Intelligent Transportation Systems Devices Research Articles

Privacy-preserving and efficient data sharing for blockchain-based intelligent transportation systems

Recent years have witnessed the development and adoption of blockchain technology in intelligent transportation systems (ITS) because of its authenticity and traceability. However, increasing ITS devices impose grand challenges in privacy-preserving and efficient data sharing. Recent research has demonstrated that integrating searchable symmetric encryption in blockchain enables privacy-preserving data sharing among ITS devices. However, existing solutions focus only on single-keyword searches over encrypted ITS data on the blockchain and suffer from privacy and efficiency issues when extended to multi-keyword scenarios. This work proposes a bloom filter-based multi-keyword search protocol for ITS data with enhanced efficiency and privacy preservation. We design a bloom filter to select a low-frequency keyword from the multiple keywords input by the ITS data owner. The low-frequency keyword can filter out a large portion of the ITS data from the search result, thus significantly reducing the computational cost. Furthermore, each identifier-keyword pair is attached with a pseudorandom tag that enables the completion of a search operation in only one round. In this manner, privacy is preserved because there are no intermediate rounds and results. In addition to the multi-keyword search protocol, we specify the addition and deletion protocols to enable dynamic updates of data records. We conducted a comprehensive performance evaluation of the protocols. The experimental results indicate that the proposed multi-keyword search protocol saves 14.67% query time and 59.96% financial cost.

Simulation of traffic light disruptions in street networks

Intelligent transportation systems (ITS) use information technology and control devices like traffic lights and sensors to help commuters in achieving flow coordination, improving safety and smoothness in mobility and providing valuable information to public managers. But ITS devices are not free of vulnerabilities against cyber-attacks and failures. The impacts of traffic light disruption will be different for different street network structures. Complex network analysis can be used to evaluate critical infrastructures like street networks, revealing patterns of flow distribution, path redundancy, efficiency and robustness. But, at the present moment, the correlations between street network topology and the impact of traffic light disruptions were not sufficiently addressed in the literature. This work aims to evaluate the role of topological configuration and resilience of street networks when subjected to traffic light disruption, 50000 trips were simulated using three attack strategies that prioritize traffic lights based on complex network centrality metrics. Complex street, network analysis is used to interpret the results of the trip simulation.

Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture.

Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview

Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultralow latency and reliable data analytics solutions that combine, in real time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this article, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level to overcome the ITS's latency and reliability challenges. With a higher capability of passengers' mobile devices and intravehicle processors, such a distributed edge computing architecture leverages deep-learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyberphysical security are investigated. Then, different deep-learning solutions for such challenges are revealed. The discussed deep-learning solutions enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the introduced edge analytics architecture, coupled with the power of deep-learning algorithms, provides a reliable, secure, and truly smart transportation environment.

Applicability of adaptive clear channel assessment mode-1 range in intelligent transportation systems devices

Abstract Quality of Service (QoS) performance of Vehicular Ad-hoc NETworks (VANETs) in Intelligent Transportation Systems (ITS) can be improved by varying the Clear Channel Assessment (CCA) mode-1 power. The CCA mode-1 threshold power is the minimum amount of energy required to declare the channel state busy by the physical layer. This paper identifies the applicability of adaptive CCA power in ITS devices by analyzing the impact of different CCA powers on the QoS of a VANET. QoS performance metrics such as packet delivery fraction, mean delay per packet, mean delay per hop and mean jitter are evaluated using the NS-3.26 simulator.

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

Different wireless communication technologies, such as wireless fidelity (Wi-Fi), 4G cellular technologies and worldwide interoperability for microwave access (WiMAX) have been used as alternatives or supplement to wired communication in intelligent transportation systems (ITS). Widespread deployment of wireless technologies in ITS require a comprehensive understanding of their performance, limitations and advantages in different field conditions. The authors evaluated performance of Wi-Fi wireless communication between adjacent roadside ITS devices (i.e., nodes) in different field conditions with varying characteristics of Wi-Fi technology. Field tests revealed that modulation rates, transmission power, line of sight, distance between nodes play critical roles in the performance of Wi-Fi communication in different roadway conditions. To achieve a desired level of performance requirements between adjacent nodes, minimum network performance thresholds must be realized in the field. Transportation agencies can identify the achievable performance, such as saturated throughput, delivery ratio and received signal strength at a particular location, by following the field test procedure developed in this research. The evaluation strategies and results presented in this study will contribute to the future planning and design of Wi-Fi communication for a roadway wireless sensor or device network considering corresponding communication performance requirements for specific ITS applications.

Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction

Recent catastrophic bridge failures clearly indicate the urgent need for improving interval-based bridge inspection procedures that are qualitative and subjective in nature. Structural Health Monitoring (SHM) can mitigate the deficiencies of interval-based inspection techniques and provide real-time diagnostic information regarding the bridge structural health. SHM is not flawless however; the variability in the vehicle characteristics and traffic operational conditions makes it prone to false diagnosis. Recent advancements in the integration of SHM with intelligent transportation systems (ITS) demonstrate the successful use of ITS devices (e.g., traffic cameras, traffic detectors) in the analysis of bridge responses to multimodal traffic with varying loads or during the critical events that cause excess vibration beyond the normal limit. In an ITS-informed SHM system, the ITS device collected data can be integrated with SHM to increase the reliability and accuracy of the SHM system. This integration would reduce the possibility of false diagnosis of damages detected by the SHM system (e.g., vibrations caused by heavy vehicles on a bridge could be read by a SHM sensor as a structural health problem of the bridge), which would eventually decrease the bridge maintenance costs. Similarly, in SHM-informed ITS system, SHM sensors can provide data on bridge health condition for ITS applications, where ITS uses this bridge health condition information for real-time traffic management. In this paper, literature related to both ITS-informed SHM and SHM-informed ITS is reviewed. Based on the literature review, potential challenges and future research directions associated with ITS-SHM integration are also discussed.

Assessing driver acceptance of Intelligent Transport Systems in the context of railway level crossings

Intelligent Transport Systems (ITS) have the potential to substantially reduce the number of crashes caused by human errors at railway levels crossings. Such systems, however, will only exert an influence on driving behaviour if they are accepted by the driver. This study aimed at assessing driver acceptance of different ITS interventions designed to enhance driver behaviour at railway crossings. Fifty-eight participants, divided into three groups, took part in a driving simulator study in which three ITS devices were tested: an in-vehicle visual ITS, an in-vehicle audio ITS, and an on-road valet system. Driver acceptance of each ITS intervention was assessed in a questionnaire guided by the Technology Acceptance Model and the Theory of Planned Behaviour. Overall, results indicated that the strongest intentions to use the ITS devices belonged to participants exposed to the road-based valet system at passive crossings. The utility of both models in explaining drivers’ intention to use the systems is discussed, with results showing greater support for the Theory of Planned Behaviour. Directions for future studies, along with strategies that target attitudes and subjective norms to increase drivers’ behavioural intentions, are also discussed.

Traffic Safety at Road–Rail Level Crossings Using a Driving Simulator and Traffic Simulation

Several intelligent transportation systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions were tested: video in vehicle, audio in vehicle, and on-road flashing marker. The results from the driving simulator were inputs for a developed model that used traffic micro-simulation (VISSIM 5.4) to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of changes in driver speed and compliance rate was greater at passive crossings than at active crossings. The slight difference in speed of drivers approaching ITS devices indicated that ITS helped drivers encounter crossings in a safer way. Since the traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varied depending on ITS safety devices, some modifications were made to the traffic simulation. The results showed that exposure to ITS devices at active crossings did not influence drivers' behavior significantly according to the traffic performance indicator, such as delay time, number of stops, speed, and stopped delay. However, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

An application of ITS devices for powered two-wheelers safety analysis: the Rome case study

Researchers, practitioners and local administrators tend to pay poor attention to Powered Two-Wheelers (PTWs), thus affecting safety levels. The poor availability of Intelligent Transport Systems (ITS) to increase PTWs safety is evidence of the underestimation of this mode's role, although ITS applications in this field are largely beneficial to increasing road safety. A specific ITS tool to collect PTWs accidents data, tested on 23 sites in Rome, is described. The objective was to have the tested sites ranked according to a series of safety indicators, which describe the road characteristics in the most comprehensive way. Such safety indicators were selected after a review of the scientific literature in this field, focusing on those which resulted the most consistent with the Rome urban environment. The methodology began with a survey of useful ITS to collect data and feed the indicators, the selection of the most appropriate ITS tool for the Rome case study, and the process to apply the PTWs' safety indicators within the selected ITS tool's environment, before the test phase. After the test, results stressed that the use of the ITS tool applied could largely improve accuracy in the safety analysis, as it allowed the merging of GIS and GPS-based data in one single processing environment and improvement in the quality of indicators by integrating the accident dynamic parameters, features of road geometry, and vehicle behaviour during the accident. Language: en

A sustainable city environment through child safety and mobility—A challenge based on ITS?

