Ramkumar Venkatanarayana

Realizing the Promise of Intelligent Transportation Systems (ITS) Data Archives

Intelligent transportation systems (ITS) are playing increasingly important roles in addressing traffic congestion, safety, and environmental concerns. Archiving and “reusing” the vast amounts of data initially collected by ITS for real-time operations holds the potential to significantly improve a wide range of transportation analyses. Effectively archiving and deriving information from ITS data requires the application of technology and algorithms recently developed and proven in research. This paper describes and summarizes this research, particularly in areas such as data warehousing, complex systems development, traffic data aggregation, traffic data imputation, and traffic data characterization. The paper concludes with a list of key future research needs required to allow expanded use of ITS data archives.

Quantum-Frequency Algorithm for Automated Identification of Traffic Patterns

Knowledge of the normal traffic flow pattern is required for a number of transportation applications. Traditionally, the simple historic average has been considered as the best way to derive the traffic pattern. However, this method may often be significantly biased by the presence of incidents. One solution to avoid this bias is through visual inspection of the data by experts. The experts could identify anomalies caused by incidents and thereby identify the underlying normal traffic patterns. Three main challenges of this approach are (a) the bias introduced because of subjectivity, (b) the additional time required to analyze the data manually, and (c) the increasing sizes of the available traffic data sets. To address these challenges and also to exploit the potential of information technology, new data analysis tools are essential. In this research, a new tool, the quantum-frequency algorithm, was developed. This algorithm can aid in the automated identification of traffic flow patterns from large data sets. The paper presents the algorithm along with its theoretical basis. Finally, in the case study presented in the paper, the algorithm was able to identify a reasonable traffic pattern automatically from a large set of archived data. When compared with the historic average, it was found that the pattern identified by the quantum-frequency algorithm resulted in 39% lower cumulative deviation from the pattern identified manually by experts.

New Methodology for Customizing Quality Assessment Techniques for Traffic Data Archives

Traffic sensors are being deployed widely across the nation, and their data are increasingly being archived for use in multiple applications. However, before the data can be used, it is important to ask whether they can be trusted. That question may be addressed through two basic and important value-added services of a traffic data archive. First, data screening checks the feasibility (i.e., are the data reasonable?) and usability of the data already collected. In the second phase, the health of the detection system is continuously monitored to support proactive maintenance of the sensor infrastructure. This research effort focused on developing a methodology to tailor these functions for specific archives. The paper presents the methodology as well as examples of its application in the regional integrated transportation information system of the Washington, D.C., area.

Exploration of Operational Concepts of Interoperability for Robust Interregional Transportation System Operations

Regional transportation operations centers (TOCs) are at the core of managing and operating a complex surface transportation system. To ensure reliable transportation services, particularly during emergency situations when transportation is vital, uninterrupted operations of TOCs should be guaranteed. Achieving TOC interoperability is of great importance. Interoperability of TOCs can be understood as the ability of one TOC to access and manage some set of the resources of another (remote) TOC. TOC interoperability can allow each metropolitan area or in some cases each state to achieve more robust and seamless interregional operations. In addition, interoperability may provide each region with a better understanding of each partners practices and thus promote more consistent and coordinated transportation systems operations. Although these benefits are compelling, TOC interoperability cannot be easily established because of the complex technical and institutional situations of existing centers. Given this background, this study explored various operational concepts and developed a methodology to achieve interregional TOC interoperability. The methodology consists of four steps: (a) develop an emergency backup plan for each TOC, (b) establish relationships among TOCs, (c) develop a communications network, and (d) establish institutional interoperability between designated TOCs. In a case study, the proposed methodology was applied to the five regional TOCs in the Commonwealth of Virginia.

Usage analysis of first-generation intelligent transportation systems data archive : Lessons learned in development of novel information technology application

ADMS Virginia is an archived data management system (ADMS) established to provide access to archived intelligent transportation systems data for use in numerous offline applications. The development team of ADMS Virginia faced the challenge—with limited experience—of developing an information technology-based system, that effectively anticipates uses of the system and users of the system. The team attempted to address this challenge, faced by many information technology professionals in the field of transportation, by using a spiral software development methodology and incorporating significant stakeholder input. This paper presents a usage analysis of ADMS Virginia. This analysis highlights key lessons learned that are of use to those developing ADMSs specifically and new transportation information technology systems in general. Key lessons learned include the following: (a) do not overspecialize early in a systems life, (b) anticipate a wide range of users, and (c) spiral development creates the risk o...

