Quantifying the effect of roadway, driver, vehicle, and location characteristics on the frequency of longitudinal and lateral accelerations.

The purpose of this study is to understand and quantify the simultaneous effects of roadway speed category, driver age, driver gender, vehicle class, and location on the rates of longitudinal and lateral acceleration epochs. The rate of usual as well as harsh acceleration epochs are used to extract insights on driving risk and driver comfort preferences. However, an analysis of acceleration rates at multiple thresholds incorporating various effects while using a large-scale and diverse dataset is missing. This analysis will fill this research gap. Data from the 2nd Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) was used for this analysis. The rate of occurrence of acceleration epochs was modeled using negative binomial distribution based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as the fixed effects and the driver identifier was used as the random effect. Incidence rate ratios were then calculated to compare subcategories of each fixed effect. Roadway speed category has the strongest effect on longitudinal and lateral accelerations of all magnitudes. Acceleration epoch rates consistently decrease as the roadway speed category increases. The difference in the rates depends on the threshold and is up to three orders of magnitude. Driver age is another significant factor with clear trends for longitudinal and lateral acceleration epochs. Younger and older drivers experience higher rates of longitudinal accelerations and decelerations. However, the rate of lateral accelerations consistently decreases with age. Vehicle class also has a significant effect on the rate of harsh accelerations with minivans consistently experiencing lower rates.

Land use, REDD+ and the status of wildlife populations in Yaeda Valley, northern Tanzania.

REDD+ projects primarily focus on reducing carbon emissions from deforestation and forest degradation in developing countries. These projects are regularly evaluated against their core objective of conserving carbon stocks, but their contribution to biodiversity conservation has rarely been assessed. To assess the conservation value of the area and the relative performance of a REDD+ land use plan in Yaeda Valley, a semi-arid savannah ecosystem in northern Tanzania, we implemented an annual wildlife monitoring scheme. Based on direct sightings and indirect signs of wildlife, obtained from stratified walking transects conducted annually from 2015-2018, we estimated annual trends of mammal species richness and wildlife densities in three REDD+ and three non-REDD+ land-use strata. Our surveys document a near complete mammal community in the area. Species accumulation curves, and subsequent statistical comparisons, indicated highest mammal species richness in the woodland habitats (both REDD+ and non REDD+ strata) as compared to more human and livestock impacted areas, and suggested constant species richness from 2015-2018. To estimate stratum- and year-specific livestock and wildlife densities (cattle, donkey, goat and sheep combined, Thomson's gazelle, Kirk's dik-dik) and wildlife sign densities (aardvark, bushbuck, bushpig, Kirk's dik dik, eland, elephant, Maasai giraffe, greater kudu, hyena, impala, lesser kudu, warthog, wildebeest, Plains zebra), we fitted species-specific detection functions in a distance sampling framework. Species-specific densities varied between 2015 and 2018 and showed substantial increases and occasional declines in other species-stratum combinations. However, population growth rates were not systematically associated with specific land-use strata. Although our results do not explicitly provide evidence that REDD+ land-use plans directly co-benefit wildlife conservation, they show that REDD+ areas have the potential to maintain intact wildlife assemblages. To ensure effective long-term conservation outcomes, we advocate for a more formal integration of wildlife conservation goals in the REDD+ scheme.

Motorcyclists' self-reported riding mileage versus actual riding mileage in the following year.

INTRODUCTION: Accurate motorcyclist mileage estimates are important because self-evaluation of riding experience is related to riding behavior, the relationship of self-reported to actual or future mileage is necessary in targeting training and considering survey responses, and motorcycle crash statistics require accurate travel data. METHOD: This study collected real-world data from motorcyclists over the course of two months to two years per rider. This paper explores motorcyclists' self-reported annual riding mileage (obtained via pre-study surveys) and the actual amount of riding during the study (based upon odometer readings and GPS data). RESULTS: Of the 91 riders who had been riding for at least a year before the study, significantly more (73%) rode less the following year than reported for the previous year. The recorded annualized mileage averaged 89% of the reported mileage from the previous year. Analyses based on estimated average annual mileage were similar to those using the previous year estimation, and the pattern held regardless of age group, motorcycle type, or gender. The exception was novice or returning riders, who tended to either significantly underestimate or increase actual mileage as they began (or continued) to ride. CONCLUSIONS: Motorcyclists' estimation of riding experience expressed as mileage may not be indicative of current or future mileage. PRACTICAL APPLICATIONS: Reliance on self-reported mileage during training to categorize groups, for interpretation of studies, or to develop motorcycle travel data and safety statistics may be unrealistic. Certainly any use of self-reported mileage should incorporate the concept that mileage overestimation seems likely. Because questions about previous year and average annual mileage may elicit similar responses, motorcyclist surveys should be constructed to prompt the most thoughtful responses in terms of mileage estimations. In general, reported mileage should not be relied upon as an accurate predictor of future actual mileage.

Driver performance while text messaging using handheld and in-vehicle systems.

This study presents an evaluation of driver performance while text messaging via handheld mobile phones and an in-vehicle texting system. Participants sent and received text messages while driving with an experimenter on a closed-road course, using their personal mobile phones and the vehicle's system. The test vehicle was an instrumented 2010 Mercury Mariner equipped with an OEM in-vehicle system that supports text messaging and voice control of mobile devices via Bluetooth, which was modified to allow text message sending during driving. Twenty participants were tested, 11 younger (19-34) and 9 older (39-51). All participants were regular users of the in-vehicle system, although none had experience with the texting functions. Results indicated that handheld text message sending and receiving resulted in higher mental demand, more frequent and longer glances away from the roadway, and degraded steering measures compared to baseline. Using the in-vehicle system to send messages showed less performance degradation, but still had more task-related interior glance time and higher mental demand than baseline; using the system's text-to-speech functionality for incoming messages showed no differences from baseline. These findings suggest that using handheld phones to send and receive text messages may interfere with drivers' visual and steering behaviors; the in-vehicle system showed improvement, but performance was not at baseline levels during message sending.

A method for evaluating collision avoidance systems using naturalistic driving data.

This paper describes a method for use in evaluating the performance of collision avoidance systems (CASs) using naturalistic driving data collected during real crashes and near-crashes. The method avoids evaluation of algorithms against specific assumptions of reaction times or response inputs. It minimizes interpretation of the involved driver's perception and response levels which permits generalizing findings beyond the performance of the involved driver. The method involves four parts: input of naturalistic crash data into alert models to determine when alerts would occur, kinematic analysis to determine when different responses would be required to avoid collision, translation of the time available into an estimate of the percentage of the population able to avoid the specific event, and an evaluation of the frequency of alerts that would be generated by the CASs. The method permits comparison of CAS performance and provides guidance for CAS development. The method is described primarily in the context of Forward Collision Warning CASs, but is applicable to other CAS types.