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Can i Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

Cai, J.; Zhao, J.; Xiang, Yusheng; Liu, J.; Chen, G.; Hu, Y.; Chen, J.

Abstract:
Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000129137
Veröffentlicht am 29.01.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 24.12.2020
Sprache Englisch
Identifikator ISSN: 0197-6729, 2042-3195
KITopen-ID: 1000129137
Erschienen in Journal of advanced transportation
Verlag Hindawi
Band 2020
Seiten Art.-Nr.: 6678996
Nachgewiesen in Web of Science
Scopus
Dimensions
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