IDENTIFICATION OF RANGE OF THRESHOLDS FOR FUZZY INPUTS IN TRAFFIC FLOW CIV8331
- 1. BAYERO UNIVERSITY KANO FACULTY OF ENGINEERING ADVANCED TRAFFIC ENGINEERING ASSIGNMENT IDENTIFYING THE RANGE OF THRESHOLDS FUZZY INPUTS IN TRAFFIC FLOW UMAR ALI UMAR SPS/21/MCE/00036 umaralimar88@gmail.com COURSE LECTURER: PROF. H.M. ALHASSAN NOVEMBER 2023
- 2. INTRODUCTION With rapid growth in motorway traffic, driver behaviour is becoming of increasing important to safety and capacity. The design and assessment of potential measures to address these issues must be developed off-line because of the cost and risks of potential field trials. Simulation is a crucial tool in this process, although such techniques depend for their validity on the quality of the underlying models of driver behaviour. Fuzzy logic allows the introduction of a quantifiable degree of uncertainty into the modelled process in order to reflect ‘natural’ or subjective perception of real variables and these can include measures of degrees of ‘desire’ and ‘confidence’ in each information source. This is accomplished by dividing the parameter space of real world observables (e.g. Speed, headway) into a number of overlapping sets and associating each one with a particular concept (e.g. ‘close’), hence allowing one term to be classified in a number of ways, each with differing degrees of confidence (or membership) as one would in real life.
- 3. INTRODUCTION The application of fuzzy numbers helps to deal with imprecise traffic data and to describe uncertainty of the simulation results. In fact, it is impossible to predict unambiguously the evolution of a traffic stream. The fuzzy logic car-following model was developed by the Transportation Research Group (TRG) at the University of Southampton (Wu et al., 2000). McDonald collected car following behaviour data on real roads and developed and validated the proposed fuzzy logic car-following model based on the real- world data. The fuzzy logic model uses relative velocity and distance divergence (DSSD) (the ratio of headway distance to a desired headway) as input variables.
- 4. STATEMENT OF PROBLEM Understanding the driver’s behaviour in is very crucial in microscopic traffic flow modelling so as to comprehend driver’s psychological and physiological reactions in a traffic stream. Fuzzy logic helps in understanding driver’s reaction in a traffic stream through identifying the sets of fuzzy parameters in traffic flow and understanding the range of thresholds associated with those parameters.
- 5. AIM The Aim is to identify a set of fuzzy parameters in traffic flow modelling and describe a range of thresholds for those parameters. OBJECTIVES Describe the application of fuzzy logic in microscopic traffic models Identify a set of fuzzy parameters Formulate a range of threshold for each fuzzy parameter
- 6. CURRENT RESEARCH IN THE AREA The developed fuzzy microscopic traffic model was validated in terms of reproducing a single vehicle’s car-following behaviour, as well as reproducing traffic flow under car following conditions (a platoon of vehicles). The results validated that the fuzzy microscopic traffic model could reproduce both stable and unstable traffic behaviour (Wu et al., 2003). A study to examine individual differences in car- following behaviours to clarify which cognitive function influences changes in car- following behaviour with aging, to assess the relationship between car- following behaviour on a real road and elderly drivers’ cognitive functions (e.g., attention, working memory, and planning (Kitajima & Toyota, 2012)) measured in a laboratory experiment. Analysis of the relationship between driving behaviour and a driver’s cognitive functions will help determine how driver support systems may assist driving behaviour and detect the driver’s cognitive functions based on natural driving behaviour.
- 7. PROBLEMS OF RESEARCH IN THE AREA Microscopic car-following model deals mainly with two vehicles: the vehicle in front and the driver’s own vehicle When a driver approaches intersection with a traffic light under car- following conditions, they may pay more attention to the signal in front of the leading vehicle and manage their acceleration based on the traffic light Drivers allocate their attention to the forward road structure instead of the leading vehicle when they approach a tight curve; thus, they may reduce their driving speed before entering the curve even if the headway distance is opening. The car-following behaviour before intersections or tight curves can be influenced by environmental factors other than a lead vehicle.
- 8. DISCUSSION Fuzzy sets for car following relative distance RANGE DISTANCE 0.75-1 Very FAR 0.50-0.75 FAR 0.25-0.50 CLOSE 0-0.25 Very CLOSE •Fuzzy sets for car following for relative distance
- 9. DISCUSSION Fuzzy sets in relation to distance from obstruction, checkpoints etc RANGE DISTANCE 0.75-1 Very FAR 0.50-0.75 FAR 0.25-0.50 CLOSE 0-0.25 Very CLOSE •Fuzzy sets for car following for relative distance
- 10. DISCUSSION Fuzzy sets for lane changing opportunity in relation to approaching vehicle RANGE FOR SPEED OPPORTUNITY 0.75-1 Very HIGH 0.50-0.75 HIGH 0.25-0.50 LOW 0-0.25 Very LOW
- 11. DISCUSSION RANGE FOR SPEED SPEED 0.75-1 Very HIGH 0.50-0.75 HIGH 0.25-0.50 LOW 0-0.25 Very LOW Fuzzy sets for car average speed based on traffic density
- 12. DISCUSSION RANGE FOR CONDITIONS WEATHER 0.75-1 Very CLEAR 0.50-0.75 CLEAR 0.25-0.50 BAD 0-0.25 Very BAD Fuzzy sets due to weather conditions
- 13. CONCLUSION Identifying fuzzy set from traffic parameters and using those sets to develop a range of thresholds can be very important in traffic studies and can help in developing automatic traffic control systems, adaptive cruise control and self-diving vehicles.
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