Performance Metrics for Freeway Sensor

Some transportation planning and traffic operations–intelligent transportation system (ITS) groups are cooperating on data-sharing initiatives, including the use of archived ITS data for planning statistics. However, conventional procedures for the calculation of annual average traffic statistics need to be updated to accommodate the incomplete nature of archived ITS data. The effects of various missing data patterns on several existing and modified annual average statistic calculation procedures were tested. Five locations were chosen for the study: two urban Interstate highways with significant commuter traffic, one urban parkway with commuter traffic and recreational trips, and two rural roads with pronounced seasonal patterns. A complete year of data was obtained for each study location; then missing data patterns (as identified from an empirical study) were simulated by randomly or systematically removing data. At urban locations in which commuter traffic dampens seasonality patterns, a significant amount of missing data (up to 8 months of consecutive missing data) can be tolerated with little to no effect on annual average traffic statistics. All calculation procedures provided similar results at the urban locations. For the two rural locations, 1 month of missing data still resulted in tolerable error for most procedures, whereas 2 months of missing data were close to or exceeded tolerable error levels for most procedures. Modifications of conventional calculation procedures (that account for small gaps in data on a daily basis) are better suited to archived ITS data.

With the recent advent of Intelligent Transportation Systems (ITS), and their associated data collection and archiving capabilities, there is now a rich data source for transportation professionals to develop capacity values for their own jurisdictions. Unfortunately, there is no consensus on the best approach for estimating capacity from ITS data. The motivation of this paper is to compare and contrast four of the most popular capacity estimation techniques in terms of (1) data requirements, (2) modeling effort required, (3) estimated parameter values, (4) theoretical background, and (5) statistical differences across time and over geographically dispersed locations. Specifically, the first method is the maximum observed value, the second is a standard fundamental diagram curve fitting approach using the popular Van Aerde model, the third method uses the breakdown identification approach, and the fourth method is the survival probability based on product limit method. These four approaches were tested on two test beds: one is located in San Diego, California, U.S., and has data from 112 work days; the other is located in Shanghai, China, and consists of 81 work days. It was found that, irrespective of the estimation methodology and the definition of capacity, the estimated capacity can vary considerably over time. The second finding was that, as expected, the different approaches yielded different capacity results. These estimated capacities varied by as much as 26 % at the San Diego test site and by 34 % at the Shanghai test site. It was also found that each of the methodologies has advantages and disadvantages, and the best method will be the function of the available data, the application, and the goals of the modeler. Consequently, it is critical for users of automatic capacity estimation techniques, which utilize ITS data, to understand the underlying assumptions of each of the different approaches.

A novel block-chain based secure cross-domain interaction approach for intelligent transportation systems

Intelligent transportation systems (ITS) typically rely on massive sources of data to monitor the transportation system, estimate its current state, forecast its future state, and react with a set of control and management strategies in real time. This requires the prompt processing of a variety of data sources that is consistently changing with time and typically challenging to combine (and make sense of) in real time. This paper presents thoroughly the studies related to big data applications in ITS and its associated challenges.

Design and implementation of a vehicle interface protocol using an IEEE 1394 network

Vehicle-to-anything (V2X) communication is considered worldwide to be an appropriate technology to enable a large number of the intelligent transportation system (ITS) applications for traffic safety and efficiency. However, since there is no completed underlying V2X communication standard in China, it is difficult for Chinese automotive enterprises and research institutions to develop ITS applications in large scale. Therefore, the Society of Automotive Engineers of China (C-SAE) together with China ITS Industry Alliance (C-ITS) decided to develop Application Layer Specification and Data Exchange Standard first to motivate the development of the Chinese cooperative intelligent transport system industry. This paper introduces the details of the Chinese Cooperative Intelligent Transportation System Vehicular Communication Application Layer Specification and Data Exchange Standard. Included in the discussion are the description of the application scenarios and their technique requirements, the summary of the message set in the data exchange standard, and the design of the application layer data interactive interfaces. It is noteworthy that the message set in this standard is complete for the included application scenarios and the designed application layer interfaces are compatible with the underlying V2X communication protocols like DSRC and LTE-V. Finally, several directions worth further consideration are suggested for future standardization.

Spatio-temporal Anomaly Detection in Intelligent Transportation Systems

Study on the Method of Road Transport Management Information Data Mining based on Pruning Eclat Algorithm and MapReduce

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.

This article provides a comprehensive overview of the novel idea of real-time traffic safety improvement on freeways. Crash prone conditions on the freeway mainline and ramps were identified using loop detector data, then intelligent transportation systems (ITS) strategies to reduce the crash risk in real-time are proposed. Separate logistic regression models for assessing the risk of crashes occurring under two speed regimes were estimated. The results show that the variables in the two models are consistent with probable mechanisms of crashes under the respective regimes (high-to-moderate and low speed). This study also discusses the analysis of parameters and conditions that affect crash occurrence on freeway ramps by type (on-/off-ramp) and configurations (diamond, loop, etc.) using five-minute traffic flow data obtained from the loop detectors upstream and downstream of ramps to reflect actual traffic conditions prior to the time of crashes. Finally, several traffic management strategies are evaluated for the resulting traffic safety improvement in real-time using PARAMICS microscopic traffic simulation and the measures of crash potential determined through the logistic regression models. The results show that, while variable speed limit strategies reduced the crash potential under moderate-to-high speed conditions, ramp metering strategies were effective in reducing the crash potential during the low-speed conditions.

