Truck and Passenger Car Connected Vehicle Penetration on Indiana Roadways

Sakhare, Rahul Suryakant; Hunter, Margaret; Mukai, Justin; Li, Howell; Bullock, Darcy M.

doi:10.4236/jtts.2022.124034

Cited by 16 publications

(16 citation statements)

References 36 publications

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“…Approximately 500 billion anonymized CV waypoint records for the month of December 2022 were aggregated into nearly 1 billion CV trip records for the entire United States. This data represents approximately 5% of the trips in the United States [22].…”

Section: Discussionmentioning

confidence: 99%

“…Emerging connected vehicle (CV) data can act as an alternative data source to potentially deliver a more scalable method to measure temporal and spatial usage of passenger vehicles in near real-time for a variety of applications, including trip origin-destinations, quantifying traffic congestion, travel mode choice, traffic safety, and infrastructure planning [16][17][18][19][20]. Early studies have shown that these CV datasets capture around 500 billion vehicle records in a month nationwide [21] for the United States, with a rough market penetration of 5-7% [22].…”

Section: Emerging Connected Vehicle Data Opportunitiesmentioning

confidence: 99%

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Using Connected Vehicle Data to Evaluate National Trip Trends

Desai,

Mathew,

Mahlberg

et al. 2023

Applied Sciences

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The National Household Travel Survey (NHTS), conducted by the Federal Highway Administration, has historically been used for documenting personal mobility trends. Current techniques using surveys to collect this data are labor-intensive and difficult to scale. Emerging connected vehicle (CV) data can provide an alternative data source to potentially provide a more scalable method to measure the temporal and spatial usage of passenger vehicles in near real-time. With an impending shift in the automobile industry towards alternative fuel vehicles (AFV), agile monitoring of trip trends is important to help guide state and national investments in AFV infrastructure. This study presents methodologies and visualizations summarizing observed trip characteristics using a sample of more than 500 billion CV records and nearly 1 billion CV trips for December 2022 in the United States. The analysis found very close agreement between trip lengths for internal combustion engine vehicles (ICEV) for CVs and those reported by the 2017 NHTS. Mean trip lengths and trip durations from CVs and NHTS for ICEVs are within 7.8% and 6.6% of each other. The 85th percentile comparison was similarly close, within 0.7% and 8.3%. A comparison of trip trends among states for ICEVs and AFVs as well as US census places and temporal trends for a selection of states, including Indiana, Texas, Wyoming, and California, is provided. The paper concludes that CV data is an important source to monitor trip characteristics across ICEVs and AFVs in near real-time, which will be particularly important to track during the anticipated change to AFVs.

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Section: Discussionmentioning

confidence: 99%

Section: Emerging Connected Vehicle Data Opportunitiesmentioning

confidence: 99%

Using Connected Vehicle Data to Evaluate National Trip Trends

Desai,

Mathew,

Mahlberg

et al. 2023

Applied Sciences

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“…Utah CV trajectory data provided by a third-party vehicle data vendor, with an estimated statewide MPR of 2.7% ( 37 ), is used. The data, obtained from passenger vehicles that were factory-equipped with the required technology, consists of vehicle trajectory waypoints with a 3-s reporting interval and 3.0 m of spatial accuracy.…”

Section: Data Descriptionmentioning

confidence: 99%

“…In this study, the available 2.7% MPR ( 37 ), aggregated over the analyzed five-day period, assuming CV data represents stochastic samples and similar traffic conditions, provides visibility equivalent to 13.5% of one day. This level of penetration is above the recommended values in the previous literature and is therefore assumed to provide accurate on-the-ground conditions ( 24 , 29 ).…”

Section: Data Descriptionmentioning

confidence: 99%

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Saldivar-Carranza

Gayen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Many agencies use Automated Traffic Signal Performance Measures (ATSPMs), which require vehicle detection and communication equipment, to evaluate traffic signal efficiency. ATSPMs usually rely on projections based on spatially limited vehicle trajectory samples to estimate performance. Recently, connected vehicle (CV) data has become available that provides entire vehicle trajectories that can be used to generate accurate signal performance measures without the need for projections or infrastructure upgrades. This paper analyzes over 50 intersections in Utah to evaluate how traditional detector-based arrivals on green (AOG) calculations compare with their CV-based calculations. The effects that saturation and queue- lengths have on estimations are analyzed. In general, there is close correlation between computations when queues are short and undersaturated conditions exist. However, if queues extend past the advance detector, detector-based calculations tend to overestimate AOG since vehicles are detected during green but may stop before. This impact is particularly large when the approach is oversaturated, which led to overestimations of around 40% in this study. In addition, detector-based estimations can underestimate AOG in undersaturated scenarios with short queues if vehicles reach the advance detector on red but reduce their speed afterwards, allowing them to not stop. In some cases, the detector-based technique underestimated AOG by over 50%. The findings can help practitioners understand how detector-based estimations vary by traffic conditions. This is particularly important as the industry moves toward a hybrid blend of detector- and CV-based signal performance measures. It is recommended that CV trajectories be used to measure AOG during periods with long queues, oversaturated conditions, or both.

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“…May and June 2022 Indiana CV trajectory data, with an estimated penetration rate of 4.5% ( 27 ), is used in this study. The data consists of individual passenger vehicle trajectory waypoints with a 3-s reporting interval and nominal 3-m spatial accuracy.…”

Section: Data Descriptionmentioning

confidence: 99%

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

Saldivar-Carranza

Platte

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the U.S.A. It is important for agencies to be able to systematically monitor traffic signal performance and have procedures to identify locations as proactively as possible where the redistribution of green time has the greatest potential of reducing congestion. Currently, Automated Traffic Signal Performance Measures (ATSPMs) require extensive communication networks and detectors on all phases at each intersection to effectively monitor the traffic signal performance for every traffic movement. This study utilizes commercially available connected vehicle (CV) trajectory data to identify overcapacity movements where there is availability of unused green time and no downstream blockage by an adjacent signal. Over 100 intersections, 400,000 trajectories, and 6,800,000 Global Positioning System points are evaluated using this method to prioritize signal retiming opportunities in a proposed Relative Performance Diagram (RPD) by assessing split failures and downstream blockage of all traffic movements. Out of the intersections identified as having opportunities for operational improvements, three were selected for retiming. A post-retiming review of the traffic signal performance measures showed up to 30% reductions in split failures, 53 s/veh reductions in control delay, and 21% increase in arrivals on green at the benefited movements. The techniques easily scale to any intersection where CV data is available without the need for vehicle detection or communication technology.

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Truck and Passenger Car Connected Vehicle Penetration on Indiana Roadways

Cited by 16 publications

References 36 publications

Using Connected Vehicle Data to Evaluate National Trip Trends

Using Connected Vehicle Data to Evaluate National Trip Trends

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

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