Signalized Corridor Timing Plan Change Assessment Using Connected Vehicle Data

Updates to traffic signal timing plans are expected to either improve operations or mitigate the effects of increased volumes. Longitudinal before-after studies are important when validating changes to traffic signal systems, but they have historically required field data collection as well as deployment of extensive detection and communication equipment. These infrastructure-based techniques are costly and hard to scale. This study utilizes commercially available connected vehicle (CV) trajectory data to assess the change in performance between August 2020 and August 2021 on a 22-intersection corridor associated with the implementation of a semi-automated adaptive control system. Approximately 1 million trajectories and 13.5 million GPS points are analyzed for weekdays in August 2020 and August 2021. The vehicle trajectory data is used to compute corridor travel times and linear referenced relative to the far side of each intersection to generate Purdue Probe Diagrams (PPD). Using the PPDs, operational measurements such as arrivals on green (AOG), split failures (SF), and downstream blockage (DSB) are calculated. Additionally, traditional Highway Capacity Manual (HCM) level of service (LOS) is estimated. Even though there was a 35% increase in annual average daily traffic (AADT), the weighted average vehicle delay only increased by two seconds, LOS did not change, AOG improved by 1%, and SF and DSB remained the same. Based on the small changes in operational performance and considering the increase in traffic volume it is concluded that the implementation of the semi-automated adaptive control system had a significant positive impact in the corridor. The presented framework can be utilized by agencies to use CV data to perform before-after studies to evaluate the impact of signal timing plan changes. The presented methodology can be applied to any location where CV trajectory data is available.

Using Anonymous Connected Vehicle Data to Evaluate Impact of Speed Feedback Displays, Speed Limit Signs and Roadway Features on Interstate Work Zones Speeds

Annually, there are over 120,000 crashes in work zones in the United States. High speeds in construction zones are a well-documented risk factor that increases the frequency and severity of crashes. This study used connected vehicle data to evaluate the spatial and temporal impact that regulatory signs, speed feedback displays, and construction site geometry had on vehicle speed. Over 27,000 unique trips over 2 weeks on a 15-mile interstate construction work zone near Lebanon, IN were analyzed. Spatial analysis over a 0.2-mi segment before and after the posted speed limit signs showed that the regulatory signs had no statistical impact on reducing speeds. A before/after analysis was also conducted to study the impact of radar-based speed feedback that displays the motorists’ speed on a sign below a regulatory speed limit sign. Results showed a maximum drop in median speeds of approximately 5 mph. Speeds greater than 15 mph above the speed limit dropped by 10% - 15%. The reduction in speeds began approximately 1000 feet ahead of the sign and results were found to be statistically significant. The analysis also revealed that larger speed drops inside the work zone were due to geometric constraints that required additional driver workloads, especially during shoulder width changes and lane shifts. The results from this study will be helpful for agencies to understand driver behavior in the work zones and to identify proper speed limit compliance techniques that significantly reduce driver speeds in and around work zones.

Calibration and Confidence in Snowplow Fleet Operations and Fleet Telematics

The Indiana Department of Transportation (INDOT) spends approximately $30 to $60 million a year on deicing salt and operates a fleet of 1,000 winter operations trucks distributed among 140 locations. The entire fleet is now instrumented with location telematics, and all new trucks have integrated dash cameras, salt spreader application rate and plow up/plow down integrated into the telematics link. When winter storms occur, they have varying regional impacts and INDOT monitors several data sources including National Weather Service (NWS) live doppler, National Severe Storms Laboratory’s (NSSL) Multi-Radar Multi-Sensor (MRMS) products, road weather monitoring stations, and connected vehicles (CV) that provide roadway segment operating speeds. This paper discusses how telematics has been integrated to provide a comprehensive view of conditions, truck asset locations, and material distribution maps. The telematics identified widely varying salt spreader rates for the same calibration settings and equipment in preliminary analysis. A calibration box is developed to allow offload calibration to occur within 10 minutes without weighing or transporting the fleet vehicle. The method is deployed across six districts at INDOT for over 1000 snowplows. A sampling of eight trucks in the fleet found the proposed calibration method reduced salt application on average of 45%. This paper describes a series of telematics dashboards for managing winter operations and details the methods developed for 140 geographically distributed truck units to conduct simple, fast, and effective calibration.

