [23]. Sweta proved that carpooling mode can help to reduce congestion and fuel consumption [24]. Zhang built a model of multiple passengers carpooling, analyzed carpooling advantages, and proved that carpooling can bring benefits to passengers and drivers by simulation [25]. On the other hand, the problem of carpooling matching and route optimization is studied. Jamal put forward route planning and ride matching algorithms and designed a system that can supply the users with alternative routes for their trips [26]. Mallus proposed a dynamic carpooling route matching algorithm, and the method is verified by the experiment [27]. Huang proposed carpool route and matching algorithm and solved carpool service problems in cloud computing based on genetic algorithm [28]. Chang designed a vehicular information system that combines the improved carpooling algorithm and the VANET-based route planning and computed the optimal carpooling sequence and 2 precise fuel costs shared [29]. Ma built a taxi carpooling path optimization model, solved it based on the improved genetic algorithm, and obtained optimized path results [30]. He proposed an intelligent routing scheme based on GPS data. The carpooling system provides many-to-many services with multiple pickup and dropping points [31]. Xiao proposed a taxi carpooling matching algorithm based on fuzzy clustering and fuzzy recognition [32]. In summary, the above researches proved the feasibility and effectiveness of carpooling mode and solved the problem of route planning in carpooling process. However, detour is a common phenomenon of taxi carpooling in reality. Destinations of passengers are different, but they would go to the same direction. Due to the factors, such as having difficulty getting another taxi and lower cost in carpooling, some passengers agree to detour for taking the same taxi. His travel time has to be delayed, but he can get more discount than other passengers in the same carpooling travel. Passengers’ payments have important influences on driver’s income. The reduction of the detour passenger’s cost will depress the driver’s income. How to control the payments of passengers to protect interest of the driver? It is important to study the problem, which can ensure implementation of carpooling policy. However, there are limited researches on the problem. For the problem of taxi detour carpooling, this paper builds a multiobjective optimization model, designs an algorithm to solve the model based on genetic algorithm, and gets reasonable pricing stagey of detour carpooling which ensures interests of passengers and drivers simultaneously. The method makes carpooling detour a reality. The works have a certain guide significance to formulate taxi carpooling policy. 2. Problem Description and Notation 2.1. Problem Description. Suppose two passengers intend to share the same taxi. Of course, it can also be two groups. There may be more than a passenger in each group, but the sum of passengers is not more than taxi capacity. The destinations of the passengers in the same group are the same ones. Therefore, the two groups carpooling can be considered as two passengers carpooling. The problem of two passengers carpooling is studied in this paper. Suppose source of passenger 𝐴 is 𝐴 1 and the destination is 𝐴 2 . The best route of passenger 𝐴 is 𝐴 1 󳨀→ 𝐴 2 . Source of passenger 𝐵 is 𝐵1 , and the destination is 𝐵2 . The best route of passenger 𝐵 is 𝐵1 󳨀→ 𝐵2 . The destinations of the two passengers are different; that is, 𝐴 2 ≠ 𝐵2 , but 𝐴 2 and 𝐵2 are in the same direction. Passenger 𝐵 agrees to make a detour in order to share t (...truncated)