Abstract
Having the big availability of transportation data, an emergent need to exploit these data in order to address different transportation issues become even more required. This paper introduces a Big Data solution to the road congestion problem that aim to reduce and optimize the traffic flow in urban areas. The proposed system uses real time traffic data to compute the congestion index for each road in the network and then generates recommendations to reassign the traffic flow. The computed congestion indexes are used in the system’s traffic network generation, where the cartography is represented by a weighted graph. The weights are changed dynamically according to the congestion indexes and path properties. The detailed approach adopts Hadoop framework in the data gathering and analysis, which has improved the performance of the proposed system significantly and uses the Dijkstra algorithm over Hadoop MapReduce framework to search for the shortest path in the road network.
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References
Weisbrod, G., Vary, D., Treyz, G.: Economic Implications of Congestion
Allie, É.: Road Congestion Measures Using Instantaneous Information from the Canadian Vehicle Use Study (CVUS), vol. 9029 (2016)
Figueiredo, L., Jesus, I.: Towards the development of intelligent transportation systems. … Transp. … [Internet], vol. 81, pp. 1206–1211 (2001). Available from: http://www.universelle-automation.de/1984_Columbus.pdf
Hadoop, W.T.: The definitive guide [Internet]
Fan, D., Shi, P.: Improvement of Dijkstra’s algorithm and its application in route planning. In: 2010 Seventh International Conference on Fuzzy System Knowledge Discovery [Internet] (FSKD), pp. 1901–1904 (2010). Available from: http://ieeexplore.ieee.org/document/5569452/
Cabinet, K.: Transportation. Congestion Measures
Lomax, T.J., Schrank, D.L., Eisele, W.: The keys to estimating mobility in urban areas, applying definitions and measures that everyone understands. Texas Transportation Institute, Texas A&M University System College Station. Texas, May 2005 (2005)
Highway, T., Monitoring, P.: Effectiveness of potential solutions, pp. 27–32 (1987)
Bertini, R.L., Lovell, D.J.: From Portland’s archived advanced traffic, Nov 2001
Schrank, D., Turner, T.: The 2001 urban mobility report [Internet]. Available from: http://linkinghub.elsevier.com/retrieve/pii/S147444221070048X
Federal Highway Administration (FHWA) [Internet]. Available from: https://ops.fhwa.dot.gov/perf_measurement/index.htm
Minnesota Department of Transportation (Mn/DOT). Available from: http://www.dot.state.mn.us/
The Washington State Department of Transportation (WSDOT). Available from: http://www.wsdot.wa.gov/
Shunping, J.I.A., Hongqin, P., Shuang, L.I.U.: Urban traffic state estimation considering resident travel characteristics and road network capacity. J. Transp. Syst. Eng. Inf. Technol. [Internet]. 11(5), 81–85 (2011). Available from: http://dx.doi.org/10.1016/S1570-6672(10)60142-0
Quiroga, C.A.: Performance measures and data requirements for congestion management systems, 8 (2000)
Abadie, R.R.J., Ehrlich, T.F.: Contrasting time-based and distance-based measures for quantifying traffic congestion levels analysis of New Jersey counties. J. Transp. Res. Rec. 2, 143–148 (1817)
Coifman, B., Kim, S., with Transit Vehicles (2121), pp. 90–101 (2009)
Hueper, J., Dervisoglu, G., Muralidharan, A., Gomes, G., Horowitz, R., Varaiya, P.: Macroscopic modeling and simulation of freeway traffic flow. IFAC Proc. Internet. 42(15), 112–116 (2009). Available from: http://linkinghub.elsevier.com/retrieve/pii/S1474667016317839
Mathew, T.V., Rao, K.K.V.: Microscopic traffic flow modeling. In: Introduction to Traffic Engineering [Internet], pp. 1–9 (2007). Available from: http://nptel.ac.in/courses/105101087/downloads/Lec-34.pdf
Abouaissa, H., Yoann, K., Morvan, G., Abouaissa, H., Yoann, K., Mod, G.M.: Modelisation hybride dynamique de flux de trafic (2015)
Khazaei, H., Zareian, S., Veleda, R., Litoiu, M.: Sipresk: a big data analytic platform for smart transportation. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 166(November), pp. 419–430 (2016)
Shtern, M., Mian, R., Litoiu, M., Zareian, S., Abdelgawad, H., Tizghadam, A.: Towards a multi-cluster analytical engine for transportation data. In: Proceedings—2014 International Conference on Cloud Autonomic Computing. ICCAC 2014, pp. 249–257 (2015)
Xia, D., Wang, B., Li, H., Li, Y., Zhang, Z.: A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing [Internet] 179, 246–26 (2016). Available from: http://dx.doi.org/10.1016/j.neucom.2015.12.013
Yu, J., Jiang, F., Zhu, T.: RTIC-C: a big data system for massive traffic information mining. Proceedings—2013 International Conference on Cloud Computing Big Data, CLOUDCOM-ASIA 2013, pp. 395–402 (2013)
Transportation Research Board. Highway Capacity Manual
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Berrouk, S., El Fazziki, A., Boucetta, Z. (2019). Traffic Regulation and Recommendation System Based on Measuring the Road Congestion. In: Ben Ahmed, M., Boudhir, A., Younes, A. (eds) Innovations in Smart Cities Applications Edition 2. SCA 2018. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-11196-0_86
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DOI: https://doi.org/10.1007/978-3-030-11196-0_86
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