Exploring the Effects of Cooperative Adaptive Cruise Control in Mitigating Traffic Congestion

The aim of this research is to examine the impact of CACC (Cooperative Adaptive Cruise Control) equipped vehicles on traffic-flow characteristics of a multilane highway system. The research identifies how CACC vehicles affect the dynamics of traffic flow on a road network and demonstrates the potential benefits of reducing traffic congestion due to stop-and-go traffic conditions. An agent-based traffic simulation model is developed specifically to examine the effect of these intelligent vehicles on the traffic flow dynamics. Traffic performance metrics characterizing the evolution of traffic congestion throughout the road network, are analyzed. Different CACC penetration levels are studied. The positive impact of the CACC technology is demonstrated and shown that it has an impact of increasing the highway capacity and mitigating traffic congestions. This effect is sensitive to the market penetration and the traffic arrival rate. In addition, a progressive deployment strategy for CACC is proposed and validated

A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks

Traffic management centers (TMCs) need high-quality data regarding the status of roadways for monitoring and delivering up-to-date traffic conditions to the traveling public. Currently this data is measured at static points on the roadway using technologies that have significant maintenance requirements. To obtain an accurate picture of traffic on any road section at any time requires a real-time probe of vehicles traveling in that section. We envision a near-term future where network communication devices are commonly included in new vehicles. These devices will allow vehicles to form vehicular networks allowing communication among themselves, other vehicles, and roadside units (RSUs) to improve driver safety, provide enhanced monitoring to TMCs, and deliver real-time traffic conditions to drivers. In this dissertation, we contribute and develop a framework for dynamic trafficmonitoring (DTMon) using vehicular networks. We introduce RSUs called task organizers (TOs) that can communicate with equipped vehicles and with a TMC. These TOs can be programmed by the TMC to task vehicles with performing traffic measurements over various sections of the roadway. Measurement points for TOs, or virtual strips, can be changed dynamically, placed anywhere within several kilometers of the TO, and used to measure wide areas of the roadway network. This is a vast improvement over current technology. We analyze the ability of a TO, or multiple TOs, to monitor high-quality traffic datain various traffic conditions (e.g., free flow traffic, transient flow traffic, traffic with congestion, etc.). We show that DTMon can accurately monitor speed and travel times in both free-flow and traffic with transient congestion. For some types of data, the percentage of equipped vehicles, or the market penetration rate, affects the quality of data gathered. Thus, we investigate methods for mitigating the effects of low penetration rate as well as low traffic density on data quality using DTMon. This includes studying the deployment of multiple TOs in a region and the use of oncoming traffic to help bridge gaps in connectivity. We show that DTMon can have a large impact on traffic monitoring. Traffic engineers can take advantage of the programmability of TOs, giving them the ability to measure traffic at any point within several km of a TO. Most real-time traffic maps measure traffic at midpoint of roads between interchanges and the use of this framework would allow for virtual strips to be placed at various locations in between interchanges, providing fine-grained measurements to TMCs. In addition, the measurement points can be adjusted as traffic conditions change. An important application of this is end-of-queue management. Traffic engineers are very interested in deliver timely information to drivers approaching congestion endpoints to improve safety. We show the ability of DTMon in detecting the end of the queue during congestion

Evaluating aggregation protocols for vehicular networks in simulation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 83-85).Routing for vehicular networks has so far presented special challenges such as high node mobility and velocity, and until now there have been few experiments performed using real data. This thesis uses a dataset of 538 taxicabs driving around the San Francisco Bay Area to gather measurements about connectivity to lay down a framework with which to reason about these networks. Additionally. this thesis describes a simulation framework written in Python to perform rapid evaluation of aggregation protocols as well as simulations where a fraction of the cars are assumed to be internet access points.by Subha Gollakota.M.Eng

Annual Research Report, 2009-2010

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and governmenthttps://digitalcommons.odu.edu/or_researchreports/1001/thumbnail.jp

Road Traffic Congestion Analysis Via Connected Vehicles

La congestion routière est un état particulier de mobilité où les temps de déplacement augmentent et de plus en plus de temps est passé dans le véhicule. En plus d’être une expérience très stressante pour les conducteurs, la congestion a également un impact négatif sur l’environnement et l’économie. Dans ce contexte, des pressions sont exercées sur les autorités afin qu’elles prennent des mesures décisives pour améliorer le flot du trafic sur le réseau routier. En améliorant le flot, la congestion est réduite et la durée totale de déplacement des véhicules est réduite. D’une part, la congestion routière peut être récurrente, faisant référence à la congestion qui se produit régulièrement. La congestion non récurrente (NRC), quant à elle, dans un réseau urbain, est principalement causée par des incidents, des zones de construction, des événements spéciaux ou des conditions météorologiques défavorables. Les opérateurs d’infrastructure surveillent le trafic sur le réseau mais sont contraints à utiliser le moins de ressources possibles. Cette contrainte implique que l’état du trafic ne peut pas être mesuré partout car il n’est pas réaliste de déployer des équipements sophistiqués pour assurer la collecte précise des données de trafic et la détection en temps réel des événements partout sur le réseau routier. Alors certains emplacements où le flot de trafic doit être amélioré ne sont pas surveillés car ces emplacements varient beaucoup. D’un autre côté, de nombreuses études sur la congestion routière ont été consacrées aux autoroutes plutôt qu’aux régions urbaines, qui sont pourtant beaucoup plus susceptibles d’être surveillées par les autorités de la circulation. De plus, les systèmes actuels de collecte de données de trafic n’incluent pas la possibilité d’enregistrer des informations détaillées sur les événements qui surviennent sur la route, tels que les collisions, les conditions météorologiques défavorables, etc. Aussi, les études proposées dans la littérature ne font que détecter la congestion ; mais ce n’est pas suffisant, nous devrions être en mesure de mieux caractériser l’événement qui en est la cause. Les agences doivent comprendre quelle est la cause qui affecte la variabilité de flot sur leurs installations et dans quelle mesure elles peuvent prendre les actions appropriées pour atténuer la congestion.----------ABSTRACT: Road traffic congestion is a particular state of mobility where travel times increase and more and more time is spent in vehicles. Apart from being a quite-stressful experience for drivers, congestion also has a negative impact on the environment and the economy. In this context, there is pressure on the authorities to take decisive actions to improve the network traffic flow. By improving network flow, congestion is reduced and the total travel time of vehicles is decreased. In fact, congestion can be classified as recurrent and non-recurrent (NRC). Recurrent congestion refers to congestion that happens on a regular basis. Non-recurrent congestion in an urban network is mainly caused by incidents, workzones, special events and adverse weather. Infrastructure operators monitor traffic on the network while using the least possible resources. Thus, traffic state cannot be directly measured everywhere on the traffic road network. But the location where traffic flow needs to be improved varies highly and certainly, deploying highly sophisticated equipment to ensure the accurate estimation of traffic flows and timely detection of events everywhere on the road network is not feasible. Also, many studies have been devoted to highways rather than highly congested urban regions which are intricate, complex networks and far more likely to be monitored by the traffic authorities. Moreover, current traffic data collection systems do not incorporate the ability of registring detailed information on the altering events happening on the road, such as vehicle crashes, adverse weather, etc. Operators require external data sources to retireve this information in real time. Current methods only detect congestion but it’s not enough, we should be able to better characterize the event causing it. Agencies need to understand what is the cause affecting variability on their facilities and to what degree so that they can take the appropriate action to mitigate congestion