Effectiveness of Using Diagonal Yellow Arrows on Lane-Use Control Signals

Lane-use control signals (LUCSs) are important elements of a traffic management strategy for mitigating congestion and enhancing safety, especially during peak travel times. The Manual on Uniform Traffic Control Devices (MUTCD) allows the use of a downward green arrow indication as well as a yellow X-indication and a red X-indication. In 2014, the Virginia Department of Transportation requested and obtained FHWA approval for an experiment to evaluate the diagonal downward yellow arrow indication. Previous studies had shown that this indication had several benefits relative to the yellow X-indication. A stated preference survey was conducted to evaluate driver understanding of LUCSs on I-66 and on I-95 express lanes. Survey results indicated that more than six times as many participants preferred the diagonal downward yellow arrow indication to the yellow X-indication, regardless of driver age or travel frequency on the test corridors. Traffic operations center staff anecdotally observed several positive changes in driver behavior while the diagonal downward yellow arrow indication was in use. Preliminary operational and safety results were reviewed after deployment of the diagonal downward yellow arrow indication. Results indicated that driver understanding of the diagonal downward yellow arrow indication was superior to that of the MUTCD-approved yellow X-indication, and no negative effects on safety or operations were observed. The diagonal downward yellow arrow indication appears to be a useful new LUCS indication and could improve traffic management.

Analysis of Standard Operating Procedures for Enhanced Regional Transportation Systems Operations

Traditionally, transportation system operations have been constrained by jurisdictional and administrative boundaries. Local governments and state agencies have operated facilities for which they are responsible with little coordination. As surface transportation mobility challenges continue to increase, it is evident that although different localities and agencies may still operate their own systems, there is a need for coordinated regional operations. An important component of enabling such coordination is the achievement of consistency in the standard operating procedures (SOPs) used by the individual transportation operations centers (TOCs). Historically, each TOC has developed its own SOPs, suited to its own unique needs and the resources available. Although these individual SOPs have served their TOCs well, such customized operations procedures of a TOC are often not compatible with its neighboring TOCs. As such, establishing regional coordination, especially to manage large-scale incidents or emergencies, is difficult. In this research effort a methodology was developed to analyze TOC SOPs to find opportunities to increase regional consistency. This methodology was applied in a case study to analyze the SOPs of the five TOCs and the statewide emergency operations center in the state of Virginia. The results of this study indicate that several differences indeed hamper coordination among the centers. At the same time a number of procedures with the potential for increased consistency are also identified. Information contained in this paper will benefit systems integrators, planners, researchers, and other transportation professionals involved in regional integration efforts and in policy and procedures review.

Research to Production: Lessons Learned from an Innovative Information System Development Approach

The development and delivery of large-scale, complex information systems is difficult, resulting in a low success rate industry-wide. Most production information systems are developed with the use of a top-down approach often called the waterfall model. In developing a complex information system, many variables must be considered and many potential solutions evaluated simultaneously; a linear approach is not conducive to this activity. Also, projects of this scale and complexity can be disruptive and unmanageable in a production environment. The research and development (R&D) environment is better suited to address the challenges presented by these kinds of projects. Development and transfer should not be considered separately, because the former will have a significant effect on the latter. Development in an R&D environment can provide important advantages for ensuring a successful transfer. A real-world case study of transferring an archived data management system from research to production is used to highlight the issues involved and the solutions required to effect the transition. It considers timing and method, emphasizing and detailing important areas of focus for the project partners. Lessons learned from examining this transfer process can benefit researchers, information technology and transportation professionals, and others who are or will be involved in developing information systems.

Scenario-Driven Computer-Based Regional Incident Management Training

Incident management is one of the most important functions of a transportation management center (TMC). To efficiently and effectively manage complex regional incidents that affect transportation facilities under the jurisdiction of multiple agencies, TMC operators must be trained appropriately. Recently, a team of universities including the University of Maryland, University of Virginia, and Rensselaer Polytechnic Institute developed a computer-based training tool for regional incident management to be used by the I-95 Corridor Coalition. In its role on the team, the University of Virginia conducted research to support the development of detailed regional incident scenarios to provide content for the computer-based simulation tool. The method created to develop the incident scenarios is presented, with clear examples. Major findings from the research effort include the benefits of rapid prototyping and the usefulness of extensible markup language (XML) for structured content development.

Automated Identification of Traffic Patterns from Large Data Archives

The notion of traffic patterns plays a vital role in the transportation profession. Demand patterns, for example the time-series of traffic volumes experienced on highways during a.m. and p.m. peak commuter travel, are used in many applications such as performance measurement, planning and operations. However, due to limited data collection in the past for the specific purposes, there was no need for automated algorithms to identify these patterns. Now, huge data archives are increasingly available for many locations with the vast deployments of ITS systems across the nation. Taking advantage of this unique opportunity, this paper identifies and applies solutions to automatically identify traffic patterns. Clustering algorithms, logistic regression, and tree-based methods are adapted and applied. Based on application to real datasets, clustering algorithms hold high potential for use in automated traffic pattern identification.