scholarly journals Estimation of Connected Vehicle Penetration Rate on Indiana Roadways

2021 ◽  
Author(s):  
Margaret Hunter ◽  
Jijo K. Mathew ◽  
Ed Cox ◽  
Matthew Blackwell ◽  
Darcy M. Bullock
Keyword(s):  
Standard Deviation ◽  
Monitoring Program ◽  
The United States ◽  
Connected Vehicle ◽  
Traffic Counts ◽  
Vehicle Trajectories ◽  
Vehicle Data ◽  
Operational Characteristics ◽  
Geographically Distributed ◽  
Vehicle Trajectory

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.

3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data

2018 ◽  
Vol 2672 (45) ◽  
pp. 14-22 ◽  
Author(s):  
Yuan Sun ◽  
Hao Xu ◽  
Jianqing Wu ◽  
Jianying Zheng ◽  
Kurt M. Dietrich
Keyword(s):  
High Resolution ◽  
Data Processing ◽  
Extraction Procedure ◽  
Estimation Method ◽  
Connected Vehicle ◽  
Vehicle Trajectories ◽  
Vehicle Data ◽  
Stop Sign ◽  
Processing Procedure ◽  
Data Processing Procedure

High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.

scholarly journals Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

2017 ◽  
Vol 2620 (1) ◽  
pp. 54-68 ◽  
Author(s):  
Christopher M. Day ◽  
Howell Li ◽  
Lucy M. Richardson ◽  
James Howard ◽  
Tom Platte ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Sampling Period ◽  
Signalized Intersections ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Trajectory Data ◽  
Vehicle Data ◽  
Low Levels ◽  
Vehicle Trajectory

Signal offset optimization recently has been shown to be feasible with vehicle trajectory data at low levels of market penetration. Offset optimization was performed on two corridors with that type of data. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for two corridors, comprising 25 signalized intersections. After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, with variations by approach. Then those arrival profiles were compared statistically with those measured with physical detectors, and most approaches showed statistically significant goodness of fit at a 90% confidence level. Finally, the arrival profiles created with virtual detection were used to optimize offsets and compared with a solution derived from arrival profiles obtained with physical detectors. Results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data. In addition, a sensitivity analysis of the sampling period indicated that 2 weeks may be sufficient for data collection at current penetration rates.

scholarly journals Review of Usage of Real-World Connected Vehicle Data

2020 ◽  
Vol 2674 (10) ◽  
pp. 939-950
Author(s):  
Yun Zhou ◽  
Raj Bridgelall
Keyword(s):  
Real World ◽  
Large Scale ◽  
The United States ◽  
Connected Vehicles ◽  
United States Department ◽  
Connected Vehicle ◽  
Pilot Model ◽  
Vehicle Data ◽  
World Information ◽  
Vehicle Technologies

GPS loggers and cameras aboard connected vehicles can produce vast amounts of data. Analysts can mine such data to decipher patterns in vehicle trajectories and driver–vehicle interactions. Ability to process such large-scale data in real time can inform strategies to reduce crashes, improve traffic flow, enhance system operational efficiencies, and reduce environmental impacts. However, connected vehicle technologies are in the very early phases of deployment. Therefore, related datasets are extremely scarce, and the utility of such emerging datasets is largely unknown. This paper provides a comprehensive review of studies that used large-scale connected vehicle data from the United States Department of Transportation Connected Vehicle Safety Pilot Model Deployment program. It is the first and only such dataset available to the public. The data contains real-world information about the operation of connected vehicles that organizations are testing. The paper provides a summary of the available datasets and their organization, and the overall structure and other characteristics of the data captured during pilot deployments. Usage of the data is then classified into three categories: driving pattern identification, development of surrogate safety measures, and improvements in the operation of signalized intersections. Finally, some limitations experienced with the existing datasets are identified.

scholarly journals A Framework of an Integrated Livestock Vehicle Trajectory Database Using Digital Tachograph Data

