This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.

Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead

Prediction-based protocols for vehicular Ad Hoc Networks: Survey and taxonomy

In recent years, the network technology known as Internet of Vehicles (IoV) has been developed to improve road safety and vehicle security, with the goal of servicing the digital demands of car drivers and passengers. However, the highly dynamical network topology that characterizes these networks, and which often leads to discontinuous transmissions, is one of the most significant challenges of IoV. To address this issue, IoV infrastructure-based components known as roadside units (RSU) are designed to play a critical role by providing continuous transmission coverage and permanent connectivity. However, the main challenges that arise when deploying RSUs are balancing IoVs’ performances and total cost so that optimal vehicle service coverage is provided with respect to some target Quality of Service (QoS) such as: service coverage, throughput, low latency, or energy consumption. This paper provides an in-depth survey of RSU deployment in IoV networks, discussing recent research trends in this field, and summarizing of a number of previous papers on the subject. Furthermore, we highlight that two classes of RSU deployment can be found in the literature—static and dynamic—the latter being based on vehicle mobility. A comparison between the existing RSU deployment schemes proposed in existing literature, as well as the various networking metrics, are presented and discussed. Our comparative study confirms that the performance of the different RSU placement solutions heavily depends on several factors such as road shape, particularity of road segments (like accident-prone ones), wireless access methods, mobility model, and vehicles’ distribution over time and space. Besides that, we review the most important RSU placement approaches, highlighting their strengths and limitations. Finally, this survey concludes by presenting some future research directions in this domain.

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Roadside Unit Deployment in Internet of Vehicles Systems: A Survey

The present invention discloses an intelligent traffic system. The system at least comprises a traffic control center, a traffic information service center, a cellular mobile communication system, a road toll collection system and an in-vehicle terminal, which are connected with each other via a wired communication network. The in-vehicle terminal communicates with the traffic control center, the road toll collection system and the traffic information service center respectively via the cellular mobile communication system. The present invention further claims an in-vehicle terminal used in the intelligent traffic system and a method for realizing the intelligent traffic system with the cellular mobile communication system. By adopting the intelligent traffic system, construction cost of the intelligent traffic system is reduced substantially.

Intelligent transport system

The accurate prediction of the vehicle mobility in Vehicular Ad-hoc Networks (VANETs) has a significant effect in the rapid deployment of Intelligent Transport Systems (ITSs) as well as in building efficient vehicular communication protocols. Thus, this article aims to briefly review the contemporary research progress in the field of mobility prediction in VANETs. The fundamental objective here is to explore the major mobility prediction aims, techniques, and use cases in VANETs, with focusing on the prerequisites, advantages, and limitations. Furthermore, performance and usage analysis are presented with the aim to deduce the appropriateness of utilizing each prediction aim/technique for the varied use cases. Finally, the relevant research challenges are identified to shape the future of the mobility prediction in VANETs. A thorough knowledge and understanding of the mobility prediction are provided, with the aim of enhancing the robustness and reliability of the mobility prediction in VANETs.

Mobility Prediction in Vehicular Ad-Hoc Networks: Prediction Aims, Techniques, Use Cases, and Research Challenges

With day to day exponential increase in the number of vehicles, it is essential to communicate travel information for road travellers to enhance their travelling experience. One of the recent technologies that aid in real time and faster communication of information is Vehicular Ad-hoc Network (VANET). To ensure significant connectivity and continuous data flow, Road Side Units (RSU) are installed in VANET. RSUs receive the traffic information from vehicles and communicate it to other RSUs, Traffic Management Centre (TMC) or other adjacent vehicles. However, identifying the appropriate locations to deploy RSUs to maintain network connectivity and reduce cost of installation in a given area are still challenging and cannot be decided based on a single parameter. This paper proposes an Optimal RSU Distribution Planner (ORDP) using a Fusion Algorithm (FA) comprising of Evolutionary Genetic Algorithm (EGA) and D-Trimming. Further it also provides the feasibility to choose the appropriate parameter configuration based on user requirement which makes the model feasible and efficient. The scalability and efficiency of the planner are tested against simulated and realistic datasets and it is seen that ORDP has proved to deliver improved results compared with other greedy approaches.

A Feasible RSU Deployment Planner Using Fusion Algorithm

With growing road traffic, it becomes essential to predict the travel time of road travelers well ahead of their travel to avoid traffic mismanagement. Vehicular Adhoc Network (VANET) infrastructure is an ideal solution for such requirements. VANET uses Road Side Units (RSU) that remain as the primary and critical source to predict the traffic in any road. This paper proposes an Optimized RSU based Travel Time (ORTT) model using a Genetic Algorithm (GA) to calculate the travel time for any type of vehicle. However accurate the predictions, identifying the right locations to place RSUs and minimizing cost of placing RSUs on these roads are still challenging. Further, this paper optimizes the number of RSUs for estimating the travel time and thus reduces the cost of VANET infrastructure.

