USDOT Region V Regional University Transportation Center

Research in Progress

2015-2016 Projects

• Driving Simulator Based Interactive Experiments: Understanding Driver Behavior, Cognition and Technology Uptake under Information and Communication Technologies 143

• A Novel Decision-Support Tool to Develop Link-Driving Schedules for MOVES 153

• Synthesis of Best Practices for Agency-wide Freight Data and Information Management

  UTC Project Information

  RIP Database link: https://rip.trb.org/view/2015/P/1359038

  Research Summary

  While states have data and information systems for managing spatially related data to support decision making, those systems are not populated to support freight-related planning and operations decisions. For example, at least one agency has constructed roundabouts on designated truck routes and then endured the extreme dismay of trucking companies. To address coordination issues, some agencies establish new offices for freight but the comprehensive scope of business activities and issues do not fit neatly into the organizational structures of typical transportation agencies. Another approach is to enhance access to the agency-wide data and information necessary to support development of transportation infrastructure that balances the needs of both passenger and freight users.

  This research will build on the investigators' expertise in asset management and relationships with the DOTs in the Mid America Freight Coalition. This project will review existing data sources, identify new data sources and create a catalog of business processes, data and information items necessary to support consistency, and quality in decision making and asset management of freight infrastructure and operations. The results will support implementation of the MAP-21 requirements for freight performance.

  This project deals with strategies for managing and integrating spatially related freight transportation data both intra- and inter-agency from the perspective of a state transportation agency. The scope of data is freight data, defined as data about freight for making decisions regarding the planning, design, construction, operation, and maintenance of transportation infrastructure. Some of the freight data items include truck routes, ESAL charts, truck traffic, variable speed limits, intermodal connectors, spring-thaw restrictions, bridge clearances, posted bridges, steep grades, rail crossings, port entries, foreign trade zones, longer combination vehicles rules, OS/OW routes, weight stations, and road uses agreements.

• Region V Transportation Workforce Assessment and Summit

• Bayesian Updating Procedure for Prediction of Corrosion-Induced Cracking in Prestressed Concrete Bridges using Visual Inspection Data

  UTC Project Information

  RIP Database link: https://rip.trb.org/view/2015/P/1373280

  Research Summary

  Bridges are key components of transportation infrastructure systems that are exposed to various uncertain environmental stressors and loading conditions. The proper functionality of these structures is critical for local and regional economic prosperity along with the assurance of public safety. Aging and deterioration are primary concerns regarding the performance bridges degrading to deficient or obsolete states. According to a recent ASCE infrastructure report (ASCE 2013b), it is estimated that every day more than 200 million trips in 102 largest metropolitan regions in the US are taken across deficient bridges. As the demands on public funds increase, it is becoming even more critical to determine and implement optimal decisions aimed at maintaining and improving public infrastructure. Bridge deck deterioration forecasts provide the necessary inputs in support of such decisions. More accurate forecasts are expected to lead to more effective decision. The ability to use bridge inspection data to update the parameters of bridge deck deterioration models on an ongoing basis following each inspection season will lead to more accurate forecasts. The enhanced accuracy of predictions of future states of bridges will lead to more effective maintenance decisions that results in increased reliability of assets, enhanced ride comfort, and less interruptions and time delays for passing traffic. Moreover, the quantification of the value of such updates with respect to the state-of-the practice will allow researchers and state agencies to better allocate the research efforts and investments vis-à-vis the various aspects of deterioration modeling and parameter estimation efforts.

• Advancing Traffic Flow Theory Using Empirical Microscopic Data

• Tracking bicyclists route choices, case study: The Ohio State University

  UTC Project Information

  RIP Database link: https://rip.trb.org/view/2015/P/1373604

  Research Summary

  This study will generate data on bicycling patterns to, from and on a large university campus as well as data on bicyclists’ perceptions and preferences. The results will help transportation planners make informed infrastructure investments. The methodology developed will be applicable elsewhere for other studies. Bicycles have low access costs and moderate travel speeds, they help protect the environment, reduce congestion and bring many health benefits (Clifton & Akar, 2009). Within these considerations, several researchers have explored the factors associated with bicycling choice to understand the needs of bicyclists and increase bicycle mode share. Existing literature identified socio-demographics, built environment, road conditions and land-use patterns as factors associated with bicycling choice in general (Pucher et al., 2011; Dill and Carr, 2003). Among these, presence of bicycle facilities, motor vehicle traffic characteristics, surface quality, neighboring land-uses are cited as factors affecting bicycling route choices (Broach et al. 2012). There is increasing interest among colleges and universities in ways to reduce local congestion, contributions to greenhouse gases, and provide leadership in sustainable transportation. This study brings these two emerging areas together: analyzing campus transportation patterns and identifying the factors associated with bicycle trip generation and bicycle route choices using state-of-the-art data collection techniques at a large university campus, The Ohio State University (OSU). The origins, destinations and routes of bicycle trips will be collected through a cell phone app: CycleTracks. This study includes 4 major tasks: (i) collect data on bicycle trips (origin, destination, purpose, time and route); (ii) conduct an online survey to capture bicyclists’ perceptions toward several street attributes; (iii) develop maps illustrating the collected data; (iv) develop models to understand the determinants of bicycle trip generation and route choices;

