Microscopic traffic stream model
- 1. MICROSCOPIC TRAFFIC STREAM MODEL PRESENTED BY: PEAKA RAKESH (2021423) SAURABH KUMAR (2021424) 1
- 2. Contents: Introduction Objectives of traffic flow modelling Classification of traffic flow models Microscopic models Conclusion Reference 2
- 3. Introduction: TRAFFIC FLOW: It is the flow of the vehicles on the road or highways etc. By the effective study of traffic facilities such as freeways , signalized and un-signalized intersections gives the better designing of traffic facilities. 3
- 4. Objectives of traffic flow modelling: To reduce the traffic congestion. Understanding the reasons for traffic jam. To optimize the traffic flow. It is used to stimulate traffic. These models can be used to improve road safety. 4
- 5. Classification of traffic models: Macroscopic traffic flow model Mesoscopic traffic flow model Microscopic traffic flow Sub-microscopic traffic model 5
- 6. Microscopic traffic flow modelling: Microscopic model of traffic flow attempt to analyze the flow of a traffic by modelling driver-driver, and driver-road interactions within a traffic stream. Driver-Driver interactions means how a driver reacts to the actions of a another driver and driver-road interactions means how a driver reacts to various features of roads such as narrow width, curves etc. So, basically microscopic traffic flow model is a driver behaviour model. 6
- 7. Fundamental variables: Distance headway is defined as the distance from a selected point (usually front bumper) on the lead vehicle to the corresponding point on the following vehicles. Time headway is defined as the time difference between any two successive vehicles when they cross a given point. 7
- 8. Over the years various models of driver behaviour in different driving situations have been developed. Some are based on the presence of static obstacles and some are based on driver behaviour in car following situations. The car following models are as follows: 1) Pipes model 2) Forbes model 3) General motors model 4) Optimal velocity model 5) Wiedemann model 8
- 9. 1) Pipes model: The first car-following models were prepared by pipes (1953). * The basic assumption of this model is “A good rule for following another vehicle at a safe distance is to allow yourself at least the length of a car between your vehicle and the vehicle ahead at which you are traveling”. * A disadvantage of this model is that at low speeds, the minimum headways proposed by the theory are considerably less than the corresponding field measurements. 2) Forbe’s model: A similar approach was proposed by Forbe’s at (1956). * In this model, the reaction time needed for the following vehicle to aware of the need to decelerate and apply the brakes is considered. * A disadvantage of this model is that, similar to Pipe’s model, there is a wide difference in the minimum distance headway at low and high speeds. 9
- 10. 3) General motor’s model : The General Motors’ model is the most popular of the car-following theories because of the following reasons: 1. This models shows good correlation to the field data. 2.Greenberg’s logarithmic model for speed-density relationship can be derived from General motors car following model. Major problem with this model is it only considers the relative speed i.e The Rate of change of distance headway. 10
- 11. 4) Optimal velocity model: Whereas the previous car-following models mostly describe the behaviour of a vehicle that is following a leader, the OVMs modify the acceleration mechanism, such that a vehicle’s desired speed is selected on the basis of its space headway, instead of only considering the speed of the leading vehicle. The formulation is based on the assumption of desired speed based on the distance headway of nth vehicle. Where 1/T is called sensitive coefficient. Where nth vehicle tries to maintain the safe speed. 11
- 12. 5)Wiedeman’s model :The Wiedemann model uses random numbers in order to create heterogeneous traffic stream behavior * These random numbers are meant to simulate behaviour of different drivers. * The naturalistic data is a perfect match for this situation because the data is collected by individual drivers. Data for three different drivers was selected and processed in order to calibrate the Wiedemann car-following model. * Future research is recommended in the development and implementation of driver’s aggression profile in this model. * More no.of profiles needed to gain more accurate stimulation of traffic flow. 12
- 13. Conclusion: Microscopic traffic flow modeling focuses on the minute aspects of traffic stream like vehicle to vehicle interaction and individual vehicle behavior. They help to analyze very small changes in the traffic stream over time and space. Car following model is one such model where in the stimulus- response concept is employed. The traffic stream models attempt to establish a better relationship between the traffic parameters. 13
- 14. Reference: Principles of Transportation engineering by Partha Chakroborty and Animesh Das. NEPTEL Traffic Engineering and Management Analysis of the Wiedemann Car Following Model over Different Speeds by Bryan Higgs Transportation Planning and Traffic Flow Models by Sven Maerivoet and Bart De Moor. Google images. 14
- 15. THANK YOU 15