Intelligent Transportation Systems Technologies Research Articles

Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

A More General Information Theoretic Study of Wireless Location Verification System

In recent years, the issue of location verification system (LVS) has received significant attention in wireless communication networks, especially in Intelligent Transportation Systems (ITSs) and vehicular technology, where the location information is important for users' safety and security. In this article, we consider a vehicular network and analyze a more general Information Theoretic (ITc) study of LVS, specifically, aiming at finding an optimum decision threshold for detection a spoofed location and increasing the system ability in correctly detecting malicious users. For this purpose, we propose different information theoretic measures (Renyi divergence, Renyi mutual information, Kullback-Leibler divergence, Kullback-Leibler mutual information and Jensen-Shannon divergence) for identifying malicious user. Simulation results and comparison between proposed measures show that the optimum decision threshold can be achieved by applying the Jensen-Shannon divergence measure.

Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

5G-V2X: standardization, architecture, use cases, network-slicing, and edge-computing

Vehicular communication is one of the critical technologies in intelligent transportation system to provide connectivity between vehicles, road side units, and pedestrians. Multiple wireless accessing technologies designed to provide connectivity in vehicular networks such as conventional Wi-Fi, IEEE 802.11p, and cellular communications. Recently, cellular V2X (C-V2X) is standardized and designed by the third generation partnership project (3GPP) for automotive services. C-V2X supports two communication modes through a single platform to provide both Wi-Fi and cellular communication. LTE-V2X is the current 3GPPRelease 14 standard that has many enhancements to provide the new 3GPPRelease 16 for the new 5G radio generation. 5G-new radio (NR) is expected to address the automotive capabilities, improvement, and services for 2020 and beyond. 5G-NR becomes a competitive technology compared with other wireless technologies because of extensive coverage, high capacity, high reliability, and low delay support. In this paper, we propose the Optimizing of 5G with V2X, and analyzing the current V2X standards, introducing the development of 5G, challenges, features, requirements, design, and technologies.

A NOVEL TECHNOLOGY FOR MACHINE TO MACHINE COMMUNICATION USING ETHEREUM SMART CONTRACTS

In the emerging world of IoT applications, machines are going to be at the endpoints of the Internet engaging in complex Machine to Machine (M2M) communications. Be it a personal assistant (softbot)making an appointment with a doctor or an autonomous car filling fuel or charging at a refueling station, in the future, it is going to be M2M communications without human intervention. In such a scenario, robust and secure technology is essential to record every M2M transactions. This paper makes use of blockchain, a distributed ledger technology for intelligent transportation systems. It is proposed that blockchain networks such as Ethereum have the foundations to record and satisfy the transaction that has happened between the machines. A permissioned Ethereum blockchain – the smart contract –is used for recording each every transaction that come off between the car and electric station. An algorithm is proposed to recharge the autonomous electric vehicles as a case study.

Vehicle Detection and Traffic Estimation with Sensors Technologies for Intelligent Transportation Systems

The paper reviews the vehicle detection methods using the intrusive and non-intrusive sensors. The objective of literature review is to summarize the sensors and technologies used in vehicle detection and traffic estimation. Integrating these sensors will give vital information by communicating with the monitoring station related to the presence of the vehicle on the road. Sensors and communicating technologies have a widespread application in intelligent transportation systems. The modern devices and technologies are discussed that determine the vehicle count, classification, location, speed, traffic volume, density, traffic estimation. Sensors fusion can further integrate information from different sources and provides more accuracy.

Comprehensive study of the response of inexpensive low energy wireless sensors for traffic noise monitoring

