PROBLEM STATEMENT The US Department of Transportation has advised planning agencies across the country to begin considering how their local transportation systems will function in a connected vehicle environment. Connected vehicles will revolutionize the way we travel and will be a major, if not the major, contributor that drives the infrastructure, policy and practices of state agencies. The real time information provided by such systems will enable improved management of transportation systems through real-time information. The majority of these efforts to date have focused on improved operations. However, management of safety performance has FIGURE 1: V2V and V2I Connectivity largely been reactive by increasing responsiveness to incidents as opposed to focusing on crash prevention. This is largely due to the random nature of crashes, but also the limited development of safety models which typically examine static variables such as roadway geometry and ADT, and do not include temporal effects such as congestion or weather related variables. With the connected vehicle infrastructure, significantly more data on vehicle operations is available and there is the potential to explore these temporal effects on crash performance from data generated from vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) in real time. This opens up an abundance of opportunities to identify high crash probability locations based on real time conditions. There is a need to develop a strategic framework that not only aims at identifying congestion and alleviating it but extend it to estimate the potential crash risk at network scale utilizing the extensive data available in a connected vehicle environment. While we are currently just stepping into the connected vehicle environment, we must take the initial steps to understand how this data may be used to enhance current understanding of congestion and safety performance. It is proposed that the active datasets from V2V and V2I can be integrated with a crash database and congestion-monitoring index using a composite index of the two. This will assist in identifying high-risk locations in a connected vehicle environment. RESEARCH OBJECTIVES The objective of this research is to leverage existing V2I datasets to link high resolution traffic performance and weather information data to safety performance data. This high resolution dataset can then be used to identify weather, geometric and operational conditions that may lead to increased crash experience. By identifying potential high crash conditions, future efforts can then identify alternative FIGURE 2 BLUETOAD devices deployed in Lexington, KY operational control strategies that may mitigate adverse conditions, or even send advisory information to drivers, provide alternative routing or call for alternative vehicle operating characteristics such as following distances, in a fully connected V2V University of Kentucky 1 Southeastern Transportation Center
environment. This project will assist in the development of understanding these relationships by leveraging high resolution operational condition data from a city-wide deployment of BlueToad devices in Lexington, KY (Figure 2), joined with individual crash records for the study period and hour by hour historical weather data from Automated Surface Observation System (ASOS) datasets. RESEARCH APPROACH TASKS Task 1: Research and Practice Review A comprehensive search for published and ongoing work related to vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) on traffic management and safety analysis will be undertaken. This task will identify studies which focus on operational and weather related conditions that impact safety performance. This task will also identify strategies of V2V or V2I, such as variable speed limits, that may be used to identify and mitigate hazardous conditions. In the absence of V2I resources, this task will look at secondary resources such as traveler information systems practices that could be enhanced through transfer to V2I systems. In addition, this task will explore next generation V2I technology that could provide more information beyond the metrics provided by the Bluetooth data collection devices which would potentially identify next generation datasets. The Kentucky Transportation Center has a state-of-the-art transportation research library that maintains current information relating to all aspects of transportation. The library possesses copies of most general transportation journals and articles. The KTC library has access to national information services (TRIS, Transport, NTIS, Dialog, etc.) and the Center employs a full-time research librarian to assist with all literature search activities. These resources will help the team obtain the latest research information. Task 2: Data Collection Traffic Flow Data for Congestion Index V2V and V2I send and receive vehicle location information at a set period and frequency. An initial concept of V2I applications is the use of Bluetooth detection devices that gather individual vehicle characteristics along a corridor. The City of Lexington has deployed several corridors with Bluetooth devices that gather vehicle travel information such as speed and origin destination data throughout the city. These devices gather real-time vehicle information including location, speed and direction of vehicle and archive historical data in a database. Figure 3 shows an example of high resolution travel speed data obtained from this infrastructure. This data allows for monitoring of both recurring and non-recurring congestion in real-time for the roadway network on a link-by-link basis. Additional travel metrics can be derived from these datasets to develop congestion metrics. Historical data from these devices will be gathered for 1-year period (2015) for the Lexington network. Additional site visits will be made to the corridor to document the corridor infrastructure and roadway characteristics. While the V2I data does not provide accurate volume estimates as it only captures a sample of traveling vehicles, it can provide volume trends that can be joined with other permanent county stations within the network to estimate fluctuations in segment volumes. University of Kentucky 2 Southeastern Transportation Center
