Browsing by Author "Lord, Dominique"

Now showing 1 - 3 of 3
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    Analyzing Highway Safety Datasets: Simplifying Statistical Analyses from Sparse to Big Data
    Lord, Dominique; Geedipally, Srinivas Reddy; Guo, Feng; Jahangiri, Arash; Shirazi, Mohammadali; Mao, Huiying; Deng, Xinwei (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
    Data used for safety analyses have characteristics that are not found in other disciplines. In this research, we examine three characteristics that can negatively influence the outcome of these safety analyses: (1) crash data with many zero observations; (2) the rare occurrence of crash events (not necessarily related to many zero observations); and (3) big datasets. These characteristics can lead to biased results if inappropriate analysis tools are used. The objectives of this study are to simplify the analysis of highway safety data and develop guidelines and analysis tools for handling these unique characteristics. The research provides guidelines on when to aggregate data over time and space to reduce the number of zero observations; uses heuristics for selecting statistical models; proposes a bias adjustment method for improving the estimation of risk factors; develops a decision-adjusted modeling framework for predicting risk; and shows how cluster analyses can be used to extract relevant information from big data. The guidelines and tools were developed using simulation and observed datasets. Examples are provided to illustrate the guidelines and tools.
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    Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles
    Talebpour, Alireza; Lord, Dominique; Manser, Michael P.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to car-following scenarios between AVs and human drivers. Braking and accelerating decision mismatches in this car-following scenario can lead to rear-end near-crashes and therefore warrant further study. This project aims to investigate the behavior of human drivers following an AV leader vehicle in a car-following situation and compare the results with a scenario in which the leader is a vehicle with human-modeled braking behavior. In this study, speed trajectory data was collected from 48 participants using a driving simulator.The results indicated a significant difference between the overall deceleration rates and braking speeds of the participants and the designated AV lead vehicle; however, no such difference was found between the participants and the human-modeled lead vehicle.
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    Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements
    Tsapakis, Ioannis; Das, Subasish; Khodadadi, Ali; Lord, Dominique; Morris, Jessica; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-03)
    States are required to have access to annual average daily traffic (AADT) for all public paved roads, including non-federal aid system (NFAS) roadways. The expectation is to use AADT estimates in data-driven safety analysis. Because collecting data on NFAS roads is financially difficult, agencies are interested in exploring affordable ways to estimate AADT. The goal of this project was to determine the accuracy of AADT estimates developed from alternative data sources and quantify the impact of AADT on safety analysis. The researchers compared 2017 AADT data provided by the Texas and Virginia Departments of Transportation against probebased AADT estimates supplied by StreetLight Data Inc. Further, the research team developed safety performance functions (SPFs) for Texas and Virginia and performed a sensitivity analysis to determine the effects of AADT on the results obtained from the empirical Bayes method that uses SPFs. The results showed that the errors stemming from the probe AADT estimates were lower than those reported in a similar study that used 2015 AADT estimates. The sensitivity analysis revealed that the impact of AADT on safety analysis mainly depends on the size of the network, the AADT coefficients, and the overdispersion parameter of the SPFs.