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Abstract
Design and Development of a GIS-Based Fuzzy Logic Approach for Urban Traffic Congestion Monitoring
Delavar, M.R.
Head,GIS-LIS Division, Department of Engineering Faculty, University of Tehran, Iran
Email: mdelavar@ut.ac.ir
Hajibabai L.
Department of Engineering Faculty
University of Tehran, Iran
Email: lbabai@geomatics.ut.ac.ir
Neisany Samany N.
Department of Engineering Faculty
University of Tehran, Iran
Email: nsamany@geomatics.ut.ac.ir
Abstract
Decisions are often evaluated on the quality of the process supported. It is in this context that geo-spatial information systems (GIS) and spatial decision support system (SDSS) increasingly are being used to generate alternatives to aid decision-makers in their deliberations. Among so many applications of GIS, GIS for transportation (GIS-T) faces ever increasing popularity among geospatial information (GI) community. It is possible to state unequivocally that GIS-T has arrived and now represents one of the most important application areas of GIS science and technology. Advanced Traffic Management System (ATMS) is one component of the Intelligent Transport Systems (ITS) that currently being developed to improve the safety and efficiency of automobile traveling. Transportation management is therefore an essential component of ATMS. The main reasons are classified as bellow:
The number of vehicles on the roads is steadily increasing whereas the roads and the land available for building new roads are very limited.• Managing, redirecting and decongesting the traffic within the existing roads and space are indeed an important challenging task.
In transportation management, the quantification and evaluation of congestion severity have been taken as important research ideas to give a modification to the generalized design procedures and also to suggest the remedial solutions for releasing congestion.
A number of standards and specifications for design parameters to the generalized situations of congestion qualification have been suggested. However, the congestion severity on an urban road is affecting the general design condition in diverse ways and the recommendations become ineffective. The type and intensity of congestion depends on many quantifiable factors such as volume, speed and ratio of slow as well as fast moving vehicles. Traffic congestion is a severe and growing problem in many urban areas due to rapid population and job growth in metropolitan areas, more intensive use of automobiles, failure to build new roads (although this is debatable) and failure to make drivers bear the full cost of driving.
Conventionally, congestion is defined using volume (V) and capacity (C) as V/C ratio. However, Passenger Car Unit (PCU) used to estimate the volume as well as capacity is subjective in nature and these are not directly measurable units. Therefore, the actual capacity of the road is not determined and thus the value of congestion becomes subjective in nature. Hence determining congestion using volume and capacity is not matching with the actual traffic conditions. Therefore, the measurable parameters such as Speed and Inter Vehicular Distance (IVD) are some of the inputs which best quantify the congestion in reality.
In this context, the development of systems capable of reasoning about the traffic behavior and evolution in similar terms to an expert traffic operator is then required. This research integrates the advantages of both fuzzy logic and GIS to provide public, policy makers and traffic managers a new means to assess and alleviate congestion as short and medium term measures. These types of systems may not be conceived to replace the human operator, however, they can be used to act as intelligent assistants that cooperate in the task of defining and applying traffic control decisions.
The directly and precisely measurable quantities such as speed and Inter Vehicular Distance (IVD) are the two parameters considered for input in the fuzzy model. The main objective of this paper is to quantify congestion using fuzzy logic and to help the policy makers and traffic managers to alleviate congestion in traffic management using network analysis for urban roads. Our proposed method gives a comprehensive insight as to how GIS can be effectively used to manage and plan transportation in an urban environment.
Experience shows that the designed implementation method is effective in terms of computation time and complexity. Tests of qualification of traffic congestion for a moderate complicated network are conducted and their results show the efficiency of the algorithm and support our analyses. Further efforts will be made to enhance the adaptability of the algorithm and expand its applications into broader GIS topics.
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