FIGURE 3 Sample Travel Metrics that can be derived from Bluetooth Data Crash and Incident Data for Safety Index Historical crash data will be procured from the Kentucky State Police crash database. These data will be plotted by GPS location and date stamped then matched to the road network which will link the crash data to the traffic characteristics and geometric data for the corridor. The crash data will also be disaggregated by time, type and severity on a link-by-link basis to match the travel speed and volume data obtained from the Bluetooth receiver data. The locational accuracy of the crash data is well understood in Kentucky s datasets and the project team has extensive experience working with the crash dataset. Care will be taken to ensure the crashes are properly linked to the correct corridors. Weather Data for Safety and Congestion Index Variability in weather conditions also impacts mobility and safety through visibility impairments, precipitation, high winds, and temperature extremes. Between 2002 and 2012, an average of 23% of all vehicle crashes was reported as weather-related. Additionally, weather impacts traffic operations and congestion which can also lead to increased crashes. Historical weather data including temperature, pressure, wind speed, wind direction and precipitation will be procured from the Automated Surface Observations System (ASOS). An example of this hourly dataset is shown in Figure 4. This data will be analyzed against the traffic flow distribution and crash frequency to be included in the development of the hazard index. University of Kentucky 3 Southeastern Transportation Center
FIGURE 4: Sample weather variability by TOD (January 3, 2015) Task 3: Safety Performance Model Development This task will focus on developing a safety model based on the temporal conditions such as operational and weather data sources using the vehicular data from Bluetooth devices and other datasets including the weather and roadway inventory. The first step will be to analyze the variability within each dataset and represent all datasets in a common resolution such as 15-min interval by time of day (TOD) and day of week (DOW) for a year. The crash data will be disaggregated by crash type and severity for 15-min interval by TOD and DOW for the selected corridor. This data will be represented by link or segment of the roadway. The vehicular data from Bluetooth receivers (speed and direction) will be aggregated in 15-min bins by TOD and DOW. The weather data will also be aggregated in similar resolution and matched with other datasets. Once all datasets are assembled the next step will determine crash frequency (#crash/ link length) and crash rate (#crash/ length-vol). Travel metrics such as speed, Travel Time Index (TTI) and Congestion Index (CI) will also be estimated using the traffic flow data from the Bluetooth devices. The impact of weather conditions on crash rate and crash frequency will also be determined. Statistical analysis will be conducted to determine if links during congestion periods show higher crash rates or crash frequency. To combine the two indices into a composite index, a statistical model based on common Factor Analysis will be applied. This technique uses an estimate of common variance among the variables to generate the factor solution. This is achieved by grouping correlated indices to form a composite indicator that captures as much common information among sub-indicators as possible. This task will be extended to determine if there is any relationship between crash severity and congestion. The impact of different weather condition will be studied at varying congestion levels. These efforts will result in multiple numerical models quantifying the effect of varying congestion levels on safety. The outcome of this task will potentially be a composite indicator that will estimate the risk factor with congestion levels. Task 4: Testing and Evaluation This task will conduct a pilot test to validate the framework and the composite index developed in the previous task. For the development of the framework in the previous task, data from the year University of Kentucky 4 Southeastern Transportation Center
2015 was utilized. For testing and validating the framework, at least 6-months of data from 2016 will be utilized. Multiple corridors identified earlier in the study will be monitored and vehicular characteristics data will be collected for January to July of 2016. The developed models will be applied to these datasets and potential crash risks will be compared with actual crashes from the crash database. Statistical analysis will be conducted to quantify the relationship between the estimated crash risk and the observed crash data. In addition, cumulative residual plots (CURE Plots) will be used to test the reliability of the SPF development. Below is an example CURE plot including upper and lower confidence limits, indicating a reliable model between specific traffic volumes. FIGURE 5: Cumulative Residual Plots (CURE Plots) Task 5: Potential Applications This research will have several potential applications specifically in the field of ITS, traffic operations and safety. From an operations standpoint, this framework can assist in Active Traffic Management and Incident Management by highlighting the congestion prone areas. Within a connected vehicle environment, sites with Variable Speed Limits can find useful information to adjust speed limits based on the hazard index and increased probability of a crash. Agencies can develop Congestion Mitigation Strategies based on the network performance indication developed using this framework. This framework can highlight problem areas and safety measures can be proactively taken. Utilizing ITS infrastructure, this framework will provide the necessary information (such as warning and speed limit information) for V2I interaction. This task will identify and document potential applications and recommend implementation guidelines. Task 6: Results and Recommendations The outcome of this project will be a document that outlines the methodology to develop the framework to quantify the congestion index that translates into crash risk in a network. This is a crucial step for the safety initiative as we embark into the next generation of vehicle and infrastructure technology and begin to understanding how we may leverage information gained from this data rich environment. Agencies can utilize this framework to develop network wide risk assessment tool. Commercial equipment such as BlueToad can incorporate this to offer additional University of Kentucky 5 Southeastern Transportation Center