• Roadway Traffic Data Collection from Mobile Platforms

• Campus Transit Laboratory: Infrastructure for Research, Education, and Outreach

  UTC Project Information

  RIP Database link: https://rip.trb.org/view/2013/P/1258397

  Research Summary

  The Ohio State University (OSU) Campus Transit Laboratory (CTL) is a living laboratory that provides the infrastructure for integrated transit-related research investigations, educational activities, and applied studies. The CTL benefits from advanced automatic data collection and information technologies deployed on the OSU Campus Area Bus Service (CABS), accessibility of the CABS system and the OSU community to researchers, instructors, and students for data collection and in situ observations, and regular interaction between CTL investigators and CABS operators and decision makers. This NEXTRANS project would continue to: Sustain, develop, and showcase the CTL; Collect, process, and archive CTL data; Exploit the CTL for research, education, and outreach activities; and Develop collaborations with transit agencies and investigators.

  This project is expected to have impacts related to transit planning and operations on each of the research, education, and outreach dimensions. Improvements in transit planning and operations should lead to a more efficient, sustainable, and environmentally responsive balance in the use of urban transportation modes. It is also hoped that publicizing a set of integrated activities centered on the use of a living lab would result in the expanded development and use of such labs in multiple transportation areas.

• Mobile air quality monitoring for local high-resolution characterization of vehicle-sourced criteria pollutants

• A Study of Potential Community and Faculty/Staff Use of an Improved 95th Street Metra Stop

2013-2014 Projects

• Segmenting, Grouping and Tracking Vehicles in LIDAR Data

  UTC Project Information

  RiP database: https://rip.trb.org/view/2013/P/1324050

  Summary of Research

  Roadway congestion impacts almost all aspects of our lives in the US, from safety, to the environment, to the quality of life, to the cost of goods and services. A comprehensive understanding of the traffic conditions over space that give rise to congestion remains elusive. To date, these issues have been studied predominantly with macroscopic data from point detectors (e.g., loop detectors) aggregated over fixed time periods ranging from 20 sec to 15 min. Many new theories have emerged in recent years to explain several on-going anomalies in traditional traffic flow theory. At the core of these new theories is the presence of non-trivial disturbances that last far less than the fixed time aggregation periods commonly used to study traffic, and thus, these micro-disturbances have not been empirically observed. If these theories are proven empirically, they should lead to better congestion management and control.

  The proposed research seeks to develop the tools to measure traffic flow at a resolution sufficiently precise to measure the micro-disturbances and prove or disprove the traffic flow theories that depend on their presence. Under support from NSF and FTA, OSU has developed an instrumented probe vehicle that includes positioning sensors (DGPS and inertial navigation) and ranging sensors (six LIDAR, one radar). The focus of the RNS is the one forward facing and one rear facing LIDAR sensors. These LIDAR collect a rich, 180° scan out to 80 m, in a plane approximately 0.5 m above the roadway, at 40 Hz. Although hundreds of hours of data have been collected, the tools to automatically reduce this vast quantity of data to useful information still need to be developed. The proposed research would undertake the task of segmenting the vehicle returns from the non-vehicle objects in the LIDAR data, grouping the vehicle returns into discrete vehicles, and tracking the resulting vehicle groups across scans. Once these tools are developed, they would be used to mine hundreds of hours of existing instrumented probe vehicle data.

• Integration of ground access to airports in measures of inter-urban accessibility

• LIDAR Based Vehicle Classification

• Using Naturalistic Driving Performance Data to Develop an Empirically Defined Model of Distracted Driving

• Driving Simulator Laboratory: Traveler Behavior Modeling and Interactive Experiments to Address Mobility and Safety Needs

• Campus Transit Laboratory: Infrastructure for Research, Education, and Outreach

  Research Information pdf

  RiP Database
  Status: Active
  Summary of Research

  The Ohio State University (OSU) Campus Transit Laboratory (CTL) is a living laboratory that provides the infrastructure for integrated transit-related research investigations, educational activities, and applied studies. The CTL benefits from advanced automatic data collection and information technologies deployed on the OSU Campus Area Bus Service (CABS), accessibility of the CABS system and the OSU community to researchers, instructors, and students for data collection and in situ observations, and regular interaction between CTL investigators and CABS operators and decision makers. This NEXTRANS project would continue to: -Sustain, develop, and showcase the CTL -Collect, process, and archive CTL data -Exploit the CTL for research, education, and outreach activities -Develop collaborations with transit agencies and investigators.