期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania 被引量:3
1
作者 Margaret Hunter Jijo K. Mathew +1 位作者 Howell Li Darcy M. Bullock 《Journal of Transportation Technologies》 2021年第4期597-610,共14页
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.... 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> 展开更多
关键词 connected vehicle trajectory Data Penetration Traffic Counts Big Data
下载PDF
Integrated Performance Measures for Bus Rapid Transit System and Traffic Signal Systems Using Trajectory Data
2
作者 Jijo Kulathintekizhakethil Mathew Howell Li +2 位作者 Enrique Saldivar-Carrranza Matthew Duffy Darcy Michael Bullock 《Journal of Transportation Technologies》 2022年第4期833-860,共28页
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, ... 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. 展开更多
关键词 connected vehicle trajectory Bus Rapid Transit Performance Traffic Signal Retiming Schedules
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部