Vehicular Variables Research Articles

The Viscosity Effect on Velocity of a Macroscopic Vehicular Traffic Model

Traffic in Mexico City poses a serious problem of vehicle saturation that causes a decrease in speed and increased transport time in the streets that suffer mobility collapses. A macroscopic model of vehicular traffic is used to show the effect of viscosity on the vehicular variables (speed and vehicle density), applied to two avenues in Mexico City, is studied. The input parameters were calculated following the Greenberg model. As the original model presents numerical divergences, the two assumptions corresponding to conservation of the vehicle’s mass and the viscous term are modified. The results suggest that the viscosity depends on time and that it can be adapted to recommend modifications in urban mobility parameters, or even to implement the public planning policies in construction of infrastructure for urban transport, to make vehicle flow more efficient.

Investigation on Identifying Road Anomalies using In-Vehicle Sensors for Cooperative Applications and Road Asset Management

One of the main challenges for road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the condition of vehicles, and the comfort of occupants of vehicles. Strategies such as pavement management systems use pavement evaluation vehicles that are equipped with state-of-the-art devices to assist road authorities in identifying and repairing these anomalies. The quantity of data available is limited, however, by the limited availability and, therefore, coverage of these vehicles. To address this problem, several investigations have been conducted on the use of smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in production vehicles to identify road anomalies. If production vehicles could be used to identify road anomalies, then road authorities would be equipped with an additional fleet of mobile sensors (vehicles traveling on a particular road) to receive initial insights into the presence of anomalies. This information could then be used to assist road authorities to deploy their staff and equipment more precisely at these locations, such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speed signals, which are commonly available vehicular variables, was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with real life test scenarios.

Forecasting Markers of Habitual Driving Behaviors Associated With Crash Risk

Both distracted and aggressive driving are habitual in nature, constituting an insurance risk, which has been difficult to quantify. Here, in this paper, we propose a method that produces short term predictions for these two dangerous driving behaviors. The method feeds an Extreme Gradient Boosting (XGB) algorithm with the most informative features of a set of physiological and vehicular variables. The XGB algorithm operates on a learning window covering the last 30 seconds to make fast track predictions (FT) for the next 10 seconds. For aggressive driving, FT predictions are final, while for distracted driving, FT predictions are weighted over one minute, to form a meta-prediction. This more deliberative process for predicting distractions fits their intermittent manifestation. The method has been tested on SIM 1, a publicly available dataset from a distracted driving experiment. In this dataset, the drivers ( $n=59$ ) are labeled as distracted based on the presence of mental activity or physical interactions antagonistic to the driving task; their driving style is defined by steering and acceleration, and is classified as aggressive or normal. The method attains classification performance that exceeds 87%. Alerting drivers when distractions and aggressiveness have taken hold on them can provide sobering awareness, given that people drift into these states subconsciously. The behavioral modification effects of such awareness mechanisms are rooted in Cognitive Behavioral Theory. The proposed method can also be used in future vehicles with advanced automation, weighing in the computer’s decision to wrest vehicular control from an unrepentant driver.

Crash scene photography in motor vehicle crashes without air bag deployment.

To determine whether vehicle characteristics, measured using crash scene photography, are associated with anatomic patterns of injury and severity of injury sustained in motor vehicle crashes (MVCs) without air bag deployment. A prospective observational study was conducted over 22 months, using 12 fire departments serving two hospitals. Two vehicle photographs (exterior and interior) were taken at each MVC. Vehicular variables were assigned by grading the photographs with a standardized scoring system, and outcome information on each patient was collected by chart review. Five hundred fifty-nine patients were entered into the study. Frontal crashes and increasing passenger space intrusion (PSI) were associated with head, facial, and lower-extremity injuries, while rear crashes were associated with spinal injuries. Restraint use had a protective effect in head, facial, and upper and lower extremity injuries, yet was associated with higher odds of spinal injury. Lack of restraint use, increasing PSI, and steering wheel deformity were associated with an increased hospital length of stay and hospital charges, yet only steering wheel deformity was associated with increasing injury severity when adjusting for other crash variables. Out-of-hospital variables, as obtained from crash vehicle photography, are associated with injury site, injury severity, hospital length of stay, and hospital charges in patients involved in MVCs without air bag deployment.

Crash Scene Photography in Motor Vehicle Crashes without Air Bag Deployment

Abstract Objective: To determine whether vehicle characteristics, measured using crash scene photography, are associated with anatomic patterns of injury and severity of injury sustained in motor vehicle crashes (MVCs) without air bag deployment. Methods: A prospective observational study was conducted over 22 months, using 12 fire departments serving two hospitals. Two vehicle photographs (exterior and interior) were taken at each MVC. Vehicular variables were assigned by grading the photographs with a standardized scoring system, and outcome information on each patient was collected by chart review. Results: Five hundred fifty‐nine patients were entered into the study. Frontal crashes and increasing passenger space intrusion (PSI) were associated with head, facial, and lower‐extremity injuries, while rear crashes were associated with spinal injuries. Restraint use had a protective effect in head, facial, and upper and lower extremity injuries, yet was associated with higher odds of spinal injury. Lack of restraint use, increasing PSI, and steering wheel deformity were associated with an increased hospital length of stay and hospital charges, yet only steering wheel deformity was associated with increasing injury severity when adjusting for other crash variables. Conclusions: Out‐of‐hospital variables, as obtained from crash vehicle photography, are associated with injury site, injury severity, hospital length of stay, and hospital charges in patients involved in MVCs without air bag deployment.