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The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen-Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the epi -centers (EC) and the nation are analyzed. In addition, the methods and results of this paper quantify the impact of COVID-19 on air passenger flow at a more detailed level under multiple disturbances. They could provide a basis for differentiated policy formulation of airlines and government in the future., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier Ltd. All rights reserved.)

Deploying dedicated lanes for automated vehicles (AVs) can effectively alleviate the coordination issues between AVs and manual vehicles (MVs). However, AV platoons running on dedicated AV lanes (DAVLs) have a prominent collective behavior characteristic of small inter-vehicle distance. The nearby MV drivers' imitation of this characteristic may reduce their car-following time headway (THW). The researchers conducted a simulation experiment to investigate the influence of DAVL assignments, inter-vehicle distances of AV platoons and AV platoon speed on the car-following performance of nearby MV drivers. The data of mean THW, standard deviation of THW, standard deviation of lateral position, standard deviation of velocity, standard deviation of horizontal gaze position and mean saccadic peak velocity were collected from 36 participants. Statistical analysis results show that the three factors considerably affected the MV drivers' car-following performance. In particular, the MV drivers showed a worse car-following safety but a better driving stability when the left lane was dedicated to AVs than when the right lane was dedicated to AVs (Note the experiments were done in a drive-on-the-left environment.). With respect to the inter-vehicle distances of AV platoons, the MV drivers' car-following safety was poorer under the 4 m condition than that under the 10 and 18 m conditions. In addition, the MV drivers showed a poorer car-following safety and bore a larger mental workload when driving next to the AV platoons running at 110 km/h. This study may provide some suggestions for DAVLs. Assigning the right lane of a three-lane motorway as the DAVL may have a slighter negative impact on the nearby MV drivers in China. In terms of traffic management in DAVLs, the inter-vehicle distance of AV platoons can be reduced to 10 m, and the speed of AVs should not be higher than the design speed of adjacent MV lanes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The takeover process in level 3 automated driving determines the controllability of the functions of automated vehicles and thereby traffic safety. In this study, we attempted to explain drivers' takeover performance variation in a level 3 automated vehicle in consideration of the effects of trust, system characteristics, environmental characteristics, and driver characteristics with a structural equation model. The model was built by incorporating drivers' takeover time and quality as endogenous variables. A theoretical framework of the model was hypothesized on the basis of the ACT-R cognitive architecture and relevant research results. The validity of the model was confirmed using data collected from 136 driving simulator samples under the condition of voluntary non-driving-related tasks. Results revealed that takeover time budget was the most critical factor in promoting the safety and stability of takeover process, which, together with traffic density, drivers' age and manual driving experience, determined drivers' takeover quality directly. In addition, the pre-existing experience with an automated system or a similar technology and self-confidence of the driver, as well as takeover time budget, strongly influenced the takeover time directly. Apart from the direct effects mentioned above, trust, as an intermediary variable, explained a major portion of the variance in takeover time. Theoretically, these findings suggest that takeover behavior could be comprehensively evaluated from the two dimensions of takeover time and quality through the combination of trust, driver characteristics, environmental characteristics, and vehicle characteristics. The influence mechanism of the above factors is complex and multidimensional. In addition to the form of direct influence, trust, as an intermediary variable, could reflect the internal mechanism of the takeover behavior variation. Practically, the findings emphasize the crucial role of trust in the change in takeover behavior through the dimensions of subjective trust level and monitoring strategy, which may provide new insights into the function design of takeover process., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Understanding driver behavior of conditionally automated driving is necessary to ensure a safe transition from automated to manual driving. This study aimed to examine the difference in take-over performance between high crash risk (HCR) and lower crash risk (LCR) drivers in emergency take-over situations during conditionally automated driving. In the current simulator study, a 3 × 3 (within-subjects) factorial design was used, including the task factors (no task, reading the news, and watching a video) and time budget factors (time budget = 3 s, 4 s, and 5 s). Forty-eight participants completed a test drive on an approximately 10 km long two-way six-lane urban road. The participants firstly were in manual control and then switched to the automated driving mode at a speed of 50 km/h. The automated driving system was able to detect a broken car in the ego-lane and requested the driver to take over the control of the vehicle. There are at least one or two other vehicles or motorcycles on each side of the ego-vehicle, resulting in fewer escape paths. For the two non-handheld non-driving-related tasks (NDRTs), the participants were asked to be fully engaged in a task without any need to monitor the road environments. Each participant completed nine emergency take-over situations. The participants were classified into two groups that were labeled LCR (N ≤ 2) and HCR drivers (N ≥ 3) according to the number of accidents per driver. The results show that LCR drivers had shorter brake reaction time compared to HCR drivers. For all drivers, the engagement in a task led to longer response times, and the time budget affected the longitudinal vehicle control. In addition, the task affected the response times for LCR and HCR drivers, but only the time budget affected the longitudinal vehicle control for LCR drivers. For all drivers, LCR and HCR drivers, the time budget and task affected the safety of take-over. Especially, the two non-handheld everyday tasks seem to have a similar effect on the drivers' workload. Therefore, the HCR drivers had a lower hazard perception compared to the LCR drivers, and the factor regarding the individual difference of driving ability in take-over situations should be considered to design safe take-over concepts for automated vehicles., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Quinine (QN) remains an effective drug for malaria treatment. However, quinine resistance (QNR) in Plasmodium falciparum has been reported in many malaria-endemic regions particularly in African countries. Genetic polymorphism of the P. falciparum Na + /H + exchanger (pfnhe1) is considered to influence QN susceptibility. Here, ms4760 alleles of pfnhe1 were analysed from imported African P. falciparum parasites isolated from returning travellers in Wuhan, Central China., Methods: A total of 204 dried-blood spots were collected during 2011-2016. The polymorphisms of the pfnhe1 gene were determined using nested PCR with DNA sequencing., Results: Sequences were generated for 99.51% (203/204) of the PCR products and 68.63% (140/204) of the isolates were analysed successfully for the pfnhe1 ms4760 haplotypes. In total, 28 distinct ms4760 alleles containing 0 to 5 DNNND and 1 to 3 NHNDNHNNDDD repeats were identified. For the alleles, ms4760-1 (22.86%, 32/140), ms4760-3 (17.86%, 25/140), and ms4760-7 (10.71%, 15/140) were the most prevalent profiles. Furthermore, 5 undescribed ms4760 alleles were reported., Conclusions: The study offers an initial comprehensive analysis of pfnhe1 ms4760 polymorphisms from imported P. falciparum isolates in Wuhan. Pfnhe1 may constitute a good genetic marker to evaluate the prevalence of QNR in malaria-endemic and non-endemic regions.

