Influência das condições climáticas e de acidentes na caracterização do comportamento do tráfego em rodovias

Authors

DOI:

https://doi.org/10.14295/transportes.v24i4.1104

Keywords:

Clustering, accidents, rain, speed-flow relationship.

Abstract

Meteorological and safety-related data have been integrated to several techniques aimed at characterizing the fundamental relationships of traffic flow on highways, in order to better understand traffic behavior in adverse conditions such as rain, and when incidents occurs. In this context, this paper proposes the use of clustering methods to characterize the traffic behavior in highway BR-290/RS, located in the metropolitan area of Porto Alegre - RS. The clustering techniques tested, K-means and fuzzy clustering (FCM), enabled the determination of behavioral patterns influenced by variables (flow, speed, accidents and rain). Clusters related to periods with rain indicate speed drop by about 10 km/h. Clusters periods related to accident conditions indicate speeds between 10 and 35 km/h, and volumes below 1000 veic/h.

Downloads

Download data is not yet available.

References

Agarwal, M., Maze, T. H., e R. R. Souleyrette (2005) Impacts of Weather on Urban Freeway Traffic Flow Characteristics and Facility Capacity. Final Technical Report, Center for Transportation Research and Education Iowa State University, USA

.

Azimi, M., e Y. Zhang (2010) Categorizing Freeway Flow Conditions by Using Clustering Methods. Transportation Research Record: Journal of the Transportation Research Board, 2173, 105–114. doi:10.3141/2173-13

Caceres, N., Romero, L. M., e F. G. Benitez (2012) Estimating Traffic Flow Profiles According to a Relative Attractiveness Factor. Procedia - Social and Behavioral Sciences, 54, 1115–1124. doi:10.1016/j.sbspro.2012.09.826

Chunchun, H., Nianxue, L., Xiaohong, Y., e S. Wenzhong (2011) Traffic Flow Data Mining and Evaluation Based on Fuzzy Clustering Techniques. International Journal of Fuzzy Systems, 13(4), 344–349.

Dunn, J. C. (1973) A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32–57. doi:10.1080/01969727308546046

Gettman, D., Pu, L., Sayed, T., e S. Shelby (2008) Surrogate Safety Assessment Model and Validation: Final Report. Federal Highway Administration, Report No. FHWA-HRT-08-051.

Jain, A. K., Murty, M. N., e P. J. Flynn (1999) Data Clustering: A Review. ACM Computing Surveys (CSUR), 31(3), 264–323.

Jia, Y., Wu, J., Du, Y., e G. Qi (2014) Impacts of rainfall weather on urban traffic in beijing: analysis and modeling. Physics and Society, 1–13.

Kianfar, J., e P. Edara (2013) A Data Mining Approach to Creating Fundamental Traffic Flow Diagram. Procedia - Social and Behavioral Sciences, 104, 430–439. doi:10.1016/j.sbspro.2013.11.136

Lloyd, S. (1982) Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. doi:10.1109/TIT.1982.1056489

Mirshahi, M., Obenberger, J., Fuhs, C. A., Howard, C. E., Krammes, R. A., Kuhn, B. T., Mayhew, R. M., Moore, M. A., Sahebjam, K., Stone, C. J., e J. L. Yung (2007) Active Traffic Management: The Next Step in Congestion Management. Federal Highway Administration, FHWA-PL-07-012, Washington D.C.

Montazeri-Gh, M., e A. Fotouhi (2011) Traffic condition recognition using the -means clustering method. Scientia Iranica, 18(4), 930–937. DOI: 10.1016/j.scient.2011.07.004

Park, B. (2002) Hybrid Neuro-Fuzzy Application in Short-Term Freeway Traffic Volume Forecasting. Transportation Research Record, 1802, 190–196. doi:10.3141/1802-21

Rousseeuw, P. J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. DOI:10.1016/0377-0427(87)90125-7

Sun, L., e J. Zhou (2005) Development of Multiregime Speed-Density Relationships by Cluster Analysis. Transportation Research Record, 1934, 64–71. doi:10.3141/1934-07

Thakuriah, P., Yanos, G., Lin, J., Metaxatos, P., Sulo, R., Pu, W., e L. Mbekeani (2008) Including Weather Effects In a Real-Time Traffic Information Environment - Exploratory Analysis and Alternative Models. In Proceedings of the 15th Intelligent Transportation Systems World Congress, New York City - USA.

Weijermars, W., e E. van Berkum (2005) Analyzing highway flow patterns using cluster analysis. Proceedings. 2005 IEEE Intelligent Transportation Systems, 831–836. doi:10.1109/ITSC.2005.1520157

Xia, J., e M. Chen (2007a) A Nested Clustering Technique for Freeway Operating Condition Classification. Computer-Aided Civil and Infrastructure Engineering, 22(6), 430–437.

Xia, J., e M. Chen (2007b) Defining Traffic Flow Phases Using Intelligent Transportation Systems-Generated Data. Journal of Intelligent Transportation Systems, 11, 15–24. doi:10.1080/15472450601122322

Downloads

Published

2016-12-01

How to Cite

Caleffi, F., Lucchesi, S. T., Anzanello, M. J., & Cybis, H. B. B. (2016). Influência das condições climáticas e de acidentes na caracterização do comportamento do tráfego em rodovias. TRANSPORTES, 24(4), 57–63. https://doi.org/10.14295/transportes.v24i4.1104

Issue

Vol. 24 No. 4 (2016)

Section

Artigos

License

Authors who submit papers for publication by TRANSPORTES agree to the following terms:

  1. Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.

  3. Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.

  4. Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.

  5. Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.