Short-term real-time traffic prediction methods: A survey

Abstract

Short-term traffic prediction provides tools for improved road management by allowing the reduction of delays, incidents and other unexpected events. Different real-time approaches provide traffic managers with varying but valuable information. This paper reviews the literature regarding modeldriven and data-driven approaches focusing on short-term realtime traffic prediction. We start by analyzing real-time traffic data collection, referring network state acquisition and description methods which are used as input to predictive algorithms. According to the input variables available, we describe common and useful traffic prediction outputs that should contribute to understand the panorama verified on a road network. We then discuss metrics commonly used to assess prediction accuracy, in order to understand a standardized way to compare the different approaches. We list, detail and compare existing model-driven and data-driven approaches that provide short-term real-time traffic predictions. This research leads to an understanding of the many advantages, disadvantages and trade-offs of the approaches studied and provides useful insights for future development. Despite the predominance of model-driven solutions for the last years, data-driven approaches also present good results suitable for Traffic Management usage.

Congestion Pattern Prediction for a Busy Traffic Zone Based on the Hidden Markov Model

“…The model is widely used to solve scientific problems in the fields of image processing, speech recognition, pattern recognition and signal processing. Recently, it has applications in short-term traffic flow prediction, traffic jam situation recognition, and vehicle trajectory prediction [32][33][34][35][36][37].…”

Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix

“…• Short-term predictions [6], which usually reflect a prediction horizon of up to one or two hours (typical example: predict the next bus arrival time information at the bus station). Such predictions are usually clientbased, they take place in consumer applications and board notifications (located at the bus stop stations).…”

Big data actionable intelligence architecture

“…Recently, improvement in traffic prediction accuracy using social media data has been demonstrated [18]. Despite many researches on traffic prediction [19], many existing research focuses on using few data sources for traffic prediction. Based on our research, we were not aware of any existing work utilizing a combination of data sources such as Twitter, web camera imageries, satellite imagery, dash camera video, Mapquest, and GDELT to support near-real-time traffic prediction.…”