Our cities should be designed to accommodate everybody, including children. We will not move toward a more sustainable society unless we accept that children are people with transportation needs, and ‘bussing’ them around, or providing parental limousine services at all times, will not lead to sustainability. Rather, we will need to make our cities walkable for children, at least those above a certain age. Safety has two main aspects, traffic safety and personal safety (risk of assault). Besides being safe, children will also need an urban environment with reasonable mobility, where they themselves can reach destinations with reasonable effort; else they will still need to be driven. This paper presents the results of two expert questionnaires focusing on the potential safety and mobility benefits to child pedestrians of targeted types of intelligent transportation systems (ITS). Five different types of functional requests for children were identified based on previous work. The first expert questionnaire was structured to collect expert opinions on which ITS solutions or devices would be, and why, the most relevant ones to satisfy the five different functional requests of child pedestrians. Based on the first questionnaire, fifteen problem areas were defined. In the second questionnaire, the experts ranked the fifteen areas, and prioritized related ITS services, according to their potential for developing ITS services beneficial to children. Several ITS systems for improving pedestrian quality are discussed. ITS services can be used when a pedestrian route takes them to a dangerous street, dangerous crossing point or through a dangerous neighborhood. An improvement of safety and other qualities would lead to increased mobility and a more sustainable way of living. Children would learn how to live to support their own health and a sustainable city environment. But it will be up to national, regional and local governments, through their ministries and agencies and public works departments, to promote, fund, and possibly mandate such systems. It is clear that we need to offer an acceptable level of convenience, efficiency, comfort, safety and security to pedestrians but it is less clear if society will prioritize resources toward this.

Analysis of Interference between UWB and ITS

In this paper, we have analyzed the effect of interference between ultra-wideband(UWB) and intelligent transport systems(ITS). The maximum possible UWB emission power and minimum possible distance between UWB devices and ITS are found. In order to analyze the interference, we employ the Monte-Carlo(MC) method. We consider six situations, which are indoor office line-of-sight(LOS), indoor office non-line-of-sight(NLOS), indoor residential LOS, indoor residential NLOS, outdoor rural LOS, and outdoor rural NLOS environments. From the simulation results, it is confirmed that coexistence between UWB and ITS devices can be realized in accordance with the emission mask of 19.3 dB for indoor application or 19.3 dB for an image system. And in the outdoors, coexistence between UWB and ITS devices can be realized if the emission mask is at least 1.6 dB for vehicles' radar systems.

Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information

This article evaluates dynamic driver behaviour models that can be used, in the context of intelligent transport systems (ITS), to predict drivers’ compliance with traffic information. The inputs to this type of models comprise drivers’ individual socio-economic characteristics and other variables that may influence their compliance behaviour. The output is a binary integer representing whether drivers comply with travel advice or not. Two approaches are available for formulating this category of classification problems: discrete choice models and artificial neural networks (ANNs). The literature on this topic clearly points to the limitations of the discrete choice approach which suffers from assumptions of perfect information about travel conditions, infinite information processing capabilities of drivers and inability to model the uncertainty in driver decision making or the vagueness in information received from ITS devices. ANNs, on the other hand, are able to deal with complex non-linear relationships, are fault tolerant in producing acceptable results under imperfect inputs and are suitable for modelling reactive behaviour which is often described using rules, linking a perceived situation with appropriate action. This study aims to evaluate the performance of these two categories of models based on a common data set of driver behaviour, collected from a field behavioural survey on a congested commuting corridor in Brisbane, Australia. This article proposes the combination of fuzzy logic and neural networks as a viable approach for overcoming the limitations of existing algorithms by modelling drivers as heterogeneous individuals. The results showed superior performance for a neuro-fuzzy model over binary choice models in terms of classifying or predicting the categories of drivers most likely to comply (or not comply) with traffic advice. The accuracy of the proposed model, in terms of classification rate, ranged between 95 and 97% compared to 50–73% for the discrete choice models.

