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Citation |
Chen Y, Quddus M, Wang X. Transp. Res. C Emerg. Technol. 2018; 86: 346-359. |
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Copyright |
(Copyright © 2018, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
Lane departures are responsible for many side-swipe, rear-end and single-vehicle run-off-road crashes. There is a dearth of research, however, on how lane departures are impacted by roadway alignments. The objective of this paper is to examine which geometric design characteristics, including road alignment at the current segment and the adjacent segments, have significant influence on lane departure. Lane departure data from a total 30 drivers were collected from a driving simulator study of a four-lane (two lanes in each direction) divided mountainous freeway. Lane departures were classified into lane keeping, lane departure to the left and lane departure to the right for all-alignments (Dataset I), and lane keeping, lane departure to the inside and lane departure to the outside for curves-only (Dataset II). A mixed multinomial logit model for each dataset was employed to examine the contributory factors. This approach allows for the possibility that the estimated model parameters can vary randomly to account for unobserved effects potentially relating to heterogeneous driver behaviors. Fixed parameters that had a significant increase on lane departure were horizontal curvature at the current segment, and the difference (max-min) in horizontal curvature within the 300-m adjacent upstream alignment. Downward slope and upward slope with fixed parameters significantly decreased lane departure. Estimated parameters related to the direction of the curve, driving lane (bordering median or hard shoulder) and driving speed had found to have randomly distributed over the drivers. This indicates that driver behavior is not consistent in the effect of these three variables on lane departure. These results can assist engineers in designing safer mountainous freeways. Language: en |
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Keywords |
Driving simulator; Combined alignments; Lane departure; Mixed multinomial logit model; Mountainous freeways |