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Traffic Flow models for Road Networks

Traffic Flow models for Road Networks

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Traffic Flow models for Road Networks

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  1. Traffic Flow models for Road Networks By Team-2

  2. INTRODUCTION • Traffic congestion is a serious problem, that we have to deal with in order to achieve smooth traffic flow conditions in road networks. • Expanding infrastructure of road networks such as widening the roads was just not sufficient to handle the smooth traffic flow conditions in road networks due to increased traffic demands • Some traffic flow modeling method are required to model the traffic flow conditions

  3. MOTIVATION Traffic congestion is one of the major problems affecting the whole world Intelligent transportation systems like ATMS and ATIS face a big challenge in controlling traffic congestion and estimating the traffic flow in road networks. To model efficient Traffic flow in road networks, clear understanding on traffic flow operations like what causes congestion, how congestion propagation takes place in road networks etc are required In our presentation we are going to explain some traffic flow models , their classification and their applications in the road network.

  4. PROBLEM STATEMENT Due to the improved economic conditions of many countries, there is a tremendous increase in motor vehicles use from many years. The current road infrastructure in almost all the countries is just not sufficient to handle the current traffic conditions Expanding the road infrastructures just solves the problem to certain extent but cannot fully solve the traffic congestion problem There arise a need for some traffic flow modelling methods to control the congestion which gave rise to many traffic flow theories

  5. CLASSIFICATION Traffic flow models classified in many ways based on Levels Microscopic Traffic flow models Macroscopic Traffic flow models Mesoscopic Traffic flow models .

  6. BLOCK DIAGRAM Traffic flow models Microscopic Mesoscopic Macroscopic Cell Automation Model Car following Model Queue based AMS DTA LWR

  7. Driver individual maximum speed is considered to enable the model to reflect the external environment and driver characteristics. • Explains why speeds and spacing differ among drivers even when the driving conditions are identical. • The model applies individual maximum speed as a model variable. • Examined traffic flow phenomena, such as: equilibrium speed-flow relationship, capacity drop and traffic hysteresis. A Car-Following Model for Intelligent Transportation Systems Management

  8. Cellular Automation Model A stochastic discreet automation model is introduced to simulate free way trafic. Monte carlo simulations of the model show transition from laminar traffic flow to start –stop waves with increase in vehicular density. Different control mechanisms used at intersections such as cycle duration, green split, and coordination of traffic lights have a significant effect on intervehicle spacing distribution and traffic dynamics. It is computationally advantageous

  9. LWR Model Hybrid traffic flow modelscouples a microscopic (vehicle based) and a macroscopic (flow based) representations of traffic flow. The hybrid model presented here combines a flow and a vehicular representations of the same model, which is the classical Lighthill-Witham-Richards model. Homogeneous hybrid model correctly translates boundary conditions from a model to the other, both under fluid and congested conditions

  10. A Macroscopic traffic Flow Model for Integrated Control of Freeway and Urban Traffic Networks An extended version of the METANET traffic flow model to describe the evolution of the traffic flows in the freeway part of the network Anew model For the urban network is proposed based on the Kashanimodel The model has been developed for use in a model predictive control approach, and offer an appropriate trade-off between accuracy and computational Complexity The coupling between the freeway and the urban part of the model is also described.

  11. A queue-based macroscopic model for performance evaluation of congested urban traffic networks This model considers explicitly queues in the links, in order to take into account congestion phenomena which usually characterize urban traffic neworks The traffic network is modelledby means of a directed graph, and the equations which drive the dynamics of the system derive from the well-known LWR model. Links of the model are divided into a running section andqueue section.

  12. Short-Term Traffic Flow Forecasting Using Macroscopic Urban Traffic Network Model Short-term traffic flow forecasting method is described based on the macroscopic urban traffic network model. A macroscopic UTN model is established and used to forecast traffic flow in short term. The model is founded based on the mechanism of the traffic flow movement, and takes all the spatial relationship of the links into consideration through the network topology It also has a good real-time quality when guaranteeing the forecasting effectiveness.

