Sustainable Transport in a Smart City: Prediction of Short-Term Parking Space through Improvement of LSTM Algorithm

Abstract

The carbon emission of fuel vehicles is a major consideration that affects the dual carbon goal in urban traffic. The problem of “difficult parking and disorderly parking” in static traffic can easily lead to traffic congestion, an increase in vehicle exhaust emissions, and air pollution. In particulate, when vehicles make an invalid detour and wait for parking with long hours, it often causes extra energy consumption and carbon emission. In this paper, adding a weather influence feature, a short-term parking occupancy rate prediction algorithm based on the long short-term model (LSTM) is proposed. The data used in this model is from Melbourne public data sets, and parking occupancy rates are predicted through historical parking data, weather information, and location information. At the same time, three commonly prediction models, i.e., simple LSTM model, multiple linear regression model (MLR), and support vector regression (SVR), are also used as comparison models. Taking MAE and RMSE as evaluation indexes, the parking occupancy rate during 10 min, 20 min, and 30 min are predicted. The experimental results show that the improved LSTM method achieves better accuracy and stability in the prediction of parking lots. The average MAE and RMSE of the proposed LSTM prediction during intervals of 10 min, 20 min, and 30 min with the weather influence feature algorithm is lower than that of simple LSTM, MLR and that of SVR.