Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Abstract

Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.

Traffic Flow Prediction at Intersections: Enhancing with a Hybrid LSTM-PSO Approach

“…While ML emerged as a prominent method, it's crucial to acknowledge the limitations of ML models, including their sensitivity to data distributions and potential challenges in adapting to dynamic traffic conditions. N. Katambire et al [14] investigated the impact of rising travel demand and vehicle ownership on traffic efficiency, particularly at intersections. They explored time series forecasting methods like LSTM and ARIMA models to predict future traffic rates, favoring LSTM for monthly traffic flow prediction.…”