2021 ◽  
Vol 13 (5) ◽  
pp. 2694
Author(s):  
Heehyeon Jeong ◽  
Jungyeol Hong ◽  
Dongjoo Park
Keyword(s):  
Infectious Diseases ◽  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Disease Spread ◽  
Transportation Engineering ◽  
The Public ◽  
Vehicle Trajectories ◽  
History Of ◽  
Vehicle Trajectory ◽  
Livestock Diseases

The outbreak of African swine fever virus has raised global concerns regarding epidemic livestock diseases. Therefore, various studies have attempted to prevent and monitor epidemic livestock diseases. Most of them have emphasized that integrated studies between the public health and transportation engineering are essential to prevent the livestock disease spread. However, it has been difficult to obtain big data related to the mobility of livestock-related vehicles. Thus, it is challenging to conduct research that comprehensively considers cargo vehicles’ movement carrying livestock and the spread of livestock infectious diseases. This study developed the framework for integrating the digital tachograph data (DTG) and trucks’ visit history of livestock facility data. The DTG data include commercial trucks’ coordinate information, but it excludes actual livestock-related vehicle trajectories such as freight types and facility visit history. Therefore, the integrated database we developed can be used as a significant resource for preventing the spread of livestock epidemics by pre-monitoring livestock transport vehicles’ movements. In future studies, epidemiological research on infectious diseases and livestock species will be able to conduct through the derived integrating database. Furthermore, the indicators of the spread of infectious diseases could be suggested based on both microscopic and macroscopic roadway networks to manage livestock epidemics.

scholarly journals 1414. #Vaccine Twitter Influencers: is it Just About Reach and Followers?

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S714-S715
Author(s):  
Jean-Etienne Poirrier ◽  
Theodore Caputi ◽  
John Ayers ◽  
Mark Dredze ◽  
Sara Poston ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Standard Deviation ◽  
Small Proportion ◽  
The United States ◽  
Research Support ◽  
Policy Makers ◽  
Media Messages ◽  
Social Media Platforms ◽  
The Impact

Abstract Background A small number of powerful users (“influencers”) dominates conversations on social media platforms: less than 1% of Twitter accounts have at least 3,000 followers and even fewer have hundreds of thousands or millions of followers. Beyond simple metrics (number of tweets, retweets...) little is known about these “influencers”, particularly in relation to their role in shaping online narratives about vaccines. Our goal was to describe influential Twitter accounts that are driving conversations about vaccines and present new metrics of influence. Methods Using publicly-available data from Twitter, we selected posts from 1-Jan-2016 to 31-Dec-2018 and extracted the top 5% of accounts tweeting about vaccines with the most followers. Using automated classifiers, we determined the location of these accounts, and grouped them into those that primarily tweet pro- versus anti-vaccine content. We further characterized the demographics of these influencer accounts. Results From 25,381 vaccine-related tweets available in our sample representing 10,607 users, 530 accounts represented the top 5% by number of followers. These accounts had on average 1,608,637 followers (standard deviation=5,063,421) and 340,390 median followers. Among the accounts for which sentiment was successfully estimated by the classifier, 10.4% (n=55) posted anti-vaccine content and 33.6% (n=178) posted pro-vaccine content. Of the 55 anti-vaccine accounts, 50% (n=18) of the accounts for which location was successfully determined were from the United States. Of the 178 pro-vaccine accounts, 42.5% (n=54) were from the United States. Conclusion This study showed that only a small proportion of Twitter accounts (A) post about vaccines and (B) have a high follower count and post anti-vaccine content. Further analysis of these users may help researchers and policy makers better understand how to amplify the impact of pro-vaccine social media messages. Disclosures Jean-Etienne Poirrier, PhD, MBA, The GSK group of companies (Employee, Shareholder) Theodore Caputi, PhD, Good Analytics Inc. (Consultant) John Ayers, PhD, GSK (Grant/Research Support) Mark Dredze, PhD, Bloomberg LP (Consultant)Good Analytics (Consultant) Sara Poston, PharmD, The GlaxoSmithKline group of companies (Employee, Shareholder) Cosmina Hogea, PhD, GlaxoSmithKline (Employee, Shareholder)

