Abstract
Traffic congestions problem could affect everyday life especially in urban area. In order to solve the issue, an excellent traffic flow prediction needs to be developed for a better traffic management. Hence, this study was conducted in order to predict traffic flow by using the data of total volume of vehicles per hour at two main roads located in urban areas namely Selangor and Kuala Lumpur, Malaysia by using application of chaos theory. Phase space reconstruction was used to determine the chaotic behaviour of the total volume of vehicles per hour data. The reconstruction of phase space involves a single variable of the total volume of vehicles per hour data to m-dimensional phase space. Meanwhile, the inverse approach as well as local linear approximation method was used to develop prediction model of the traffic flow time series data. This study found that (i) the time series data were chaotic behaviour based on the phase space plot and (ii) inverse approach can provide prediction on the traffic flow time series data besides give excellent prediction with the value of correlation coefficient more than 0.7500. Hence, inverse approach of chaos theory can develop to prediction model towards the traffic flow in urban area; thus may help the local authorities to provide good traffic management.
Highlights
Traffic congestion is a normal scenario to be seen in urban areas while it can endanger human and environment and can cause pollution [1]
Traffic flow prediction is important for traffic management as it provides accurate information for excellent traffic system [2]
A prediction model that can give an accurate prediction of traffic flow has become a crucial need nowadays which gives a strong reason for its study and development
Summary
Traffic congestion is a normal scenario to be seen in urban areas while it can endanger human and environment and can cause pollution [1]. Traffic flow prediction is important for traffic management as it provides accurate information for excellent traffic system [2]. Efficient prediction of traffic flows can help to monitor and analyse urban traffic needs and give citizens better choices of public transport [3]. A prediction model that can give an accurate prediction of traffic flow has become a crucial need nowadays which gives a strong reason for its study and development. Traffic flow or the total number of vehicles crossing a particular point per unit time period is a point process [4]. The system is influenced by several variables such as volume, speed, density, travel time, and headways, which are important in traffic planning and design
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