<p>  </p> <p>Historically, agencies have been reliant on physical infrastructure, crash data, manual data collection, and modeling to evaluate their road networks. Over the past several years, enhanced probe data has become commercially available and has shown itself to be a relatively inexpensive and scalable way to evaluate the performance of road networks. In January 2022 alone, 11.3 billion passenger vehicle trajectory waypoints and 279 million passenger vehicle event records were logged in the state of Indiana. This data, typically segmented into vehicle trajectory waypoints and vehicle event records, contains a variety of information including, but not limited to, location, speed, heading, and timestamp. </p> <p>One use for this enhanced probe data is the evaluation of traffic signals for safety improvements. Typically, agencies require 3 – 5 years of crash data to be able to statistically identify intersections in need of safety improvements. This study compared crash data over a 4.5-year period at 8 signalized intersections to one month of weekday hard-braking and hard-acceleration data from July 2019. A Spearman’s rank-order correlation test was used, and a strong to very strong correlation between event data and crashes could be found indicating that just one month of event data could be an adequate substitute for 3 – 5 years of crash data. </p> <p>The representativeness of this data is often a major concern for many agencies as the usefulness of the data is only as good as the data itself. This paper describes and demonstrates a methodology for measuring connected vehicle penetration using data provided by state highway performance monitoring stations. This study looked at 1.7 billion count station vehicle counts and 70 million connected vehicle records across 381 count stations in 11 different states (California, Connecticut, Georgia, Indiana, Minnesota, North Carolina, Ohio, Pennsylvania, Texas, Utah, and Wisconsin). Across the 11 states and 381 stations, the average percent penetration was 3.8% in August 2020 and 3.9% in August 2021. Drilling down to August 2021, the percent penetration observed among the 187 interstate stations varied from 1.6% in Indiana to 10.0% in Wisconsin. A similar comparison of 162 non-interstate count stations showed a variation of 2.1% in MN and 18.0% in WI on non-interstates. </p>

<p>  </p> <p>Advancements in technology have propelled the availability of enriched and more frequent information about traffic conditions as well as the external factors that impact traffic such as weather, emergency response etc. Most newer vehicles are equipped with sensors that transmit their data back to the original equipment manufacturer (OEM) at near real-time fidelity. A growing number of such connected vehicles (CV) and the advent of third-party data collectors from various OEMs have made big data for traffic commercially available for use. Agencies maintaining and managing surface transportation are presented with opportunities to leverage such big data for efficiency gains. The focus of this dissertation is enhancing the use of CV data and applications derived from fusing it with other datasets to extract meaningful information that will aid agencies in data driven efficient decision making to improve network wide mobility and safety performance.   </p> <p>One of the primary concerns of CV data for agencies is data sampling, particularly during low-volume overnight hours. An evaluation of over 3 billion CV records in May 2022 in Indiana has shown an overall CV penetration rate of 6.3% on interstates and 5.3% on non-interstate roadways. Fusion of CV traffic speeds with precipitation intensity from NOAA’s High-Resolution Rapid-Refresh (HRRR) data over 42 unique rainy days has shown reduction in the average traffic speed by approximately 8.4% during conditions classified as very heavy rain compared to no rain. </p> <p>Both aggregate analysis and disaggregate analysis performed during this study enables agencies and automobile manufacturers to effectively answer the often-asked question of what rain intensity it takes to begin impacting traffic speeds. Proactive measures such as providing advance warnings that improve the situational awareness of motorists and enhance roadway safety should be considered during very heavy rain periods, wind events, and low daylight conditions.</p> <p>Scalable methodologies that can be used to systematically analyze hard braking and speed data were also developed. This study demonstrated both quantitatively and qualitatively how CV data provides an opportunity for near real-time assessment of work zone operations using metrics such as congestion, location-based speed profiles and hard braking. The availability of data across different states and ease of scalability makes the methodology implementable on a state or national basis for tracking any highway work zone with little to no infrastructure investment. These techniques can provide a nationwide opportunity in assessing the current guidelines and giving feedback in updating the design procedures to improve the consistency and safety of construction work zones on a national level.  </p> <p>CV data was also used to evaluate the impact of queue warning trucks sending digital alerts. Hard-braking events were found to decrease by approximately 80% when queue warning trucks were used to alert motorists of impending queues analyzed from 370 hours of queueing with queue trucks present and 58 hours of queueing without the queue trucks present, thus improving work zone safety. </p> <p>Emerging opportunities to identify and measure traffic shock waves and their forming or recovery speed anywhere across a roadway network are provided due to the ubiquity of the CV data providers. A methodology for identifying different shock waves was presented, and among the various case studies found typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were between 5.78 to 16.54 mph. The significance of this is illustrated with a case study of  a secondary crash that suggested  accelerating the clearance by 9 minutes could have prevented the secondary crash incident occurring at the back of the queue. Such capability of identifying and measuring shock wave speeds can be utilized by various stakeholders for traffic management decision-making that provide a holistic perspective on the importance of both on scene risk as well as the risk at the back of the queue. Near real-time estimation of shock waves using CV data can recommend travel time prediction models and serve as input variables to navigation systems to identify alternate route choice opportunities ahead of a driver’s time of arrival.   </p> <p>The overall contribution of this thesis is developing scalable methodologies and evaluation techniques to extract valuable information from CV data that aids agencies in operational decision making.</p>