features to help state agencies monitor networks. This project will provide the tool required by agencies when the connected vehicles fully integrated into the roadway network. RESEARCH DURATION AND COST It is anticipated that this project will take 12 months to complete for a total budget of $100,000. The majority of the efforts involved in this effort will be data collection and numerical model development efforts which can be executed concurrently with other ongoing research projects currently underway at the Kentucky Transportation Center. A 50/50 match of the STC Grant is proposed. QUALIFICATION OF RESEARCH TEAM The Kentucky Transportation Center team members have extensive experience in operations and safety. Team members have not only worked with the datasets being utilized in the study but have also worked with the controlling agencies to refine and improve the datasets and technology deployments in questions. The Universities of Kentucky also offers well established civil engineering department that focus on transportation research from which to draw knowledgeable and interested graduate and undergraduate students. Dr. Adam Kirk, PE, PTOE, is a Principal Research Associate at the Kentucky Transportation Center at the University of Kentucky and will serve as the Principal Investigator for this research. Dr. Kirk leads the research team within the Center for Advanced Traffic Solutions Laboratory (CATSLab; catslab.ukytc.com), which focuses on advances within traffic signal systems and practices. In this role he has lead efforts within Lexington and Louisville Kentucky to implement and evaluate adaptive traffic control systems to ease high levels of congestion and associated safety problems. Dr. Kirk also has extensive safety experience, having utilized similar modeling efforts to develop surrogate safety performance measures for innovative intersection applications through his PhD research. Mr. Green has been working for the Kentucky Transportation Center since 1998. He is currently a research engineer in the Traffic Operations and Safety section. His focuses are in safety analysis and asset management. Mr. Green has conducted research in areas such as identification of high crash locations, secondary crashes, distracted driving, intersection crash rates, and pavement marking performance. Mr. Green has also been involved in the development and maintenance of several software packages used by the Kentucky Transportation Cabinet and other agencies. He has developed software for GPS-based data collection, asset management, crash analysis, mapping and database integration. Mr. Green is involved in a sign retro-reflectivity training course given to the transportation cabinet and local Kentucky governments. He is involved with Kentucky's traffic records improvement group known as KTRAC. He works closely with Kentucky's Highway Safety Improvement Program team in an effort to implement the Highway Safety Manual in Kentucky's safety prioritization. Dr. Agarwal has worked as a research engineer on various projects including Bluetooth device evaluations, intersection design, traffic modeling and safety implications. He has extensive experience in crash analysis, data modeling and statistical analyses. Dr. Agarwal has utilized Hardware-in-the-Loop system to conduct several studies including evaluation of adaptive traffic University of Kentucky 6 Southeastern Transportation Center
control deployments, development of a tool to evaluate pedestrian safety at intersections, feasibility of road diet conversion and studying the alternative signal system strategies for corridor preemption. Dr. Agarwal has also developed an online traffic technician training course that is utilized by the State of Kentucky. Dr. Agarwal has presented at national conferences including Transportation Research Board and has coauthored several transportation center reports and is the current secretary of the Signal Timing Manual Subcommittee of TRB. Full resumes for the project team are provided at the end of the proposal. STUDENT INVOLVEMENT Both undergraduate and graduate students from the University of Kentucky will be involved in the proposed project to provide them with increased learning opportunities and hands on experience. Erin Lammers, an undergraduate student in Civil Engineering will lead the development of the database having extensive experience in the USRAP program and operational evaluations of an ongoing evaluation of a Zipper Merge evaluation. Graham Winchester, will be a Master s student during the course of the project and will support manipulation and statistical analysis in the development of the safety models. He brings extensive experience in SPF model development having support efforts by KTC in calibrating HSM SPFs to Kentucky Data. Students are routinely involved in project meetings and discussions with the full project team to provide them with a global view of the project, as well as for the project team to gain innovative and new insight into the project approach. TECHNOLOGY TRANSFER The results of this research have the potential to be disseminated widely among the transportation community. This research will layout the methodology that may be used by other agencies to develop real time indices to monitor network operations and safety. It is anticipated that journal articles will be published based on the findings of this safety and operations research. Additionally, the results of the proposed study may also be presented at conference venues, like the Annual Transportation Research Board Meeting and be shared with state and local engineers during workshops accessible to transportation professionals. The results of this research will also be readily incorporated into the education and professional training courses regularly delivered by the researchers. Finally, the findings will also be available online at the Southeastern Transportation Center website and a special effort will be made to disseminate the results to federal and state agencies, policy makers as well as to relevant private sector. This research will be critical in establishing the way forward for agencies to manage transportation networks within the coming connected vehicle environment. University of Kentucky 7 Southeastern Transportation Center
