Safe-D: Safety through Disruption

Webinar Archive

Recording Title/Project/Date Speaker Webinar Overview
Link (YouTube) Title: Older Drivers and Transportation Network Companies: Investigating Opportunities for Increased Safety and Improved Mobility

Project: Safe-D Project 02-016

Date: January 23, 2020

Melissa Tooley
Texas A&M Transportation Institute
Transportation network companies (TNCs) such as Uber and Lyft offer an increasingly popular alternative to driving a personal vehicle. This project investigated the potential of TNCs to increase the safety and enhance the mobility of older adults who are experiencing a decline in driving ability. Interviews with commercial and non-profit transportation providers and focus groups of adults ranging from age 65 to over 85 identified attitudes and perceptions toward TNCs and related services targeting senior adults, as well as ongoing barriers to TNC use by this demographic. Barriers include insufficient familiarity and comfort with using smartphone applications, a lack of knowledge among older adults about how TNCs operate, and lack of availability of TNC services in many rural areas. Increased availability of TNC services targeted toward older adults may help to overcome some of these barriers. The project team developed outreach and education materials for older adults on how to access and use TNC services.
Link (YouTube) Title: Model Selection Heuristics based on Characteristics of Data & Rare Events Modeling

Project: Safe-D Project 01-001

Date: February 5, 2020

Ali Shirazi
Texas A&M University

Feng Guo
Virginia Tech Transportation Institute

Part 1: Model Selection Heuristics Based on Characteristics of Data. Transportation analysts usually employ post-modeling methods, such as Goodness-of-Fit statistics or Likelihood-based Ratio Tests for selecting the best distribution or model. These metrics require all competitive distributions or models to be fitted to the data before any comparisons can be accomplished. Given the continuous growth in introducing new statistical distributions, choosing the best one using such post-modeling methods is not a trivial task, especially given all theoretical or numerical issues the analyst may face during the analysis. Furthermore, and most importantly, these measures or tests do not provide any intuitions about why a specific distribution or model is preferred over another (Goodness-of-Logic). This presentation describes a methodology to design heuristics for Model Selection based on the characteristics of data, in terms of descriptive summary statistics, before the competitive models are fitted. The proposed methodology employs two analytic tools: (1) Monte-Carlo Simulations and (2) Machine Learning Classifiers, to design simple heuristics to predict the label of the ‘most-likely-true’ distribution for analyzing data.

Part 2: Rare Event Modeling. The rare event nature of crashes brings challenges in crash modeling and prediction. This study focuses on the following two aspects: 1) propose bias adjustment for more accurate estimation of the safety impact of a risk factor; 2) develop a decision-adjusted modeling framework to predict high risk drivers based on telematics data. The decision-adjusted framework optimizes predictive performance based on the objective of the study, e.g., top 1% of high risk drivers. In a case study, we developed an optimal driver level risk prediction model based on the telematics data (high G-force events) and driver demographic information using the SHRP2 NDS.

Link (YouTube) Title: Street Noise Relationship to Vulnerable Road User Safety

Project: Safe-D Project 02-027

Date: February 12, 2020

Greg Griffin
Texas A&M Transportation Institute
This webinar shares results of recent research that related bicycle crash rates to noise levels – measured from the bicycle handlebar in two cities. The study developed a method for evaluating street noise and documented crash rates for roadways in Austin, Texas, and Washington, D.C., in a manner that is replicable by researchers and practitioners. Researchers collected street-level noise in both cities over a range of locations, facility types, and times, and compared these against crash records, normalized by bicycle volumes, and other explanatory variables. Modeling explained 87% of the variation in crash risk in our Washington, DC Capital Area route, after controlling for infrastructure differences and nearby bicycle commute mode shares. Further explorations of street noise are needed to improve further guidance for transportation planning and design.