Search

Historically, a network of roadside sensors and cameras have been used to monitor freeway conditions. Although these systems are effective, they are typically not operational in and around work zones. Furthermore, it is often not financially viable to deploy in-road sensors and cameras in rural areas. Connected vehicle trajectory data has emerged as a viable source of data and provides a unique opportunity for monitoring freeways. This monograph describes how these connected vehicles can be used to directly measure queue lengths and travel times and this description is summarized in a graphical format easily used by agencies to make management decisions. Approximately 50 use cases are described to demonstrate these techniques under diverse conditions, such as lane reductions, short term closures, rolling slowdowns, work zone set up, work zone removal and inclement weather. A number of the use cases were selected from Indiana locations that had good ITS camera coverage to provide context-sensitive information to help the reader understand the graphics. In addition, several case studies are presented from selected states around the country to demonstrate the scalability of these techniques.

Traffic Incident Management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring the safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile-marker based with cameras that operate in a Pan-Tilt-Zoom (PTZ) coordinate system relies on dispatchers having detailed knowledge of hundreds of cameras and perhaps some presets. During real-time incident dispatching, reducing the time it takes to identify the most relevant cameras and view the incident improves incident management dispatch times. This research developed a camera-to-mile marker mapping technique that automatically sets the camera view to a specified mile marker within the field-of-view of the camera. A new performance metric on verification time (TEYE) that captures the time it takes for TMC operators to have the first visual on roadside cameras is proposed for integration into the FHWA TIM event sequence. Performance metrics that summarize spatial camera coverage and image quality for use in both dispatch and long-term statewide planning for camera deployments were also developed. Using mobile mapping and LiDAR geospatial data to automate the mapping of mile markers to camera PTZ settings, and the integration of connected vehicle trajectory data to detect incidents and set the nearest camera view on the incident are both discussed for future studies.

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.

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.

For over a decade, segment-based probe data has been extensively used by transportation stakeholders for monitoring mobility on Indiana roadways. However, enhanced probe data from connected vehicles includes a richer dataset that can provide more detailed real-time and after-action reviews. This enhanced data includes detailed vehicle trajectories, at 3s resolution, and “event data.” This event data is near real-time and includes hard-braking events, hard-acceleration events, weather-related data, including wiper activations and some seat belt usage data. This project developed a set of methodologies and resulting visualizations that enables the use of emerging connected vehicle data in operational decision-making on work zone management and winter operations activities. Each month approximately 13 billion connected vehicle records are ingested for Indiana. During peak periods, approximately 625,000 records per minute are ingested. Without substantial processing, this large data set is “data-rich, information-poor.” This study developed techniques to rapidly assign relevant data to interstate segments so that visual graphics could be efficiently generated. This provided the ability for both real-time monitoring as well as after action assessment to identify opportunities to improve both work zone operations and winter operation activities. The summaries derived from these datasets have helped promote effective actionable dialog among agencies, contractors, and public safety colleagues towards the overarching goal of improving interstate safety and mobility.

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.

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.

In November 2022, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 470 billion connected vehicle records covering all 50 states for the month of August 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone analytics graphics and electric/hybrid vehicle counts at a national level. This report illustrates several performance measures developed using this dataset for all interstate routes, both state wise and cross-country. Statewide graphics are presented for each interstate in the 50 states. There are also a series of multi-state graphics for I-5, I-10, I-15, I-35, I-55, I-65, I-75, I-80, I-90, and I-95. Performance measures include absolute and normalized trip counts classified by type of trip (electric vehicle or hybrid vehicle or internal combustion engine vehicle), weekly heatmaps based on vehicle speed overlaid with hard-braking events and finally, speed profiles by interstate mile markers. The generated performance measures can be found in the supplemental files.

In January 2023, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 503 billion connected vehicle records covering all 50 states for the month of December 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone/winter weather analytics graphics and electric/hybrid vehicle counts at a national level as well as visualize the mobility impacts of winter weather activity on the national interstate network. This report illustrates several performance measures developed using this dataset for all interstate routes, both state wise and cross-country. Statewide graphics are presented for each interstate in the 50 states. There are also a series of multi-state graphics for I-5, I-10, I-15, I-35, I-55, I-65, I-75, I-80, I-90, and I-95 and selected other routes that witnessed winter storm impacts. Performance measures include absolute and normalized trip counts classified by type of trip (electric vehicle or hybrid vehicle or internal combustion engine vehicle), weekly heatmaps based on vehicle speed (for the two-week period from December 12-25, 2022) and finally, speed profiles by interstate mile markers. Traffic tickers depicting miles of congestion as well as mile-hours of congestion for the national interstate network and selected multi-state routes are also included to provide a unified visual of nationwide mobility impact of recurring congestion as well as non-recurring congestion caused by winter weather. The generated performance measures can be found in the supplemental files.

Systemwide interstate performance measures that detail hours and location of congestion on an interstate provide important information for decision makers to plan capital projects and assess operations. This report presents summary of mile-hours of congestion across 8 Indiana interstates and the Indiana toll road. Hours of operation by speed bins (0 to 14 mph, 15 mph to 24 mph, 25 mph to 34 mph, 35 mph to 44 mph, 45 mph to 54 mph, 55 mph to 64 mph, more than 65 mph) for every 0.1 mile of the interstates across a month were tabulated for every hour of every day during the month. The quantities of those six different speed bins are plotted as a stacked bar plot from lower to higher speeds by mile marker for each month. The vertical axis shows the mile marker of the interstate. Horizontally, these stacked bars are cropped at maximum of 250 hours (a little more than 10 days) per month to focus on the lower speeds. To produce these plots, approximately 60 billion records from INRIX across 5 years were analyzed. These speed profiles help identify areas with congestion at system level as well as regions impacted by severe winter storms and construction projects.

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.

With the advent of probe data, there is a need to virtualize many of the Traffic Management Center (TMC) tools used for analyzing work zones, severe crashes, winter operations, moving maintenance operations, and providing dashboards characterizing overall system mobility. Traditional tools have evolved over the past several years and it is important to develop training materials and make them more accessible to a broad range of Indiana Department of Transportation (INDOT) users and other stakeholder. Over the past several years, agencies have used probe data, mainly 1-minute aggregated segment-based probe data to assess and manage roadways. This study extended traditional segment-based probe data concepts to include enhanced trajectory-based connected vehicle (CV) data, which provides anonymous individual vehicle waypoints at a reporting interval of 3 seconds within a 1.5-meter fidelity radius. The study discusses some of the near-term opportunities, nationwide scalability, and some of the limitations of trajectory data for managing roadways and infrastructure assessment. The tools developed in this study will assist INDOT and other stakeholders in visualizing interstate queues, identifying back-of-queue hard braking events and crashes, identifying alternate diversions during incidents and road closures, enhancing agile management of work zones, estimating traffic signal performance measures without infrastructure investment, and understanding the impact of construction diversions on traffic signals performance.