Total Word Count = 3,963 words + 8 Figures * Tables*250 = 6,713 words

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1 0 0 0 Application of Wider Economic Benefits Tools on I- Active Traffic and Safety Management System Project Daniela Gonzales Undergraduate Research Assistant Civil and Environmental Engineering Department Old Dominion University Kaufman Hall, Norfolk, VA dgonz0@odu.edu Total Word Count =, words + Figures *0 + Tables*0 =, words Submission Date: Friday, July, 0

2 Gonzales ABSTRACT This paper investigates the wider economic benefits (WEB) of the Interstate Active Traffic and Safety Management System (ATSMS) by using the Second Strategic Highway Research Program s Tools for Assessing Wider Economic Benefits (C). This study is part of a larger Virginia case study exploring the WEB impacts of Intellegent Transportation System (ITS) instrumentation implementations. Research suggests no prior work has been done in assessing these benefits for ITS equipment making this research one of the first in its field. This paper covers the data, methodology and results of applying the SHRP C tools on the I- study corridor. It finds that while tools are relatively easy to run and get simple results, understanding and gathering data applicable requires some degree of manipulation making results have limited applicability, particularly when it concerns ITS projects. Keywords: Wider Economic Benefits, Intelligent Transportation System

3 Gonzales INTRODUCTION In the August of 0, Virginia Department of Transportation (VDOT) research division, Virginia Center for Transportation Innovation and Research (VCTIR), was awarded a Lead Adopter Incentive for the Tools for Assessing Wider Economic Benefits (C) by the Second Strategic Highway Research Program (SHRP) Implementation Assistance Program (IAP) (). In the past, though transportation WEB have been acknowledged to exist, benefits have been harder to quantify. These tools were developed as part of the SHRP C project to measure wider economic benefits (WEB) of capacity projects not captured in typical cost-benefit analysis (CBA). Typical CBA tools analyze user benefits such as travel time, travel cost, and safety. SHRP C tools are a pack of excel-based tools intended to augment typical CBA tools during the programming and prioritization stages by providing insight into project impacts on WEB through reliability, intermodal connectivity and accessibility measures (). As lead adopters in the implementation of SHRP C tools, VCTIR proposed to apply SHRP C tools on several different Intelligent Transportation System (ITS) projects throughout Virginia and plans implement the tool in the planning and prioritization phases of future transportation projects (). VCTIR, in collaboration with UVA researchers, has chosen two ITS project corridors to study for its research seen that no prior work had been done to analyze the WEB of ITS projects making the research one of the first instances. The Active Traffic and Safety Management System (ATSMS) project on Interstate was chosen as one the current projects to be reviewed as part of this study. Project Background This ITS project is located in a mountainous, fog-prone section of I- along the Fancy Gap area in Carroll County where a high percentage of weather-related incidents occur. VDOT is currently implementing an ATSMS project in the area which includes a series of cameras, dynamic messages signs, variable speed limits and weather detection stations installed along a -mile stretch of the highway. The primary aim of this project was to reduce incidents by alerting drivers of road and weather conditions. This new ITS technology deployment is expected to be completely operational by summer 0, but some of the cameras have become operational as of March 0 (). The objective of including I- ATSMS within this study is to evaluate the changes in traffic and incident conditions before and after installing these devices along the corridor using appropriate performance measures and the SHRP C tools. This rest of this paper focuses on applications of the C tools on I- ATSMS. The remainder of this paper is divided into five sections. First, a description of the SHRP C tools is provided. Next, the Data and Study Area are defined followed by a methodologies section describing each of the specific tools methods. In the next section, the results are presented. The paper closes with conclusions and assessments of the tools. SHRP C TOOLS Three different SHRP C tools were tested for this paper: the Reliability Tool, the Intermodal Connectivity Tool, and the Effective Density Accessibility Tool. The description of SHRP C tools in this section refer to the versions downloaded in May and June 0 from Reliability Tool