An increasing number of vehicles travel on freeways result not only in traffic congestions but also accidents. Rear-end crashes in freeways can be collectively attributed to drivers, vehicles, and road infrastructure, but driving behavior plays a key role in influencing car-following safety. This study aims to investigate the impact of heterogeneity of driving behavior on rear-end crash risk. Driving behavior depends on perceived risk levels, acceleration and deceleration habits, and driver reaction characteristics. Thus, the influencing factors of rear-end crash risk were initially analyzed by using the desired safety margin (DSM) model. Subsequently, five driving behavior parameters, including upper and lower limits of DSM, sensitivity coefficients of acceleration and deceleration, and response time, were calibrated by using the vehicle trajectories from the Next Generation Simulation I-80 datasets. Simulation experiments were designed to evaluate the impact of heterogeneity of car-following behavior on rear-end crash risk. Results showed that decreasing the lower (or upper) limit of the DSM, increasing the response time, increasing the sensitivity coefficient for acceleration, or decreasing the sensitivity coefficient for deceleration can increase rear-end crash risk. In addition, if stable and unstable driving styles coexist, then their proportions have important influences on rear-end crash risk. These results imply that two critical factors affect shock waves, namely, driving behavior characteristics and proportion of different driving styles. Thus, a potential strategy for the adjustment of the proportions of unstable driving styles can attenuate shock waves and reduce rear-end crash risk to a certain extent. Moreover, a wide extent of driving behavior heterogeneity can attenuate shock waves and subsequently reduce rear-end crash risk. Overall, driving behavior heterogeneity has an important impact on rear-end crash risk. Exploring the effect of each driving behavior parameter on rear-end crash probability is useful for urban road traffic control, and it can provide improved understanding of abnormal driving behavior characteristics to minimize rear-end crash risks., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The schedule of urban road network recovery caused by rainstorms, snow, and other bad weather conditions, traffic incidents, and other daily events is essential. However, limited studies have been conducted to investigate this problem. We fill this research gap by proposing an optimal schedule for urban road network repair with limited repair resources based on the greedy algorithm. Critical links will be given priority in repair according to the basic concept of the greedy algorithm. In this study, the link whose restoration produces the ratio of the system-wide travel time of the current network to the worst network is the minimum. We define such a link as the critical link for the current network. We will re-evaluate the importance of damaged links after each repair process is completed. That is, the critical link ranking will be changed along with the repair process because of the interaction among links. We repair the most critical link for the specific network state based on the greedy algorithm to obtain the optimal schedule. The algorithm can still quickly obtain an optimal schedule even if the scale of the road network is large because the greedy algorithm can reduce computational complexity. We prove that the problem can obtain the optimal solution using the greedy algorithm in theory. The algorithm is also demonstrated in the Sioux Falls network. The problem discussed in this paper is highly significant in dealing with urban road network restoration., Competing Interests: The authors have declared that no competing interests exist.

