Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data  

作　　者：Enrique D. Saldivar-Carranza Howell Li Darcy M. Bullock Enrique D. Saldivar-Carranza;Howell Li;Darcy M. Bullock(Purdue University, West Lafayette, USA)

机构地区：[1]Purdue University, West Lafayette, USA

出　　处：《Journal of Transportation Technologies》2021年第4期628-643,共16页交通科技期刊（英文）

摘　　要：Since the first Diverging Diamond Interchange (DDI) implementation in 2009, most of the performance studies developed for this type of interchange have been based on simulations and historical crash data, with a small numbe<span style="font-family:Verdana;">r of studies using Automated Traffic Signal Performance Measures (ATS</span><span style="font-family:Verdana;">PM). Simulation models require considerable effort to collect volumes and to model actual controller operations. Safety studies based on historical crashes usually require from 3 to 5 years of data collection. ATSPMs rely on sensing equipment. This study describes the use of connected vehicle trajectory data to analyze the performance of a DDI located in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe Diagram (PPD) is proposed to assess the levels of delay, progression, and saturation. Further, an additional PPD variation is presented that provides a convenient visualization to qualitatively understand progression patterns and to evaluate queue length for spillback in the critical interior crossover. Over 7000 trajectories and 130,000 GPS points were analyzed between the 7</span>^{<span style="font-family:Verdana;">th</span>}<span style="font-family:Verdana;"> and the 11</span>^{<span style="font-family:Verdana;">th</span>}<span style="font-family:Verdana;"> of June 2021 from 5:00 AM to 10:00 PM to estimate the DDI’s arrivals on green, level of service, split failures, and downstream blockage. Although this technique was demonstrated for weekdays, the ubiquity of connected vehicle data makes it very ea</span><span style="font-family:Verdana;">sy to adapt these techniques to analysis during special events, winter sto</span><span style="font-family:Verdana;">rms, and weekends. Furthermore, the methodologies presented in this paper can be applied by any agency wanting to assess the performance of any DDI in their jurisdiction.</span>Since the first Diverging Diamond Interchange (DDI) implementation in 2009, most of the performance studies developed for this type of interchange have been based on simulations and historical crash data, with a small numbe<span style="font-family:Verdana;">r of studies using Automated Traffic Signal Performance Measures (ATS</span><span style="font-family:Verdana;">PM). Simulation models require considerable effort to collect volumes and to model actual controller operations. Safety studies based on historical crashes usually require from 3 to 5 years of data collection. ATSPMs rely on sensing equipment. This study describes the use of connected vehicle trajectory data to analyze the performance of a DDI located in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe Diagram (PPD) is proposed to assess the levels of delay, progression, and saturation. Further, an additional PPD variation is presented that provides a convenient visualization to qualitatively understand progression patterns and to evaluate queue length for spillback in the critical interior crossover. Over 7000 trajectories and 130,000 GPS points were analyzed between the 7</span>^{<span style="font-family:Verdana;">th</span>}<span style="font-family:Verdana;"> and the 11</span>^{<span style="font-family:Verdana;">th</span>}<span style="font-family:Verdana;"> of June 2021 from 5:00 AM to 10:00 PM to estimate the DDI’s arrivals on green, level of service, split failures, and downstream blockage. Although this technique was demonstrated for weekdays, the ubiquity of connected vehicle data makes it very ea</span><span style="font-family:Verdana;">sy to adapt these techniques to analysis during special events, winter sto</span><span style="font-family:Verdana;">rms, and weekends. Furthermore, the methodologies presented in this paper can be applied by any agency wanting to assess the performance of any DDI in their jurisdiction.</span>

关 键 词：Diverging Diamond Interchange Performance Measures Connected Vehicle Big Data 

分 类 号：O17[理学—数学]