4 Gonzales The Reliability tool estimates the WEB with respect to improved travel time reliability of transportation projects. A unique aspect of this tool is that not only does it consider the value of travel time reliability but also the value of reliability itself as a quantifiable measure. It does so by assessing changing volume capacity ratios, incident frequency and incident duration and combining those with travel time costs and reliability ratios. The specifics of the inputs will be further discussed in the methodology section. As outputs, the C Reliability tool measures enhanced reliability for business-related travel with several performance metrics including: Year of analysis (the future year), Recurring delay in hours, Incident delay in hours, Total delay in hours, Overall travel time index, th percentile travel time index, 0th percentile travel time index, Percent of trips < mph, Percent of trips < 0 mph, Cost of recurring delay, Cost of unreliability, and Total congestion cost (). Connectivity Tool The Intermodal Connectivity Tool estimates the WEB of transportation projects on intermodal facility terminals and connectivity. The tool generates connectivity indices given the general form: connectivity index = activity * value per activity unit * number of connections. The tool combines this index with user inputs to produce a relative connectivity index which is then multiplied by savings created by the improvement associated with each facility. The value of the tool lies on the comparison of connectivity indexes of facilities with and without improvements and in comparing how an improvement will affect distinct facilities differently. These comparisons serve as a base to prioritize investments in transportation projects (). Accessibility Tool The Accessibility Tool estimates the WEB of transportation projects by developing sketch plan approximations to the economic value for market access. It comes as two separate tools: Access to Buyer-Seller Markets tool and Fixed Threshold Specialized Labor Market tool. For this study we tested on the Access to Buyer-Seller Markets tool which is a broad-based regional measure that aims to approximate productivity gains from a wide range of economic triggers such as scale economies and economies of dispersion (). The default settings outputs reflect Productivity Impacts as GDP increase or added value into the system due to change in market access (). This tool however was found to be difficult to understand the inputs and the meaning of the outputs when not used outside of default settings. The specific challenges will be further discussed in the methodologies and tool assessment sections. DATA AND STUDY AREA The map in Figure shows the study corridor, located between the North Carolina State Borderline at the Southern end and mile marker past US-/ Carrollton Pike (US-) exit at the Northern end, and the location of devices that were installed in the field. Both directions (NB and SB) are included in the study comprising a total length of 0 miles.

5 Gonzales FIGURE Map of I- ATSMS study corridor. Given the installation date of these cameras in March 0, the immediate three months following the installation, April to June 0, were considered for the After period. The "Before" period was therefore chosen to be April to June 0 to avoid biases due to snow and other weather related incidents during winter months. Since this I- corridor is primarily a truck route with no distinguishable peak times, the entire day was initially selected as the only period of the day considered for changes traffic and incident conditions. Due to constrictions in Reliability Tool inputs, which will be further discussed in the Methodologies Section, the period of hours between AM and PM were later also considered for study. Performance Measures Performance measures for assessing the impact of the technology deployment on the corridor included: total number of incidents, incident severity, priority, and duration. We observed shift in incident priority from 0% of all incidents racked minor in our before period to % in our after period (FIGURE ). There was also an observed overall reduction in incident frequency in total frequency duration in our before and after periods (TABLE ). TABLE Incident Duration Performance Measures Incident Duration (Mins) Before hrs After % Reduction Before AM - PM After % Reduction Total Duration -.% -.% Average Duration.0. 0.%...% Count -.% -.%

6 Gonzales (a) (b) 0 (c) FIGURE Number of incidents (a) by severity, (b) by priority, and (c) by type. METHODOLOGY The SHRP C tools have been tested on construction-based transportation improvements. SHRP Project C provides applications of the SHRP C Tools on nine existing Transportation Project Impact Case Studies (TPICS) (). The C tools user manual and SHPR Project C were referenced to provide instructions on tool Methodology. Reliability Tool The Reliability tool requires few and relatively simple inputs and yields fairly straightforward outputs. It was the first ran in early June 0 for projected improvement results on I- for the SHRP C Third Quarterly Report for the IAP. Inputs were then revised and re-ran in mid-july as full data for June incidents was made available Figure provides a screenshot of the Reliability tool inputs.