Correlating Connected Vehicle Estimated Deceleration Events with Crash Incidents near Interstate Interchanges

Historical roadway safety analyses have used labor and time-intensive crash data collection procedures. However, crash reporting is often delayed and crash locations are reported with varying levels of spatial accuracy and detail. Recent advances in connected vehicle (CV) data provide an opportunity for stakeholders to proactively identify areas of safety concerns in near-real time with high spatial precision. Public and private sector stakeholders including automotive original equipment manufacturers (OEM) and insurance providers may independently define acceleration thresholds for reporting unsafe driver behavior. Although some OEMs have provided fixed threshold hard-braking event data for a number of years, this varies by OEM and there is no published literature on the best thresholds to use for identifying emerging safety issues. This research proposes a methodology to estimate deceleration events from raw CV trajectory data at varying thresholds that can be scaled to any CV. The estimated deceleration events and crash incident records around 629 interstate exits in Indiana were analyzed for a three-month period from March 1-May 31, 2023. Nearly 20 million estimated deceleration events and 4800 crash records were spatially joined to a 2-mile search radius around each exit ramp. Results showed that deceleration events between -0.5 g and -0.4 g had the highest correlation with an R2 of 0.69. This study also identifies the top 20 interstate exit locations with highest deceleration events. The framework presented in this study enables agencies and transportation professionals to perform safety evaluations on raw trajectory data without the need to integrate external data sources.

Longitudinal Performance Assessment of Traffic Signal System Impacted by Long-Term Interstate Construction Diversion Using Connected Vehicle Data

Local arterials can be significantly impacted by diversions from adjacent work zones. These diversions often occur on unofficial detour routes due to guidance received on personal navigation devices. Often, these routes do not have sufficient sensing or communication equipment to obtain infrastructure-based traffic signal performance measures, so other data sources are required to identify locations being significantly affected by diversions. This paper examines the network impact caused by the start of an 18-month closure of the I-65/70 interchange (North Split), which usually serves approximately 214,000 vehicles per day in Indianapolis, IN. In anticipation of some proportion of the public diverting from official detour routes to local streets, a connected vehicle monitoring program was established to provide daily performances measures for over 100 intersections in the area without the need for vehicle sensing equipment. This study reports on 13 of the most impacted signals on an alternative arterial to identify locations and time of day where operations are most degraded, so that decision makers have quantitative information to make informed adjustments to the system. Individual vehicle movements at the studied locations are analyzed to estimate changes in volume, split failures, downstream blockage, arrivals on green, and travel times. Over 130,000 trajectories were analyzed in an 11-week period. Weekly afternoon peak period volumes increased by approximately 455%, split failures increased 3%, downstream blockage increased 10%, arrivals on green decreased 16%, and travel time increase 74%. The analysis performed in this paper will serve as a framework for any agency that wants to assess traffic signal performance at hundreds of locations with little or no existing sensing or communication infrastructure to prioritize tactical retiming and/or longer-term infrastructure investments.

Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data

Since the first Diverging Diamond Interchange (DDI) implementation in 2009, most of the performance studies developed for this type of interchange have been based on simulations and historical crash data, with a small number of studies using Automated Traffic Signal Performance Measures (ATSPM). Simulation models require considerable effort to collect volumes and to model actual controller operations. Safety studies based on historical crashes usually require from 3 to 5 years of data collection. ATSPMs rely on sensing equipment. This study describes the use of connected vehicle trajectory data to analyze the performance of a DDI located in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe Diagram (PPD) is proposed to assess the levels of delay, progression, and saturation. Further, an additional PPD variation is presented that provides a convenient visualization to qualitatively understand progression patterns and to evaluate queue length for spillback in the critical interior crossover. Over 7000 trajectories and 130,000 GPS points were analyzed between the 7th and the 11th of June 2021 from 5:00 AM to 10:00 PM to estimate the DDI’s arrivals on green, level of service, split failures, and downstream blockage. Although this technique was demonstrated for weekdays, the ubiquity of connected vehicle data makes it very easy to adapt these techniques to analysis during special events, winter storms, and weekends. Furthermore, the methodologies presented in this paper can be applied by any agency wanting to assess the performance of any DDI in their jurisdiction.

Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania

Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures. A common concern by agencies interested in using crowd sourced probe data is the penetration rate across different types of roads, different hours of the day, and different regions. This paper describes and demonstrates a methodology that uses data from state highway performance monitoring systems in Indiana, Ohio and Pennsylvania. The study analyzes 54 locations over the 3 states for select Wednesdays and Saturdays in 2020 and 2021. Overall, across all locations and dates, the median penetration was approximately 4.5%. The median penetration for August 2020 for Indiana, Ohio, and Pennsylvania was 4.6%, 4.3%, and 4.0%, respectively. The median penetration for those same states in August 2020 on interstates and non-interstates was 3.9% and 4.6%, respectively. Additionally, the study conducted a longitudinal evaluation of Indiana penetration for selected months between January 2020 and June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This paper concludes by recommending that the techniques described in this paper be scaled to other states so that traffic engineers can make informed decisions on the use and limitations of connected vehicle data for various use cases.

