Deep learning-based prediction model of road traffic conditions using bus probe data

Abstract

This paper proposes a deep learning-based model for predicting road traffic conditions utilizing bus probe data. In recent years, the development of traffic state prediction models using probe vehicle data has become increasingly prominent. Such models provide valuable information to travelers, although they are often subject to statistical uncertainties, especially concerning data sampling. To enhance the reliability of traffic predictions, this study focuses on the use of bus probe data, aiming to demonstrate how this data can provide more stable and reliable predictions, thereby offering more dependable information to drivers and travelers alike. Buses operate on fixed routes and adhere to strict timetables, potentially overcoming the spatial and temporal sampling biases inherent in traditional probe vehicle data collection. Specifically, this paper conducts a case study on the development of a traffic state prediction model across several bus routes in Kyoto city area. The city is a major tourist destination, featuring numerous attractions throughout its center and an extensive bus network designed to cover the entire road system. Therefore, this study targets specific road links to compare the accuracy of traffic predictions, considering the frequency of bus passages and varying levels of traffic congestion. This paper employs the Macroscopic Fundamental Diagram (MFD) to represent the traffic state, a methodology well known for describing traffic conditions by analyzing the relationship between traffic flow and demand within a target area at specific timestamps. This paper also employs Long Short-Term Memory (LSTM) model, which is a well-suited for time-series data due to its ability to remember long-term dependencies. Case studies in six target street showed that congestion level and the number of bus vehicle may affect the prediction accuracy when using ETC2.0 bus probe vehicle data.