7 Gonzales 0 0 FIGURE Reliability inputs: build year. In order to choose the input data for the Traffic Data inputs of reliability tool, a series of calculations were made. Traffic Count Data by VDOT provided annual average daily traffic (AADT), link length, truck percentages per link for each of the links, northbound and southbound, comprising the 0 miles study corridor for the years 00 to 0 (). Link weighted averages were used to calculate average AADT for both northbound and southbound directions. The sum of the 0 northbound and southbound AADT s was used as the current AADT input value. Combined AADTs were found for years 00 to 0 to calculate annual growth rate. The median value of annual traffic growth rate from 00 to 0 was chosen as the input value for the tool. The percent trucks in traffic were found using the same method as for finding AADTs and the median value for 00 to 0 was chosen. Peak capacity was derived by using the Highway Capacity Manual (HCM) equation found in the reliability tool documentation section of (). The mountainous terrain option was used. The Scenario Data describes our study corridor. The time horizon describes the future year in question. The broadest analysis period was chosen from AM to PM. Our highway type was freeway. The beginning and ending milepoints were the same as our corridors beginning and end mile markers. With the exception of 0. miles of lanes, the number of lanes was the same throughout the entire of the corridor. The free flow speed was determined from the posted speed limit which was obtained from VDOT and Google Street View imagery where data was unclear or not available. The Reliability tool was run 0 times with time horizons between and years, once for a build case and once for a base case. The base case was considered without the ITS deployment project, while the build case was with the ITS deployment project. The difference between the base and build cases was in the inputs for the "Effect of Incident Management Strategies" boxes. The recommendations in () for the input values for the effects of incident management strategies were adapted to the first run in our case of study (% reductions) and then taken from the performance measures for the second run. In the final run, the base case was zero reduction in incident frequency and duration for the base case and (no improvements), and.% reduction in incident frequency and 0% reduction in incident duration for the build case. Connectivity Tool The Connectivity Tool has two input sheets. First, to test the Connectivity Tool, we had to choose up to three terminals that were linked to our corridor. We chose the Norfolk Southern s

8 Gonzales Charlotte Intermodal shipping facility in North Carolina, which housed both rail freight and airport freight facilities, and the Roanoke Regional Airport as an airport freight facility to the north. These facilities were chosen because Charlotte is one of the largest, closest intermodal facility south of our corridor and Roanoke airport is the closest, reasonable northbound facility. For conservative measures, instead of using the default fractions, it was assumed that a fraction equal to 0.0 of all truck traffic was associated with each airport, while 0. of truck traffic was associated with the larger Charlotte NC rail facility. This can be seen in part f of FIGURE b. (a) (b) FIGURE Connectivity tool: (a) intermodal facility inputs and (b) roadway improvement facility input. As with the Reliability Tool test, the Connectivity Tool was tested over a time horizon of years. For the first year, the number of trucks within the study area was calculated using the AADT values from the before year. The sum of the average annual weekday traffic times the number of weekdays and the average annual weekend traffic multiplied by both the weekends and holidays were taken to be the total number of trucks per year. Annual Truck growth rate was found from as in the same way as finding annual traffic growth rate for the reliability tool

9 Gonzales The data provider for Virginia traffic speed data stores daily -hour directional travel time data for the road links. These links making up our corridor were then combined to get daily -hour corridor travel times and were averaged for both before and after periods. The difference between the before and after periods was considered the hours saved per truck. The default value per truck hour was kept and the distance between improvement and facility was input. Accessibility Tool Though the Accessibility Tool was later found to be inapplicable to our study, this section explores the data and methodology that lead to that conclusion. The Accessibility Tool requires zonal activity input and impedance matrices. Three regions of zonal impacts were taken into consideration. FIGURE shows the three regions with their respective counties (zones) highlighted. 0 (a) (b) FIGURE Map of (a) surrounding counties and (b) northern and southern counties regions. The tool allows the user to input multiple effective density parameters. The Base year and Build year were the before and after years, respectively. The default selections for calculations and activity type were kept as shown FIGURE. Population is also an activity option but outputs do not include productivity impact. Though some guidelines are given, the user manual is unclear on how to choose appropriate impedance decay factor (alpha) and productivity elasticity. The Accessibility tool examples in SHRP Project C presented cases for transportation projects that improved access in commuting regions and for business supply chains. Though these examples provided sample decay factors and elasticities, our study concentrated on freight and thus the parameters would not be true representatives of our corridor. To test the tool, the surrounding counties zone was assumed to behave as a commuting region and the northern and southern counties zone were assumed to behave as business supply chains and took on the decay and elasticity shown in SHPR Project C ().

