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

Typical intelligent transportation systems (ITS) are comprised of geographically distributed ITS devices including sensors, cameras and dynamic message signs (DMS). There are several options for providing data communication between these field devices and traffic management centers (TMC). Wireless networks are attractive due to their relatively lower cost and ease of deployment compared to fixed networks. However, these face unique security and signal interference problems, and deploying new wireless networks can require significant equipment investment. In this work, a new extremely low-cost wireless strategy for ITS network communication is presented. This approach applies control channels of the Network Trunking System (NTS), a licensed radio frequency to support the voice communication, to control DMS operation under the National Transportation Communications for ITS Protocol (NTCIP). Because the NTS network has already installed in the metropolitan, urban, and rural areas in Oklahoma, it provides a cheap deployment solution requiring only additional adapter devices. Long term operation and maintenance costs can further be amortized between voice and data services. The proposed technique is compared to the existing ITS wireless networking strategies and hybrid network strategies merging wired and wireless networks deployed in Oklahoma. Details of the strategy for using wireless networking in the Oklahoma ITS and experiences with wireless ITS device deployment are also provided.

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.

This paper examines the relationship between public and private investments in intelligent transportation system (ITS) technology and the effects on transportation performance through the highway system. ITS technology is an infrastructure that promotes integrated information sharing that can be used to improve system performance. ITS combines better infrastructure with information and control technologies to ( a) reduce traffic congestion by maintaining traffic flow, ( b) reduce transportation-generated pollution, ( c) improve transport efficiency, and ( d) produce economic benefits. A statistical analysis of partial least squares structural equation modeling was used to test three hypothesized relationships. Primary data were collected from U.S.-based commercial vehicle transportation service providers. Respondents were parceled with a geographic information system analysis of secondary data from the U.S. Department of Transportation National ITS Deployment Tracking Survey. On the basis of their level of regional ITS experience, primary data respondents were matched to regions and split between regions with low ITS experience and high ITS experience as a proxy for regional public investments in ITS. Performance measures were based on efficiency, reliability, responsiveness, quality, carbon emissions reduction, and equipment utilization for commercial vehicle operators. Results indicate that private investments in ITS significantly positively affect the responsiveness performance measure for commercial vehicle operators in regions with high public ITS investments. The same relationship is not significant in regions with low public ITS investments, which indicates at least some transportation performance improvements for regions with higher investments in ITS. Implications for research and practice are discussed.

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.

Aim. Threats of a non-physical nature have a significant effect on the security of intelligent transportation systems (ITS). They may have the form of unlawful interference in the development and implementation of ITS. The creation of a trusted framework for the development and implementation of ITS is examined as in the case of intelligent water transportation systems (IWTS). Problems. By decree of the President of the Russian Federation, ITS, telecommunications and security of information processing are among the priority areas of scientific and technological development. ITS technologies, as well as those involved in the creation of trusted, secure system and application software are among the most important critical high technologies. The operation of ITS involves wide use of computerised systems that implement the latest information and telecommunication technologies, automated and automatic control technologies, artificial intelligence that can pose security threats. ITS are to be developed and operated in a trusted environment. Methods. The paper used the methodology for ensuring the security of IWTS, development of secure hardware and software platforms for secure automated systems, methods of system analysis, dependability theory, information protection, and law. Results. The paper defines the problem of creating a trusted framework for the development and implementation of IWTS, the applicable terminology is developed. The author examined the effect of IWTS on the security of critical information infrastructure (CII) and national security, developed a model of relationships between the IWTS security domains taking into account threats of physical and non-physical origin. Examples of computer incidents within IWTS that caused consequences at the national and international levels are given. The composition of the IWTS facilities attributed to CII is defined, critical processes implemented by standard CII facilities as part of the IWTS are set forth. The author lists conceptual problems of IWTS security, defines the principles of creating a trusted framework for the development and implementation of IWTS. Conclusion. Ensuring the security of IWTS against modern threats requires solving a number of problems associated with the creation of a trusted framework for the development and implementation of IWTS. For the purpose of improving the timeliness and quality of their solution, the paper proposes an intuitive terminology that reflects the subject area and helps finding a common understanding of the security domain by experts from various industries. IWTS facilities have an effect on CII security and national security in general. That is taken into account in the model of relationships between IWTS security domains, demonstrated using cases of computer incidents within IWTS that caused consequences at the national and international level. Given the above, the paper lists IWTS facilities attributed to CII and sets forth examples of critical processes implemented by standard CII facilities as part of the IWTS. The defined list of conceptual IWTS security problems take into account the growing landscape of IWTS security threats that includes insecure software, hardware and software platforms, software and hardware systems and emerging technologies. When developing the principles for the creation of a trusted IWTS development and implementation framework, the author took into account the best practice of implementing the methodology for creating national secure hardware and software platforms of CII facilities that enables the creation of secure automated systems for various applications that are based on domestically-developed solutions. The examined matters are of a systemic nature, which allows using the findings in the development and implementation of ITS in other modes of transportation.