STC Research Schedule/Timeline Task / Month 1 2 3 4 5 6 7 8 9 10 11 12 Task 1: Research and Practice Review Deliverable: Literature Review Task 2. Data Collection Deliverable: Data Summary and Review Task 3: Safety Performance Model Deliverable: Model Documentation Task 4: Testing and Evaluation Task 5: Potential Applications Task 6: Results and Recommendations Deliverable: Final Report University of Kentucky 8 Southeastern Transportation Center
STC Research Project Description Project Title: Development of Safety Performance Indices in a Connected Vehicle Environment Principal Investigator: Adam Kirk University: University of Kentucky Telephone: 859-257-7310 Email Address: adam.kirk@uky.edu External Project Contact (if applicable): Address Street: City: State: Zip: Telephone: Email Address: Project Start Date: 06/01/2016 End Date: 05/30/2017 Other Milestones, Dates: Literature Review (August 31, 2016) Data Summary (September 30, 2016) Safety Model Documentation (February 28, 2017 Final Report (May 30, 2017) Project #: Project Objective: The objective of this research is to leverage existing V2I datasets to link high resolution traffic performance and weather information data to safety performance data. This high resolution dataset will then be used to develop crash performance models that account for temporal variations in weather, geometric and operational conditions. Project Abstract: This research will develop safety performance models based on temporal conditions such as congestion and weather from existing V2I datasets. Models will be developed from high resolution operational condition data from a city-wide deployment of BlueToad devices in Lexington, KY, joined with individual crash records for the study period and hour by hour historical weather data from Automated Surface Observation System (ASOS) datasets. These models can be used to identify alternative operational control strategies that may mitigate adverse conditions, or even send advisory information to drivers, provide alternative routing or call for alternative vehicle operating characteristics such as following distances, to fully manage transportation systems in a fully connected V2V/V2I environment. Task Description: Task 1: Research and Practice Review Task 2: Data Collection Task 3: Developing Framework for Integrating Indices Task 4: Testing and Evaluation Task 5: Potential Applications Task 6: Results and Recommendations Total Budget: $ 100,000 Student Involvement (Thesis, Assistantships, Paid Employment): This research will support 1 undergraduate and 1 graduate student in their current employment as research assistances at the Kentucky Transportation Center. In addition, the data and analysis of this project may be used in the Relationship to Other Projects: This project builds on the current efforts at KTC, developing Safety Performance Functions for Kentucky, and a SHRP2 project analyzing roadway segment speed data from Bluetooth and other second party data systems. Technology Transfer Activities: It is anticipated that the results of this research will be disseminated through existing publications of the STC, University o University of Tennessee, as well as submitted for inclusion in other application journals and presentations. Potential Benefits of Project: Benefits include improved management of transportation networks to increase efficiency and safety of travel. Additional insight into information available and applications of V2V and V2I infrastructure will also be provided. TRB Keywords: Connected Vehicles, Connected Infrastructure, Safety Performance Functions, Advanced Traffic Management Systems (ATMS),
PEER REVIEW FORM Peer Reviewer #1 Name: Organization/University Affiliation: Address: Phone #: Fax #: Email address: Please submit a brief overview of why this individual is qualified to review the material. Qualifications of reviewer: Peer Reviewer #2 Name: Organization/University Affiliation: Address: Phone #: Fax #: Email address: Please submit a brief overview of why this individual is qualified to review the material. Qualifications of reviewer: Peer Reviewer #3 Name: Organization/University Affiliation: Address: Phone #: Fax #: Email address: Please submit a brief overview of why this individual is qualified to review the material. Qualifications of reviewer: *Two peer reviewers will be selected for each final report. Other appropriate reviewers may be selected at the discretion of the STC.
Southeastern Transportation Center Proposed Budget O/E Grant 2016-2017 Title: Development of Safety Performance Indices in a Connected Vehicle Environment University: Salaries: Other Direct Costs: University of Kentucky Federal Funds Matching Funds Faculty Administrative Staff Other Staff 27200 27200 Graduate Student Salaries/Stipends 5500 5500 Undergraduate Student Salaries/Stipends 5500 5500 Total Salaries/Stipends 38200 38200 Benefits (including student health insurance) 0 0 Total Salaries and Benefits 38200 38200 Permanent Equipment Expendable Equipment and Supplies Computer Costs Non-salary Education Costs tuition/fees Other Costs: (specify) Printing / duplication Postal expense Communication Conference Registration / Fees Travel 1482.54 1482.54 Computer Costs Other miscellaneous costs: Total Other Direct Costs 1482.54 1482.54 Indirect Costs at 26% 10317.46 10317.46 TOTAL COSTS 50,000 50,000