Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections

Drivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with...

Full description

Bibliographic Details
Main Authors: Juan Li, Boyu Jiang, Chunjiao Dong, Jue Wang, Xuan Zhang
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2020/2023093
id doaj-dfd1df100d1e4eed97e7fec0547ddad3
record_format Article
spelling doaj-dfd1df100d1e4eed97e7fec0547ddad32020-11-25T03:36:10ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/20230932023093Analysis of Driver Decisions at the Onset of Yellow at Signalized IntersectionsJuan Li0Boyu Jiang1Chunjiao Dong2Jue Wang3Xuan Zhang4School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaCenter for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, ChinaSam’s Club Technology, Austin, TX, USADrivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with other known factors to analyze stop/go decision-making. Initially, the driving reliability index is extracted using a Hidden Markov Model (HMM). The dangerous driving index is calculated based on statistics extracted from dangerous driving records. A latent class logit model is then proposed to explore the factors which influence drivers’ decisions. Drivers are classified for analytical purposes into “low-risk” and “high-risk” categories according to driving styles and age. Results indicate that those considering “low-risk” tend to stop, while drivers considering “high-risk” are inclined to pass intersections. Furthermore, distractions from cell phones have different influences on each group of drivers. These findings help to determine driver preferences and may be used to formulate strategies to reduce unsafe driving occurring at signalized intersections.http://dx.doi.org/10.1155/2020/2023093
collection DOAJ
language English
format Article
sources DOAJ
author Juan Li
Boyu Jiang
Chunjiao Dong
Jue Wang
Xuan Zhang
spellingShingle Juan Li
Boyu Jiang
Chunjiao Dong
Jue Wang
Xuan Zhang
Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
Journal of Advanced Transportation
author_facet Juan Li
Boyu Jiang
Chunjiao Dong
Jue Wang
Xuan Zhang
author_sort Juan Li
title Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
title_short Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
title_full Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
title_fullStr Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
title_full_unstemmed Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
title_sort analysis of driver decisions at the onset of yellow at signalized intersections
publisher Hindawi-Wiley
series Journal of Advanced Transportation
issn 0197-6729
2042-3195
publishDate 2020-01-01
description Drivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with other known factors to analyze stop/go decision-making. Initially, the driving reliability index is extracted using a Hidden Markov Model (HMM). The dangerous driving index is calculated based on statistics extracted from dangerous driving records. A latent class logit model is then proposed to explore the factors which influence drivers’ decisions. Drivers are classified for analytical purposes into “low-risk” and “high-risk” categories according to driving styles and age. Results indicate that those considering “low-risk” tend to stop, while drivers considering “high-risk” are inclined to pass intersections. Furthermore, distractions from cell phones have different influences on each group of drivers. These findings help to determine driver preferences and may be used to formulate strategies to reduce unsafe driving occurring at signalized intersections.
url http://dx.doi.org/10.1155/2020/2023093
work_keys_str_mv AT juanli analysisofdriverdecisionsattheonsetofyellowatsignalizedintersections
AT boyujiang analysisofdriverdecisionsattheonsetofyellowatsignalizedintersections
AT chunjiaodong analysisofdriverdecisionsattheonsetofyellowatsignalizedintersections
AT juewang analysisofdriverdecisionsattheonsetofyellowatsignalizedintersections
AT xuanzhang analysisofdriverdecisionsattheonsetofyellowatsignalizedintersections
_version_ 1715167689023946752