Roundabout Performance Analysis Using Connected Vehicle Data

There are over 8000 roundabouts in the United States. The current techniques for assessing their performance require field counts to provide inputs to analysis or simulation models. These techniques are labor-intensive and do not scale well. This paper presents a methodology to use connected vehicle (CV) trajectory data to estimate delay and level of service for roundabout approaches by adapting the Purdue Probe Diagram used for traffic signal analytics. By linear referencing vehicle trajectories with a particular movement based on the location and time they exit a roundabout, delay can be calculated. The scalability is demonstrated by applying these techniques to assess over 100 roundabouts in Carmel, IN during the weekday afternoon peak period in July 2021. Over 264,000 trajectories and 3,600,000 GPS points were analyzed to rank over 300 roundabout approaches by delay and summarize in Pareto-sorted graphics and maps. The paper concludes by discussing how these techniques can also be used to analyze queue lengths and origin-destination characteristics at roundabouts. The methodology presented in this study can be used by any agency that wants to assess the performance of all roundabouts in their system.

Leveraging Telematics for Winter Operations Performance Measures and Tactical Adjustment

The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15th, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long-term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.

Evaluation of the Impact of Queue Trucks with Navigation Alerts Using Connected Vehicle Data

Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist’s assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue trucks present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.

Diamond Interchange Performance Measures Using Connected Vehicle Data

Diamond interchanges are frequently used where a freeway intersects a two-way surface street. Most of the techniques to evaluate the performance of diamond interchanges rely on the Highway Capacity Manual (HCM), simulation, Automated Traffic Signal Performance Measures (ATSPMs), and historical crash data. HCM and simulation techniques require on-site data collection to obtain models’ inputs. ATSPMs need high-resolution controller event data acquired from roadway sensing equipment. Safety studies typically need 3 to 5 years of crash data to provide statistically significant results. This study utilizes commercially available connected vehicle (CV) data to assess the performance and operation of a three- and four-phase diamond interchange located in Indianapolis, Indiana, and Dallas, Texas, respectively. Over 92,000 trajectories and 1,400,000 GPS points are analyzed from August 2020 weekdays CV data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) from which arrivals on green (AOG), split failures, downstream blockage, and movement-based control delay are estimated. In addition, an extension of the PPD is presented that characterizes the complete journey of a vehicle travelling through both signals of the diamond interchange. This enhanced PPD is a significant contribution as it provides an analytical framework and graphical summary of the operational characteristics of how the external movements traverse the entire system. The four-phase control showed high internal progression (99% AOG) compared to the moderate internal progression of the three-phase operation (64% AOG). This is consistent with the design objectives of three- and four-phase control models, but historically these quantitative AOG measures were not possible to obtain with just detector data. Additionally, a graphical summary that illustrates the spatial distribution of hard-braking and hard-acceleration events is also provided. The presented techniques can be used by any agency to evaluate the performance of their diamond interchanges without on-site data collection or capital investments in sensing infrastructure.

Truck and Passenger Car Connected Vehicle Penetration on Indiana Roadways

Commercially available connected vehicle (CV) probe data has been demonstrated to provide scalable and near-real-time methodologies to evaluate the performance of road networks for various applications. However, one of the major concerns of probe data for agencies is data sampling, particularly during low-volume overnight hours. This paper reports on an evaluation that looked at both connected passenger cars and connected trucks. This study analyzed 40 continuous count stations in Indiana that recorded more than 10.8 million vehicles and more than 13 million trips (3 billion records) from CV data over a 1-week period from May 9th to 15th in 2022. The average truck penetration was observed to be 3.4% during overnight hours from 1 AM to 5 AM when the connected passenger car penetration was at the lowest. When both connected trucks and connected car penetration were analyzed, the overall CV penetration was 6.32% on interstates and 5.30% on non-interstate roadways. The paper concludes by recommending that both connected car and connected truck data be used by agencies to increase penetration and reduce the hourly variation in CV penetration. This is particularly important during overnight hours.

