A Prediction Method for Short-term Parking Demands in Variable Interval Based on Particle Swarm Optimization and LSTM Model

LIU Donghui; XIAO Xue; ZHANG Jue

doi:10.3963/j.jssn.1674-4861.2021.04.010

Volume 39 Issue 4

Aug. 2021

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Article Navigation > Journal of Transport Information and Safety > 2021 > 39(4): 77-83

LIU Donghui, XIAO Xue, ZHANG Jue. A Prediction Method for Short-term Parking Demands in Variable Interval Based on Particle Swarm Optimization and LSTM Model[J]. Journal of Transport Information and Safety, 2021, 39(4): 77-83. doi: 10.3963/j.jssn.1674-4861.2021.04.010

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LIU Donghui, XIAO Xue, ZHANG Jue. A Prediction Method for Short-term Parking Demands in Variable Interval Based on Particle Swarm Optimization and LSTM Model[J]. Journal of Transport Information and Safety, 2021, 39(4): 77-83. doi: 10.3963/j.jssn.1674-4861.2021.04.010

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A Prediction Method for Short-term Parking Demands in Variable Interval Based on Particle Swarm Optimization and LSTM Model

doi: 10.3963/j.jssn.1674-4861.2021.04.010

1.
Jilin Police College Department of Information Engineering, Changchun 130000, China
2.
School of Communication, Jilin University, Changchun 130021, China
3.
Traffic Police Detachment of Hangzhou Public Security Bureau, Hangzhou 310000, China

Received Date: 2020-08-02

Abstract

Abstract

An intelligent parking guidance system is widely considered to solve the problem of difficult parking at present, providing management, traffic participants, and parking operators with immediate and future parking information. A prediction method for short-term parking demands in variable interval based on particle swarm optimization and LSTM model is studied due to the importance of parking information. Based on the birth and death of the Markov process, the characteristics of the temporal parking demand are analyzed. It is formulated as a combination of the arrival rate and departure rate of parking calibrated by the temporal parking quantity. Dynamic prediction intervals are determined according to the calibrated arrival rate and departure rate. The improved LSTM network is used as the basic prediction model, and the network parameters are optimized by the particle swarm optimization algorithm. The parking lot in the Nanling campus of Jilin University is selected as a research object, and its parking data are predicted and compared with other prediction models. The results show that the MAE and MSE of the proposed parking demand prediction model are 2.53 vehicles and 11.89 vehicles in working days, respectively. For non-business days, the MAE is2.32 vehicles and the MSE is 10.89 vehicles. Therefore, a predictable prediction model of parking demands proposed in the work can predict the real-time and future parking demands, providing a reliable reference for management, traffic participants, and parking operators.
- smart parking,
- real-time and future parking information,
- short-term prediction of parking demand,
- birth and death of the Markov process,
- LSTM

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References

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A Prediction Method for Short-term Parking Demands in Variable Interval Based on Particle Swarm Optimization and LSTM Model

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