Populating SAE J2735 Message Confidence Values for Traffic Signal Transitions Along a Signalized Corridor

The communication between connected vehicles and traffic signal controllers is defined in SAE Surface Vehicle Standard J2735. SAE J2735 defines traffic signal status messages and a series of 16 confidence levels for traffic signal transitions. This paper discusses a statistical method for tabulating traffic signal data by phase and time of day and populating the SAE J2735 messages. Graphical representation of the red-green and green-yellow transitions are presented from six intersections along a 4-mile corridor for five different time of day timing plans. The case study provided illustrates the importance of characterizing the stochastic variation of traffic signals to understand locations, phases, and time of day when traffic indications operate with high predictability, and periods when there are large variations in traffic signal change times. Specific cases, such as low vehicle demand and occasional actuation of pedestrian phases are highlighted as situations that may reduce the predictability of traffic signal change intervals. The results from this study also opens up discussion among transportation professionals on the importance of consistent tabulation of confidence values for both beginning and end of green signal states. We believe this paper will initiate dialog on how to consistently tabulate important data elements transmitted in SAE J2735 and perhaps refine those definitions. The paper concludes by highlighting the importance of traffic engineers and connected vehicle developers to work together to develop shared visions on traffic signal change characteristics so that the in-vehicle use cases and human-machine interface (HMI) meet user expectations

Utilization of Dedicated Electric Vehicle Plug-In Charging Stations in a College Campus Environment

As electric mobility is expanding at a rapid pace, the standardized availability of gas stations compared to a scarcity of charging stations continues to be the greatest challenge for electric vehicles. With cities, university campuses and businesses promoting electric vehicle infrastructure and incentives, it is necessary to develop key performance metrics and visualizations that can track the utilization of the charging infrastructure. This study performs a manual data collection at dedicated plug-in charging stations across Purdue University to assess their utilization. Approximately 2,800 observations were conducted over 50 days across seven level 2 plug-in charging stations. Results showed that for large portion of the observations, vehicles were parked at the spots (40%) but not plugged in. Vehicles plugged in to charging stations accounted for 34% of observations. Charging station spots were vacant for 25% of observations indicating that current infrastructure meets the demand. There were 74 unique vehicles that used the spots, of which 27% were plugged in more than 10 times. Illegally parked vehicles accounted for less than 1% with only 4 repeat offenders who used these spots more than once. As electric deployment continues to increase, performance metrics will be an integral tool for agencies and decision makers to help with the maintenance and expansion of electric vehicle infrastructure

Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods

Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of these sampling techniques is to employ filtering techniques to remove outliers associated with trip chaining, but not remove important features in the data associated with incidents or traffic congestion. This paper describes a curated data set that was developed from high-fidelity GPS trajectory data. The curated data contained 31,621 vehicle observations spanning 42 days; 2,550 observations had travel times greater than 3 minutes more than normal. From this baseline data set, outliers were determined using GPS waypoints to determine if the vehicle left the route. Two performance measures were identified for evaluating three outlier-filtering algorithms by the proportion of true samples rejected and proportion of outliers correctly identified. The effectiveness of the three methods over 10-minute sampling windows was also evaluated. The curated data set has been archived in a digital repository and is available online for others to test outlier-filtering algorithms

Using Crowdsourced Vehicle Braking Data to Identify Roadway Hazards

Modern vehicles know more about the road conditions than transportation agencies. Enhanced vehicle data that provides information on “close calls” such as hard braking events or road conditions during winter such as wheel slips and traction control will be critical for improving safety and traffic operations. This research applied conflict analyses techniques to process approximately 1.5 million hard braking events that occurred in the state of Indiana over a period of one week in August 2019. The study looked at work zones, signalized intersections, interchanges and entry/exit ramps. Qualitative spatial frequency analysis of hard-braking events on the interstate demonstrated the ability to quickly identify temporary and long-term construction zones that warrant further investigation to improve geometry and advance warning signs. The study concludes by recommending the frequency of hard-braking events across different interstate routes to identify roadway locations that have abnormally high numbers of “close calls” for further engineering assessment

Probe Vehicle Bluetooth Study for Travel Time Savings Estimation

Interstate 69 is a new highway envisioned to eventually travel the length of the United States, from Canada to Mexico. In Indiana, the construction is being completed in a series of segments, as shown below. With the opening of new sections, the travel time savings for travelers can be quantified. By deploying Bluetooth data loggers to capture unique MAC addresses of travelers’ devices, timestamps for corresponding MAC addresses along the study route can be matched to determine travel time. Compared to the posted speeds and lengths of new interstate sections, the travel time saved by using I-69 can be estimated. One such study was completed for the previous opening of I-69 Sections 1-3, and researchers completed a study for Section 4, which opened to public traffic on December 6, 2015

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

Estimation of Connected Vehicle Penetration Rate on Indiana Roadways

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

Characterization of Knots and Links Arising From Site-specific Recombination on Twist Knots

We develop a model characterizing all possible knots and links arising from recombination starting with a twist knot substrate, extending previous work of Buck and Flapan. We show that all knot or link products fall into three well-understood families of knots and links, and prove that given a positive integer $n$, the number of product knots and links with minimal crossing number equal to $n$ grows proportionally to $n^5$. In the (common) case of twist knot substrates whose products have minimal crossing number one more than the substrate, we prove that the types of products are tightly prescribed. Finally, we give two simple examples to illustrate how this model can help determine previously uncharacterized experimental data.Comment: 32 pages, 7 tables, 27 figures, revised: figures re-arranged, and minor corrections. To appear in Journal of Physics

Travel Time Observations Using Bluetooth MAC Address Matching: A Case Study on the Rajiv Gandhi Roadway: Chennai, India

Bluetooth MAC Address matching has become a useful approach for determining travel times on corridors in the United States. In September 2013, an international collaborative study was performed using this technology along a busy urban corridor in Chennai, India. Two Purdue University graduate students traveled to Chennai, India to interact and understand the dynamics of exchanging knowledge and implementing technologies in different environments. The students worked with students from IIT Madras to determine the feasibility of Bluetooth probe vehicle technology along a typical Indian corridor. The study determined that it is feasible to expand Bluetooth use in India. Using the technology, the impact of weather, holiday and peak hour related traffic events were determined and evaluated. Of particular note were the relative high penetration of Bluetooth devices, and the exceptionally strong impact of precipitation on the heterogeneous traffic stream in Chennai, India