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Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania 被引量:3

Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania
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摘要 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<span style="font-family:;" "=""> </span><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">and</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This pap</span><span style="font-family:Verdana;">er concludes by recommending that the techniques described in this paper</span><span style="font-family:Verdana;"> 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.</span></span> 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<span style="font-family:;" "=""> </span><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">and</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This pap</span><span style="font-family:Verdana;">er concludes by recommending that the techniques described in this paper</span><span style="font-family:Verdana;"> 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.</span></span>
作者 Margaret Hunter Jijo K. Mathew Howell Li Darcy M. Bullock Margaret Hunter;Jijo K. Mathew;Howell Li;Darcy M. Bullock(Purdue University, West Lafayette, USA)
机构地区 Purdue University
出处 《Journal of Transportation Technologies》 2021年第4期597-610,共14页 交通科技期刊(英文)
关键词 Connected Vehicle Trajectory Data Penetration Traffic Counts Big Data Connected Vehicle Trajectory Data Penetration Traffic Counts Big Data
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