RANGE OF THRESHOLD FOR FUZZY INPUT IN TRAFFIC FLOW
- 1. KURE, ALHAMDU ABBA SPS/21/MCE/00051 A PRESENTATION ON THE RANGE OF THRESHOLDS FOR FUZZY INPUTS IN TRAFFIC FLOW BAYERO UNIVERSITY KANO.
- 2. WHAT IS FUZZY? The word fuzzy refers to things, events, process, or function that is changing continuously which cannot always be defined as either true/false or which are not clear or are vague. In traffic flow, fuzzy inputs play a significant role in understanding and modelling traffic flow, where the tendency to choose a particular mode of travel between an origin-destination pair depends on a number of factors such as travel-time, cost of travel, comfort, safety, privacy, and income
- 3. AIM OF FUZZY IN TRAFFIC FLOW The aim is to solve complex problems in traffic flow, where the parameters may be unclear or imprecise OBJECTIVES OF FUZZY LOGIC WHAT DO YOU INTEND TO ACHIEVE WITH FUZZY IN TRAFFIC FLOW??? 1. To Improve traffic prediction accuracy 2. To optimize traffic management system using intelligent transport system to facilitate drivers’ decision making 3. To enhance traffic control strategies Perception, input of the driver and traffic conditions
- 4. PROBLEMS OF THE STUDY One major problem of fuzzy logic is iidentifying the appropriate input/output variables, acquiring training data, and optimizing the model parameters to achieve the desired performance.
- 5. ADVANTAGES/DISADVANTAGES OF FUZZY LOGIC SYSTEM ❑ This system can work with any type of inputs whether it is imprecise, distorted or noisy input information. ❑ The construction of Fuzzy Logic Systems is easy and understandable. ❑ It provides a very efficient solution to complex problems in all fields of life as it resembles human reasoning and decision-making. DISADVANTAGES: ❑ There is no systematic approach to solve a given problem through fuzzy logic. ❑ Proof of its characteristics is difficult or impossible in most cases because every time we do not get a mathematical description of our approach. ❑ As fuzzy logic works on precise as well as imprecise data so most of the time accuracy is compromised.
- 6. APPLICATIONS OF FUZZY LOGIC SYSTEM • It has been used in the automotive system for speed control, traffic control. • It is used for decision-making support systems and personal evaluation in the large company business. • Fuzzy logic is used in Natural language processing and various intensive applications in Artificial Intelligence.
- 7. FUZZY LOGIC THRESHOLDS Thresholds are decision points or boundaries that helps guide actions or decisions based on specific criteria in a wide range of fields and applications Many of the inputs used in traffic flow modelling and analysis can have threshold values. These thresholds define the boundaries between different linguistic variables or fuzzy sets within a given input. There are three basic traffic parameters required for understanding traffic flow, which are 1. Speed 2. Volume 3. Density.
- 8. RANGE OF THRESHOLD IN SPEED • Speed differential refers to the difference in speeds between vehicles traveling in the same direction. • Thresholds for speed differential can be defined to identify situations where significant speed differences may lead to safety concerns. For example the ranges could be • low differential ( 0-10 km/h) • moderate differential (10-20 km/h) • high differential (above 20 km/h). what does it mean in a car following situation???
- 9. RANGE OF THRESHOLD IN TRAFFIC DENSITY • Vehicle density refers to the number of vehicles occupying a unit length of road. • The thresholds for vehicle density can be defined based on the desired levels of congestion. For example, the ranges could be • low density (0-20 vehicles per kilometre) • moderate density (20-40 vehicles per kilometre) • high density (above 40 vehicles per kilometre).
- 10. RANGE OF THRESHOLD IN REACTION TIME • Reaction time represents the time it takes for a driver to respond to a stimulus or change in the environment. • Thresholds for reaction time can be established based on human performance and safety considerations. The ranges could be • fast reaction ( 0-1 second) • moderate reaction (1-2 seconds) • slow reaction (above 2 seconds). This explains the Perception – reaction of the driver…
- 11. CONCLUSION/RECOMMENDATION • Fuzzy logic provides a powerful framework for dealing with the imprecise and uncertain nature of these factors by incorporating fuzzy inputs. Hence,we can capture and represent the vagueness and fuzziness associated with traffic flow, such as vehicle density, traffic volume, speed differential, and reaction time can be fuzzified and mapped to linguistic variables, enabling us to reason about concepts that are not precisely defined. This allows for more realistic representation of the traffic system RECOMMENDATION To better ascertain an accurate level of threshold range in fuzzy, the use of real data that integrates other technologies like machine learning to develop methods and ensure an ideal vehicle experience should be incorporated A case study that has used real data from the busiest intersections is in Bogor, Indonesia, to determine how to mitigate traffic congestion at a road intersection (Jafari et al., 2022)