Road user behavior: Describing, inferring, predicting and beyond

Birsen Donmez; Anthony D. McDonald; John D. Lee; Linda Ng Boyle

doi:10.1016/j.trip.2023.100932

Road user behavior: Describing, inferring, predicting and beyond

Birsen Donmez, Anthony D. McDonald, John D. Lee, Linda Ng Boyle

Civil and Urban Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Data collection and analysis perspectives differ widely across disciplines. In the study of road user behavior, data can be used to explore characteristics of the road user, verify theories, or predict road user behavior. The choice of descriptive, inferential, or predictive analysis greatly depends on the research question(s), which affects the data processing and visualization needs. In this paper, we present differences among the three analysis approaches with examples using a subset of the Strategic Highway Research Program (SHRP2) naturalistic driving data. The findings showcase 11 guidelines that should be considered irrespective of the analysis type.

Original language	English (US)
Article number	100932
Journal	Transportation Research Interdisciplinary Perspectives
Volume	22
DOIs	https://doi.org/10.1016/j.trip.2023.100932
State	Published - Nov 2023

Keywords

Experimental design
Machine learning
Statistical modeling
Transportation analysis

ASJC Scopus subject areas

Civil and Structural Engineering
Geography, Planning and Development
Automotive Engineering
Transportation
General Environmental Science
Urban Studies
Management Science and Operations Research

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10.1016/j.trip.2023.100932

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title = "Road user behavior: Describing, inferring, predicting and beyond",

abstract = "Data collection and analysis perspectives differ widely across disciplines. In the study of road user behavior, data can be used to explore characteristics of the road user, verify theories, or predict road user behavior. The choice of descriptive, inferential, or predictive analysis greatly depends on the research question(s), which affects the data processing and visualization needs. In this paper, we present differences among the three analysis approaches with examples using a subset of the Strategic Highway Research Program (SHRP2) naturalistic driving data. The findings showcase 11 guidelines that should be considered irrespective of the analysis type.",

keywords = "Experimental design, Machine learning, Statistical modeling, Transportation analysis",

author = "Birsen Donmez and McDonald, {Anthony D.} and Lee, {John D.} and Boyle, {Linda Ng}",

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year = "2023",

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doi = "10.1016/j.trip.2023.100932",

language = "English (US)",

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KW - Statistical modeling

KW - Transportation analysis

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Road user behavior: Describing, inferring, predicting and beyond

Abstract

Keywords

ASJC Scopus subject areas

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