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This report describes an assessment of using dedicated short-range communications(DSRC) technology to perform travel time monitoring and automated incident detectionon a segment of rural freeway. The assessment used the CORSIM traffic simulation toolto simulate traffic and incidents on a segment of rural freeway. Output data from thesimulation was subjected to post-processing to produce the "probe and beacon" data thatwould be produced by a DSRC-based system. An incident detection algorithm wasdeveloped, which used a travel time threshold and a counter. Travel times exceeding thethreshold incremented the counter, while travel times below the threshold decrementedthe counter (unless it was at zero). An alarm was generated when the counter reached apre-selected level. This algorithm was tested on selected data files, and the results wereused to identify the "best" values of the threshold and counter alarm level. Using these"best" values, the algorithm was then applied to the "probe and beacon" data todetermine how quickly the system could detect various traffic incidents. The analysisshowed that the system could provide rapid and reliable detection of incidents.During the simulation and analysis, several parameters were varied to observe theirimpacts on the system performance. These parameters included traffic volume, incidentseverity, percentage of vehicles with transponders, spacing of roadside readers, andlocation of the incident relative to the next downstream reader. Each parameter proved tohave a significant effect on the detection time, and the observed impacts were consistentwith logical expectations. In general, the time to detect an incident was reduced inresponse to (1) an increase in traffic volume, (2) an increase in incident severity, (3) anincrease in transponder population, (4) a reduction in reader spacing, and (5) a reductionin distance from incident location to next downstream reader.Preliminary estimates were developed of the costs associated with implementing aDSRC-based traffic monitoring system. The relationship between system cost andsystem performance was explored and illustrated.Recommendations were developed and presented. These included further analysis basedon traffic simulations, followed by a limited field deployment to validate the analysisresults.

O monitoramento do trânsito de veículos rodoviários de carga em vias urbanas é dependente do trabalho de profissionais que precisam estar integralmente atentos ao tráfego de uma determinada via, e assim, identificar a presença desses veículos em meio a cenas complexas. Sistemas automáticos de identificação de veículos em quadros de vídeos podem agregar valor na qualidade de vida das cidades, pois tendem a diminuir os congestionamentos e incrementam segurança ao trânsito. Nesta pesquisa, propõe-se um modelo capaz de identificar e segregar veículos rodoviários de carga em vias urbanas, por meio de técnicas de processamento e análise de vídeos, gravados por câmeras posicionadas em diversos ângulos em dispositivos de acessos a vias urbanas. O sistema foi desenvolvido em linguagem MATLAB® e foram abordadas técnicas referentes às etapas de detecção, segmentação e processamento de objetos em fundos complexos. Ao final do trabalho, o sistema é aplicado em cenários de filmagem na cidade de São Carlos - SP e os resultados obtidos são discutidos. / The monitoring of the road freight vehicles traffic on urban roads relies on the work of professionals who need to be fully aware of the traffic on a specific road, and thereby identify the presence of these vehicles in complex scenes. Automatic vehicle identification in video shots may add value to the city life quality, due to congestion diminishment and traffic security improvement. In this research, a model able to identify and segregate road freight vehicles on urban roads is proposed, by means of video analysis and processing, that were recorded by cameras positioned at different angles on highways accesses on urban roads. The system was developed using MATLAB®. Techniques related to detecting, targeting and processing steps have been used to identify objects in complex backgrounds. At the end of the work, the system is applied to several scenarios that have been filmed in the city of São Carlos - SP and the results are discussed.

O monitoramento do trânsito de veículos rodoviários de carga em vias urbanas é dependente do trabalho de profissionais que precisam estar integralmente atentos ao tráfego de uma determinada via, e assim, identificar a presença desses veículos em meio a cenas complexas. Sistemas automáticos de identificação de veículos em quadros de vídeos podem agregar valor na qualidade de vida das cidades, pois tendem a diminuir os congestionamentos e incrementam segurança ao trânsito. Nesta pesquisa, propõe-se um modelo capaz de identificar e segregar veículos rodoviários de carga em vias urbanas, por meio de técnicas de processamento e análise de vídeos, gravados por câmeras posicionadas em diversos ângulos em dispositivos de acessos a vias urbanas. O sistema foi desenvolvido em linguagem MATLAB® e foram abordadas técnicas referentes às etapas de detecção, segmentação e processamento de objetos em fundos complexos. Ao final do trabalho, o sistema é aplicado em cenários de filmagem na cidade de São Carlos - SP e os resultados obtidos são discutidos. / The monitoring of the road freight vehicles traffic on urban roads relies on the work of professionals who need to be fully aware of the traffic on a specific road, and thereby identify the presence of these vehicles in complex scenes. Automatic vehicle identification in video shots may add value to the city life quality, due to congestion diminishment and traffic security improvement. In this research, a model able to identify and segregate road freight vehicles on urban roads is proposed, by means of video analysis and processing, that were recorded by cameras positioned at different angles on highways accesses on urban roads. The system was developed using MATLAB®. Techniques related to detecting, targeting and processing steps have been used to identify objects in complex backgrounds. At the end of the work, the system is applied to several scenarios that have been filmed in the city of São Carlos - SP and the results are discussed.