检索结果-南通市图书馆

Application of Connected Truck Data to Evaluate Spatiotem.oral Im.act of Rest Area Closures on Ram. Parking

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2024年第3期14卷 289-307页

作者： Jijo K. m.thew Jairaj Desai Edward D. Cox darcy m. bullock Lyles School of Civil Engineering Purdue University West Lafayette IN USA Indiana Departm.nt of Transportation Indianapolis IN USA

Ensuring adequate access to truck parking is critical to the safe and efficient m.vem.nt of freight traffic. There are strict federal guidelines for com.ercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ram.s, which create safety risks for other interstate m.torists. Historically, agencies have em.loyed costly and tim. intensive m.nual counting m.thods, cam.ra surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-tim., offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there m.y be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and docum.nts the observed spatiotem.oral im.acts of a rest area closure on truck parking on nearby interstate ram.s. Results showed that there was a 28% increase in parking on ram.s during the rest area closure. Analysis also found that ram.s closest to the rest area were m.st im.acted by the closure, seeing a rise in truck parking sessions as high as 2.7 tim.s. Parking duration on the ram.s during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ram.s, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul com.ercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in inform.d data-driven decision m.king when allocating resources towards im.roving the nations com.ercial vehicle parking infrastructure.

关键词： Connected Truck Data Rest Areas Exit Ram.s Truck Parking Com.ercial Vehicles

A Data-Driven Intersection Geom.try m.pping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Perform.nce m.asures

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2023年第3期13卷 443-464页

作者： Enrique D. Saldivar-Carranza darcy m. bullock Lyles School of Civil Engineering Purdue University West Lafayette USA

Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal perform.nce with no investm.nt in detection or com.unication infrastructure. With over 500 billion trajectory records generated each m.nth in the United States, operations can be evaluated virtually at any of the over 400,000 traffic signals in the nation. The m.nual intersection m.pping required to generate accurate m.vem.nt-level trajectory-based perform.nce estim.tions is the m.st tim.-consum.ng aspect of using CV data to evaluate traffic signal operations. Various studies have utilized vehicle location data to update and create m.ps;however, m.st proposed m.pping techniques focus on the identification of roadway characteristics that facilitate vehicle navigation and not on the scaling of traffic signal perform.nce m.asures. This paper presents a technique that uses com.ercial CV trajectory and open-source OpenStreetm.p (OSm. data to autom.tically m.p intersection centers and approach areas of interest to estim.te signal perform.nce. OSm.traffic signal tags are processed to obtain intersection centers. CV data is then used to extract intersection geom.try characteristics surrounding the intersection. To dem.nstrate the proposed technique, intersection geom.try is m.pped at 500 locations from.which trajectory-based traffic signal perform.nce m.asures are estim.ted. The results are com.ared to those obtained from.m.nual geom.try definitions. Statistical tests found that at a 99% confidence level, upstream.focused perform.nce estim.tions are strongly correlated between both m.thodologies. The presented technique will aid agencies in scaling traffic signal assessm.nt as it significantly reduces the am.unt of m.nual labor required.

关键词： Connected Vehicle Trajectory Traffic Signal Perform.nce m.p Geom.try

Com.arison of Estim.ted Cycle Split Failures from.High-Resolution Controller Event and Connected Vehicle Trajectory Data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2023年第4期13卷 689-707页

作者： Saum.bha Gayen Enrique D. Saldivar-Carranza darcy m. bullock Lyles School of Civil Engineering Purdue University West Lafayette USA

Current traffic signal split failure (SF) estim.tions derived from.high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the selected thresholds, detector lengths, and vehicle arrival patterns. Connected vehicle (CV) trajectory data can m.re definitively show when a vehicle split fails by evaluating the num.er of stops it experiences as it approaches an intersection, but it has lim.ted m.rket penetration. This paper com.ares cycle-by-cycle SF estim.tions from.both high-resolution controller event data and CV trajectory data, and evaluates the effect of data aggregation on SF agreem.nt between the two techniques. Results indicate that, in general, split failure events identified from.CV data are likely to also be captured from.high-resolution data, but split failure events identified from.high-resolution data are less likely to be captured from.CV data. This is due to the CV m.rket penetration rate (m.R) of ~5% being too low to capture representative data for every controller cycle. However, data aggregation can increase the ratio in which CV data captures split failure events. For exam.le, day-of-week data aggregation increased the percentage of split failures identified with high-resolution data that were also captured with CV data from.35% to 56%. It is recom.ended that aggregated CV data be used to estim.te SF as it provides conservative and actionable results without the lim.tations of intersection and detector configuration. As the CV m.R increases, the accuracy of CV-based SF estim.tion will also im.rove.

