Dynamic pricing of free-floating carsharing networks with sensitivity to travellers’ attitudes towards risk

Wu, Chenyang; Vine, Scott Le; Sivakumar, Aruna; Polak, John

doi:10.1007/s11116-021-10190-8

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…Car-sharing companies are encouraged to maximize efficiency when determining optimal locations for car-sharing stations, particularly in densely populated areas where cars occupy a substantial quantity of expensive parking space. Wu et al [43] proposed a novel choice-based framework for optimizing the dynamic pricing of Free-Floating Car-sharing Networks (FFCS) operators under various forms of consumers' risk preferences. The numerical study demonstrated the adequacy of the proposed approach to cover a range of user attitudes towards risk, i.e., neutrality, risk aversion, and riskseeking.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

et al. 2022

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Motorization has been escalating rapidly in developing countries, posing a severe challenge to sustainable urban mobility. In the past two decades, car-sharing has emerged as one of the most prominent alternatives to facilitate smart mobility solutions, thereby helping to reduce air pollution and traffic congestion. However, before its full-scale deployment, it is essential to understand the consumers’ acceptance of car-sharing systems. This study aimed to assess the public perception and acceptance of the car-sharing system through a stated preference (SP) questionnaire in the city of Lahore, Pakistan. The collected data contained detailed information on various service attributes of three alternative modes (car-sharing, private car, and taxi) in addition to the sociodemographic attributes of respondents. Data analysis and interpretation were performed using econometric models such as the Multinomial Logit Model (MNL), the Nested Logit Model (NL), and the Random Parameter Logit Model (RPL). Study findings revealed that several generic attributes such as travel time, travel cost, waiting time, and privacy were predicated as significant influential factors towards the adoption of car-sharing. Sociodemographic attributes, including age, education, monthly income, the individuals who had driver’s licenses, and frequency of travel in a week, were also found to be significant. The findings of the current study can provide valuable insights to stakeholders and transportation planners in formulating effective policies for car-sharing.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

et al. 2022

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“…Intersections and road segments in road network will be nodes and edges in a graph. Meanwhile, the traffic conditions [40], travel distance [41], travel time [42], pricing (ridesharing) [43,44], etc. could be described as the weight of the graph.…”

Section: Route Recommendationmentioning

confidence: 99%

Contextual Route Recommendation System in Heterogeneous Traffic Flow

et al. 2021

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The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

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“…Upon receiving requests, the CSO assigns vehicles to users, plans staff-based relocations and determines incentives for customers, to whom a car has already been assigned, in exchange for a change of destination. The focus on [49] is instead on free-floating services. The system they consider is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Exact solutions to a carsharing pricing and relocation problem under uncertainty

Pantuso¹

2022

Preprint

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In this article we study the problem of jointly deciding carsharing prices and vehicle relocations. We consider carsharing services operating in the context of multi-modal urban transportation systems. Pricing decisions take into account the availability of alternative transport modes, and customer preferences with respect to these. In order to account for the inherent uncertainty in customer preferences, the problem is formulated as a mixedinteger two-stage stochastic program with integer decision variables at both stages. We propose an exact solution method for the problem based on the integer L-Shaped method which exploits an efficient exact algorithm for the solution of the subproblems. Tests on artificial instances based on the city of Milan illustrate that the method can solve, or find good solutions to, moderately sized instances for which a commercial solver fails. Furthermore, our results suggest that, by adjusting prices between different zones of the city, the operator can attract significantly more demand than with a fixed pricing scheme and that such a pricing scheme, coupled with a sufficiently large fleet, significantly reduces the relevance of staff-based relocations. A number of issues, that remain to be addressed in future research, are pointed out in our conclusions.

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Dynamic pricing of free-floating carsharing networks with sensitivity to travellers’ attitudes towards risk

Cited by 13 publications

References 35 publications

Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

Contextual Route Recommendation System in Heterogeneous Traffic Flow

Exact solutions to a carsharing pricing and relocation problem under uncertainty

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