Airport ramp safety and intelligent transport systems

The possibilities for intelligent transport systems (ITS) to address safety problems on airport ramps are examined. In addition, a structured approach to aid the safe design and implementation of such systems to aid injury prevention in different industry and transport domains are presented. The work begins by reviewing worldwide ramp accidents and incidents. Thereafter, it presents ramp safety data collected at an Australian international airport. Building on this identification of problems, the research shows that a large number of ITS devices are theoretically possible to address such safety issues, and then proposes criteria upon which decisions to implement such technology could be based. The paper closes by giving general recommendations for which of the identified ITS devices should be further deployed on airport ramps and makes overall conclusions regarding the further development of a structured approach to establish the injury prevention benefits of ITS in different domains.

Extending Older Drivers’ Access to Freeways with Intelligent Transportation System Technologies

The vanguard of the baby boom generation is approaching retirement age. They received their driver's licenses at the age of 16 at a time when freeways were reshaping urban land patterns. For those living in suburbs and the urban edge, freeway driving is a way of life. Nevertheless, as these drivers age, they too will experience changes in visual acuity and a reduction in their ability to respond quickly to changes in road conditions. Many studies document problems that older drivers face on city streets. The current study funded by the Arizona Department of Transportation and FHWA suggests that freeway driving might be less taxing for some. The study explores the possibility that intelligent transportation system (ITS) devices–-including variable message signs; portable, changeable message signs; and ramp meters–-may respond to older drivers’ challenges with freeway driving. Eleven focus groups held with diverse groups of freeway drivers from 65 to 80 years of age in 2003–2004 focused on these ITS devices and suggested minor changes in application that would enhance their value. Drivers said that fixed variable message signs with a positive contrast caught their attention and were easier to read than standard signs. They urged their use for short simple messages centered on the signs. Portable signs attracted attention, but they were subject to glare and were difficult to read at the roadside, particularly those with multiple phases. Ramp meters were enthusiastically endorsed especially when combined with acceleration lanes. A parallel study using Arizona accident data provided some statistical evidence to support positive views of ramp meters.

Extending Older Drivers' Access to Freeways with Intelligent Transportation System Technologies

The vanguard of the baby boom generation is approaching retirement age. They received their driver's licenses at the age of 16 at a time when freeways were reshaping urban land patterns. For those living in suburbs and the urban edge, freeway driving is a way of life. Nevertheless, as these drivers age, they too will experience changes in visual acuity and a reduction in their ability to respond quickly to changes in road conditions. Many studies document problems that older drivers face on city streets. The current study funded by the Arizona Department of Transportation and FHWA suggests that freeway driving might be less taxing for some. The study explores the possibility that intelligent transportation system (ITS) devices—including variable message signs; portable, changeable message signs; and ramp meters—may respond to older drivers' challenges with freeway driving. Eleven focus groups held with diverse groups of freeway drivers from 65 to 80 years of age in 2003-2004 focused on these ITS devices a...

Behavioral Adaptation, Safety, and Intelligent Transportation Systems

It is intrinsic to human nature to modify behavior to suit new conditions. How drivers are likely to change the way they drive if their vehicles are equipped with intelligent transportation system (ITS) devices is considered. It is clear from the antilock braking system experience that improvements in safety cannot be predicted on the basis of proof-of-concept studies alone, in which one simply looks at changes in performance of the task being aided, whether that is braking, navigation, or detection of hazards. One also must look at changes in other aspects of the driving task and at the type of driving being done to determine the likely effect on safety. In particular, one should assume that there may be trade-offs of mobility for safety, that is, more driving in more difficult conditions and at higher speeds leading to more crashes. Further, one should expect drivers to attempt to increase productivity while driving, given reduced driving task demand. The prolific use of cellular phones is evidence of this behavior. Research is needed on driver mental models of ITS devices, to ensure that drivers understand how they function. The best design from a mechanistic point of view may not be the most effective for drivers.