INCIDENT DETECTION OF CONGESTIONED URBAN TUNNELS: A CASE STUDY OF REBOUÇAS TUNNEL IN RIO DE JANEIRO

Marina Leite de Barros Baltar, Paulo Cezar Martins Ribeiro

Abstract


The objective of this research is to develop a theoretical method capable of eliminating the large number of false alarms resulted of traffic stopping on the automatic incident detection system installed in urban tunnels with large traffic flow. This method took in consideration the concept of shocks wave and have with the purpose of predicting the moment that this wave will reach a certain point in the tunnel. After the development of the method, a case study was conducted in the tunnel Rebouças, in Rio de Janeiro city, where there is an automatic incident detection system that generates a large number of false alarms during rush hours. With the suggested methodology, it was observed that it is possible to lower the number of false alarms by predicting the shock waves that hit the tunnel galleries.

Keywords


Incidentes, túnel urbano, sistemas inteligentes de transporte

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References


CET-RIO. Relatório - Sistemas Inteligentes de Transportes CET-RIO. Prefeitura da Cidade do Rio de Janeiro, Rio de Janeiro, 2010

CHEN, L., CAO, Y., JI, R. Automatic Incident Detection Algorithm Based on Support Vector Machine. Revista IEEE, pp 864-866, 2010.

COELHO, E. C. Avaliação dos níveis de congestionamento em vias arteriais com a utilização da micro-simulação. Dissertação de M.Sc., Programa de Pós-graduação Engenharia de Transportes da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ., Brasil, 2009.

GARBER, N.J E HOEL, L.A. Traffic and Highway Engeering. Second Edition. Revised Printing. PWS Publishing. 1996

GU, Y., QIAN, Z., CHEN, F. From Twitter to detector: Real-time traffic incident detection using social media data, Transportation Research Part C: Emerging Technologies, v.67, Junho de 2016, p. 321-342

HCM (2010) Highway Capacity Manual, Transportation Research Board, Washington D. C., USA.

IMMERS, L. H., LOGGHE, S. Traffic Flow Theory, Notas de aula sobre Teoria do fluxo de tráfego da Katholieke Universiteit Leuven, 2002.

JEONG, Y., CASTRO, M. E HAN, M. K. J. D. A wavelet-based freeway incident detection algorithm with adapting threshold parameters, Transportation Research Part C , n.19, p.1-19, 2011.

JIANG, G., NIU, S., LI, Q. CHANG, A. E JIANG, H. Automated Incident Detection Algorithms for Urban Expressway, Revista IEEE, v.3, p.70-74, 2010. Yantai, Shandong, Agosto.

JINGLEI, Z., JIN, X., E SHAOYI, L. Abnormal traffic incident detection based on hidden markov models”, ICTE 2011, p.3098-3103.

KAMIJO E FUJIMURA. Incident Detection in Heavy Traffics in Tunnels by the Interlayer Feedback Algorithm”,Int. J. ITS Res, v.8, p. 121-130, 2010

KINOSHITA, A., TAKASU, A., ADACHI, J. Real-time traffic incident detection using a probabilistic topic model, Information Systems, v.54, p.169-188, Dezembro de 2015.

LOU, Y., YIN, Y. E LAWPHONGPANICH, S. Freeway service patrol deployment planning for incident management and congestion mitigation, Transportation Research Part C, v.19, p. 283-295, 2011

LU, J., CHEN, S., WANG, W. E RAN, B. Automatic traffic incident detection based on nFoil, Expert Systems with Applications, v.39, n.7, p.6547–6556, 2012.

SCHWABACH, H.,HARRER, M., HOLZMANN, W., BISCHOF, H., DOMÍNGUEZ, G., NÖLLE, M., PFLUGFELDER, R., STROBL, B., TACKE, A., WALTL, A. Video based image analysis for tunnel safety – vitus-1: a tunnel video surveillance and traffic control system, TRB, Fevereiro, San Francisco California, USA, 2005.

ŠKORPUT, P., MANDŽUKA, S. JELUŠIĆ:,N. Real-time Detection of Road Traffic Incidents”, Promet – Traffic&Transportation, v. 22, n. 4, p.273-283, 2010.

SMTR-RJ. Disponível em . Acesso em: 8 de setembro de 2011.

STEENBRUGGEN, J., Tranos, E. RIETVELD, P. Traffic incidents in motorways: An empirical proposal for incident detection using data from mobile phone operators, Journal of Transport Geography, v. 54, p. 81-90, Junho de 2016.

TRAFFICINFRATECH . Disponível em . Acesso em maio de 2012.

TRB. Traffic Flow Theory, Washington D. C., Transportation Research Board, 1976.

VALENTI, G., LELLI, M. E CUCINA, D. A comparative study of models for the incident duration prediction. Department of Statistics, La Sapienza University, Roma, Italy, 2010.

WANG, Q. Traffic incident detection based on artificial neural network, Revista IEEE, 2011, p.657-659, 2011.

ZHANG, Z., LIN, X. E HU, B. Algorithm Design of Traffic Incident Automatic Detection Based on Mobile Detection, Revista IEEE, p.331 a 335, 2011.

ZHAO, X., WENG, J. E RONG, J. Urban Expressway Incident Detection Algorithm Based on Floating Car Data, Integrated Transportation Systems – Green Intelligent Reliable, p.2132 a 2139, 2010.

ZHENG, C., ZHOU, Q., CHEN,S. E YU,Z. Urban Road Traffic Incident Auto-detecting Based on Decision Fusion, ICCTP, p. 1348 a 1359, 2011.




DOI: https://doi.org/10.32358/rpd.2017.v3.243

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