Abstract
A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week.
Highlights
With the growth in population and economy, a car-sharing system becomes an alternative public transportation that alleviates road traffic congestion [1]
Results of Relocation Algorithms. is experiment aims to compare various relocation algorithms based on two measures. e first measure is the Root Mean Square Error (RMSE), which is the evaluation of model precision. e second measure is the opportunity loss, which is the amount of search failures resulting in less services. e third measure is the number of relocation trips, which is the amount of efforts by staff to relocate vehicles in order to have vehicles available in the stations with more demands
Ere are four methods in the comparison: three models are based on different forecasting techniques (BiGRU, Gated-Recurrent Unit (GRU), and Long ShortTerm Memory (LSTM)) and the last one is a relocation by experts. e experiment was conducted on the real usage data of three months (September, October, and November)
Summary
With the growth in population and economy, a car-sharing system becomes an alternative public transportation that alleviates road traffic congestion [1]. Experts in the transportation field predicted that car sharing can significantly increase in the ten years, especially in Asia-Pacific [2], and can become a possible bridging mode between private cars and traditional public transportation such as bus and train. E first design is a round-trip car-sharing system. In this design, a customer needs to reserve a vehicle in advance and return the vehicle at the station they picked. Multiple vehicles are available for customers at multiple stations. If the one-way car-sharing system operates efficiently, it can be more beneficial for both customers and operators, compared to the round-trip car-sharing system [4]. In the one-way car-sharing system, the number of vehicles at each station can become imbalanced in the sense
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