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Contributions of measurements for detecting drowsy driving are determined by calculation parameters, which are directly related to the accuracy of drowsiness detection. The previous studies utilized the same Unified Calculation Parameters (UCPs) to compute each driver's measurements. However, since each driver has unique driving behavior characteristics, namely, driver fingerprinting, Individual Drivers' Best Calculation Parameters (IDBCPs) making measurements more discriminative for drowsiness are various. Regardless of the difference in driver fingerprinting among the drivers being tested, using UCPs instead of IDBCPs to compute measurements will limit the drowsiness-detection performance of the measurements and reduce drowsiness-detection accuracies at the individual driver level. Thus, this paper proposed a model to optimize calculation parameters of individual driver's measurements and to extract individual driver's measurements that effectively distinguish drowsy driving. Through real vehicle experiments, we collected naturalistic driving data and subjective drowsy levels evaluated by the Karolinska Sleepiness Scale. Eight nonintrusive drowsiness-related measurements were calculated by double-layer sliding time windows. In the proposed model, we firstly applied the Wilcoxon test to analyze differences between measurements of the awake state and drowsy state, and constructed the fitness function reflecting the relationship between the calculation parameters and measurement's drowsiness-detection performance. Secondly, the genetic algorithms were used to optimize fitness functions to obtain measured IDBCPs. Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers' optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. The mean drowsiness-detection accuracies of DF_U and DF_I were, respectively, 85.25% and 91.06%. It indicated that IDBCPs could enhance measurements' drowsiness-detection performance and improve the drowsiness-detection accuracies. This paper contributed to the establishment of personalized drowsiness-detection models considering driver fingerprinting differences., Author(s): Yifan Sun [1]; Chaozhong Wu [1]; Hui Zhang (corresponding author) [1]; Yijun Zhang [1]; Shaopeng Li [1]; Hongxia Feng [1] 1. Introduction Drowsy driving is a typically dangerous driving [...]

In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles., Author(s): Depeng Chen [1]; Zhijun Chen (corresponding author) [1]; Yishi Zhang (corresponding author) [2]; Xu Qu [3]; Mingyang Zhang [4]; Chaozhong Wu [1] 1. Introduction Automated vehicle technology has been [...]

Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway., 1. Introduction With the rapid development of Chinese cities, residents' demand for travel and the increasing number of motor vehicles put forward higher requirements for the operation efficiency of cities. [...]

Driver fatigue level was considered an accumulated result contributed by circadian rhythms, hours of sleep before driving, driving duration, and break time during driving. This article presents an investigation into the regression model between driver fatigue level and the above four time-related variables. With the cooperation of one commercial transportation company, a Naturalistic Driving Study (NDS) was conducted, and NDS data from thirty-four middle-aged drivers were selected for analysis. With regard to the circadian rhythms, commercial drivers operated the vehicle and started driving at around 09:00, 14:00, and 21:00, respectively. Participants' time of sleep before driving is also surveyed, and a range from 4 to 7 hours was selected. The commercial driving route was the same for all participants. After getting the fatigue level of all participants using the Karolinska Sleepiness Scale (KSS), the discrete KSS data were converted into consecutive value, and curve fitting methods were adopted for modeling. In addition, a linear regression model was proposed to represent the relationship between accumulated fatigue level and the four time-related variables. Finally, the prediction model was verified by the driving performance measurement: standard deviation of lateral position. The results demonstrated that fatigue prediction results are significantly relevant to driving performance. In conclusion, the fatigue prediction model proposed in this study could be implemented to predict the risk driving period and the maximum consecutive driving time once the driving schedule is determined, and the fatigue driving behavior could be avoided or alleviated by optimizing the driving and break schedule., 1. Introduction Driving fatigue is a major safety issue in transportation, which has been identified to be associated with an increased risk of traffic collisions on roads because fatigued drivers [...]

Evacuation management for large crowds after events involves a number of processes and factors with socioeconomic and environmental implications. These processes and factors, as well as their interactions, are associated with a variety of uncertainties. In this study, an inexact fuzzy robust programming model for evacuation management (IFR-EM) has been developed for supporting the management of event-related evacuation under uncertainty. Through the proposed method, parameters presented as interval numbers and/or fuzzy boundary intervals are acceptable as uncertain inputs, such that the uncertainties can be directly communicated into the optimization process. Programming robustness can thus be highly enhanced. The proposed method is applied to a case study and then solved through an interactive method. A number of decision alternatives could be directly generated based on results of the model, which might be favored by decision makers because of the increased flexibility and applicability. Results of the IFR-EM model can reflect a compromise between optimality and stability of the study system, and are also useful for gaining insights into trade-offs among objectives of system efficiency, environmental protection, and economic cost. Also, the results suggest that the proposed IFR-EM method can explicitly address complexities and uncertainties in evacuation management systems and is applicable to practical evacuation problems. CE Database subject headings: Evacuation; Transportation management; Optimization; Uncertainty principles; Fuzzy sets.

Rollover accidents are more likely to occur with Heavy-Duty Vehicles (HDV) due to the high center of gravity and large size. A dynamic rollover index (RI) can describe the transient nature and responses of vehicle states. This study proposes an improved RI to detect real-time rollover events in tripped and untripped conditions. The RI combines the roll-plane dynamics of the sprung and unsprung mass and even fully considers vehicle suspension dynamics under uncertain external road inputs. Moreover, a low-cost unknown input observer (UIO) is designed to estimate vehicle roll angle under the uncertain disturbance of road bank. The updating improved RI is in terms of quasi-constant parameters, estimated states and external perturbation. A parameter estimation algorithm based on the Normalized Least Mean Square (NLMS) is developed to observe unmeasurable and time-varying parameters. Furthermore, by investigating the thresholds of roll angle and lateral acceleration, a dynamic predictive rollover threshold is determined with state constraints. The results demonstrate that the proposed parameters estimator is capable of estimating the c. g. height, roll moment of inertial, suspension roll stiffness, and suspension roll damping within acceptable errors in different driving scenarios. Moreover, the improved RI can strictly detect rollover in a complex driving scenario. Finally, the proposed dynamic RI threshold reduces invalid rollover warnings, predicates the rollover risk and guarantees sufficient response time for controller delay. [ABSTRACT FROM AUTHOR]