10 Gonzales 0 0 FIGURE Screenshot of accessibility parameter inputs and selections. The Activity Type employment was retrieved from (). Gross Regional Product per Employee (GRP proxies) was a multi-step calculation. First, for each zone within the three studied regions, total Earnings by Place of Work were divided by total zonal employment to obtain earnings by place of work per employee. Then, the ratio of state gross domestic product (GDP) to Total Earnings by Place of Work was taken. That ratio was applied Earnings by Place of Work per number of Employees to get GRP proxy. The result was then adjusted for inflation to 0 currency ().The BEA Regional Data, Economic Profiles did not provide data for Carroll County alone but instead group it with the small, nearby independent city of Galax, thus for this section of the paper Carroll + Galax and Carroll County will be used interchangeably to refer to the same area where the I- ATSMS ITS instrumentation project was implemented. The Impedance Matrices built considered travel times in minutes. The rows represent the origin points while the columns are the destination points and the diagonal represent the intra-zonal travel times. Inter-zonal travel times were gathered from Google maps Intra-zonal travel time was calculated using suggested equations the user guide (). As with the connectivity tool, average travel times through our corridor from our before and after period were used for the No Build and Build scenarios impedance matrices, respectively. The build impedance travel times for zones impacted by the new travel time through Carroll County were then calculated by subtracting base Carroll County intra-zonal travel time from the inter-zonal base travel time and adding the build Carroll County intra-zonal travel time. For inter-zonal that did not run through Carroll County, impedance base and build times do not change. RESULTS FIGURE depicts the Reliability Results into one chart showing the predicted vehicle hours of delay with the associated reliability costs over years after the deployment of the I- ATSMS ITS project. The base (no improvement) and build scenarios show increasing amount of vehicle delay hours as the annual traffic grows. The cumulative savings over years predicted by the tool total roughly % of the base case delays and costs. Ten years after the project implementation, the reliability results estimate over 0% of $,, project proposal price

11 Gonzales would be recouped in reliability savings alone (). FIGURE Reliability results: vehicle hours of delay and reliability costs by year. The Connectivity Results were twofold showing truck hours of delay with the associated connectivity savings and weighted connectivities for the facilities over a period of ten years after ITS deployment. FIGURE reveals relatively, small positive linear trend in related connectivity savings. Both of the airport freight facilities were assumed to have an equal fraction of truck traffic associated with each facility since they were equidistant from the corridor. Thus, the connectivity savings predictions overlap on part (a) of FIGURE. The number of connections, activity (tons/containers of freight), or value of time savings per facility are all positively correlated with the weighted connectivity. In our tests, it was found that Charlotte airport had more connections than Roanoke airport, and Charlotte rail facility had higher connectivity index and value of time savings than Charlotte airport hence higher weighted connectivity.

12 Gonzales (a) (b) FIGURE Connectivity results: (a) truck hours of delay and connectivity savings by year and (b) weighted connectivities by year. The Accessibility Results yielded a wide range of results that were largely due to lack of clarity on parameter inputs. A sensitivity test was run on the tool testing decay parameters and after inconsistent results the tool was then considered inapplicable towards our research. TABLE and TABLE show the results of testing the two suggested alpha parameters on the Surrounding Counties Region. Counties with Productivity Impacts are different and the magnitude of results is times as large. Testing intermediate alphas from to. for the Surrounding Counties Region displayed productivity impacts shifting from county to county with overall productivity savings increasing and decreasing unpredictably as alpha values increased.

