Operations-oriented traffic signal performance measures are important for identifying the need for retiming to improve traffic signal operations. 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. Over 400 billion vehicle trajectory points are generated each month in the United States. This paper proposes using high-fidelity vehicle trajectory data to produce traffic signal performance measures such as: split failure, downstream blockage, and quality of progression, as well as traditional Highway Capacity Manual level of service. Geo-fences are created at specific signalized intersections to filter vehicle waypoints that lie within the generated boundaries. These waypoints are then converted into trajectories that are relative to the intersection. A case study is presented that summarizes the performance of an eight-intersection corridor with four different timing plans using over 160,000 trajectories and 1.4 million GPS samples collected during weekdays in July 2019 between 5:00 a.m. and 10:00 p.m. The paper concludes by commenting on current probe data penetration rates, indicating that these techniques can be applied to corridors with annual average daily traffic of ~15,000 vehicles per day for the mainline approaches, and discussing cloud-based implementation opportunities.

Amyotrophic lateral sclerosis (ALS), known as motor neurone disease (MND) in Britain, poses special problems in rehabilitation by virtue of its nature, trajectory and the age of patients with the disease. Many practical difficulties of ALS are well known, but there has been little research on the psychological parameters of the disease. This study of 181 ALS patients, from a national register in England and Wales, indicates that, contrary to some previous research, psychological distress (measured by the General Health Questionnaire) is widespread among patients at all stages of the disease. Severity of functional impairment is significantly related to psychological distress, but explains only a small part of the variance. Rehabilitation in relation to ALS must take account of the high incidence of psychological difficulties, as well as the considerable functional problems associated with the disease.

Typical safety improvements at signalized intersections are identified and prioritized using crash data over 3–5 years. Enhanced probe data that provides date, time, heading, and location of hard-braking events has recently become available to agencies. In a typical month, over six million hard-braking events are logged in the state of Indiana. This study compared rear-end crash data over a period of 4.5 years at 8 signalized intersections with weekday hard-braking data from July 2019. Using Spearman’s rank-order correlation, results indicated a strong correlation between hard-braking events and rear-end crashes occurring more than 400 ft upstream of an intersection. The paper concludes that using a month or two of hard-braking events occurring upstream from the stop bar may be a useful tool to screen potential locations with elevated rear-end crashes. Using these techniques described in this paper, new commercially available hard-braking data sources will provide an opportunity for agencies to follow up with mitigation measures addressing emerging problems much quicker than typical practices that rely on 3–5 years of crash data.

Over 400 billion passenger vehicle trajectory waypoints are collected each month in the United States. This data creates many new opportunities for agencies to assess operational characteristics of roadways for more agile management of resources. This study compared traffic counts obtained from 24 Indiana Department of Transportation traffic counts stations with counts derived by the vehicle trajectories during the same periods. These stations were geographically distributed throughout Indiana with 13 locations on interstates and 11 locations on state or US roads. A Wednesday and a Saturday in January, August, and September 2020 are analyzed. The results show that the analyzed interstates had an average penetration of 4.3% with a standard deviation of 1.0. The non-interstate roads had an average penetration of 5.0% with a standard deviation of 1.36. These penetration levels suggest that connected vehicle data can provide a valuable data source for developing scalable roadway performance measures. Since all agencies currently have a highway monitoring system using fixed infrastructure, this paper concludes by recommending agencies integrate a connected vehicle penetration monitoring program into their traditional highway count station program to monitor the growing penetration of connected cars and trucks.

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 9 th to 15 th 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.