Connected Vehicle Corridor Deployment and Performance Measures for Assessment

In November 2016, the American Association of State Highway and Transportation Officials (AASHTO) announced the Signal Phase and Timing (SPaT) challenge to state and local agencies to kick start infrastructure deployments for V2I communications. The challenge involved the deployment of Dedicated Short Range Communication (DSRC) infrastructure with SPaT broadcasts (current intersection signal light phase) on at least 20 signalized intersections in all of the 50 states by 2020. Although the roadmap for agencies to partner with the automotive industry is still evolving, it is important for Indiana to not only support the SPaT challenge, but also identify mutually beneficial opportunities for INDOT to partner with the automotive industry as Indiana has the second largest automotive related Gross Domestic Product (GDP) in the country. During this study, connected traffic signal infrastructure was deployed at several locations around the state. The West Lafayette corridor SPaT message deployment was done using both traditional Dedicated Short Range Communication (DSRC) as well cellular communication. This report details the deployment locations, the various public and private sector stakeholders that were engaged during the field testing, and several vehicle-infrastructure communication experiments that were used to evaluate connected vehicle use cases. The findings of this research were as follows: The team successfully demonstrated use cases for placing virtual vehicle detection calls to a traffic signal controller using SPaT messages and evaluated latency. The team developed a scalable methodology for characterizing the probability of a traffic signal phase changing by time of day. This methodology of using agency traffic signal data for green light prediction and engine shut down at red lights is particularly useful to the automotive industry. The team successfully demonstrated that split failures, reduced roadway friction and hard braking events can be identified on the vehicle and transmitted to an agency. This enhanced probe data information is particularly valuable to agencies for identifying traffic signal timing problems, segments impacted by winter weather and location where drivers are encountering roadway conditions required hard braking. DSRC provides the lowest latency communication, but in general commercial cellular interface between vehicles and infrastructure provided acceptable latency for most use cases. For most applications, the team believes a commercial cellular interface between vehicles and infrastructure is the most scalable and feasible for an agency to maintain

Connected Vehicle-Centric Dashboards for TMC of the Future

SPR-4625The adoption of dashboards and tools into Traffic Management Centers (TMC) has been growing with advancements in connected vehicle (CV) data. These tools are now being utilized\u2014not only for analyzing work zones, severe crashes, winter operations, and traffic signals\u2014but also to provide measures for characterizing overall system mobility, resiliency, and after-action assessments. Previous studies have extended the concepts to include the enhanced trajectory-based CV data into dashboards that aid agencies in assessing and managing roadways. This study presents the extension of these tools that further improve the value and insights provided. It also highlights the evolution of CV data in Indiana. CV data in Indiana has grown to over 364 billion statewide records. Average overall penetration rate of CV data on interstates has increased to 6.32% in May 2022 with trucks accounting for 1.7%. Sections of this study also present the impact of rain intensity on interstate traffic and incorporation of such weather data into heatmap and other tools. Updates to existing dashboards and a summary of newly developed dashboards are synopsized in this report. Finally, this report presents a case study that highlights the use of these tools to assess and analyze the impact of tornadoes on interstate traffic in Indiana. As interest in these tools has grown, this project facilitated continued improvements and added features to meet the needs of INDOT and their partners

Scalable Operational Traffic Signal Performance Measures from Vehicle Trajectory Data

Operations-oriented traffic signal performance measures are important for identifying retiming needs to improve traffic signal operations. Enhancements on traffic signal timings can lead to a decrease on delays, fuel consumption, and air pollutants. Currently, most traffic signal performance measures are obtained from high-resolution traffic signal controller event data, which provides information on an intersection-by-intersection basis and requires significant initial capital investment. Further, maintenance of the required sensing and communication equipment can represent a significant cost. Over 400 billion vehicle trajectory points are generated each month in the United States. This high volume of data provides more than 95% of road network coverage. This thesis proposes using vehicle trajectory data to produce traffic signal performance measures such as: traditional Highway Capacity Manual (HCM) Level of Service (LOS), quality of progression, split failure, and downstream blockage. Geo-fences are created at specific signalized intersections to filter vehicle’s waypoints that lie within the generated boundaries. These waypoints are then converted into trajectories that are relative to the intersection. Subsequently, trajectory attributes, such as delay and location and number of stops, are analyzed to produce the mentioned performance measures. A case study is presented to demonstrate the methodology, which summarizes the performance of an 8-intersection corridor with 4 different timing plans using over 117,000 trajectories and 1.5 million GPS samples collected during weekdays in July 2019. Graphics to analyze entire corridors and to effectuate temporal comparisons are proposed. The thesis concludes by discussing the required effort and recommendations for scalability, cloud-based implementation opportunities and costs, reviewing current probe data penetrations rates, and indicating that these techniques can be applied to corridors with Annual Average Daily Traffic (AADT) of ~15,000 vehicles-per-day (VPD) for the mainline approaches

Actionable Traffic Signal Performance Measures from Large-scale Vehicle Trajectory Analysis

Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the United States. It is therefore important for agencies to systematically monitor signal performance to identify locations where operations do not function as desired and where mobility could be improved. Currently, most signal performance evaluations are derived from infrastructure-based Automated Traffic Signal Performance Measures (ATSPMs). These performance measures rely on high-resolution detector and phase information that is collected at 10 Hz and reported via TCP/IP connections. Even though ATSPMs have proven to be a valid approach to estimate signal performance, significant initial capital investment required for infrastructure deployment can represent an obstacle for agencies attempting to scale these techniques. Further, fixed vehicle detection zones can create challenges in the accuracy and extent of the calculated performance measures. High-resolution connected vehicle (CV) trajectory data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, this data set provides unique opportunities to derive accurate signal performance measures without the need for infrastructure upgrades. This dissertation provides a comprehensive suite of CV-based techniques to generate actionable and scalable traffic signal performance measures. Turning movements of vehicles at intersections are automatically identified from attributes included in the commercial CV data set to facilitate movement-level analyses. Then, a trajectory-based visualization from which relevant performance measures can be extracted is presented. Subsequently, methodologies to identify signal retiming opportunities are discussed. An approach to evaluate closely-coupled intersections, which is particularly challenging with detector-based techniques, is then presented. Finally, a data-driven methodology to enhance the scalability of trajectory-based traffic signal performance estimations by automatically mapping relevant intersection geometry components is provided. The trajectory data processing procedures provided in this dissertation can aid agencies make data-driven decisions on resource allocation and signal system modifications. The presented techniques are transferable to any location where CV data is available, and the scope of analysis can be easily varied from the movement to intersection, corridor, region, and state level.</p