Fuzzy Logic Inference Applications in Road Traffic and Parking Space Management
Author(s)
Ahmed Tijjani Dahiru
ABSTRACT
In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.
In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.
KEYWORDS
Fuzzy Logic Inference, Universe of Discourse, Membership Functions, Parking Space, Traffic, Simulations, Surface Views, Rule Views
Fuzzy Logic Inference, Universe of Discourse, Membership Functions, Parking Space, Traffic, Simulations, Surface Views, Rule Views
Cite this paper
Dahiru, A. (2015) Fuzzy Logic Inference Applications in Road Traffic and Parking Space Management. Journal of Software Engineering and Applications, 8, 339-345. doi: 10.4236/jsea.2015.87034.
Dahiru, A. (2015) Fuzzy Logic Inference Applications in Road Traffic and Parking Space Management. Journal of Software Engineering and Applications, 8, 339-345. doi: 10.4236/jsea.2015.87034.
References
[1] International Organisation of Motor Vehicle Manufacturers (2015) World Vehicles in Use.
http://www.oica.net/category/vehicles-in-use [2] Retscher, G. (2006) An Intelligent Multi-Sensor System for Pedestrian Navigation. Journal of Global Positioning Systems, 5, 110-118. http://dx.doi.org/10.5081/jgps.5.1.110 [3] Campbell, A. and Choudhury, T. (2012) From Smart to Cognitive Phones. IEEE Pervasive Computing, 11, 7-11. [4] Mohan, P., Padmanabhan V.N. and Ramjee, R. (2008) Demo Abstract: Nericell—Using Mobile Smartphones for Rich Monitoring of Road and Traffic Conditions. Microsoft Research India, Bangalore. [5] Ross, V. (2012) Smart Parking System Steer Drivers to Open Spaces. Popular Mechanics.
http://www.popularmechanics.com/technology/gadgets/news/smart-parking-systems-steer-drivers-to-open-spaces [6] Build Mamdani Systems (GUI) (2015) How to Build Mamdani Systems Using Fuzzy Logic Toolbox Graphical User Interface Tools.
http://www.mathworks.com/help/fuzzy/building-systems-with-fuzzy-logic-toolbox-soft [7] Ibrahim, A.M. Introduction to Applied Fuzzy Electronics. Eastern Economy Edition, PHI. DeVry Institute of Technology, Toronto, Canada. [8] Neagu, D. (2012) Building Fuzzy Inference System: A Case Study. School of Informatics and Media, University of Bradford, Bradford. [9] Buckley, P. (2012) Smart Parking Sensor Platforms Helps City Motorist Save Time and Fuel. EE Times News and Analysis, UBM Tech.http://www.eetimes.com/electronics-news/4216891/Smart-parking-sens or-platform-helps-city-motorists-save-time-and-fuel
[1] International Organisation of Motor Vehicle Manufacturers (2015) World Vehicles in Use.
http://www.oica.net/category/vehicles-in-use [2] Retscher, G. (2006) An Intelligent Multi-Sensor System for Pedestrian Navigation. Journal of Global Positioning Systems, 5, 110-118. http://dx.doi.org/10.5081/jgps.5.1.110 [3] Campbell, A. and Choudhury, T. (2012) From Smart to Cognitive Phones. IEEE Pervasive Computing, 11, 7-11. [4] Mohan, P., Padmanabhan V.N. and Ramjee, R. (2008) Demo Abstract: Nericell—Using Mobile Smartphones for Rich Monitoring of Road and Traffic Conditions. Microsoft Research India, Bangalore. [5] Ross, V. (2012) Smart Parking System Steer Drivers to Open Spaces. Popular Mechanics.
http://www.popularmechanics.com/technology/gadgets/news/smart-parking-systems-steer-drivers-to-open-spaces [6] Build Mamdani Systems (GUI) (2015) How to Build Mamdani Systems Using Fuzzy Logic Toolbox Graphical User Interface Tools.
http://www.mathworks.com/help/fuzzy/building-systems-with-fuzzy-logic-toolbox-soft [7] Ibrahim, A.M. Introduction to Applied Fuzzy Electronics. Eastern Economy Edition, PHI. DeVry Institute of Technology, Toronto, Canada. [8] Neagu, D. (2012) Building Fuzzy Inference System: A Case Study. School of Informatics and Media, University of Bradford, Bradford. [9] Buckley, P. (2012) Smart Parking Sensor Platforms Helps City Motorist Save Time and Fuel. EE Times News and Analysis, UBM Tech.http://www.eetimes.com/electronics-news/4216891/Smart-parking-sens or-platform-helps-city-motorists-save-time-and-fuel