13 Gonzales 0 TABLE Effective Density and Productivity Impact Results Calculated by Accessibility Tool for Surrounding Counties Zone, with Decay Parameter Alpha =. NO BUILD BUILD % Per PRODUCTIVITY 0 0 Change Worker Zonal IMPACT ZONES EFFECTIVE EFFECTIVE DENSITY DENSITY in ED GRP Employment ($) Surry, NC 0 0.% $, $, Floyd, VA 0.00% $,0 $0 Grayson, VA 0.00% $, $0 Patrick, VA 0.00% $, 0 $0 Wythe, VA 0.00% $,0 $0 Carroll + Galax, VA 0 0.% $, $0,00 TOTAL 0.% $,0, TABLE Effective Density and Productivity Impact Results Calculated by Accessibility Tool for Surrounding Counties Zone, with Decay Parameter Alpha = NO BUILD BUILD % Per PRODUCTIVITY 0 0 Change Worker Zonal IMPACT ZONES EFFECTIVE EFFECTIVE DENSITY DENSITY in ED GRP Employment ($) Surry 0.00% $, $0 Floyd 0.0% $,0 $, Grayson 0.00% $, $0 Patrick 0.00% $, 0 $0 Wythe 0.0% $,0 $, Carroll + Galax 0.% $, $, TOTAL % $, Testing the Accessibility tool on the Northern and Southern Counties Regions yielded more results on the same magnitude. For the Northern Region productivity impacts were spread over of counties in contrast to of counties for the Southern Region. The counties impacted were usually the closest to Carroll County but in several cases some counties further away received productivity impacts while closer ones didn t. It is unclear as to which of the predicted productivity impacts should be considered in these cases. Thus this section on accessibility results was not included in the full report for the collaborative UVA-VCTIR research. CONCLUSIONS AND ASSESSMENTS To conclude, we were able to assess some of the wider economic benefits of the I- ATSMS ITS project using SHPR C tools.this research was done as a part of a larger Virginia Case study to see if these tools are applicable for the the planning and prioritization of transportation projects statewide. We found that while the tools themselves were simple to run and assess the results, gathering the information was not always simple. This was either because information was hard to find or not specific enough. This limited scope of applicability of the tools. Tools also needed to be thoroughly calibrated to yield more consistent predictions. While these tools did not always apply to our study, we feel that practionters who have a wider data access and travel demand models to

14 Gonzales appropriately calibrate these tools may find these tools useful to compare the WEB impacts of multiple projects. ACKNOWLEDGEMENTS I would personally like to thank Simona Babiceanu and Dr. Emily Parkany for their outstanding mentorship, support, and endless effort throughout the whole research experience. Your encouragement and guidance has definitely made a lasting impact beyond this summer experience. I would also like to thank Britanny Hungate, who also collaborated on the larger Virginia Case Study, and who was always happy to help and was a joy to work with. It has been a privilege to work with you.

15 Gonzales 0 0 REFERENCES. U.S. Department of Transportation, Federal Highway Administration. SHRP Solutions Implementation Assistance Program, Accessed July, 0. Naomi Stein, Glen Weisbrod, Economic Development Research Group, Training for EconWorks Wide Economic Benefit Tools, Online Training Session, March, 0. Virginia Department of Transportation, New Interstate Traffic Cameras Available on Virginia, VDOT Press Release, Mar 0, 0, traffic.asp, Accessed July, 0. Economic Development Research Group, Inc., Cambridge Systematics, Inc., ICF International, Texas A&M Transportation Institute, Weris Inc., Development of Tools for Assessing Wider Economic Benefits of Transportation, Report S-C-RW-, Transportation Research Board of The National Academies, 0. Economic Development Research Group, Susan Moses and Associates, Texas A&M Transportation Institute, SHRP Project C: Application of the SHRP C Tools to a Sample of Existing TPICS Cases, Dec 0, CS%0Cases%0(DRAFT).pdf, Accessed July, 0. Virginia Department of Transportation. Traffic Data. ct-trafficcounts.asp. Accessed July, 0.. Williges, C., B.McCullough,Y.Y.Chu,N.Amatya,R.Kuo,M.Lin,and.L.Chu. Pilot Testing of SHRP Reliability Data and Analytical Products: Southern California. Transportation Research Record: Journal of the Transporation Research Board, Online Publication., 0. Bureau of Economic Analysis. Regional Data, Economic profiles. Accessed June, 0. Bureau of Labor Statistics Consumer Price Index, Inflation Calculator. Accessed June, 0. Commonwealth of Virginia. I- Active Traffic and Safety Management System Design-Build Project, Contract Award, Feb, 0, ard_letter.pdf. Accessed June, 0