Continuous Flow Intersection Performance Measures Using Connected Vehicle Data

Continuous flow intersections (CFIs), also known as displaced left turns (DLTs), are a type of alternative intersection designed to improve operations at locations with heavy left-turn movements by reallocating these vehicles to the left side of opposing traffic. Currently, simulation is commonly used to evaluate operational performance of CFIs. However, this approach requires significant on-site data collection and is highly dependent on the analyst’s ability to correctly model the intersection and driver behavior. Recently, connected vehicle (CV) trajectory data has become widely available and presents opportunities for the direct measurement of traffic signal performance measures. This study utilizes CV trajectory data to analyze the performance of a CFI located in West Valley City, UT. Over 4500 trajectories and 105,000 GPS points are analyzed from August 2021 weekday data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) and extended PPDs to estimate split failures (SF), arrivals on green (AOG), traditional Highway Capacity Manual (HCM) level of service (LOS), and the distribution of stops. The estimated operational performance showed effective progression during the PM peak period at all the critical internal storage areas with AOG levels at exit traffic signals between 83% and 100%. In contrast, all external approaches with longer queue storage areas had AOG values ranging from 2% to 81% during the same time period. The presented analytical techniques and summary graphics provide practitioners with tools to evaluate the performance of any CFI where CV trajectories are available without the need for on-site data collection.

Analysis of Electric and Hybrid Vehicle Usage in Proximity to Charging Infrastructure in Indiana

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

Integrated Performance Measures for Bus Rapid Transit System and Traffic Signal Systems Using Trajectory Data

Bus rapid transit (BRT) systems have been implemented in many cities over the past two decades. Widespread adoption of General Transit Feed Specification (GTFS), the deployment of high-fidelity bus GPS data tracking, and anonymized high-fidelity connected vehicle data from private vehicles have provided new opportunities for performance measures that can be used by both transit agencies and traffic signal system operators. This paper describes the use of trajectory-based data to develop performance measures for a BRT system in Indianapolis, Indiana. Over 3 million data records during the 3-month period between March and May 2022 are analyzed to develop visualizations and performance metrics. A methodology to estimate the average delay and schedule adherence is presented along a route comprised of 74 signals and 28 bus stations. Additionally, this research demonstrates how these performance measures can be used to evaluate dedicated and non-dedicated bus lanes with general traffic. Travel times and reliability of buses are compared with nearly 30 million private vehicle trips. Results show that median travel time for buses on dedicated bi-directional lanes is within one minute of general traffic and during peak periods the buses are often faster. Schedule adherence was observed to be more challenging, with approximately 3% of buses arriving within 1 minute on average during the 5AM hour and 5% of buses arriving 6 - 9 minutes late during the 5PM hour. The framework and performance measures presented in this research provide agencies and transportation professionals with tools to identify opportunities for adjustments and to justify investment decisions.

Using Connected Truck Trajectory Data to Compare Speeds in States with and without Differential Truck Speeds

Historically, researchers and practitioners have utilized spot speeds and microscopic simulation methodologies to evaluate the operational impact of differential or uniform speed limits for trucks and passenger vehicles. This paper presents a methodology that uses connected truck data to develop a statistical characterization of both passenger car and truck speeds. These techniques were applied to three adjacent states, Illinois, Indiana and Ohio. Illinois and Ohio have 70 mph speed limits for both trucks and cars. Indiana has a differential speed limit for heavy trucks (65 mph) and passenger cars (70 mph). The statistical distribution of truck speeds was then compared among Illinois, Indiana and Ohio. These speeds were derived from over 8 million connected truck records traveling along Interstate 70 in Illinois, Indiana and Ohio during a one-week period from May 8-14, 2022. Statistical test results over selected 20-mile sections in each state showed that median truck speeds in Indiana with its differential speed limit of 65 mph were only 1 - 2 mph lesser than the neighboring states of Illinois and Ohio who observe a uniform speed limit of 70 mph for all traffic.

Dashboards for Monitoring Congestion and Crashes in Interstate Work Zones

Work zones present challenges to safety and mobility that require agencies to balance limited resources with vital traffic management activities. Extensive literature exists regarding the impact of congestion and recommendations for work zone design to provide safe and efficient traffic operations. However, it is often infeasible or unsafe to inspect every work zone within an agency’s jurisdiction, so it is important to obtain operational feedback regarding congestion and crashes in work zones to prioritize inspection activities. This paper outlines the use of connected vehicle speed data and crash report data to identify operational performance problems in work zones. This is a way to provide feedback to queuing models used to design maintenance-of-traffic (MOT) plans. A weekly work zone report and dashboards were developed for use by the Indiana Department of Transportation (INDOT) for the purpose of assessing and improving both mobility and safety in work zones. The study has developed a mile-hours of congestion graph, frequency of speed delta heat map, congestion profile graph, and the Route Builder interactive application to comprehensively visualize work zone performance. This weekly report provides a mechanism for agency staff to maintain situational awareness of which work zones were most challenging for queues and during what periods those were likely to occur. In one case study, the reports were used to identify and mitigate operational performance problems in a work zone within 4 weeks, reducing congestion and crash rates. The integration of these data provided project managers with quantitative information about traffic mobility and performance of work zones for informed decision-making during the construction season.