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A Protocol for Simulation Modeling of Ridesourcing Services: Optimisation of Fleet Size in an Urban Environment

机译:Ridesourcing服务仿真建模的协议:城市环境中机队规模的优化

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摘要

Ridesourcing services have emerged as an alternative transit option for commuters in metropolitan areas. Western Australia has exhibited the highest growth of ridesourcing services as compared to the other regions of Australia. Although the ridesourcing services have attracted a considerable number of riders, whether such services meet the demand still remains a major concern. The introduction of a pricing strategy, ‘surge pricing’, in order to attract drivers during peak hours has led to concerns related to congestion and emissions. Therefore, it is required to devise strategies to determine the optimal distribution of vehicles in order to meet the spatio-temporal demand of the ridesourcing services. Therefore, this study would develop a simulation model to determine the optimal allocation of drivers to converge to demand through simulation through the principles of Cellular Automata (CA) Theory in order to minimise the drivers and riders’ waiting time and overall travel distance of drivers. The boundary conditions for the simulation would be updated through the feedbacks of drivers and riders based on the field survey. The model would be validated through the open-sourced historical data provided by the Perth Uber Company.
机译:Ridesourcing服务已成为大都市区通勤者的替代交通选择。与澳大利亚其他地区相比,西澳大利亚州的拼车服务增长最快。尽管拼车服务已经吸引了大量的骑手,但是这些服务是否满足需求仍然是一个主要问题。为了在高峰时段吸引驾驶员,引入了定价策略“激增定价”,这引起了与交通拥堵和排放相关的担忧。因此,需要设计策略来确定车辆的最佳分配,以满足拼车服务的时空需求。因此,本研究将开发一种仿真模型,通过基于元胞自动机(CA)理论的仿真来确定要收敛到需求的驾驶员的最佳分配,从而最大程度地减少驾驶员和驾驶员的等待时间以及驾驶员的总体行驶距离。模拟的边界条件将根据现场调查通过驾驶员和骑手的反馈来更新。该模型将通过珀斯Uber公司提供的开源历史数据进行验证。

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