Objective: This study aims to investigate the crossing behavior of straight-moving drivers when they encounter other straight-moving drivers at unsignalized intersections in China., Background: In China, when two vehicle drivers encounter at an unsignalized intersection, neither driver completely stops his or her vehicle in most cases. Instead, one driver gradually approaches the intersection and dynamically decides to either yield or preempt by gaming with the other vehicle. This process increases the probability of accidents., Method: A total of 305 crossing cases were collected at an unsignalized intersection in Kunming City. Motion parameters were extracted from the video detection program designed by our research group. Based on a logistic regression method, we analyzed decision making moment of straight-moving drivers under crossing conditions, established crossing behavior models, and identified the main factors that affected drivers' decisions. In all, 68 cases observed at a separate intersection were used to validate the established models., Results: For crossing processes at unsignalized intersections in China, straight-moving drivers from the right side completed preemptive/yielding decisions at 1.3 s before reaching the crossing point. However, the majority of straight-moving drivers from the left side completed decisions at 1.1 s before reaching the crossing point. The most important parameter that influenced the drivers' decisions was the difference between the speeds of the two vehicles., Conclusion: An effective method for preventing traffic conflict between two straight-moving drivers is to control the speeds of the vehicles before they enter the intersection., Application: This study explores crossing behavior of straight-moving drivers and provides significant insights for controlling driver behavior., (© 2015, Human Factors and Ergonomics Society.)

A critical phenomenon is an intrinsic feature of traffic dynamics, during which transition between isolated local flows and global flows occurs. However, very little attention has been given to the question of how the local flows in the roads are organized collectively into a global city flow. Here we characterize this organization process of traffic as "traffic percolation," where the giant cluster of local flows disintegrates when the second largest cluster reaches its maximum. We find in real-time data of city road traffic that global traffic is dynamically composed of clusters of local flows, which are connected by bottleneck links. This organization evolves during a day with different bottleneck links appearing in different hours, but similar in the same hours in different days. A small improvement of critical bottleneck roads is found to benefit significantly the global traffic, providing a method to improve city traffic with low cost. Our results may provide insights on the relation between traffic dynamics and percolation, which can be useful for efficient transportation, epidemic control, and emergency evacuation.

Objective: Road traffic conflicts can be used to estimate the probability of accident occurrence, assess road safety, or evaluate road safety programs if the relationship between road traffic accidents and conflicts is known. To this end, we propose a model for the relationship between road traffic accidents and conflicts recorded by drive recorders (DRs)., Methods: DRs were installed in 50 cars in Beijing to collect records of traffic conflicts. Data containing 1366 conflicts were collected in 193 days. The hourly distributions of conflicts and accidents were used to model the relationship between accidents and conflicts. To eliminate time series and base number effects, we defined and used 2 parameters: average annual number of accidents per 10,000 vehicles per hour and average number of conflicts per 10,000 vehicles per hour. A model was developed to describe the relationship between the two parameters., Results: If A(i) = average annual number of accidents per 10,000 vehicles per hour at hour i, and E(i) = average number of conflicts per 10,000 vehicles per hour at hour i, the relationship can be expressed as [Formula in text] (α>0, β>0). The average number of traffic accidents increases as the number of conflicts rises, but the rate of increase decelerates as the number of conflicts increases further., Conclusions: The proposed model can describe the relationship between road traffic accidents and conflicts in a simple manner. According to our analysis, the model fits the present data.