Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 21, Pages 21723-21735Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3181591
Keywords
Automobiles; Data mining; Internet of Things; Public transportation; Planning; Roads; Optimization; Commuting trip; customized bus; differential evolution (DE) algorithm; electronic registration identification (ERI) of vehicles
Categories
Funding
- National Natural Science Foundation of China [62073049]
- Key Research and Development Program of Chongqing [cstc2018jszx-cyztzxX0019]
- Ford University Research Program [DEPT2018-J030.1]
- Fundamental Research Funds for the Central Universities [2020CDJKYJH027, 2022CDJKYJH038]
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This article designs customized buses for private cars using electronic registration identification (ERI) data. By mining the spatial-temporal and high-frequency characteristics of commuting trips, the article proposes methods for discovering private car commuters and designing customized bus schemes, and verifies their superior performance in solving urban traffic congestion and environmental problems through experiments.
With the deepening of the urbanization process, the ownership of urban private cars continues to increase, resulting in severe urban traffic congestion and environmental problems. The customized bus, as an emerging public transportation mode, is considered an effective means to alleviate the above problems. This article employs electronic registration identification (ERI) data of vehicles to design customized buses for private cars, consisting of two components: 1) discovering private car commuters and 2) designing customized bus schemes. First, based on the spatial-temporal similarity and high-frequency characteristics of commuting trips, we mined the urban private car commuters and their corresponding commuting trips as the demand for customized buses. Then, we constructed the customized bus model, which targets the number of served passengers with the constraints, such as the trip time window, bus capacity, passenger load rate, etc. In the model, intermediate stops are not set to ensure bus punctuality and passenger experience, and buses of various capacities are employed to ensure effectiveness and efficiency. The differential evolution algorithm was utilized to find the optimal solution for the model. In the experiments, we carried out relevant verification based on Chongqing's one-week ERI data. The experimental results showed customized bus schemes for various cases and verified the superior performance of our algorithm by comparing it with general optimization algorithms. Besides, through numerical calculation and traffic simulation, the excellent potential for customized buses in reducing urban transportation energy consumption and urban road congestion is illustrated.
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