Browsing by Author "Hao, Haiyan"
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- Analyzing Intersection Gap Acceptance Behavior with Naturalistic Driving DataLi, Yingfeng (Eric); Hao, Haiyan; Gibbons, Ronald B.; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2022-09-14)Safety at unsignalized intersections continues to be a major concern for transportation agencies and roadway users. To improve intersection safety, this project conducted a comprehensive study of gap acceptance behaviors at unsignalized intersections using the second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS) data. The team collected 1,170 accepted and rejected gaps/lags based on 466 NDS trips at 60 unsignalized T-intersections in Washington state and North Carolina. The project team utilized a number of data sources, including time series data measuring vehicle kinematics for the analyzed trips, forward-facing and rear-view videos for the analyzed trips, driver demographic and driving history data, the SHRP 2 Roadway Information Database, and satellite images. First, the team identified the critical gaps for a number of common scenarios using three widely accepted methods: binary logistic regression, maximum likelihood method, and probability equilibrium method. Results showed an overall critical gap of 5.3 seconds for right-turning trips and 6.2 seconds for left-turning trips. The team then went on to develop a complete understanding of the factors affecting gap acceptance decisions using logistic regression and machine learning techniques. A number of factors were identified that affect drivers’ gap acceptance decisions, including being a gap instead of a lag, presence of leading and/or following vehicles, higher volume, intersection being unskewed, and increased number of through lanes. Finally, researchers further investigated drivers’ longitudinal and lateral acceleration behaviors during turning after accepting a gap and factors affecting their turning behaviors. Overall, both left- and right-turning vehicles initially accelerated quickly after they accepted a gap, and then reduced to a lower but prolonged acceleration rate while turning to reach a desired speed. For lateral acceleration, the peak value for the left-turning profile was reached later in the turning process than for the right-turning profile.
- Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study DataHao, Haiyan (Virginia Tech, 2018-08-30)Fixed-object crashes have long time been considered as major roadway safety concerns. While previous relevant studies tended to address such crashes in the context of roadway departures, and heavily relied on police-reported accidents data, this study integrated the SHRP2 NDS and RID data for analyses, which fully depicted the prior to, during, and after crash scenarios. A total of 1,639 crash, near-crash events, and 1,050 baseline events were acquired. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. Logistic regression analyses identified 16 and 10 significant variables with significance levels of 0.1, relevant to driver, roadway, environment, etc. for two responses respectively. The logistic regression analyses led to a series of findings regarding the effects of explanatory variables on fixed-object event occurrence and associated severity level. SVM classifiers and ANN models were also constructed to predict these two responses. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods obtained satisfactory prediction performance, that was around 88% for fixed-object event occurrence and 75% for event severity level, which indicated the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses.