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...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><sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;"> and the 11</span><sup><span style="font-family:Verdana;">th</span></sup><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>展开更多
Diamond interchanges are frequently used where a freeway intersects a two-way surface street. Most of the techniques to evaluate the performance of diamond interchanges rely on the Highway Capacity Manual (HCM), simul...Diamond interchanges are frequently used where a freeway intersects a two-way surface street. Most of the techniques to evaluate the performance of diamond interchanges rely on the Highway Capacity Manual (HCM), simulation, Automated Traffic Signal Performance Measures (ATSPMs), and historical crash data. HCM and simulation techniques require on-site data collection to obtain models’ inputs. ATSPMs need high-resolution controller event data acquired from roadway sensing equipment. Safety studies typically need 3 to 5 years of crash data to provide statistically significant results. This study utilizes commercially available connected vehicle (CV) data to assess the performance and operation of a three- and four-phase diamond interchange located in Indianapolis, Indiana, and Dallas, Texas, respectively. Over 92,000 trajectories and 1,400,000 GPS points are analyzed from August 2020 weekdays CV data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) from which arrivals on green (AOG), split failures, downstream blockage, and movement-based control delay are estimated. In addition, an extension of the PPD is presented that characterizes the complete journey of a vehicle travelling through both signals of the diamond interchange. This enhanced PPD is a significant contribution as it provides an analytical framework and graphical summary of the operational characteristics of how the external movements traverse the entire system. The four-phase control showed high internal progression (99% AOG) compared to the moderate internal progression of the three-phase operation (64% AOG). This is consistent with the design objectives of three- and four-phase control models, but historically these quantitative AOG measures were not possible to obtain with just detector data. Additionally, a graphical summary that illustrates the spatial distribution of hard-braking and hard-acceleration events is also provided. The presented techniques can be used by any agency to evaluate the performance of their diamond interchanges without on-site data collection or capital investments in sensing infrastructure.展开更多
文摘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><sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;"> and the 11</span><sup><span style="font-family:Verdana;">th</span></sup><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>
文摘Diamond interchanges are frequently used where a freeway intersects a two-way surface street. Most of the techniques to evaluate the performance of diamond interchanges rely on the Highway Capacity Manual (HCM), simulation, Automated Traffic Signal Performance Measures (ATSPMs), and historical crash data. HCM and simulation techniques require on-site data collection to obtain models’ inputs. ATSPMs need high-resolution controller event data acquired from roadway sensing equipment. Safety studies typically need 3 to 5 years of crash data to provide statistically significant results. This study utilizes commercially available connected vehicle (CV) data to assess the performance and operation of a three- and four-phase diamond interchange located in Indianapolis, Indiana, and Dallas, Texas, respectively. Over 92,000 trajectories and 1,400,000 GPS points are analyzed from August 2020 weekdays CV data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) from which arrivals on green (AOG), split failures, downstream blockage, and movement-based control delay are estimated. In addition, an extension of the PPD is presented that characterizes the complete journey of a vehicle travelling through both signals of the diamond interchange. This enhanced PPD is a significant contribution as it provides an analytical framework and graphical summary of the operational characteristics of how the external movements traverse the entire system. The four-phase control showed high internal progression (99% AOG) compared to the moderate internal progression of the three-phase operation (64% AOG). This is consistent with the design objectives of three- and four-phase control models, but historically these quantitative AOG measures were not possible to obtain with just detector data. Additionally, a graphical summary that illustrates the spatial distribution of hard-braking and hard-acceleration events is also provided. The presented techniques can be used by any agency to evaluate the performance of their diamond interchanges without on-site data collection or capital investments in sensing infrastructure.
