A Quantitative Approach for Timing the Pedestrian Walk Interval

At a typical signalized intersection, the pedestrian phase consists of a walk interval and a change/clearance interval, during which pedestrians are given the right of way. The walk interval is intended to allow pedestrians to exit the curb ramp and enter the crosswalk. The clearance interval will enable them to cross entirely to the other side of the road. Unfortunately, the literature is quite vague on how long the walk interval should be and provides values ranging from 4 to 15 seconds based on qualitative pedestrian demand ranging from Negligible to High. To provide some quantitative guidance for walk interval selection, this paper reports on a study that collected 1,500 pedestrian movement data from 12 signalized intersections with varying pedestrian demand, pedestrian storage areas, and pedestrian push-button locations. The data was used to propose a quantitative model for designers to select the appropriate walk interval. Specifically, this paper seeks to add values to the Traffic Operations Handbook walk-interval guidelines as to how many pedestrians are considered “negligible volume” and can be accommodated by the 4 second minimum time, how many pedestrians are considered “typical volume” and require 7 to 10 seconds, and how many pedestrians are considered “high volume” and require 10 to 15 seconds, or perhaps longer. In addition to examining pedestrian demand, this paper looks at the impact of storage areas and pedestrian push-button location on pedestrian start-up time and, consequently, an appropriate walk interval.

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.

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.

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.

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.

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.