A framework for early detection of incident in dense traffic using vehicular ad-hoc networks

VANETs provide the ability for vehicles to spontaneously and wirelessly network with other vehicles nearby for the purposes of providing travelers with new features and applications. Dense Traffic has become major issue at rush-hours traffic in big cities where congested roads cost over billions in lost worker productivity and over billions gallons of fuel due to traffic incidents. We dispense and prosper a framework for early detection of incident in dense traffic using vehicular ad-hoc networks. We proposed incident detection techniques in dense traffic where Incident Detections Node (IDN) collect On-Board Unit (OBU) data directly from passing vehicles and perform some analysis to detect possible incidents and to be integrate with the Internet. IDN may be used for the advertisement of self-fund generation. These things are good for traffic management can take a proactive role in managing alternative routes to avoid the accident. Therefore, early detection of incident in dense traffic would provide better management of traffic flow.

[1]  Daniel J. Dailey,et al.  AVL-Equipped Vehicles as Speed Probes , 2003 .

[2]  Joy Dahlgren,et al.  Using Vehicles Equipped with Toll Tags as Probes for Providing Travel Times , 2001 .

[3]  Billy M. Williams,et al.  Traffic Management Center Use of Incident Detection Algorithms: Findings of a Nationwide Survey , 2007, IEEE Transactions on Intelligent Transportation Systems.

[4]  K N Balke An evaluation of existing incident detection algorithms , 1993 .

[5]  P S Parsonson,et al.  TRAFFIC DETECTOR HANDBOOK , 1985 .

[6]  R. Sinnott Virtues of the Haversine , 1984 .

[7]  Stephan Olariu,et al.  Intelligent Transportation Systems and Vehicular Networks that , 2022 .

[8]  Stephan Olariu,et al.  Automatic Incident Detection In VANETs: A Bayesian Approach , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[9]  Ted Chira-chavala,et al.  The I-880 Field Experiment: Effectiveness Of Incident Detection Using Cellular Phones , 1998 .

[10]  Dipak Ghosal,et al.  Distributed automated incident detection with VGRID , 2011, IEEE Wireless Communications.

[11]  Ryan Newton,et al.  The pothole patrol: using a mobile sensor network for road surface monitoring , 2008, MobiSys '08.

[12]  Emily Parkany,et al.  A COMPLETE REVIEW OF INCIDENT DETECTION ALGORITHMS & THEIR DEPLOYMENT: WHAT WORKS AND WHAT DOESN'T , 2005 .

[13]  Dan Middleton,et al.  EVALUATION OF SOME EXISTING TECHNOLOGIES FOR VEHICLE DETECTION , 1999 .

[14]  Ernest L. Hall,et al.  Detection and avoidance of simulated potholes in autonomous vehicle navigation in an unstructured environment , 2000, SPIE Optics East.

[15]  Milos Hauskrecht,et al.  Towards a Learning Trac Incident Detection System , 2006 .

[16]  Stephan Olariu,et al.  Vehicular Networks: From Theory to Practice , 2009 .

[17]  Luca Delgrossi,et al.  IEEE 802.11p: Towards an International Standard for Wireless Access in Vehicular Environments , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[18]  Elmar Schoch,et al.  Communication patterns in VANETs , 2008, IEEE Communications Magazine.

[19]  Gerald L Ullman,et al.  BENEFITS OF REAL-TIME TRAVEL TIME INFORMATION IN HOUSTON , 1996 .