Intelligent Transportation Systems Scenarios Research Articles

Novel computational intelligence-based model for effective traffic management in intelligent transportation system

<p><span lang="EN-US">The evolution of intelligent transportation system (ITS) is essentially meant for upgrading the driving experience with more safety and accessibility of various analytical information from its extensive network. However, a significant gap is observed that doesn't cater up the complete demands of ITS. It has been also noted that computational intelligence (CI) based approach is slowly gaining pace in solving the transport related problems in ITS as compared to its other counterpart existing methodologies like artificial intelligence. The proposed manuscript introduces a novel computational framework towards assisting in relaying routing and navigational services using CI-based approach. A design of novel navigational controller unit is presented for global ITS scenario towards yielding an optimal decision of routing. The CI-based approach is implemented by integrating fuzzy process with evolutionary searching, learning and probability theory in most simplified form. The study also introduces a novel concept of relaying decision as feedback from navigational controller unit to specific vehicular node discretely unlike existing traffic controller system with an agenda to offer faster and effective clearance of queued vehicular nodes from target area. The study outcome shows higher consistency in its relay with better performance from existing study model in ITS.</span></p>

DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment.

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.

Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods

Intelligent transportation system (ITS) collects numerous data for analysis of the transportation system. The data can be used for providing services for travellers and traffic controllers in the ITS and optimizing it, for the purpose of making the transportation more efficient and safer. Due to the wide and flexible employment of video cameras in visual surveillance system (VSS), mature edge-cloud resource scheduling for data transmission and analysis, and the fast development of deep learning, computer vision (CV) methods have been employed in the visual-based ITS services successfully. In this paper, we discuss the edge-cloud surveillance resource scheduling for the CV methods and review the deep learning-based CV methods in the VSS, including detection, classification, and tracking methods, for better understanding of the relationship between the CV-based ITS services and these methods. We experimentally compare several state-of-the-art deep learning-based methods, which have been successfully applied in the CV fields under the ITS scenario, on their performance, inference speed, computational quantity, and model size. According to the comparisons, we propose four main challenges of the deep learning-based CV methods applied in the services, as a discussion of the future research directions. Code are available at https://github.com/PRIS-CV/DL-CV-ITS .

Energy-efficient cooperative transmission for intelligent transportation systems

Recent advances in cooperative multiple-input-multiple-output (CMIMO) techniques have encouraged interest in the development of intelligent transportation systems (ITS). They have the potential for use in the infrastructure to vehicle (I2V) and infrastructure to infrastructure (I2I) communications in ITS networks where the energy consumption of wireless sensor nodes embedded on the road infrastructure is constraint. Therefore, how to reduce the energy consumption becomes a hot research topic. In this paper, applications of cooperative communications in ITS networks are proposed for reducing the total energy consumption. At first, the ITS model is established based on the cooperative multiple-input-multiple-output spatial modulation (CMIMO-SM). A detailed energy consumption analysis of the proposed scheme compared with the traditional single-input-single-output (SISO) based scheme is then presented. The comparison conducted between these communication schemes helps us select the optimal one for energy reduction in energy constrained ITS networks. Additionally, under the guidance of the proposed scheme we consider the multi-hop transmission scenario where the energy efficiency improvement is achieved by finding the optimal hop number with the equal hop-length scheme. As a result, we analyze the energy consumption in different situations, and discuss the requirements on the hop-length and hop number in ITS networks. It shows that the optimal results are dependent on the ITS scenarios and choosing the appropriate transmission scheme will provide a good energy consumption performance in ITS.

Predictive Urban Traffic Flow Model using Vehicular Big Data

Objectives: Intelligent Transportation System (ITS) is raising the interests of the research community in the field of communication. The main objectives of this work are to implement ITS scenario and evaluate the Traffic Density Estimation for different cities for checking the density of vehicles on the different roads and perform a comparative evaluation of traffic densities in the different number of city based scenarios and to increase the accuracy of the density prediction technique for ITS. Method/Statistical Analysis: In this manner, an advanced density prediction technique has been proposed for the prediction of density on the road. The proposed technique is based on the number of vehicles on the road, in which vehicle on the map of the three cities at a particular instance of time has been estimated. After estimating the density of vehicle, prediction of that density has been done which shows that there is a maximum density on the lane and in future density on the road has been predicted. Finding: A range of values has been generated with the help of a dedicated simulation scenario using SUMO simulator. The computed values have been used for estimating the density of vehicles in Chandigarh, Delhi and Kolkata based on road side scenarios using Poisson distribution for randomly generated of vehicles and the obtained values has been evaluated to compare the densities of vehicles for these three cities. Improvement: When we are talking about traffic, then the term comes in our mind is congestion. To avoid that congestion on the roads, we use the Poisson distribution in which vehicle generates after every instance of time. With the help of this distribution, the amount of congestion on the roads can be decreased and this technique is very effective on the hurdle like jam. Keywords: Density Estimation, ITS, Linear Regression, Poisson Distribution, R Tool, SUMO Simulator, Vehicular Bigdata

A Cluster Management System for VANETs

Vehicular ad-hoc networks (VANETs) are a key solution for communication in intelligent transportation systems (ITS). However, high mobility of vehicles on roads may cause varying delays and message losses which can limit the use of VANETs in some ITS scenarios. This paper proposes and evaluates a cluster management system for VANETs (CMS) that makes communication in VANETs more robust and efficient, enabling the collection of data of individual vehicles that travel on roads at any time. Simulated experiments show that the proposed system achieves improved communication in VANETs enabling their use in ITS applications such as the real-time road traffic management and control.

