Samia Hireche

Connected vehicles (CVs) are an emerging technology in intelligent transportation systems. Currently, many data-driven intelligent transportation systems (D2ITS) use CV data. Unfortunately, these D2ITS still need serious improvement before they meet higher-level visualization needs. Thus, we aim to develop a new, intelligent data-driven transportation system framework. We focus on visualizing real-time CV data using a big data analytic system in urban areas. In response, we first propose an effective real-time data distribution approach within the Vehicular Ad-hoc NETwork. Second, we develop novel strategies for aggregating, extracting and ingesting data. We provide scalable and fault-tolerant delivery methods without interruption or delay. Finally, we proposed a novel visualization REpresentational State Transfer (REST) web service. We used Simulation of Urban MObility, OMNET++ and Veins to simulate a traffic incident dataset. Then, we tested the Basic Safety Messages in an experimental big data cluster. We used NIFI, Kafka and Cassandra for ingestion, distribution, delivery and storage. The results show accurate performance for packet loss, packet delivery and communication delay. Also, it indicates high throughput and low latency for distributed data delivery systems. Additionally, we obtained the smallest response time for the RESTFUL visualization web service.

Over the past decades, tremendous research efforts have been proposed to deploy Automatic Incident Detection Systems (AIDSs) onto urban roads. As a result, new challenges to the research community have been introduced to enhance the AIDS performances. To overcome these challenges, and to improve the efficiency and safety of road traffic, attention has been drawn to use Artificial Intelligence (AI) techniques as a solution. However, choosing appropriate AI technologies to process real-time traffic data is an area that needs spotlighting. The aim of this paper is to discuss recent advancements in urban AIDSs based on AI techniques. We carried out a review for twelve previous AIDS study proposed since 2009. We compared these AIDSs in term of traffic data sources, input variables, and type of incident. Also, a comparison of simulators, techniques and the evaluation metrics has been used. The results indicated that hybrid AI techniques reported the best detection performances. In this context, fixed or fusion data sources were also given good results compared to others. This review shows reliable results of AI classification techniques applied in AIDS approaches. This finding urges to continue improve the performances of the proposed AI techniques. In future research, testing others AI techniques with smart technologies may increase the incident detection efficiency.