Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

In 2015, there were 319,195 police reported vehicle-animal crashes, resulting in 275 vehicle occupant fatalities. Animal-detecting automatic emergency braking (AEB) systems are a promising active safety measure which could potentially avoid or mitigate many of these crashes by warning the driver, utilizing automatic braking, or both. The purpose of this study was to develop and characterize a target population of vehicle-animal crashes applicable to AEB systems and to analyze the potential benefits of an animal-detecting AEB system. The study was based on two nationally representative databases, Fatality Analysis Reporting System and the National Automotive Sampling System’s General Estimates System, and a naturalistic driving study, SHRP 2. The target population was restricted to vehicle-animal crashes that were forward impacts or road departures and involved cars and light trucks, with no loss of control. Crash characteristics which may influence the performance of AEB such as lighting, weather, pre-crash movement, relation to junction, and first and worst harmful events, were analyzed. The study found that the major influences on the effectiveness of animal AEB systems were: weather, lighting, pre-crash movements, and the crash location. Six potential target populations were used to analyze the potential effectiveness of an animal AEB system, with effectiveness ranging between 21.6% and 97% of police reported crashes and between 4.1% and 50.8% of fatal vehicle-animal crashes. An AEB system’s ability to function in low light and poor weather conditions may enable it to avoid a substantially higher proportion of crashes.

Assessing the Impact of Large-Scale Trends on Ontario’s Pedestrian Fatality Rate

Pedestrian-involved collisions are a key contributor to roadway fatalities in Ontario; pedestrian deaths have been growing as a proportion of total road fatalities. This study aimed first to determine trends in the pedestrian fatality rate in Ontario over time and, second, to assess the impact of select large-scale trends on pedestrian fatalities. Large-scale trends were identified through a review of the literature and hypotheses were tested using Ontario collision data from 2002 to 2016. The following four key areas were assessed for their impact: (1) the aging demographic; (2) the impact of increasing consumer preference for light trucks; (3) the potential for an increase in alcohol-consuming pedestrians associated with a decrease in alcohol-consuming drivers, and; (4) increasing inattention, caused, in part, by pedestrians and drivers using electronic devices. A quadratic model, with a minimum at 2010, best described changes in Ontario’s pedestrian fatality rate, suggesting a transition from a decreasing to increasing trend at that time. Results of the four key areas were: (1) the proportion of pedestrians aged 75 and older being killed has been increasing over time, a trend that can be fully explained by their increased representation in Ontario’s population, a trend which is expected to continue; (2) similarly, the increase in the proportion of pedestrians killed by a light truck can be explained by their increased representation in Ontario’s registered vehicle population; (3) the odds of a pedestrian being alcohol positive have been decreasing over time; and (4) the odds are higher that a driver who kills a pedestrian is inattentive.

An Improved Design of the Engine Bench Test Tool for the Dual Mass Flywheel (DMF)

At present, a lot of passenger cars and light trucks are equipped with the dual mass flywheel, which can decrease the non-uniformity of the indicated engine torque. In engine bench test, the secondary mass of the dual mass flywheel can’t be fixed because there is no transmission system on the test bench. As a result, the secondary mass of the dual mass flywheel will have radial and circumferential movements, which may lead to a damage to the dual mass flywheel. This paper presents an improved design of the crafting tool for the dual mass flywheel to protect the secondary mass during the engine bench test. For the improved design, the cylinder body is used for the positioning of the crafting tool, and a spline shaft and a ball bearing of centripetal adjustment are chosen to fix the secondary mass. Through this tool, the dual mass flywheel engine can be tested on the test bench without a starting shaft, and the disassembling of the engine flywheel bolts during the hot test can be avoided.

Trivializing Climate Change

The Trump administration has, at every turn, taken actions to reverse the climate progress achieved during the Obama administration, moving to repeal or significantly roll back three significant rules to reduce the emissions of greenhouse gases from existing power plants, passenger cars and light trucks, and oil and gas installations. To mask the true effects of its policies, the Trump administration has targeted the social cost of carbon, which quantifies the harm caused by a ton of carbon dioxide emissions. It has used two principal techniques, unsupported by economic theory, to reduce the estimate of the damages of greenhouse emissions by a factor of about 10.

Analysis of Operational Efficiencies and Costs for Extracting Thinned Woods in Small-Scale Forestry, Nasunogahara Area, Tochigi Prefecture, Japan

In this study, two operational methodologies to extract thinned woods were investigated in the Nasunogahara area, Tochigi Prefecture, Japan. Methodology one included manual extraction and light truck transportation. Methodology two included mini-forwarder forwarding and four-ton truck transportation. Furthermore, a newly introduced chipper was investigated. As a result, costs of manual extractions within 10 m and 20 m were JPY942/m3 and JPY1040/m3, respectively. On the other hand, the forwarding cost of the mini-forwarder was JPY499/m3, which was significantly lower than the cost of manual extractions. Transportation costs with light trucks and four-ton trucks were JPY7224/m3 and JPY1298/m3, respectively, with 28 km transportation distances. Chipping operation costs were JPY1036/m3 and JPY1160/m3 with three and two persons, respectively. Finally, the total costs of methodologies one and two from extraction within 20 m to chipping were estimated as JPY9300/m3 and JPY2833/m3, respectively, with 28 km transportation distances and three-person chipping operations (EUR1 = JPY126, as of 12 August 2020).

Travel Demand and Emissions from Driving Dogs to Dog Parks

This paper reports on an intercept survey of dog park visitors in Seattle, U.S., which was combined with Google Maps and Google Popular Times data to develop estimates of the number of walking trips, vehicle miles traveled (VMT), and greenhouse gas (GHG) emissions associated with traveling to dog parks. It is estimated that approximately 1.6 million VMT and more than 700 tonnes CO2-equivalent are generated annually by driving dogs to dog parks in Seattle, representing approximately 0.07% of vehicle trips and 0.04% of GHG emissions from cars and light trucks in the city. Based on a stated choice exercise, it is estimated that allowing dogs off-leash in neighborhood parks could reduce these VMT and GHG emissions by 38% and 45%, respectively, while encouraging more than 39,000 additional walking trips annually. Even limiting such use to the hours of 6:00 to 8:00 a.m. would reduce VMT and GHGs by 24% and 28%, respectively, while encouraging 22,000 additional walking trips. Although less than 20% of survey respondents expressed an interest in replacing a trip to the dog park with a visit to a neighborhood park, those who did were likely to replace driving trips to the dog park with walking trips. Thus, allowing dogs off-leash in neighborhood parks, even for limited hours each day, could increase physical activity while reducing the vehicle travel and GHG emissions associated with driving dogs to dog parks.

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.