Cooperative Evaluation of the Cause of Urban Traffic Congestion via Connected Vehicles

Abstract

We developed a distributed data mining system to elaborate a decision concerning the cause of urban traffic congestion via emerging connected vehicle (CV) technology. We observe this complex phenomena through the interactions between vehicles exchanging messages via Vehicle to Vehicle (V2V) communication. Results are based on real-time simulation generated scenarios extended from the realworld traffic Travel and Activity PAtterns Simulation (TAPAS) Cologne scenario. We evaluate a Voting Procedure (VP) useful for obtaining deeper insights using cooperation between vehicles, Belief Functions (BF) aim at improving representation of information and a Data Association Technique (DAT) aiming at data mining and extracting the association rules from the messages exchanged. Methods are tested and compared using a microscopic urban mobility simulator, SUMO and a network simulator, ns-2, for the simulation of communication between CVs. Compared to the Back-Propagation algorithm (BP) extensively used in the past literature, our performance evaluation shows that the proposed methods enhance the estimation of the cause of congestion by 48% for the proposed VP, 58% for the BF, 71% for the DAT and 70% for β-DAT. The methods also enhance detection time from 7.09% to 10.3%, and β-DAT outperforms BP by approximately 1.25% less false alarms triggered by the network, which can be significant in the context of real-time decision making. We show that a market penetration rate between 63% and 75% is enough to ensure satisfactory performance.

A Distributed Congestion Control Strategy Using Harmonic Search Algorithm in Internet of Vehicles

“…erefore, with SUMO, MOVE, and NS2 tools, a VANET-like environment can be created to evaluate the performance of proposed congestion control strategy. e experiment is considered as an urban traffic scenario (4 lanes, 2 in each direction), in which communications between vehicles and the base station follows the eyes 802.11p protocols [21,22].…”

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

“…ere is a methodology in which traffic signals are extracted from video and converted as spatial-temporal (ST) signals to find the normal and abnormal driving patterns by using SVM and adaptive boosting on these ST signals [48]. In [49], it is studied that the causes of traffic congestion using collaboration between connected vehicles and used voting procedure (VP) and belief function (BF) methods for congestion estimation, but it has very low support for real-time implementation due to lack of validity. A framework was designed for real-time distributed congestion classification on a diverse urban road network using a decision tree and probabilistic Naïve Bayes classifier [50].…”

Countermeasure Study of Urban Traffic Improvement Based on the Internet of Things

“…The speed of the vehicle is inconsistent. On the one hand, the maneuverability and the load of the vehicle are different; on the other hand, it is caused by the driving habits and technical differences of the drivers [16]. In order to reduce the traffic turbulence caused by the low-speed vehicles hindering the high-speed vehicles and vehicles overtaking each other, different lanes are defined when designing the road cross-sections.…”