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To promote the sustainable development and safety of bicycle traffic, survival analysis of the risk perception sensitivity of cyclists is proposed. The cumulative probability of survival serves as an index of risk perception sensitivity, and a Cox regression model is established. The proposed method is applied to middle school cyclists, and the factors of their risk perception are analyzed. Data are collected by questionnaire and traffic conflict survey and are quantified by factor analysis. The model results show that active and extroverted personality, negative peer influence, unsafe riding behavior intention, non-motor vehicle flow and speed, and a lack of separation facilities have negative correlations with risk perception sensitivity. Positive attitude towards traffic rules, good family education, heightened traffic safety awareness, motor vehicle flow and speed, pedestrian flow, and non-motorized lane width have positive correlations with risk perception sensitivity. The conflict type has no correlation with risk perception sensitivity. This study aims to improve the sensitivity of risk perception, prevent traffic conflicts and provide a theoretical basis for risk perception research on vulnerable traffic participants.
Dan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability 2020, 12, 2613 .
AMA StyleDan Zhao, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability. 2020; 12 (7):2613.
Chicago/Turabian StyleDan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. 2020. "Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model." Sustainability 12, no. 7: 2613.
This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver’s sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process.
Shuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation 2020, 2020, 1 -13.
AMA StyleShuaiyang Jiao, Shengrui Zhang, Zongzhi Li, Bei Zhou, Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation. 2020; 2020 ():1-13.
Chicago/Turabian StyleShuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. 2020. "An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them." Journal of Advanced Transportation 2020, no. : 1-13.