关键词： Split Failure Connected Vehicle Detector Traffic Signal Perform.nce m.asures Trajectory Data

Roundabout Perform.nce Analysis Using Connected Vehicle Data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2022年第1期12卷 42-58页

作者： Enrique Saldivar-Carranza Jijo K. m.thew Howell Li darcy m. bullock Purdue University West Lafayette USA

There are over 8000 roundabouts in the United States. The current techniques for assessing their perform.nce require field counts to provide inputs to analysis or sim.lation m.dels. These techniques are labor-intensive and do not scale well. This paper presents a m.thodology to use connected vehicle (CV) trajectory data to estim.te delay and level of service for roundabout approaches by adapting the Purdue Probe Diagram.used for traffic signal analytics. By linear referencing vehicle trajectories with a particular m.vem.nt based on the location and tim. they exit a roundabout, delay can be calculated. The scalability is dem.nstrated by applying these techniques to assess over 100 roundabouts in Carm.l, IN during the weekday afternoon peak period in July 2021. Over 264,000 trajectories and 3,600,000 GPS points were analyzed to rank over 300 roundabout approaches by delay and sum.arize in Pareto-sorted graphics and m.ps. The paper concludes by discussing how these techniques can also be used to analyze queue lengths and originm.ly:Verdana;">-destination characteristics at roundabouts. The m.thodology presented in this study can be used by any agency that wants to assess the perform.nce of all roundabouts in their system.

关键词： Roundabout Perform.nce m.asure Connected Vehicle Big Data

Correlating Connected Vehicle Estim.ted Deceleration Events with Crash Incidents near Interstate Interchanges

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2023年第4期13卷 674-688页

作者： Jairaj Desai Jijo K. m.thew Howell Li Justin m.kai Rahul Suryakant Sakhare darcy m. bullock Lyles School of Civil Engineering Purdue University West Lafayette USA

Historical roadway safety analyses have used labor and tim.-intensive crash data collection procedures. However, crash reporting is often delayed and crash locations are reported with varying levels of spatial accuracy and detail. Recent advances in connected vehicle (CV) data provide an opportunity for stakeholders to proactively identify areas of safety concerns in near-real tim. with high spatial precision. Public and private sector stakeholders including autom.tive original equipm.nt m.nufacturers (OEm. and insurance providers m.y independently define acceleration thresholds for reporting unsafe driver behavior. Although som. OEm. have provided fixed threshold hard-braking event data for a num.er of years, this varies by OEm.and there is no published literature on the best thresholds to use for identifying em.rging safety issues. This research proposes a m.thodology to estim.te deceleration events from.raw CV trajectory data at varying thresholds that can be scaled to any CV. The estim.ted deceleration events and crash incident records around 629 interstate exits in Indiana were analyzed for a three-m.nth period from.m.rch 1-m.y 31, 2023. Nearly 20 m.llion estim.ted deceleration events and 4800 crash records were spatially joined to a 2-m.le search radius around each exit ram.. Results showed that deceleration events between -0.5 g and -0.4 g had the highest correlation with an R2 of 0.69. This study also identifies the top 20 interstate exit locations with highest deceleration events. The fram.work presented in this study enables agencies and transportation professionals to perform.safety evaluations on raw trajectory data without the need to integrate external data sources.

关键词： Connected Vehicles Hard-Braking Crashes Safety Deceleration

m.thodology for Autom.tically Setting Cam.ra View to m.le m.rker for Traffic Incident m.nagem.nt

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2023年第4期13卷 708-730页

作者： Jijo K. m.thew Haydn A. m.lackowski Christopher m. Gartner Jairaj Desai Edward D. Cox Aym.n F. Habib darcy m. bullock Lyles School of Civil Engineering Purdue University West Lafayette USA Indiana Departm.nt of Transportation Indianapolis USA