Genetic Algorithm Based Efficient RSU Distribution to Estimate Travel Time for Vehicular Users

The increase in day to day vehicular traffic demands an efficient, automated and innovative approach for its management and regulation. One of the most practical and commonly used Intelligent Transportation Systems (ITS) based solution in recent times is video based traffic monitoring. The primary task of such systems is vehicle detection and the existing methods are not robust enough to handle diverse backgrounds and illumination changes in the traffic video. In order to address this issue, a Pre-processing Framework with Virtual Mono-Layered Sequence of Boxes (PF-VMSB) is proposed to make any vehicle detection process robust and efficient. The two composite modules of the proposed system include Effective Element Estimation (E3) module and Dynamic Multilevel Parallel Video Image Processing (D-MPVIP) module. The E3 module enhances the illumination of the traffic video only when necessary to produce an ideal environment for detection. Further, it classifies the type of background in video content (Static, Moderate or Dynamic) to provide an option to choose the appropriate image processing algorithm suitable for vehicle detection. In D-MPVIP module, the redundant video content are eliminated from processing to reduce the computational cost and processing time. Additionally, the spatial color information of the traffic video content is preserved and processed in parallel.The efficiency of the proposed framework of VMSB with E3 and D-MPVIP were analysed using benchmark datasets such as DETRAC, Urban Trackr, Ko-per and self recorded videos. The results shows an overall accuracy of detection rate using conventional image processing techniques such as Background Subtraction (BS) increases by 34.6% and the processing time is reduced by 37.5%. By incorporating the proposed framework into any vehicle detection process, the processing time and computational cost are improved without compromising the detection accuracy. The resultant of the detection process can be utilized by ITS application to enumerate traffic parameters such as vehicle volume count, congestion estimation, speed monitoring, travel time prediction, etc.

Pre-processing framework with virtual mono-layer sequence of boxes for video based vehicle detection applications

Rapid Urbanization and higher usage of private transport has led to increase in vehicular traffic in cities across the globe. However, there has been no drastic improvement in terms of the resources to monitor and regulate the vehicular traffic. This leads to frequent congestion in roads and the delay in reaching any destination within the city limits has become inevitable. One of the advancements in wireless technologies to address this issue is Vehicular Ad-hoc Network (VANET) Infrastructure. As one among the service application of VANET, the Congestion Rate (CR) information is essential for travelers to make adaptive decisions and avoid overcrowding. This paper proposes a novel approach to calculate CR in a target geographic area for the smart vehicles of VANET Infrastructure using fuzzy based controllers. In addition, this paper proposes a novel method to reduce the computational complexity owing to the irregularity of traffic and frequent updation of CR value in VANET. Further, simulation models of moderate traffic were created using VISSIM and their corresponding CR values are evaluated using fuzzy logic controller in MATLAB. The results also show that the proposed selective updating algorithm reduces the CR updation by 85% in comparison with conventional periodic updation.

Congestion Rate Estimation for VANET Infrastructure using Fuzzy Logic

The advancements of several real-time system applications enable us to provide better solutions to day-to-day problems. One such real-time systems that has significantly enhanced its efficiency in aiding travelers to make commutation pleasant is the intelligent transportation system (ITS). There are several aspects of an ITS application that make it efficient and resourceful, but the major significant factor is its capability to provide services within a time constraint. This chapter aims to provide the basic concepts, background, and importance of dependability on distributed real-time systems in ITS using two applications for efficient traffic management. A novel automated traffic signal (ATS) is proposed that manages traffic flow by enumerating vehicle density of road segments using image processing techniques. The other proposed work involves the estimation of congestion rate (CONGRA) for given target area using the proposed hybrid vehicular ad hoc network (VANET). The details of the modules, implementation, and result analysis of the applications are discussed and presented.

Manipriya Sankaranarayanan

Papers

A Feasible RSU Deployment Planner Using Fusion Algorithm

Genetic Algorithm Based Efficient RSU Distribution to Estimate Travel Time for Vehicular Users

Pre-processing framework with virtual mono-layer sequence of boxes for video based vehicle detection applications

Congestion Rate Estimation for VANET Infrastructure using Fuzzy Logic

Significance of Real-Time Systems in Intelligent Transportation Systems