Time- and Frequency-Varying <inline-formula> <tex-math notation="TeX">$K$</tex-math> </inline-formula>-Factor of Non-Stationary Vehicular Channels for Safety-Relevant Scenarios

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician $K$-factor. We analyze the time-frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS). This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Ricean distributed with a varying $K$-factor. The later delay bins are Rayleigh distributed. We demonstrate that the $K$-factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bi-modal Gaussian mixture model, that allows to capture the $K$-factor variability in time for safety-relevant ITS scenarios.

Evaluation of visible light communication channel delay profiles for automotive applications

Abstract In this article, we present channel delay profiles based on simulated data regarding the practical conditions for the use of visible light communication (VLC) in automotive applications such as Intelligent Transportation Systems (ITS). Practical vehicular LED headlamp and street lamp that consider the lighting regulation for transportation are used to design the ITS scenarios based on VLC. We modeled two usage scenarios, crossroad and metropolitan street, using the CATIA V5 tool. Measurements for the VLC channel delay profile evaluation were then gathered by using a ray-tracing scheme employing commercial LightTools software under the vehicle-to-vehicle and vehicle-to-infrastructure (V2I) communication links. From the obtained channel impulse responses from both scenarios, we derived the VLC channel delay profiles. From them, we found that the common property of the delay profile was composed of dominant multiple line of sight (LOS) links and a less number of non-LOS delay taps. However, the channel delay profile for the V2I link and metropolitan scenario show more dispersive channel characteristics due to the reflection and diffusion of the visible light.

Analytical efficiency evaluation of a network mobility management protocol for Intelligent Transportation Systems

One of the major concerns in an Intelligent Transportation System (ITS) scenario, such as that which may be found on a long-distance train service, is the provision of efficient communication services, satisfying users’ expectations, and fulfilling even highly demanding application requirements, such as safety-oriented services. In an ITS scenario, it is common to have a significant amount of onboard devices that comprise a cluster of nodes (a mobile network) that demand connectivity to the outside networks. This demand has to be satisfied without service disruption. Consequently, the mobility of the mobile network has to be managed. Due to the nature of mobile networks, efficient and lightweight protocols are desired in the ITS context to ensure adequate service performance. However, the security is also a key factor in this scenario. Since the management of the mobility is essential for providing communications, the protocol for managing this mobility has to be protected. Furthermore, there are safety-oriented services in this scenario, so user application data should also be protected. Nevertheless, providing security is expensive in terms of efficiency. Based on this considerations, we have developed a solution for managing the network mobility for ITS scenarios: the NeMHIP protocol. This approach provides a secure management of network mobility in an efficient manner. In this article, we present this protocol and the strategy developed to maintain its security and efficiency in satisfactory levels. We also present the developed analytical models to analyze quantitatively the efficiency of the protocol. More specifically, we have developed models for assessing it in terms of signaling cost, which demonstrates that NeMHIP generates up to 73.47% less signaling compared to other relevant approaches. Therefore, the results obtained demonstrate that NeMHIP is the most efficient and secure solution for providing communications in mobile network scenarios such as in an ITS context.

A Comprehensive Approach to WSN-Based ITS Applications: A Survey

In order to perform sensing tasks, most current Intelligent Transportation Systems (ITS) rely on expensive sensors, which offer only limited functionality. A more recent trend consists of using Wireless Sensor Networks (WSN) for such purpose, which reduces the required investment and enables the development of new collaborative and intelligent applications that further contribute to improve both driving safety and traffic efficiency. This paper surveys the application of WSNs to such ITS scenarios, tackling the main issues that may arise when developing these systems. The paper is divided into sections which address different matters including vehicle detection and classification as well as the selection of appropriate communication protocols, network architecture, topology and some important design parameters. In addition, in line with the multiplicity of different technologies that take part in ITS, it does not consider WSNs just as stand-alone systems, but also as key components of heterogeneous systems cooperating along with other technologies employed in vehicular scenarios.

Using wireless sensor networks to support intelligent transportation systems

In this paper we propose a system architecture for enabling mobile nodes to query a largely deployed wireless sensor network in an intelligent transportation system scenario. We identify three different types of nodes in the network: mobile sinks (i.e. the nodes moving and querying the WSN), vice-sinks (i.e. nodes able to communicate directly with mobile sinks) and ordinary sensor nodes (i.e. nodes sensing a phenomenon and communicating in a multihop fashion). We present protocols and algorithms specifically tailored to such a scenario, in particular at the MAC and network layers. Such a reference architecture well covers situations in which WSNs deployed in a parking place or along a road, provide to cars information on the conditions of the surrounding environment. We introduce and analyse a simple geographic routing protocol and two different load balancing techniques. The performance of the proposed solutions is evaluated through extensive simulations. The simple geographic routing is compared to load balancing techniques. Results support the capability of the proposed solutions to enable the introduction of novel intelligent transportation system applications.