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

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...

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Bibliographic Details
Published in:International journal of ITS research 2020-05, Vol.18 (2), p.267-276
Main Authors: Chakraborty, Jayita, Pandit, Debapratim, Xia, Jianhong (Cecilia), Chan, Felix
Format: Article
Language:English
Subjects:
Automotive Engineering
Boundary conditions
Car sharing
Cellular automata
Civil Engineering
Computer Imaging
Computer simulation
Demand
Electrical Engineering
Engineering
Optimization
Pattern Recognition and Graphics
Pricing
Robotics and Automation
Simulation
Urban environments
User Interfaces and Human Computer Interaction
Vision
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Summary: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.
ISSN:1348-8503
1868-8659
DOI:10.1007/s13177-019-00197-y