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Concept: Automobile


Insufficient sleep is common among high school students and has been associated with an increased risk for motor vehicle crashes (1), sports injuries (2), and occupational injuries (3). To evaluate the association between self-reported sleep duration on an average school night and several injury-related risk behaviors (infrequent bicycle helmet use, infrequent seatbelt use, riding with a driver who had been drinking, drinking and driving, and texting while driving) among U.S. high school students, CDC analyzed data from 50,370 high school students (grades 9-12) who participated in the national Youth Risk Behavior Surveys (YRBSs) in 2007, 2009, 2011, or 2013. The likelihood of each of the five risk behaviors was significantly higher for students who reported sleeping ≤7 hours on an average school night; infrequent seatbelt use, riding with a drinking driver, and drinking and driving were also more likely for students who reported sleeping ≥10 hours compared with 9 hours on an average school night. Although insufficient sleep directly contributes to injury risk, some of the increased risk associated with insufficient sleep might be caused by engaging in injury-related risk behaviors. Intervention efforts aimed at these behaviors might help reduce injuries resulting from sleepiness, as well as provide opportunities for increasing awareness of the importance of sleep.

Concepts: Sleep, Injuries, High school, Somnolence, Driver's license, Automobile, Helmet, Remove Intoxicated Drivers


Distracted driving attributable to the performance of secondary tasks is a major cause of motor vehicle crashes both among teenagers who are novice drivers and among adults who are experienced drivers.

Concepts: Vehicle, Automobile, Vehicles, Motor vehicle


The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains [Formula: see text]1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.

Concepts: United States, President of the United States, Demographics, Automobile, IPhone, Truck, Google Earth, Google Street View


Night-shift workers are at high risk of drowsiness-related motor vehicle crashes as a result of circadian disruption and sleep restriction. However, the impact of actual night-shift work on measures of drowsiness and driving performance while operating a real motor vehicle remains unknown. Sixteen night-shift workers completed two 2-h daytime driving sessions on a closed driving track at the Liberty Mutual Research Institute for Safety: (i) a postsleep baseline driving session after an average of 7.6 ± 2.4 h sleep the previous night with no night-shift work, and (ii) a postnight-shift driving session following night-shift work. Physiological measures of drowsiness were collected, including infrared reflectance oculography, electroencephalography, and electrooculography. Driving performance measures included lane excursions, near-crash events, and drives terminated because of failure to maintain control of the vehicle. Eleven near-crashes occurred in 6 of 16 postnight-shift drives (37.5%), and 7 of 16 postnight-shift drives (43.8%) were terminated early for safety reasons, compared with zero near-crashes or early drive terminations during 16 postsleep drives (Fishers exact: P = 0.0088 and P = 0.0034, respectively). Participants had a significantly higher rate of lane excursions, average Johns Drowsiness Scale, blink duration, and number of slow eye movements during postnight-shift drives compared with postsleep drives (3.09/min vs. 1.49/min; 1.71 vs. 0.97; 125 ms vs. 100 ms; 35.8 vs. 19.1; respectively, P < 0.05 for all). Night-shift work increases driver drowsiness, degrading driving performance and increasing the risk of near-crash drive events. With more than 9.5 million Americans working overnight or rotating shifts and one-third of United States commutes exceeding 30 min, these results have implications for traffic and occupational safety.

Concepts: Sleep, Termination of employment, Vehicle, Automobile, Vehicles


Pedestrians regularly engage with their mobile phone whilst walking. The current study investigated how mobile phone use affects where people look (visual search behaviour) and how they negotiate a floor based hazard placed along the walking path. Whilst wearing a mobile eye tracker and motion analysis sensors, participants walked up to and negotiated a surface height change whilst writing a text, reading a text, talking on the phone, or without a phone. Differences in gait and visual search behaviour were found when using a mobile phone compared to when not using a phone. Using a phone resulted in looking less frequently and for less time at the surface height change, which led to adaptations in gait by negotiating it in a manner consistent with adopting an increasingly cautious stepping strategy. When using a mobile phone, writing a text whilst walking resulted in the greatest adaptions in gait and visual search behaviour compared to reading a text and talking on a mobile phone. Findings indicate that mobile phone users were able to adapt their visual search behaviour and gait to incorporate mobile phone use in a safe manner when negotiating floor based obstacles.