Multi-agent approaches have a key role to play in intelligent transportation systems as such systems must service dynamically changing environments in an autonomous fashion. Such Intelligent Transportation Systems (ITS) are exemplified by Google's driverless car. The driverless car can be considered a multi-agent system because it involves multiple interacting intelligent agents analysing and detecting stimuli in its environment and mobilising suitable and safe responses to those stimuli. This position paper discusses the security threats that may result from introducing multiagent technology in such intelligent transportation systems. We argue that, while multi-agent approaches have a major role to play in this domain, their introduction in vehicle-to-vehicle communication or autonomous driving systems raises new security challenges that must be addressed by both cyber-security and multi-agent research.

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.

To explore the relationship between injury risk among child occupants involved in motor vehicle collisions according to the age of the vehicle driver. The National Automotive Sampling System Crashworthiness Data System 2000-2009 (n = 10 797) was used to identify demographic, vehicle-, collision- and injury-related characteristics among motor vehicle collision occupants ≤15 years of age. The association between the age of the driver (older vs. younger, defined as individuals <50 years of age vs. individuals ≥50 years of age, respectively) and injury occurrence was estimated using logistic regression adjusting for the potentially confounding effect of occupant, vehicle and collision characteristics. Of the child occupants in motor vehicle collisions, 2.9% were driving with an older driver, and approximately 2.9% were injured while driving with a younger driver (odds ratio 1.03; 95% confidence interval 0.55-1.91). After adjusting for child occupant age, gender, restraint use, seat position and vehicle type, there remained no significant association between the age of the driver (older vs. younger) and the risk of injury (odds ratio 0.92; 95% confidence interval 0.49-1.74). These findings add to the body of literature indicating no difference in injury risk found among children when considering the age of the driver. Research is needed to ascertain the association and further evaluate characteristics more specific to the relationship being explored in this study.

Download a PDF of "Guidebook for Implementing Intelligent Transportation Systems Elements to Improve Airport Traveler Access Information" by the National Academies of Sciences, Engineering, and Medicine for free.

In 1995, the planning and building processes began to design and develop a mobile demonstration unit that could travel across the nation and be used as an effective outreach tool. In 1997, the unit was completed; and from June 1997 until December 2000, the Federal Highway Administration (FHWA)/Federal Motor Carrier Safety Administration (FMCSA) mobilized the Technology Truck, also known as Demonstration Project No. 111, ''Advanced Motor Carrier Operations and Safety Technologies.'' The project featured the latest available state-of-the-practice intelligent transportation systems (ITS) technologies designed to improve both the efficiency and safety of commercial vehicle operations (CVO). The Technology Truck was designed to inform and educate the motor carrier community and other stakeholders regarding ITS technologies, thus gaining support and buy-in for participation in the ITS program. The primary objective of the project was to demonstrate new and emerging ITS/CVO technologies and programs, showing their impact on motor carrier safety and productivity. In order to meet the objectives of the Technology Truck project, the FHWA/FMCSA formed public/private partnerships with industry and with Oak Ridge National Laboratory to demonstrate and display available ITS/CVO technologies in a cooperative effort. The mobile demonstration unit was showcased at national and regional conferences, symposiums, universities, truck shows and other venues, in an effort to reach as many potential users and decision makers as possible. By the end of the touring phase, the ITS/CVO Technology Truck had been demonstrated in 38 states, 4 Canadian provinces, 88 cities, and 114 events; been toured by 18,099 people; and traveled 115,233 miles. The market penetration for the Technology Truck exceeded 4,000,000, and the website received more than 25,000 hits. In addition to the Truck's visits, the portable ITS/CVO kiosk was demonstrated at 31 events in 23 cites in 15 states.

Comprised of a broad range of diverse technologies, including information processing, communications, control, and electronics, Intelligent Transportation Systems (ITS) were first introduced as a means to solve the conflict between constantly increasing travel demand and insufficient transportation infrastructure. However, the impact of ITS on road safety was soon realized, and it became one of the major purposes of some ITS systems instead of only a side product. This paper summarizes the impacts of ITS technologies on road safety and the challenges of ITS safety evaluation. Based on the evaluation of collected ITS benefits information, this paper concludes that a variety of ITS technologies have the potential to make a substantial contribution to road safety and thereby have a substantial positive economic impact in the United States.

Transportation infrastructures integrate advanced Information Technology to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept that improve the efficiency and safety of road traffic are being built and will be commercially available through Intelligent Transport Systems (ITS) technology and services. To support ITS technology, Vehicle-To-Everything (V2X) is needed, and in the 3rd Generation Partnership Project, many studies have focused on Long Term Evolution (LTE)-based vehicle communications. In order to realize reliable and optimized communications performance in vehicle communications, which move in propagation environments at high speed, in this study, we propose a novel channel estimation scheme suited for LTE sidelink-based Vehicle-To-Vehicle systems. Conventional channel estimation schemes can be categorized as Decision-Directed Channel Estimation, spectral temporal averaging, and smoothing methods. In this study, unlike conventional channel estimation schemes, we propose a Novel Interference Cancellation of Channel Estimation (NICCE) using Quadratic Smoothing of the pilot symbols, which estimates a channel with greater accuracy, and a novel interference cancellation of channel estimation in data symbols. In simulation results, the proposed NICCE scheme shows improved overall performance in terms of the Normalized Mean Square Error and uncoded Bit Error Rate relative to conventional schemes.

