Adamu muhammad isah
- 1. ADVANCE TRAFFIC ENGINEERING PRSENTATION TOPIC: REVIEW OF FUZZY MICROSCOPIC TRAFFIC MODELS BY ADAMU, MUHAMMAD ISAH SPS/17/MCE/00074 COURSE FACILITATOR: ENGR. PROF. H.M.ALHASSAN FACULTY OF ENGINEERING, CIVIL ENGINEERING PROGRAM BAYERO UNIVERSITY KANO 15TH MAY, 2018.
- 2. INTRODUCTION Traffic congestion and increased number of accidents have become one of the most prior problem of populated areas. Because the existing road networks cannot satisfy the current demand and construction of new roads is usually not a solution and often not economical and socially desired. Therefore the need for new traffic management and information systems. Traffic models are thus fundamental resources in the management of road network. Microscopic models appears to be promising among the models for its ability to describe the dynamics of traffic flow at the level of each individual vehicle. However, microscopic models assumed that vehicular behavior can be described base on precise and approximated values. But this have its limitations as Human decisions imply uncertainties since most of our behaviors have fuzzy nature rather than crisp, and the application of fuzzy set theory is a useful tool to handle uncertainties.
- 3. AIM The aim of this review is to integrate fuzzy logic with microscopic traffic models
- 4. OBJECTIVES To review microscopic model assumptions To highlight the limitations of microscopic model To give the processes involved in considering fuzzy logic To highlight the benefits of fuzzy logic in microscopic traffic models.
- 5. JUSTIFICATION Due to increasing number of private car ownership, the share of public transport has reduced and therefore an increase in traffic volume. Especially in the medium sized cities, people have a strong perception that car is the most suitable mode when compared to existing public transport system and continuously thinking of owning and using a car. These perception of people will not do justice to the traffic management policies. Therefore the need to review these policies from time to time.
- 6. REVIEW OF MICROSCOPIC CAR-FOLLOWING MODEL Car-following models have been developed since 1950s (Pipes, 1953). These models describe the accelerative behavior of a driver as a function of distance headway and relative speed. The following are representative car-following models Safe Distance models Optimal Speed models (OSM) General motors model (stimulus response model)
- 7. GENERAL MOTORS MODEL The fundamental concept behind the General Motors Model is the stimulus-response theory (Chandler et al., 1958). Response= stimulus × sensitivity Response is acceleration Or deceleration of the FV, stimulus is the difference between speed of leader vehicle, 𝑉𝐿 and speed of the follower vehicle, 𝑉𝐹 , sensitivity is a function of distance headway,𝑑 𝑛 and 𝑉𝐹. Several equations have been proposed in the last 20 years (Mehmood et al., 2001)
- 8. LIMITATIONS OF GENERAL MOTORS MODEL The possibility of relative speed between the two vehicles to be equal to zero is not realistic. The assumption of symmetrical behavior. For example, a lead vehicle has a positive relative speed with a certain magnitude, and another lead vehicle has a negative relative speed with the same magnitude. Therefore deceleration rate in the first case will be equal to acceleration rate in the second case. In a real traffic, deceleration rate is greater to avoid risk.
- 9. STOP-DISTANCE MODEL This Model assumes that a following vehicle maintains a safe distance as a factor of safety incase the leading vehicle suddenly stops. This model is based on a function of the speeds of the following and leading vehicles and the follower’s reaction time. This is represented by the Kometani and Sasaki, (1959) formula: Δx(t-T) = α𝑉𝐿 2 (t-T) + β𝑣 𝐹 2 (t) + β𝑉𝐹(t)+ 𝑏0 Where Δx is the relative distance between the lead and following vehicles; 𝑉𝐿 is the speed of the lead vehicle; 𝑉𝐹 is the speed of the following vehicle; T is the driver’s reaction time; and α, β, β1and 𝑏0 are calibration constants.