Precision noise monitoring can prove to be prohibitively expensive and labour intensive. Low cost, wireless, environmental sensor systems, referred to as motes, are being developed as alternatives. Whilst considered of lower accuracy than precision sound level meters, they can be deployed for much longer periods and monitor continuously at one minute resolution. This paper reports the performance of two commercially available motes, the eMote developed to monitor traffic and the nMote to monitor airport noise, in both controlled laboratory conditions, and deployment in the field. Under controlled conditions the performance was assessed by co-location with precision sound level meters (Brüel and Kjær Type, 2260) and exposed to coloured (‘white’ and ‘pink’) noise at different levels. Both units exhibited strong linear relationships (R2 > 0.98) for LAeq when compared to the precision instruments. The performance of three individual eMotes, in terms of similarity and variability, were broadly consistent across the tested units, though levels were systematically overestimated by approximately ±3 dBA when uncorrected. The eMote units were found to exhibit a lower detection limit of ~55 dBA, potentially limiting their application to (a) urban daytime conditions when levels generally exceed that threshold and (b) at other times and locations as screening tools for excessive noise levels, to justify more expensive precision monitoring confirming findings of Marouf et al. (2018). Further research here demonstrates that the systematic error could be corrected by an algorithm resulting in a random error of better than ±1.5dBA. Both nMote units exhibited a lower detection limit (~40dBA). One nMote was electronically calibrated at the manufacturing stage and found to slightly underestimate at lower and overestimate at higher levels. Some improvement was made by developing an algorithm giving overall measurement accuracy better than ±2dBA. The other nMote not calibrated at the manufacturing stage underestimated values at low levels. This could be corrected resulting in a random error of better than ±2.5dBA. Similar results for both Mote types were observed in the field but with more variability due to the data synchronisation, local reflection and refraction etc. and the continually varying noise characteristics of traffic. Whilst the nMote was designed for airport noise, it is considered along with the eMote suitable for monitoring traffic noise in urban areas, and demonstrates much potential to be incorporated into existing Intelligent Transport System technologies to measure, manage and control population exposure to traffic noise (e.g. as a ‘noise camera’ analogous to the existing speed camera). Given that deriving correction algorithms for motes in controlled test environments is impractical, the knowledge gained from these studies has led to modifications to the design and with electronic calibration as part of the manufacturing process measurement errors of better than ±2dBA across the range of sensitivity can be achieved. With advancements in technologies and if power consumption was not an issue, then nowadays a device could quite easily be designed with a dynamic range and linearity quite close to the precision instruments for little extra cost.

Optimization of Maximum Routing Hop Count Parameter Based on Vehicle Density for VANET

Vehicular Ad Hoc Network (VANET) is the basic technology of intelligent transportation systems for providing reliable and real-time communications between vehicles and vehicles or roadside units. In order to improve the communication quality of VANET, this paper studies the effects of different maximum routing hop count parameters on the performance of the network under different vehicle densities. We establish the mathematical models of node connectivity probability and the packet delivery ratio by using the Poisson distribution model. And the maximum routing hop count selection algorithm (MRHSA) is proposed based on the theoretical model established in the paper. The simulation experiments and statistical analysis on packet delivery ratio, throughput, and end-to-end delay are performed under the straight road and urban road scenes, supported by the Vehicle in Network Simulation (Veins). The results show that the maximum routing hop count parameter is an important impact factor on the communication quality of the network. It is found that MRSHA proposed in this paper can improve the packet delivery ratio by about 9.1% at most in straight road scenarios, which indicates that MRHSA will contribute to the improvement of the communication quality of VANET.

Integrating IoT and Blockchain for Ensuring Road Safety: An Unconventional Approach.

The Internet of things (IoT), the Internet of vehicles, and blockchain technology have become very popular these days because of their versatility. Road traffic, which is increasing day by day, is causing more and more deaths worldwide. The world needs a product that would reduce the number of road accidents. This paper suggests combining IoT and blockchain technology to mitigate road hazards. The new intelligent transportation system technologies and the subsequent emergence of 5G technologies will be a blessing, delivering the necessary speed to ensure both safety and quality of service (QoS). Hashgraph technology, a distributed ledger technology is used to create communication networks between the different vehicles and other relevant parameters. Scheduling the requests according to the priorities for ensuring better QoS quotient can be effectively done using hashgraph. We demonstrated how the hashgraph outstrips other equivalents platforms. The proposed model was simulated using OMNeT++ with proper design and network description files. A hardware implementation of the proposed model was also done. Messages were transferred between the vehicles and prioritized using a hashgraph. This paper proposes an effective model in reducing the accidents in terms of parameters like speed, security, stability, and fairness.

Privacy and security challenges in smart and sustainable mobility

The current era of computing is witnessing a huge amount of data being generated with every passing moment. This massive data if nourished effectively can open new horizons for the computing world. The modern world is slowly but surely moving towards the automation age where every entity and object is being automated to perform desired tasks without the need of human interventions. This has made the lives of people more convenient and comfortable. Automation has taken over every single field of computing and even beyond. Smart mobility is one such example of automation wherein the users get real time information about the traffic conditions as well as alternate route suggestions in case of traffic jams. Transportation is considered as the backbone of every business. The automated intelligent transportation system (ITS) has completely transformed the way how people, goods and services are transported and is quite important for achieving sustainability. This paper provides an overview of the existing ITS system, concept of smart mobility and existing vulnerabilities in these systems. Their security concerns and scenarios are also analyzed. Furthermore, in this paper the importance and need for securing these intelligent systems is highlighted and future trends in ITS is also suggested. Although ITS and smart mobility technology are already providing convenient transportation and navigational facilities, there is still a huge scope to improve these facilities for the end users. The suggested future trends if integrated in an effective manner can provide exemplary means to provide state-of-the-art navigational facilities and smart mobility in a true sense.

