skip to main content
10.1145/3350546.3352537acmotherconferencesArticle/Chapter ViewAbstractPublication PageswiConference Proceedingsconference-collections
short-paper

Implementing an Urban Dynamic Traffic Model

Published:14 October 2019Publication History

ABSTRACT

The world of mobility is constantly evolving and proposing new technologies, such as autonomous driving, electromobility, shared-mobility or even new air transport systems. We do not know how people and things will be moving within cities in 30 years, but for sure we know that road network planning and traffic management will remain critical issues.

The goal of our research is the implementation of a data-driven micro-simulation traffic model for computing everyday simulations of road traffic in a medium-sized city. A dynamic traffic model is needed in every urban area, we introduce an easy-to-set-up solution for cities that already have traffic sensors installed. Daily traffic flows are created from real data measured by induction loop detectors along the urban roads in Modena. The result of the simulation provides a set of ”snapshots” of the traffic flow within the Modena road network every minute. The main contribution of the implemented model is the ability, starting from traffic punctual information on 400 locations, to provide an overview of traffic intensity on more than 800 km of roads.

References

  1. Chiara Bachechi. 2019. Traffic simulation based on sensor data: the case of Modena. Master’s Thesis.Google ScholarGoogle Scholar
  2. Chiara Bachechi and Laura Po. 2019. Traffic Analysis in a Smart City. In Web4City, International IEEE/WIC/ACM Smart City Workshop: Web for Smart Cities - In conjunction with IEEE/WIC/ACM International Conference on Web Intelligence, WI’19, Thessaloniki, Greece, Oct. 14-17, 2019. ACM (United States), Thessaloniki, Greece. to appear.Google ScholarGoogle Scholar
  3. Laura Bieker-Walz, Daniel Krajzewicz, AntonioPio Morra, Carlo Michelacci, and Fabio Cartolano. 2015. Traffic Simulation for All: A Real World Traffic Scenario from the City of Bologna. Lecture Notes in Control and Information Sciences 13 (2015), 47–60. https://doi.org/10.1007/978-3-319-15024-6_4Google ScholarGoogle Scholar
  4. Zied Bouyahia, Hedi Haddad, Nafaâ Jabeur, and Stéphane Derrode. 2017. Real-Time Traffic Data Smoothing from GPS Sparse Measures Using Fuzzy Switching Linear Models. In 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017) / 12th International Conference on Future Networks and Communications (FNC 2017) / Affiliated Workshops, July 24-26, 2017, Leuven, Belgium(Procedia Computer Science), Elhadi M. Shakshuki and Ansar-Ul-Haque Yasar (Eds.), Vol. 110. Elsevier, Leuven, Belgium, 143–150. https://doi.org/10.1016/j.procs.2017.06.136Google ScholarGoogle ScholarCross RefCross Ref
  5. Bo Huang, Chunxia Zhao, and Ya-Min Sun. 2008. Modeling of Urban Traffic Systems Based on Fluid Stochastic Petri Nets. In Fourth International Conference on Natural Computation, ICNC 2008, Jinan, Shandong, China, 18-20 October 2008, Volume 7, Maozu Guo, Liang Zhao, and Lipo Wang (Eds.). IEEE Computer Society, Jinan, Shandong, China, 149–153. https://doi.org/10.1109/ICNC.2008.90Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Daniel Krajzewicz, Georg Hertkorn, Christian Feld, and Peter Wagner. 2002. SUMO (Simulation of Urban MObility); An open-source traffic simulation, In Fundamentals of Traffic Simulation. 4th Middle East Symposium on Simulation and Modelling (MESM2002) 145, 442, 183–187.Google ScholarGoogle Scholar
  7. Daniel Krajzewicz, Georg Hertkorn, Christian Feld, and Peter Wagner. 2003. An Example of Microscopic Car Models Validation Using the Open Source Traffic Simulation SUMO. In Proceedings of the 14th European Simulation Symposium (ESS 2002). October 2002. Dresden. REVERSE-TIME SIMULATION IN PRODUCTION LINE REDESIGN. SCS European Publishing House, Dresden, Germany, 318–322.Google ScholarGoogle Scholar
  8. P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. WieBner. 2018. Microscopic Traffic Simulation using SUMO. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, Maui, HI, USA, 2575–2582. https://doi.org/10.1109/ITSC.2018.8569938Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Petru Pau and Karl-Heinz Kastner. 2014. TOMS-Traffic Online Monitoring System for ITS Austria West. In Modeling Mobility with Open Data. Lecture Notes in Mobility.Springer, Cham, Springer International Publishing Switzerland 2015. https://doi.org/10.1007/978-3-319-15024-6_11Google ScholarGoogle Scholar
  10. Laura Po, Federica Rollo, Chiara Bachechi, and Alberto Corni. 2019. From Sensors Data to Urban Traffic Flow Analysis. In 5th IEEE International Smart Cities Conference, ISC2 2019, Casablanca, Morocco, October 14-17, 2019. IEEE, Casablanca, Morocco. to appear.Google ScholarGoogle ScholarCross RefCross Ref
  11. Laura Po, Federica Rollo, Jose Ramon Rios Viqueira, Raquel Trillo Lado, Alessandro Bigi, Javier Cacheiro Lopez, and Paolo Nesi. 2019. TRAFAIR: Understanding Traffic Flow to Improve Air Quality. In The 1st IEEE African Workshop on Smart Sustainable Cities and Communities (IEEE ASC2 2019) - In conjunction with the 5th IEEE International Smart Cities Conference, ISC2 2019, Casablanca, Morocco, October 14-17, 2019. IEEE, Casablanca, Morocco. to appear.Google ScholarGoogle ScholarCross RefCross Ref
  12. Banoth Ravi, Jaisingh Thangaraj, and Shrinivas Petale. 2019. Data Traffic Forwarding for Inter-vehicular Communication in VANETs Using Stochastic Method. Wireless Personal Communications 106, 3 (2019), 1591–1607. https://doi.org/10.1007/s11277-019-06231-2Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Jorge Luis Zambrano-Martinez, Carlos T. Calafate, David Soler, and Juan-Carlos Cano. 2017. Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions. Sensors 17, 12 (2017), 2921. https://doi.org/10.3390/s17122921Google ScholarGoogle Scholar

Index Terms

  1. Implementing an Urban Dynamic Traffic Model
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format