Concepts: Mobile phone, Walking, Adaptation, Locomotion, Automobile, Pedestrian, Walkability, Sustainable transport


Promoting active commuting is viewed as one strategy to increase physical activity and improve the energy balance of more sedentary individuals thereby improving health outcomes. However, the potential effectiveness of promotion policies may be seriously undermined by the endogenous choice of commute mode. Policy to promote active commuting will be most effective if it can be demonstrated that 1) those in compact cities do not necessarily have a preference for more physical activity, and 2) that current active commuting is not explained by unobserved characteristics that may be the true source of a lower body mass index (BMI).

Concepts: Energy, Obesity, Mass, Body mass index, Cycling, Automobile, Sustainable transport, Park and ride


Human-mediated dispersal is known as an important driver of long-distance dispersal for plants but underlying mechanisms have rarely been assessed. Road corridors function as routes of secondary dispersal for many plant species but the extent to which vehicles support this process remains unclear. In this paper we quantify dispersal distances and seed deposition of plant species moved over the ground by the slipstream of passing cars. We exposed marked seeds of four species on a section of road and drove a car along the road at a speed of 48 km/h. By tracking seeds we quantified movement parallel as well as lateral to the road, resulting dispersal kernels, and the effect of repeated vehicle passes. Median distances travelled by seeds along the road were about eight meters for species with wind dispersal morphologies and one meter for species without such adaptations. Airflow created by the car lifted seeds and resulted in longitudinal dispersal. Single seeds reached our maximum measuring distance of 45 m and for some species exceeded distances under primary dispersal. Mathematical models were fit to dispersal kernels. The incremental effect of passing vehicles on longitudinal dispersal decreased with increasing number of passes as seeds accumulated at road verges. We conclude that dispersal by vehicle airflow facilitates seed movement along roads and accumulation of seeds in roadside habitats. Dispersal by vehicle airflow can aid the spread of plant species and thus has wide implications for roadside ecology, invasion biology and nature conservation.

Concepts: Plant, Transport, Seed, Walking, Vehicle, Automobile, The Road, Seed dispersal


Impaired driving is a recognized cause of major injury. Contemporary data are lacking on exposures to impaired driving behaviours and related injury among young adolescents, as well as inequities in these youth risk behaviours.

Concepts: Vehicle, Automobile


The aim of this study was to analyze the association between the characteristics of the built and social and environmental microscale and walking and bicycling for transportation in adults in Curitiba, Paraná State, Brazil. A cross-sectional study was performed in 2009 with a household survey that included 1,419 adults. Objective evaluation of environment was performed on the resident’s street segments, using an instrument for systematic observation consisting of six dimensions: “land use”, “public transportation”, “streetscape”, “conditions and aesthetics”, “places for walking and bicycling”, and “social environment”. The score for each dimension was obtained as the sum of positive items related to physical activity. The items for “public transportation” (≥ 1 items) and “places for walking and bicycling on the streets” (≥ 3 items) were dichotomized, while the scores for the other items were classified in tertiles. Walking and bicycling for transportation were assessed with the International Physical Activity Questionnaire (IPAQ). The data were analyzed using multilevel Poisson regression. Medium “streetscape” score was inversely associated with walking ≥ 150min/week (PR = 0.60; 95%CI: 0.40-0.91; VPC = 12%) and bicycling (PR = 0.54; 95%CI: 0.29-0.99; VPC = 60%). In conclusion, only “streetscape” was associated with walking and bicycling for transportation in adults.

Concepts: Regression analysis, Econometrics, Exercise, Transport, The Score, Street, Automobile, Sustainable transport


Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

Concepts: Mathematics, Engineering, Operations research, Transport, Optimization, Mathematical model, Automobile, Carpool