- 10. LIMITATIONS OF STOP DISTANCE MODEL The Stop Distance Model is widely used in microscopic traffic simulations (Gipps, 1981), because of its easy calibration based on a realistic driving behavior, requiring only the maximal deceleration of the following vehicle. However, the “safe headway” concept is not a totally valid starting point, and this assumption is not consistent with empirical observations.
- 11. OPTIMAL SPEED MODELS (OSM) The Optimal Speed Model (OSM) is a microscopic “car following type” model first proposed by Bando et al.,(1994). It is based on the principle that for each situation, there is an Optimal Speed (OS) to adopt by the driver. Any deviation from this OS causes the vehicle to adopt an acceleration proportional to the difference between the optimum and actual speed (Six,etal.,2012).
- 12. LIMITATIONS OF OSM The sensitivity and the OS function are assumed to be common to all cars. The OS model predicts that a homogenous flow becomes unstable and transits to a jammed flow if the density exceeds a critical value (Akihiro et al. (2016) Linear OS Functions do not describe realistic microscopic behaviours (Antoine and Armin, 2014) Optimal Speed Model has difficulty to avoid collision in urgent braking cases (Doudouet al., 2016)
- 13. FUZZY LOGIC In the recent past, many microscopic simulation models have been developed (Algers et al, 1997) based on probabilistic or mechanistic approach in capturing drivers’ decisions (Hidas, 2002). However, these approaches do not incorporate the uncertainties of driver perception and decisions (Wu et al, 2000). The reactions of a driver perhaps is not based on a deterministic one-to-one relationship, but on a set of vague driving rules developed through experience (Chakraborty and Kikuchi, 1999).
- 14. CONTINUETION • Fuzzy logic provides the opportunity to introduce a quantifiable degree of uncertainty into the modelling procedure to reflect the natural or subjective perception of the real variables (Wu et al. 2000). • McDonald et al (1997).; Brackstone et al (1998) and Wu et al. (2000) developed a fuzzy logic model based on fuzzy sets and systems. To model the lane changing decision, they classified the lane changing manoeuvres into two categories lane changes to the near-side lane and lane changes to the off-side lane.
- 15. CONTNUATION • The fuzzy logic model uses relative velocity and distance divergence (DD) (ratio of headway distance to desired headway) as input variables. The output variable is the acceleration or deceleration rate. The DD is the average of the headway distance that is observed when the relative speeds between vehicles are close to zero. This model adopts fuzzy functions (fuzzy sets described by membership functions) as the formula for the input-output relationship • The fuzzy logic car-following model was developed by the Transportation Research Group (TRG) at the University of Southampton (Wu et al., 2000). McDonald et al. collected car following behavior data on real roads. Then developed and validated the proposed fuzzy logic car- following model based on the real-world data
- 16. FUZZY LOGIC CAR-FOLLOWING MODEL The car-following models listed above have established a unique interpretation of drivers in a car following behaviors; • General Motors Model described the driver in a car following situation as a stimuli responder • Stop Distance Model describe the driver as a safe distance keeper and • Optimal speed model describe the driver as a state monitor who wants to maintain optimal speed below the threshold However, these models include non-realistic constraints to describe car-following behavior in real road-traffic environments: symmetry between acceleration and deceleration, the “safe headway” concept, and constant acceleration or deceleration above the threshold.
- 17. The fuzzy logic car-following model describes driving operations under car-following conditions using linguistic terms and associated rules, instead of deterministic mathematical functions. Car-following behavior can be described in a natural manner that reflects the imprecise and uncertain human sensory modalities The fuzzy logic car-following model treats a driver as a decision- maker who decides the controls based on the current information, experience and situation as inputs using a fuzzy reasoning. CONTINUATION
- 18. FUTURE RESEARCH However, the details of the inbuilt mechanism of underlying models are not been accessible in the available literature. The further refinements are still going on to improve fuzzy microscopic simulation models in terms of computational performance, accuracy of the underlying models in representing realistic traffic flow, analyze various policies related to ITS applications etc.
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