Traffic Flow models for Road Networks. By Team-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.
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.
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
Traffic flow models classified in many ways based on
Microscopic Traffic flow models
Macroscopic Traffic flow models
Mesoscopic Traffic flow models
Traffic flow models
Cell Automation Model
Car following Model
A Car-Following Model for
Intelligent Transportation Systems Management
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
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
A Macroscopic traffic Flow Model for Integrated Control of Freeway and Urban Traffic
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.
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.
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.
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
A Hybrid SUPPORT LARGE-SCALE TRAFFIC AND LOGISTIC MODELING AND ANALYSISOptimization-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
A Discrete-Event SUPPORT LARGE-SCALE TRAFFIC AND LOGISTIC MODELING AND ANALYSISMesoscopic Traffic Simulation Model for Hybrid
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.
 SUPPORT LARGE-SCALE TRAFFIC AND LOGISTIC MODELING AND ANALYSISWilcoBurghout, 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
 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
Thank you! SUPPORT LARGE-SCALE TRAFFIC AND LOGISTIC MODELING AND ANALYSIS