Speed Enforcement in Work Zones and Synthesis on Cost-Benefit Assessment of Installing Speed Enforcement Cameras on INDOT Road Network

Work zone safety is a high priority for transportation agencies across the United States. High speeds in construction zones are a well-documented risk factor that increases the frequency and severity of crashes. It is therefore important to understand the extent and severity of high-speed vehicles in and around construction work zones. This study uses CV trajectory data to evaluate the impact of several work zone speed compliance measures, such as posted speed limit signs, radar-based speed feedback displays, and automated speed enforcement on controlling speeds inside the work zone. This study also presents several methodologies to characterize both the spatial and temporal effects of these control measures on driver behavior and vehicle speeds across the work zones

Impacts to Traffic Behavior from Queue Warning Truck: Current Pilot Project

The Indiana Department of Transportation (INDOT) started deploying queue warning trucks ahead of interstate work zones to alert motorists of queued traffic. Along with visually alerting the motorists, digital alerts were integrated with navigational applications such as Apple Maps, Waze, and the in-vehicle infotainment system of Stellantis vehicles. More than 45,000 hours of alerting was provided to motorists across various interstates in Indiana over a 26-month period. This report evaluated the impact of queue warning trucks on traffic using hard braking events and traffic speeds provided by granular connected trajectory vehicle data. Evaluation of over 370 hours of queuing with the presence of queue trucks and 52 hours of queuing without the queue trucks indicated a decrease in hard braking events by 80% when trucks were present with digital alerts. It was also observed that traffic speeds started to reduce approximately 1,500 to 2,000 ft in advance of deployed queue trucks

Extraction of Vehicle CAN Bus Data for Roadway Condition Monitoring

Obtaining timely information across the state roadway network is important for monitoring the condition of the roads and operating characteristics of traffic. One of the most significant challenges in winter roadway maintenance is identifying emerging or deteriorating conditions before significant crashes occur. For instance, almost all modern vehicles have accelerometers, anti-lock brake (ABS) and traction control systems. This data can be read from the Controller Area Network (CAN) of the vehicle, and combined with GPS coordinates and cellular connectivity, can provide valuable on-the-ground sampling of vehicle dynamics at the onset of a storm. We are rapidly entering an era where this vehicle data can provide an agency with opportunities to more effectively manage their systems than traditional procedures that rely on fixed infrastructure sensors and telephone reports. This data could also reduce the density of roadway weather information systems (RWIS), similar to how probe vehicle data has reduced the need for micro loop or side fire sensors for collecting traffic speeds

Crowdsourcing/Winter Operations Dashboard Upgrade

INDOT has recently completed the deployment of Parsons telematics-based dash-cameras, automatic vehicle locator (AVL) positions, and spreader rate monitoring across their winter operations fleet. The motivation of this study was to develop dashboards that integrate connected vehicle data into the real-time monitoring and after-action review of winter storms. Each month approximately 13 billion connected vehicle records are ingested for the state of Indiana and almost 99 billion weather data records are ingested nationwide in 15-minute intervals. This study developed techniques to utilize this connected vehicle data and weather data to monitor real-time mobility of interstates and post storm after-action assessments to identify improvement opportunities of winter operations activities. In multiple instances, these agile reviews have influenced operational changes in snow removal and maintenance around the state, leading to a marked improvement in observed mobility and safety

Connected Vehicle-Centric Dashboards for TMC of the Future

The 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—not only for analyzing work zones, severe crashes, winter operations, and traffic signals—but 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

Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data

High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to relevant roadways and movements are presented. This allows for performance analyses that can range from the movement- to the system-level. A comprehensive suite of methodologies to evaluate signal performance using vehicle trajectories is then provided. Finally, uses of CV hard-braking and hard-acceleration event data to assess safety and driver behavior are discussed. To evaluate scalability and test the proposed techniques, performance measures for over 4,700 traffic signals were estimated using more than 910 million vehicle trajectories and 14 billion GPS points in all 50 states and Washington, D.C. The contents of this report will help the industry transition towards a hybrid blend of detector- and CV-based signal performance measures with rigorously defined performance measures that have been peer-reviewed by both academics and industry leaders