TY - GEN
T1 - Assessing Connected Vehicle Data Coverage on New Jersey Roadways
AU - Dimitrijevic, Branislav
AU - Zhong, Zijia
AU - Zhao, Liuhui
AU - Besenski, Dejan
AU - Lee, Joyoung
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The connected vehicle data (CVD) is one of the most promising emerging mobility data that greatly increases the ability to effectively monitor transportation system performance. A commercial vehicle trajectory dataset was evaluated for market penetration and coverage to establish whether it represents a sufficient sample of the vehicle volumes across the statewide roadway network of New Jersey. The dataset (officially named Wejo Vehicle Movement data) was compared to the vehicle volumes obtained from 46 weight-in-motion (WIM) traffic count stations during the corresponding two-month period. The observed market penetration rates of the Movement data for the interstate highways, non-interstate expressways, major arterials, and minor arterials are 2.55% (std. dev. 0.76%), 2.31% (std. dev. 1.07%), 3.25% (standard deviation 1.48 %), and 4.39% (standard deviation 2.65%), respectively. Additionally, the temporal resolution of the dataset (i.e., the time interval between consecutive Wejo vehicle trips captured at a given roadway section, time-of-day variation, day-of-month variation) was also found to be consistent among the evaluated WIM locations. Although relatively low (less than 5%), the consistent market penetration, combined with uniform spatial distribution of equipped vehicles within the traffic flow, could enable or enhance a wide range of traffic analytics applications.
AB - The connected vehicle data (CVD) is one of the most promising emerging mobility data that greatly increases the ability to effectively monitor transportation system performance. A commercial vehicle trajectory dataset was evaluated for market penetration and coverage to establish whether it represents a sufficient sample of the vehicle volumes across the statewide roadway network of New Jersey. The dataset (officially named Wejo Vehicle Movement data) was compared to the vehicle volumes obtained from 46 weight-in-motion (WIM) traffic count stations during the corresponding two-month period. The observed market penetration rates of the Movement data for the interstate highways, non-interstate expressways, major arterials, and minor arterials are 2.55% (std. dev. 0.76%), 2.31% (std. dev. 1.07%), 3.25% (standard deviation 1.48 %), and 4.39% (standard deviation 2.65%), respectively. Additionally, the temporal resolution of the dataset (i.e., the time interval between consecutive Wejo vehicle trips captured at a given roadway section, time-of-day variation, day-of-month variation) was also found to be consistent among the evaluated WIM locations. Although relatively low (less than 5%), the consistent market penetration, combined with uniform spatial distribution of equipped vehicles within the traffic flow, could enable or enhance a wide range of traffic analytics applications.
KW - Average Daily Traffic
KW - Connected Vehicle Data
KW - Market Penetration
KW - Probe Vehicle Data
KW - Roadway Functional Classifications
UR - http://www.scopus.com/inward/record.url?scp=85158904433&partnerID=8YFLogxK
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U2 - 10.1109/ICITE56321.2022.10101453
DO - 10.1109/ICITE56321.2022.10101453
M3 - Conference contribution
AN - SCOPUS:85158904433
T3 - 2022 IEEE 7th International Conference on Intelligent Transportation Engineering, ICITE 2022
SP - 388
EP - 393
BT - 2022 IEEE 7th International Conference on Intelligent Transportation Engineering, ICITE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2022
Y2 - 11 November 2022 through 13 November 2022
ER -