Research on Taxi Pricing Model and Optimization for Carpooling Detour Problem

Zhang, Wei; He, Ruichun; Chen, Yong; Gao, Mingxia; Ma, Changxi

doi:10.1155/2019/3867874

Cited by 17 publications

(10 citation statements)

References 29 publications

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“…The optimization of matching participants, routes, and schedules is widely and actively studied, as shown in Table 5. Several algorithms have been proposed to overcome traditional problems like passenger to driver matching [66] to minimize time and distance to the carpooling starting point and destination [67], to find participants within organizations [68], to determine the pricing schemes for the driver and the passengers [67], and to plan the route by integrating different means such as public transportation or bike rentals with carpooling [69]. Several studies examine the business models framework for services in smart cities.…”

Section: Literature Collection #1mentioning

confidence: 99%

Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review

Ανθόπουλος

Tzimos

2021

Sustainability

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Carpooling schemes for mutual cost benefits between the driver and the passengers has a long history. However, the convenience of driving alone, the increasing level of car ownership, and the difficulties in finding travelers with matching timing and routes keep car occupancy low. Technology is a key enabler of online platforms which facilitate the ride matching process and lead to an increase in carpooling services. Smart carpooling services may be an alternative and enrichment for mobility, which can help smart cities (SCs) reduce traffic congestion and gas emissions but require the appropriate architecture to support connection with the city infrastructure such as high-occupancy vehicle lanes, parking space, tolls, and the public transportation services. To better understand the evolution of carpooling platforms in SCs, bibliometric analysis of three separate specialized literature collections, combined with a systematic literature review, is performed. It is identified that smart carpooling platforms could generate additional value for participants and SCs. To deliver this value to an SC, a multi-sided platform business model is proposed, suitable for a carpooling service provider with multiple customer segments and partners. Finally, after examining the SC structure, a carpooling platform architecture is presented, which interconnects with the applicable smart city layers.

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Section: Literature Collection #1mentioning

confidence: 99%

Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review

Ανθόπουλος

Tzimos

2021

Sustainability

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“…The time constraint reflects the time window limitation in the process of vehicle service, and also stipulates the behavior rules of vehicles arriving at the service point in different time periods. The specific constraints are shown as Equation (11) to Equation (13). Equation (11) indicates that the actual arrival time of the taxi should be less than the latest arrival time T j2 requested by the passenger.…”

Section: Time Window Constrainsmentioning

confidence: 99%

“…Zhou et al [12] considered the problem of ride-sharing route and cost sharing, constructed the optimization model with the minimum travel time cost as the objective function, and solved it by Genetic Algorithm. Zhang et al [13] built a multi-objective optimization model for solving the taxi ride-sharing with detour problem and designed a Genetic Algorithm to determine a fair pricing scheme for riders and drivers. In order to optimize the taxi ride-sharing route, Ma et al [14] built the taxi ride-sharing route optimization model with single objective and its extended model with multiple objectives respectively.…”

Section: Introductionmentioning

confidence: 99%

The Optimization Model of Ride-Sharing Route for Ride Hailing Considering Both System Optimization and User Fairness

Cao¹,

Wang²,

Li³

2021

Sustainability

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To fully take the advantages of ride-sharing ride hailing, such as high loading rate, high operating efficiency, and less traffic resources, and to alleviate the difficulty of getting a taxi in urban hubs, the topic of ride-sharing route optimization for ride hailing is studied in this paper. For the multiple ride hailing ride-sharing demands and multiple ride hailing services in the urban road network in a specific period, the objective function is established with the shortest route of the system. The constraint conditions of the optimization model are constructed by considering factors of the rated passenger capacity, route rationality, passenger benefits, driver benefits and time window. Based on the idea of the Genetic Algorithm, the solution algorithm of the optimization model is developed. According to the supply and demand data of taxi during peak hours in the local road network in the city of Dalian, the optimization model and algorithm are used to optimize the ride-sharing route scheme. Research results indicate that the optimization model and algorithm can find the approximate optimal solution of the system in a short time. Compared with the traditional non-ride-sharing mode, the ride-sharing scheme can not only effectively reduce the taxi empty-loaded rate and the travel cost of passengers, improve the efficiency of drivers, but also save energy and reduce emissions, and promote the sustainable development of urban traffic.

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“…Ma and Zhang [12] investigated the impact of di erent shared parking charges and ridesharing payments on tra c ow, and indicated that a scheme with dynamic parking charges and a constant ridesharing payment can signi cantly improve system performance. Zhang et al [13] believed that the key of the taxi carpooling detour scheme is to determine the appropriate payment ratio and detour payment ratio, and a multi-objective optimization model for taxi carpooling detours was established.…”

Section: Introductionmentioning

confidence: 99%

Guidance Optimization of Travelers’ Travel Mode Choice Based on Fuel Tax Rate and Bus Departure Quantity in Two-Mode Transportation System

Sun

et al. 2020

Journal of Advanced Transportation

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This aim of this study is to improve the guidance role of the fuel tax rate and bus departure quantity on travel mode choice. Car and bus travel are chosen as the research object, and a day-to-day evolution model of dual-mode network traffic flow (based on a stochastic user equilibrium model and the method of network tatonnement process) is established. Subsequently, a guidance optimization model of fuel tax rate and bus departure quantity is designed. This guidance optimization model is formulated to determine the comprehensive minimum value among system total travel time of car travel, system total comprehensive cost of bus travel, and the difference between the total operating cost of bus departure increment and the total amount of fuel tax levied on car travelers. Through numerical examples, the validity of this guidance optimization model is verified, and the influence of fuel tax rate and bus departure quantity on the traffic network is analyzed. The results show that a guidance optimization scheme based on fuel tax rate and bus departure quantity can help regulate the proportion of car travel and improve bus service quality.

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Research on Taxi Pricing Model and Optimization for Carpooling Detour Problem

Cited by 17 publications

References 29 publications

Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review

Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review

The Optimization Model of Ride-Sharing Route for Ride Hailing Considering Both System Optimization and User Fairness

Guidance Optimization of Travelers’ Travel Mode Choice Based on Fuel Tax Rate and Bus Departure Quantity in Two-Mode Transportation System

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