To be successful, shared vehicle systems must be responsive, convenient, and easy to manage. By applying a variety of intelligent transportation system (ITS) technologies (e.g., vehicle location and identification, dispatching, smart cards), these attributes can be achieved. Further, intelligent transportation technology is useful for collecting data on user behavior and vehicle usage. These data add to the understanding of shared vehicle systems and of how to design systems for the future. The University of California-Riverside (UC Riverside) and Honda Motor Company have implemented a shared electric vehicle system test bed named UCR IntelliShare, which consists of 15 shared electric vehicles, moving among three stations on and near the UC Riverside campus. The system, described here, uses highly developed ITS technology, including smart cards, touch-screen registration kiosks, vehicle monitoring and tracking hardware, and sophisticated management software. The system has operated since April 1999, and abundant data are collected every day. Preliminary results are provided, describing user behavior, vehicle operation, and many shared vehicle trip characteristics. These data are being used to study shared vehicle systems and to refine shared vehicle system simulation modeling tools. These tools can assist in designing other shared vehicle systems, greatly reducing the implementation risks and liabilities that may be associated with future, full-scale shared vehicle system implementations.

Combining Ramp Metering and Hard Shoulder Strategies: Field Evaluation Results on the Ile the France Motorway Network

To the Editor: Accident rates increase steadily per mile driven beyond age 60.1 In addition, the older body is generally weaker and less likely to survive a traumatic accident, possibly contributing to a greater crash fatality rate among older persons,2 making motor vehicle crashes the leading cause of death from unintentional injury for those aged 65 to 74.3 This increased accident rate is largely accidents incurred when making left hand turns.4 If the reason for these left turn accidents could be identified, then a preventative effort could possibly be designed. In a series of published5, 6 and unpublished (Quillian et al., The effects of age and alcohol intoxication on simulated driving performance, awareness, and self-restraint) studies employing a driving simulator, we have reported that older drivers do not spend more time than younger drivers at the stop line of a left turn. This suggests that age is not associated with longer decision-making concerning when to turn. However, older people do spend significantly longer executing the left hand turn. This is consistent with the general tendency of older drivers to drive slower as a compensatory strategy.7 In making a left hand turn, the driver has to drive through potential traffic coming from ahead, right, and left. However, the longer the driver spends crossing these three lines of traffic, the greater the driver's exposure to this high-risk situation. To verify realistically the simulation results that older drivers take longer executing a left turn, we had two observers sit at an intersection, estimate the age of the driver as s/he pulled up to the stop line, and then time the driver from the point of leaving the stop line to passing a white pole located 60 feet into the left turn beyond the intersection. The intersection was a through way for North-South traffic and a stop sign for East-West traffic. We timed drivers coming from the West and turning North, which meant drivers coming to the stop sign and turning left across oncoming traffic. Seventy drivers were timed over two 1-hour samples, once on a Saturday and once, a week later, on a Sunday. Because we wanted natural behaviors, we could not ask a driver's age before the turn and could not safely stop him/her after the turn. Consequently, the two raters estimated the driver's age, and when there was a disagreement, a mutual consensus was reached. Subjects were classified into one of three distinct groups: Young, Mature, and Older drivers. Young drivers were estimated to be less than 25 years of age, Mature drivers were those estimated to be between ages 35 and 55, and Older drivers were those estimated to be more than 65 years of age. To ensure nonoverlapping age groups, drivers estimated to be ages 26 to 34 and 56 to 64 were excluded from data analysis. Subjects were also excluded if they drove a standard transmission, detected by hearing gears shift in the turn. There were 11, 24, and 11 Young, Mature, and Older drivers who fit the inclusion criteria. Raters' times correlated significantly (r =.96, P <.001). Because times were collected on two different days, when the position of the timers and the signal post, as well as the time of day, could have influenced turn times, scores for the two days were converted to z scores and then combined for comparisons. Therefore, a z score of 0 represents the mean turn time, and a z score of +1 refers to a turn time that was 1 standard deviation above the mean. As seen in Figure 1, turning time was significantly different (F = 10.357, P <.0002) among the age groups. The mean z scores for Young, Mature, and Older drivers were -.66, -.07, and +.95, respectively. This real observation confirms the simulation data indicating that older drivers have longer exposure to the high-risk situation of crossing three lines of traffic when making a left hand turn compared with younger age groups. If this increased exposure contributes to the increased accident rate among older drivers making left turns, it may be important to encourage older drivers to pass through left turns more quickly once the decision has been made to initiate the turn.