A Comprehensive Study of the Parameters Impacting the Fuel Economy of Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) are one of the most promising solutions that can improve the fuel economy and reduce emissions. The fuel consumption of PHEVs is affected by various factors. This article identifies four primary factors with significant impact on the PHEV fuel consumption. In this paper, after a brief discussion of different types of the PHEV configurations, the energy management strategies for PHEVs are classified and reviewed in detail. Moreover, the impact of driving conditions, including traffic congestion, road profile, driving distance, weather conditions, and charging behavior, on the PHEV fuel economy are comprehensively analyzed. The data regarding the trip can be obtained utilizing intelligent transportation systems (ITS) technologies. Finally, commonly used energy storage devices in PHEVs are presented and their advantages and disadvantages are highlighted.

Design and implementation path of intelligent transportation information system based on artificial intelligence technology

By using the concept, guiding ideology and design idea of artificial intelligence (AI) and Internet of Thing, this study puts forward the logistics intelligent transportation system (ITS) model, structure design and function design based on AI, focusing on the time value and information value of the intelligent logistics information system. The results show that the design of ITS model based on AI includes three modules: input, transformation and output of ITS. The ITS supply chain management platform and function design of freight vehicles with AI is feasible and prospective. The application of AI technology in ITS has great value and development prospect.

Full Bayesian Before-After Analysis of Safety Effects of Variable Speed Limit System

Variable speed limits (VSL) is a major Intelligent Transportation System (ITS) technology for controlling freeway mainline traffic, which have been increasingly used to improve traffic safety and operations efficiency of freeway traffic management. The primary objective of this study is to evaluate the safety impacts of the VSL system implemented on Interstate 5 in Seattle, United States since 2010. A Full Bayesian (FB) before-after analysis was conducted based on 9,787 crashes that occurred in a 72-month study period. The analysis was conducted for all crashes, crash severity levels, crash types, and crash causes. The FB before-after results implied that the total crash count was reduced by 32.23% with a standard deviation of 3.58% after the VSL system was applied on the freeway. The count of crashes with no injury decreased more than crashes with severe injury and possible injury. The effect of rear-end crash reduction was the most beneficial among all crash types, while the effect on sideswipe crash reduction was the least. The study also compared the traffic speed features in the before and after periods to fully evaluate the impacts of the VSL system on traffic operations. The result indicated that, with VSL control, the difference in speed was reduced by the VSL system. The results of this study are particularly valuable for policy and control strategy development, and cost-benefit evaluation associated with VSL system implementations.

A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving

As a key technology of intelligent transportation system, the intelligent vehicle is the carrier of comprehensive integration of many technologies. Although vision-based autonomous driving has shown excellent prospects, there is still a problem of how to analyze the complicated traffic situation by the collected data. Recently, autonomous driving has been formulated as many tasks separately by using different models, such as object detection task and intention recognition task. In this study, a vision-based system was developed to detect and identity various objects and predict the intention of pedestrians in the traffic scene. The main contributions of this research are (1) an optimized model was presented to detect 10 kinds of objects based on the structure of YOLOv4; (2) a fine-tuned Part Affinity Fields approach was proposed to estimate the pose of pedestrians; (3) Explainable Artificial Intelligence (XAI) technology is added to explain and assist the estimation results in the risk assessment phase; (4) an elaborate self-driving dataset that includes several different subsets for each corresponding task was introduced; and (5) an end-to-end system containing multiple models with high accuracy was developed. Experimental results proved that the total parameters of optimized YOLOv4 are reduced by 74%, which satisfies the real-time capability. In addition, the detection precision of the optimized YOLOv4 achieved an improvement of 2.6% compared to the state-of-the-art.

Accident Detection and Disaster Response Framework Utilizing IoT

The internet of things (IoT) leads the noteworthy edges above customary information and communication technologies (ICT) for Intelligent Transportation Systems (ITS). The progression in the transportation system, the increment in vehicles and the accidents happened on the roads are cumulative up to an alarming situation. Additionally, 1.256 million people expire by the road bumps every year and it is very problematic to find the precise accident location of the user. If an accident occurs, the survival rate of the victim increases, if he is given instantaneous remedial assistance. You can provide remedial assistance to the victim only when you identify the precise location of accident. The main persistence of this system is to identify an accident and find the location of the user. After tracing the location, the system will search nearby hospitals for remedial treatment. System will send a message that contains user's current location, to the nearby hospitals in case of an emergency. System will acquire recommended contacts from the cloud and also send message to them for user’s support by using API. If the user is safe, he can cancel the message that is being sent to the nearest hospital and the recommended contacts. This system will help the users in saving their lives within minimal time.

