TY - GEN
T1 - Relationship between Criticality and Travel Time Reliability in Transportation Networks
AU - Bargahi, Mahsa
AU - Barati, Hojjat
AU - Yazici, Anil
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This study examines the relationship between criticality and travel time reliability (TTR) in transportation networks, exploring whether one can serve as a proxy or an auxiliary measure for the other. Criticality identifies essential components requiring strengthening or protection during disruptive events, while TTR reflects day-to-day performance in maintaining reliable travel times. The correlation analysis in this study reveals that the majority (92%) of the calculated correlations between criticality and TTR metrics are statistically significant. The correlation coefficients range from 0.13 to 0.85, indicating diverse relationships. However, most correlations fall within the moderate strength range (0.4 to 0.6). A non-model specific feature selection analysis identifies influential network characteristics, with ' closeness average' ranking highest in predictive power. These findings can inform policymakers in infrastructure investments and disruption mitigation strategies, improving the resilience and reliability of transportation systems.
AB - This study examines the relationship between criticality and travel time reliability (TTR) in transportation networks, exploring whether one can serve as a proxy or an auxiliary measure for the other. Criticality identifies essential components requiring strengthening or protection during disruptive events, while TTR reflects day-to-day performance in maintaining reliable travel times. The correlation analysis in this study reveals that the majority (92%) of the calculated correlations between criticality and TTR metrics are statistically significant. The correlation coefficients range from 0.13 to 0.85, indicating diverse relationships. However, most correlations fall within the moderate strength range (0.4 to 0.6). A non-model specific feature selection analysis identifies influential network characteristics, with ' closeness average' ranking highest in predictive power. These findings can inform policymakers in infrastructure investments and disruption mitigation strategies, improving the resilience and reliability of transportation systems.
UR - https://www.scopus.com/pages/publications/85186534713
U2 - 10.1109/ITSC57777.2023.10421885
DO - 10.1109/ITSC57777.2023.10421885
M3 - Conference contribution
AN - SCOPUS:85186534713
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2479
EP - 2484
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Y2 - 24 September 2023 through 28 September 2023
ER -