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Roadway safety is immensely affected during adverse weather conditions. Therefore, this study seeks to quantify such effects on the injury severity of involved drivers in single-vehicle crashes on two-way highways. This should be done by identifying risk factors and accommodating the heterogeneous impacts of these factors by accounting for the heterogeneity in means and variances of random parameters in the estimated model using seven years of police-reported crash data from 2010 to 2016 in Oregon. The study findings confirm the superior performance of the mixed logit model with heterogeneity in the means and variances in terms of statistical fit comparable to the traditional mixed logit model and mixed logit model with heterogeneity in the means only. The estimation results reveal that some factors such as female drivers, fatigued drivers, crashes in the early morning (between 4:00 am and 8:00 am), and crashes that occurred in darkness with no streetlights were found to increase serious injury outcomes. The empirical findings can offer evidence-based insights to help in decision-making and countermeasure selection that is aimed at improving safety under adverse weather conditions. [ABSTRACT FROM AUTHOR]

Single vehicle (SV) run-off-road crashes are a major cause of severe injury and fatality. Such crashes can result in different levels of severity depending on the direction (i.e., left or right) in which the vehicle runs off the road. This paper investigated the factors contributing to the crash severities of right run-off-road (R-ROR) and left run-off-road (L-ROR) SV crashes. The study used SV crash data from the City of Charlotte, North Carolina, covering 2014 to 2017. Two separate random parameter ordered logit (RPOL) models were developed to estimate the contributing factors to R-ROR and L-ROR SV crash severities. The impact of the explanatory variables on the crash severity outcomes was quantified using the models' direct pseudo-elasticities. The model results showed that male drivers, Driving Under Influence (DUI), motorcycles, and dry road surfaces were significant contributing factors to R-ROR and L-ROR SV crash severities. Specifically for the R-ROR model, speeding, reckless driving, 1–2 lanes, and older drivers increased crash severity. For the L-ROR model, phone distraction, crossed centerline/median, 3–4 lanes, rain, and dark unlighted roadway increased crash severity. Based on the estimated parameters for the common significant variables in the two models, it was inferred that L-ROR SV crashes are more likely to result in severe crashes compared to R-ROR SV crashes. Hence, this study contributes to the literature on ROR SV crashes by providing additional insight into contextual factors influencing ROR crash severity for more effective countermeasures. [ABSTRACT FROM AUTHOR]

The vehicles driving causes resuspension of road dust, which deteriorates air quality and poses population health risks. Based on sliding grid technology, the distribution characteristics of resuspended PM 2.5 generated from a single vehicle driving are studied, and its population exposure for drivers and pedestrians are analyzed. The results show that the concentration of PM 2.5 at z = −0.03 m has a "double bracket" shape with one front and one back. The length of PM 2.5 concentration distribution gradually increases from 6.825 m to 60.825 m for 1 to 10 s. The maximum height of resuspension PM 2.5 increases from 3.16 m to 5.13 m from 1 to 3 s and shows a sawtooth pattern from 3 to 10 s, reaching its highest at 5.62 m at 9 s at the XZ section. The dust gradually rises from 1 to 4 s and the dust gradually spreads to both sides of the vehicle from 4 to 10 s at X = -15 m section. PM 2.5 concentration for a, b, and c ranges at 0–59.0, 0–63.1and 0–39.2 μg/m3, respectively. The driver in point a poses the highest exposure concentration within 1–10 s, which is 2.1 times that of pedestrian b and 4.1 times that of pedestrian c. [Display omitted] • This study has performed sliding grids to simulate PM 2.5 diffusion from road resuspension dust. • PM 2.5 diffusion distribution for different sections are discussed and analyzed. • PM 2.5 exposure concentration of the driver is higher than that of pedestrians. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of collision avoidance of ground vehicles. In this framework, they focus on a single vehicle, and they guarantee that a given safe set is not exited during a mission. More specifically, they consider the yaw‐lateral motion of a two‐wheel vehicle subject to a side wind, and they design a state‐feedback controller such that the vehicle follows the nominal trajectory with a given maximum error. For these purposes, they resort to a control system where a supervisor, implemented as a hybrid automata, decides to switch between two controllers, depending on the measured vehicle lateral displacement from the nominal trajectory. The two controllers are designed using an H∞ approach and the input–output finite‐time stability approach, respectively. Some simulation results are included, showing the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

In this paper, we first research on the distance distribution of human mobility with single vehicle based on the driving data from a taxi company in South China. Different from conventional exponential distribution, we discover the mobility distance with taxi follows power-law distribution. Further, we proposed a model which may explain the mechanism for the power-law distribution: mobility distance is constrained by time and fare. Specifically, the relationship between fare and mobility distance follows piecewise function, and responds to individual sensitivity; the relationship between time and mobility distance follows significant logarithmic relationship. These two factors, especially the logarithmic relationship between time and mobility distance, may contribute to a power-law distribution instead of an exponential one. Finally, with a simulation model, we verify the significant power-law distribution of human mobility behavioral distance with a single vehicle, by supplementing factors of waiting time and fare. [ABSTRACT FROM AUTHOR]

• 1,618 light vehicle impacts in real world crashes analysed by impact type. • Travel speed taken from Event Data Recorders for high levels of accuracy. • Risk curves produced for risk of serious injury (MAIS3+F) vs travel speed. • Head on impacts reached 1% risk of serious injury at just 17 km/h. • The results can be used to set speed limits according to risk. While there is a large quantity of prior research on speed and road safety, no previous studies have quantified the absolute risk of serious injury in a crash relative to travel speed. This study aimed to produce risk curves that relate travel speed to the risk of serious injury in light vehicle impacts in order to contribute to the process of selecting acceptable travel speeds. Serious injury was defined in this study as any injury having a maximum abbreviated injury scale (MAIS) of three or greater, or a fatal injury (MAIS3+F). In the context of a crash, travel speed is defined as the vehicle's speed before the driver reacts to the crash situation. Travel speed was determined by selecting the highest pre-impact speed recorded by an Event Data Recorder (EDR) in the seconds before the crash. A total of 1,618 light vehicle impacts were analysed using logistic regression. Individual risk curves were produced for front, head on, side, rear and single vehicle impacts. The analysis found significant positive relationships between the risk of serious injury and travel speed for all of these impact types. The travel speeds at which the risk of serious injury reached one per cent were 63 km/h across all impacts, 17 km/h for head on impacts, 48 km/h for single vehicle impacts, 58 km/h for side impacts, 81 km/h for front impacts and 96 km/h for rear impacts. These results have implications for the setting of speed limits and other measures that influence the speed at which vehicles travel. [ABSTRACT FROM AUTHOR]