Techniques of Early Incident Detection and Traffic Monitoring Centre in VANETs: A Review

The Vehicle Ad hoc networks (VANETs), which provide protection and warning for drivers and passengers, are playing a major role in wireless and connectivity technology and are becoming one of the major research areas in intelligent transport systems (ITS) technology. The features, problems, and deployment of VANETs are very distinct from those of mobile ad hoc networks (MANETs). The early detection of incidents provides information about traffic incidents classified by the Intelligent Transportation System of Traffic Monitoring Centre. In this review paper, we will discuss the concept of a traffic monitoring center and its techniques. Also, we will explain the concept of early incidents detection and show limitations and strong points for each approach used.

Advanced Security Attacks on Vehicular AD HOC Network (VANET)

VANET is becoming an emergent technology in Intelligent Transportation Systems. Their main purpose is to provide road safety and improve driving experience for both drivers and passengers. Other unique features of VANET include dynamic topology, short connection period, high mobility of nodes, etc However, security and privacy becomes major issues when communicating in such open wireless environment. In this paper, along with VANET characteristics, we presented security requirements and challenges that need to be kept in mind while designing a security protocol. We defined all existing security attacks and privacy issues in VANET. We categorized these security attacks into different classes based on their properties and provided the comprehensive analysis on them.

Analysis and design on intervehicle distance control of autonomous vehicle platoons

This paper presents an intervehicle distance control (IDC) to solve the problem of autonomous vehicle platooning, motivated by future automated highway system (AHS) or smart road which is proposed as intelligent transportation system (ITS) technology. First the velocity and position control of the single vehicle is studied based on internal model compensator. And then the platooning problem on multiple vehicles is solved in the light of multiagent concept. Moreover, the platoon condition is derived for the corresponding scheme. Further we analyze the influence of controller parameters on the whole system, and propose the guidance for parameter design. Finally some simulations are used to verify the effectiveness of the proposed IDC scheme with an analysis on controller parameters.

PDMAC: A Priority-Based Enhanced TDMA Protocol for Warning Message Dissemination in VANETs.

Vehicular ad hoc networks (VANETs) are the key enabling technology for intelligent transportation systems. Carrier-sense multiple access with collision avoidance (CSMA/CA) is the de facto media access standard for inter-vehicular communications, but its performance degrades in high-density networks. Time-division multiple access (TDMA)-based protocols fill this gap to a certain extent, but encounter inefficient clock synchronization and lack of prioritized message delivery. Therefore, we propose a priority-based direction-aware media access control (PDMAC) as a novel protocol for intra-cluster and inter-cluster clock synchronization. Furthermore, PDMAC pioneers a three-tier priority assignment technique to enhance warning messages delivery by taking into account the direction component, message type, and severity level on each tier. Analytical and simulation results validate the improved performance of PDMAC in terms of clock synchronization, channel utilization, message loss rate, end-to-end delays, and network throughput, as compared with eminent VANET MAC protocols.

IEEE 802.15.7-Compliant Ultra-Low Latency Relaying VLC System for Safety-Critical ITS

The integration of Visible-Light Communications technology (VLC) in Intelligent Transportation Systems (ITS) is a very promising platform for a cost-effective implementation of revolutionary ITS and cooperative ITS protocols. In this paper, we propose an infrastructure-to-vehicle-to-vehicle (I2V2V) VLC system for ITS, implementing it through a regular LED traffic light serving as a transmitter and a digital Active Decode-and-Relay (ADR) stage for decoding and relaying the received information towards further incoming units. The proposed VLC system targets the challenging and important case of ultra-low latency ADR transmission of short packets, as this is needed for emerging applications of automatic braking, car platooning and other critical automatic and/or assisted driving applications. The experimental validation of the ADR VLC chain, as well as a thorough statistical analysis of errors distribution in the transmission, has been performed for short to medium distances, up to 50 meters. The performances of the designed system are evaluated by measuring the packet error rate (PER) and latency in the whole ADR transmission chain. Our analysis shows that our system attains ultra-low, sub-ms latencies at 99.9\% confidence level for PER as high as $5\times10^{-3}$, yet granting a latency below 10 ms even for distances of 50 m. The demonstrated system prototype is compatible with IEEE 802.15.7 standard.