Abstract
In the internet era, Intelligent Transportation Sys-tem (ITS) for smart cities is gaining tremendous attention since it offers intelligent smart services for traffic monitoring and management with the help of different technologies such as micro-electronics, sensors and IoT. However, in the existing literature, very few attempts are made towards effective traffic monitoring at road junctions in terms of providing faster decision making so that the traffic present in heavily congested urban environments can be dynamically rerouted. In order to tackle this issue, this article proposes a new Controller framework that can be applied at junction-points in order to the control the traffic movement. Specifically, the proposed framework utilizes a multi-logic ruleset database to estimate the traffic density dynamically at the first stage followed by the usage of signal-time computation algorithm at the second stage in order to streamline the traffic and achieve faster clearance at the junction-points. The experimental results conducted with the help of test environment using MEMSIC nodes clearly demonstrate the improved efficiency of the proposed framework in terms various performance metrics including move command frequency, ruleset score and fluctuation score.
Highlights
Nowadays, Intelligent Transportation System (ITS) for smart cities is gaining huge attention, which offers smart as well as intelligent services towards traffic management and monitoring with the help of technologies including electronic sensing, data communication, and advanced information management systems [1],[2]
ITS system consists of the different sophisticated components such as Advanced Traffic Information Service System (ATIS), Emergency Rescue System (ERS), Freight Management System (FMS), Electronic Public Transport System (EPTS), Advanced Public Transportation System (APTS), Advanced Vehicle Control System (AVCS) and Advanced Traffic Management System (ATMs) and so on [5]
The successful deployment of ITS for facilitating effective traffic monitoring is significantly affected by these two limitations: a) To standardize the elementary factors in order to compute and predict the state of deadlock condition of traffic over larger traffic density and b) To ensure the reliability associated with the predictive concept in traffic management along with higher accuracy [6]
Summary
Intelligent Transportation System (ITS) for smart cities is gaining huge attention, which offers smart as well as intelligent services towards traffic management and monitoring with the help of technologies including electronic sensing, data communication, and advanced information management systems [1],[2]. The successful deployment of ITS for facilitating effective traffic monitoring is significantly affected by these two limitations: a) To standardize the elementary factors in order to compute and predict the state of deadlock condition of traffic over larger traffic density and b) To ensure the reliability associated with the predictive concept in traffic management along with higher accuracy [6] The former limitation is addressed up to a certain extent, in the existing literature by means of Vehicular Ad-hoc Networks using forecasting approaches [7]. The traffic density computations are assessed using certain formulated theories without suitable validations with real-world traffic systems, which results in significantly poor performances [9],[10] Due to these aspects, promising techniques are needed for traffic monitoring in ITS which can effectively make faster decisions for rerouting traffic in heavily congested urban environments.
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