Post-Dorian shoreline change at Cape Hatteras National Seashore: 2019 report

2021 ◽  
Author(s):  
Lisa Baron
Keyword(s):  
High Tide ◽  
Monitoring Program ◽  
Shoreline Change ◽  
The United States ◽  
United States Geological Survey ◽  
Vital Signs Monitoring ◽  
Cape Hatteras ◽  
Cape Hatteras National Seashore ◽  
National Seashore

In 2018 and 2019 the Southeast Coast Network (SECN), with assistance from park staff, collected long-term shoreline monitoring data at Cape Hatteras National Seashore as part of the National Park Service (NPS) Vital Signs Monitoring Program. Monitoring was conducted following methods developed by the NPS Northeast Coastal and Barrier Network and consisted of mapping the high-tide swash line using a Global Positioning System unit in the spring of each year (Psuty et al. 2010). Shoreline change was calculated using the Digital Shoreline Analysis System (DSAS) developed by the United States Geological Survey (USGS; Himmelstoss et al. 2018). Following the same field methods used for monitoring long-term shoreline change, geospatial data were collected as part of the Hurricane Dorian (or Dorian) Incident Response from September 12–16, 2019. This report summarizes the post-Dorian data and the previous two shoreline data collection efforts (spring 2019 and fall 2018).

Value in vehicles: economic assessment of automotive data

2018 ◽  
Vol 20 (6) ◽  
pp. 513-527
Author(s):  
Alexander M. Soley ◽  
Joshua E. Siegel ◽  
Dajiang Suo ◽  
Sanjay E. Sarma
Keyword(s):  
Model Performance ◽  
Economic Assessment ◽  
Connected Vehicle ◽  
Data Generation ◽  
Content Type ◽  
Monetary Value ◽  
Vehicle Data ◽  
Data Market ◽  
Data Stewardship ◽  
Value Model

Purpose The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers. Design/methodology/approach The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors. Findings Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn. Research limitations/implications This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions. Practical implications The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship. Social implications Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens. Originality/value This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.

scholarly journals Two Decades of Invasive Western Corn Rootworm Population Monitoring in Croatia

Insects ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 160 ◽  
Author(s):  
Martina Mrganić ◽  
Renata Bažok ◽  
Katarina Mikac ◽  
Hugo Benítez ◽  
Darija Lemic
Keyword(s):  
Adult Population ◽  
Monitoring Program ◽  
Population Monitoring ◽  
The United States ◽  
Western Corn Rootworm ◽  
Population Variation ◽  
Future Research ◽  
Corn Rootworm ◽  
Population Abundance ◽  
National Monitoring

Western corn rootworm (WCR) is the worst pest of maize in the United States, and since its spread through Europe, WCR is now recognized as the most serious pest affecting maize production. After the beetle’s first detection in Serbia in 1992, neighboring countries such as Croatia have established a national monitoring program. For more than two decades WCR adult population abundance and variability was monitored. With traditional density monitoring, more recent genetic monitoring, and the newest morphometric monitoring of WCR populations, Croatia possesses a great deal of knowledge about the beetle’s invasion process over time and space. Croatia’s position in Europe is unique as no other European nation has demonstrated such a detailed and complete understanding of an invasive insect. The combined use of traditional monitoring (attractant cards), which can be effectively used to predict population abundance, and modern monitoring procedures, such as population genetics and geometric morphometrics, has been effectively used to estimate inter- and intra-population variation. The combined application of traditional and modern monitoring techniques will enable more efficient control and management of WCR across Europe. This review summarizes the research on WCR in Croatia from when it was first detected in 1992 until 2018. An outline of future research needs is provided.

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