Simulator Evaluation of the Effectiveness of an Comprehensive Teen Driver Training Program

Novice teen drivers are over represented in crashes, particularly rear end, intersection and run- off-the-road crashes. Their over involvement in these crashes appears to be due to six poorly developed skills: tactical and strategic hazard anticipation, tactical and strategic hazard mitigation, and tactical and strategic attention maintenance. Previous studies had determined that a single skill could be taught in a 45 minute training session. The question addressed here was whether all six possible skills could be taught in a two hour session without reducing the effectiveness of the training of the individual skills. Specifically, the current study examines the development and evaluation on a driving simulator of a training program, ACCEL (Accelerated Curriculum to Create Effective Learning), that is designed to decrease the time it takes teens to become safer drivers over the first 18 months of independent driving by targeting for training the above six behaviors in the most risky crash scenarios. During the evaluation, eye movements were recorded and vehicle measures were collected for a total of 75 novice drivers (16 to 18 14 years with less than 6 months’ experience), of which fifty were ACCEL-trained and 25 were Placebo-trained, and 25 experienced drivers (28 to 55 with at least 10 years’ experience), all untrained. ACCEL training was found to significantly improve the performance of novice drivers in 5 out of the 6 of the trained skills when compared to Placebo trained teens: tactical and strategic hazard anticipation, tactical hazard mitigation, and tactical and strategic attention maintenance. The results are consistent with the hypothesis that combined skill training can be deliver effectively in a relatively short amount of time.

Advertisements

A Seasonal Bicycle Demand Model Using A Sinusoidal Function

As urban populations increase, there is a growing need for efficient and sustainable transportation modes, such as bicycling. Unfortunately, the lack of bicycle demand data is a substantial barrier to efforts in designing, planning, and researching bicycle transportation. Estimating bicycle demand is especially difficult not only due to limited count data, but due to the fact that bicyclists are highly responsive to a multitude of factors, particularly seasonal weather conditions. Current bicycle demand estimation methods are increasingly improving and are capable of accurately adjusting for seasonal change in demand. However, these methods often require substantial data for each calibration, which is often difficult or impossible in locations with partial or minimal continuous count data. This research aims to help mitigate this challenge by developing an estimation method which uses a sinusoidal model to fit the typical pattern of seasonal bicycle demand expected in in many locations. This sinusoidal model utilizes a single calibration factor to adjust for scale of seasonal demand change and is capable of estimating monthly average daily bicycle counts (ADB) and average annual daily bicycle counts (AADB). This calibration factor can be established using a minimum of two short term counts to represent the maximum monthly ADB in summer and minimum monthly ADB in winter, or ideally with continuous counts. The calibration factor can then be applied to other locations that are expected to have similar seasonal patterns, even if they have different overall counts. To develop the model this research uses data from bike-share systems in four cities and permanent bicycle counters in six cities. Ultimately, this model functions as an alternative, or supportive, estimation method which allows for researchers and transportation agencies to approximate expected demand in locations that suffer from minimal seasonal bicycle demand data.

By: Nicholas Fourniera,∗, Eleni Christofaa, Michael A. Knodler Jr. ; UMass Amherst