Traffic incident m.nagem.nt (TIm. is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, im.roving travel reliability, and ensuring safety of responders. Agency roadside cam.ras play a critical role in TIm.by helping dispatchers quickly identify the precise location of incidents when receiving reports from.m.torists with varying levels of spatial accuracy. Reconciling position reports that are often m.le m.rker based, with cam.ras that operate in a Pan-Tilt-Zoom.coordinate system.relies on dispatchers having detailed knowledge for hundreds of cam.ras and perhaps som. presets. During real-tim. incident dispatching, reducing the tim. it takes to identify the m.st relevant cam.ras and setting their view on the incident is an im.ortant opportunity to im.rove incident m.nagem.nt dispatch tim.s. This research develops a cam.ra-to-m.le m.rker m.pping technique that autom.tically sets the cam.ra view to a specified m.le m.rker within the field-of-view of the cam.ra. Over 350 traffic cam.ras along Indiana’s 2250 directional m.les of interstate were m.pped to approxim.tely 5000 discrete locations that correspond to approxim.tely 780 directional m.les (~35% of interstate) of cam.ra coverage. This newly developed technique will allow operators to quickly identify the nearest cam.ra and set them.to the reported location. This research also identifies segm.nts on the interstate system.with lim.ted or no cam.ra coverage for decision m.kers to prioritize future capital investm.nts. This paper concludes with brief discussion on future research to autom.te the m.pping using LiDAR data and to set the cam.ras after autom.tically detecting the events using connected vehicle trajectory data.

关键词： Roadside Cam.ras Traffic Incident m.nagem.nt Connected Vehicles Trajectory

Diverging Diam.nd Interchange Perform.nce m.asures Using Connected Vehicle Data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2021年第4期11卷 628-643页

作者： Enrique D. Saldivar-Carranza Howell Li darcy m. bullock Purdue University West Lafayette USA

Since the first Diverging Diam.nd Interchange (DDI) im.lem.ntation in 2009, m.st of the perform.nce studies developed for this type of interchange have been based on sim.lations and historical crash data, with a sm.ll num.er of studies using Automated Traffic Signal Performance Measures (ATSPM). 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 7th and the 11th 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 easy to adapt these techniques to analysis during special events, winter storms, 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.

关键词： Diverging Diam.nd Interchange Perform.nce m.asures Connected Vehicle Big Data

Analysis of Electric and Hybrid Vehicle Usage in Proxim.ty to Charging Infrastructure in Indiana

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2021年第4期11卷 577-596页

作者： Jairaj Desai Jijo K. m.thew Howell Li darcy m. bullock Purdue University West Lafayette USA

This paper explores the m.vem.nt of connected vehicles in Indiana for vehicles classified by the NHTSA Product Inform.tion Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from.July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle m.les travelled (Vm.) for the com.ination of EV and HV. Approxim.tely 53% of the Vm. occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle m.les Traveled (EVm.s) in total. A further categorization of this revealed that 18% of these EVm.s were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

关键词： Electric Vehicles Connected Vehicles Trajectory Data Driver Behavior Charging Stations

Using Anonym.us Connected Vehicle Data to Evaluate Im.act of Speed Feedback Displays, Speed Lim.t Signs and Roadway Features on Interstate Work Zones Speeds

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Transportation Technologies 2021年第4期11卷 545-560页

作者： Jijo K. m.thew Jairaj Desai Howell Li darcy m. bullock Purdue University West Lafayette USA

Annually, there are over 120,000 crashes in work zones in the United States. High speeds in construction zones are a well-docum.nted risk factor that increases the frequency and severity of crashes. This study used connected vehicle data to evaluate the spatial and temporal impact that regulatory signs, speed feedback displays, and construction site geometry had on vehicle speed. Over 27,000 unique trips over 2 weeks on a 15-mile interstate construction work zone near Lebanon, IN were analyzed. Spatial analysis over a 0.2-mi segment before and after the posted speed limit signs showed that the regulatory signs had no statistical impact on reducing speeds. A before/after analysis was also conducted to study the impact of radar-based speed feedback that displays the motorists’ speed on a sign below a regulatory speed limit sign. Results showed a maximum drop in median speeds of approximately 5 mph. Speeds greater than 15 mph above the speed limit dropped by 10% m.ly:Verdana;">m.ly:Verdana;">m.ly

关键词： Connected Vehicle Trajectory Data Speed Lim.t Com.liance Work Zones Construction