Detection of traffic congestion and incidents from GPS trace analysis. (1st May 2017)

• Record Type: Journal Article
• Authors: D'Andrea, Eleonora; Marcelloni, Francesco
• Is Part Of: Expert systems with applications. Volume 73(2017)
• Page Start: 43
• Page End: 56
• Publication Date: 2017-05-01
• Subjects: Expert systems -- GPS -- Incident detection -- Traffic congestion detection -- Urban mobility simulation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.12.018
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Real-time detection of road traffic congestions and incidents. Real-time notification to users of detected traffic events. Possible traffic states: absent, flowing, slowed, very slowed, blocked, incident . Detection only based on a spatiotemporal analysis of GPS data. Real-time GPS data collected from users' devices and GPS trackers. Abstract: This paper presents an expert system for detecting traffic congestion and incidents from real-time GPS data collected from GPS trackers or drivers' smartphones. First, GPS traces are pre-processed and placed in the road map. Then, the system assigns to each road segment of the map a traffic state based on the speeds of the vehicles. Finally, it sends to the users traffic alerts based on a spatiotemporal analysis of the classified segments. Each traffic alert contains the affected area, a traffic state (e.g., incident, slowed traffic, blocked traffic), and the estimated velocity of vehicles in the area. The proposed system is intended to be a valuable support tool in traffic management for municipalities and citizens. The information produced by the system can be successfully employed to adopt actions for improving the city mobility, e.g., regulate vehicular traffic, or can be exploited by the users, who may spontaneously decide to modify their path in order to avoid the traffic jam. The elaboration performed by the expert system is independent of the context (urban o non-urban) and may be directly employed in several city road networks with almost no change of the system parameters, and without the need for a learning process or historical data. The experimental analysis was performed using a combination of simulated GPS data and real GPS data from the city of Pisa. The results on incidents show a detection rate of 91.6%, and an average detection time lower than 7 min. Regarding congestion, we show how the system is able to recognize different levels of congestion depending on different road use.