2009Unpublished venueRequires access

Design and simulation of an artificially intelligent VANET for solving traffic congestion

Ali Ghazy, Tarık Özkul

Open publisher page 27 citations

Abstract

Traffic congestion has been plaguing motorists for years, and it progressively continues to get worse as the population continues to increase, resulting in an increase in the number of vehicles on the road. There are many factors that contribute to traffic congestion, however; there is one that plays a major role in giving rise to a phenomenon called ldquotraffic wavesrdquo, and that is driver behavior. Traffic waves also called ldquostop wavesrdquo or ldquotraffic shocksrdquo, and they are travelling disturbances in the distribution of cars on a highway, which seems to appear without any reason, propagating backwards and severely slowing traffic flow on roads. The proposed research aims at reducing/eliminating traffic waves by integrating Artificial Intelligence, and vehicular ad-hoc network (VANET) to create a driver aid that helps in combating traffic congestion as well as embedding safety awareness by dynamically rerouting traffic depending on road conditions.

About this research paper

What this paper is about

Traffic congestion has been plaguing motorists for years, and it progressively continues to get worse as the population continues to increase, resulting in an increase in the number of vehicles on the road. There are many factors that contribute to traffic congestion, however; there is one that plays a major role in giving rise to a phenomenon called ldquotraffic wavesrdquo, and that is driver behavior. Traffic waves also called ldquostop wavesrdquo or ldquotraffic shocksrdquo, and they are travelling disturbances in the distribution of cars on a highway, which seems to appear without any reason, propagating backwards and severely slowing traffic flow on roads. The proposed research aims at reducing/eliminating traffic waves by integrating Artificial Intelligence, and vehicular ad-hoc network (VANET) to create a driver aid that helps in combating traffic congestion as well as embedding safety awareness by dynamically rerouting traffic depending on road conditions.

Why it matters

OpenAlex reports 27 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Traffic congestion has been plaguing motorists for years, and it progressively continues to get worse as the population continues to increase, resulting in an increase in the number of vehicles on the road. There are many factors that contribute to traffic congestion, however; there is one that plays a major role in giving rise to a phenomenon called ldquotraffic wavesrdquo, and that is driver behavior. Traffic waves also called ldquostop wavesrdquo or ldquotraffic shocksrdquo, and they are travelling disturbances in the distribution of cars on a highway, which seems to appear without any reason, propagating backwards and severely slowing traffic flow on roads. The proposed research aims at reducing/eliminating traffic waves by integrating Artificial Intelligence, and vehicular ad-hoc network (VANET) to create a driver aid that helps in combating traffic congestion as well as embedding safety awareness by dynamically rerouting traffic depending on road conditions.

Key concepts: Vehicular ad hoc network, Traffic flow (computer networking), Traffic bottleneck, Traffic congestion, Computer science, Traffic congestion reconstruction with Kerner's three-phase theory, Traffic conflict, Traffic optimization

Related papers

Back to paper searchBrowse research topicsOriginal source
Design and simulation of an artificially intelligent VANET for solving traffic congestion — Research Paper | ScholarLens