  13. ANISOTROPIC MESOSCOPIC TRAFFIC SIMULATION APPROACH TO SUPPORT LARGE-SCALE TRAFFIC AND LOGISTIC MODELING AND ANALYSIS This paper discusses a new Anisotropic Mesoscopic Simulation (AMS) approach that carefully omits micro-scale details but nicely preserves critical traffic dynamics characteristics AMS model allows computational speed-ups in the order of magnitudes compared to the microscopic models, making it well-suited for large-scale applications It accounts for special scenarios involving stalled or particularly slow-moving vehicles

  14. A Hybrid Optimization-Mesoscopic Simulation Dynamic Traffic Assignment Model Presents a new dynamic traffic assignment model that is based on the mesoscopic space-time queue network loading Method It incorporates a route choice method inspired from optimization theory This hybrid optimization simulation method was applied to a portion of the Stockholm road network, which consists of 220 zones, 2080 links and 5000 turns. The execution times for the code developed for this algorithm are reasonable

  15. A Discrete-Event Mesoscopic Traffic Simulation Model for Hybrid Traffic simulation Presents a mesoscopic traffic simulation model, particularly suited for the development of integrated meso-micro traffic simulation models. It combines a number of recent advances in simulation modeling with new features, such as start-up shockwaves, to create the flexibility necessary for integration with microscopic models Discusses the structure of the model, presents a framework for integration with micro models, and illustrates its validity through a case study with a congested network north of Stockholm Compares its performance with a hybrid model applied to the same network.

  16. [1] Ozan K. Tonguz and WantaneeViriyasitavat, Fan Bai “ Modeling Urban Traffic: A cellular Automata Approach” IEEE Communications Magazine • May 2009 • [2] HSUN-JUNG CHO, YUH-TING WU “A Car-Following Model for Intelligent Transportation Systems Management” ISSN: 1109-9526 Issue 5, Volume 4, May 2007 • [3] K.Nagel and M.Schreckenberg,"A cellular automaton model for freeway traffic",Physics Abstracts December 1992 • [4] Shunpinglis, Zhipeng Li, Jianping Wu “Microscopic Behaviour of Traffic at a Three-staged Signalized intersection “Jianping Wu: TransporetionRaeareh Group. University of Southampton, S o u ~ p t o n , SO17 IBJ, UK. Chong Kong Scholar Pmfwr. NonhemliaotongUNvenity. Bcijiog Iwo44. China, 2003 IEEE • [5] Emmanuel Bourrel, Jean-Baptiste Lesort “Mixing Micro and Macro Representations of Traffic Flow: a Hybrid Model Based on the LWR Theory” 82th Annual Meeting of the Transportation Research Board, 12-16 January 2003, Washington, D.C. References

  17. [6] Shu Lin, Yugeng Xi, and Yanfei Yang, “Short-Term Traffic Flow forecasting Using Macroscopic Urban Traffic Network Model “11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008 • [7] Marco Ciccia, DavideGiglio, Riccardo Minciardi and MatteoViarengo, “A queue-based macroscopic model for performance evaluation of congested urban traffic networks” 2007 IEEE Intelligent Transportation Systems Conference Seattle, WA, USA, Sept. 30 - Oct. 3, 2007 • [8] R. AshaAnand, LelithaVanajakshi, and Shankar C. Subramanian, “Traffic Density Estimation under Heterogeneous Traffic Conditions using Data Fusion” 2011 IEEE Intelligent Vehicles Symposium (IV) Baden-Baden, Germany, June 5-9, 2011 • [9] M. van den Berg, A. Hegyi, B. De Schutter, and J. Hellendoom, “A Macroscopic aaffic Flow Model for Integrated Control of Freeway and Urban Traffic Networks” 42nd IEEE Conference on Decision and Control Mad, Hawaii USA, December 2003 • [10]Ye Tian,Yi-Chang Chiu , “ Anisotropic mesoscopic traffic simulation approach to support large-scale traffic and logistic modeling and analysis” 2011 Winter Simulation IEEE Conference References

  18. [11] WilcoBurghout, Haris N. Koutsopoulos and Ingmar Andreasson, “A Discrete-Event Mesoscopic Traffic Simulation Model for Hybrid Traffic simulation” IEEE ITSC 2006 IEEE Intelligent Transportation Systems Conference Toronto, Canada, September 17-20, 2006 [12] Michael Florian’, Michael Mahut’ and Nicolas Tremblay, ”A Hybrid Optimization-Mesoscopic Simulation Dynamic Traffic assignment Model”, 2001 IEEE Intelligent Transportation Systems Conference Proceedings - Oakland (CA), USA - August 25-29, 2001 References

  19. Thank you!