Through research and lessons learned from early intelligent transportation systems (ITS) deployers, it has been concluded that the procurement of goods and services to support ITS deployments represents a major obstacle for transportation agencies responsible for deploying ITS. This obstacle can be attributed to the challenges associated with the procurement of goods and services to support the deployment of complex information technology (IT) systems. In response to this challenge, innovative procurement methods have been established. In an effort to aid state and local transportation agencies with the identification of appropriate innovative procurement methods, the National Cooperative Highway Research Program (NCHRP) funded Project 3-77 with the objective of developing a guide to ITS procurement. A research team was established to develop the guide and an NCHRP research panel was established to oversee the development of the guide. As a result of this research it was determined that project, agency and environment characteristics and systems engineering and project management principles are key inputs into the process used to identify an appropriate procurement approach for a specified ITS project. A decision model was developed to represent this process.

The Paso Del Norte region includes El Paso County (Texas), Dona Ana County (New Mexico), and Ciudad Juarez (Mexico) along the southwest border region between the United States and Mexico. With a combined population of more than 2 million, the city of El Paso (Texas) and Ciudad Juarez have an intertwined transportation system with one of the nation's busiest international borders. In response to growing transportation needs in this binational region, the Texas Transportation Institute established a new transportation research center in the city of El Paso. One of the many research initiatives identified at the early stages of the center's inception was to develop a mobility information system for the Paso Del Norte region. The goals of this regional mobility information system were to demonstrate the significance and benefits of archiving intelligent transportation system (ITS) data, build a centralized repository of transportation data, provide the ability to monitor regional and cross-border mobility, and provide a platform to adopt newer ITS technologies. The objectives of the regional mobility information system are to provide archived transportation data user services and an advanced traveler information system in the region. The regional mobility information system is the first of its kind in the region and has been well received by stakeholder agencies. The system has an extremely flexible and scalable database framework and uses the concept of data warehouse and database-centric architecture. As an advanced traveler information system, the system provides access to users’ information related to everyday travel through a single-window website.

In transportation environment, Intelligent Transportation System (ITS) is being used in various stages of every transport mode (road, rail, air & sea). The aim of intelligent transportation system is to provide the On-Road Vehicles (ORVs) with reliable Internet access connectivity, emergency notification and other safety information via infrastructure-based Road Side Units (RSUs). In the past decade, intelligent transportation system has been started in many countries where RSUs are provided with all type of communication services to connect, compute, communicate and share the information between its neighboring RSUs and on-road vehicles. However, there are certain diffcuilties in which the information cannot be delivered reliably such as Line Of Sight (LOS) communication with on-road vehicles, packet loss, latency, retransmission overhead and multiple target tracking of objects over time-to-time etc. Thus, the intelligent transportation system becomes a complex network of entities. Modern intelligent transportation systems use relay-RSU nodes (i.e., small sized Unmanned Aerial Vehicles) also referred as RRSU node network deployed between satellites and on-road vehicles as they provide many advanages like coverage, reliability and information sharing without depending more on knowledge provided by satellites. In this paper, a novel Aerial Intelligent Relay-Road Side Unit (AIR-RSU) framework has been proposed to determine the network connectivity status level and analyze the communication link stability among RRSUs, and between RRSUs and on-road vehicles for every time instants Ti based on certain performance metrics discussed in this paper. The proposed AIR-RSU framework holds the precise status/information about the network connectivity needed in the transportation environment carefully and effectively accounting the frequent changes in positions. The performance of the proposed AIR-RSU framework is evaluated using rectangle waypoint mobility model for on-road vehicles and steady state random waypoint mobility model for RRSU nodes, which measures the network connectivity status and the performance metrics periodically. Furthermore, a comparative analysis is done to both individual nodes and entire network in terms of network load.

The emergence of Intelligent Transport Systems (ITS) and their further development for road transport in years to come are considered. Artificial Intelligence techniques have potential applications for the vehicle, the infrastructure, the driver or transport user, and in particular the way in which these interact dynamically to deliver a transport service. The related concept of Ambient Intelligence (AmI) embraces greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions. The overall function of ITS is to improve decision making, often in real time, by transport network controllers and other users, thereby improving the operation of the entire transport system. A future vision for ITS is described with reference to efficient road traffic management, services for “smart” travellers making inter-modal journeys, “smart” cars, co-operative systems and automated highways. The paper concludes that intelligent infrastructures are required that are dependable, manageable, adaptable and affordable.

Deployment of intelligent transportation systems (ITS) is one of the 12 key areas of research and development in China's 10th five-year plan, lasting from 2000 to 2005. China's highway system has grown significantly over the last decade. Traffic congestion and air pollution have been the two major problems facing Beijing for decades and are the two central concerns for the successful execution of the 2008 Olympics. Recently, Beijing's government has made tremendous efforts toward solving those problems, and ITS technology will likely play an important role in the solutions. This installment deals with ITS issues in China. Clearly, ITS will play a role in the future, and all major cities are working on integrating systems into their current scenarios. Along with this, we're witnessing the boost of China's economy and, particularly, its great effort constructing its new transportation network. This installment presents the architecture and the technology that will be the starting point for the construction of the ITS, that will be used in Beijing 2008 Olympics.