Shan Bao

Associate Professor

University of Michigan-Dearborn

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Shan Bao is an Associate Professor at the University of Michigan–Dearborn. His research focuses on traffic safety, pedestrian and cyclist injury patterns, as well as the design of autonomous vehicles and advanced driving assistance systems. Recent publications include studies on crash injury patterns among different demographics and an analysis of user needs for autonomous shared rides. His work employs data-driven methodologies to identify risk factors and improve safety measures in transportation.

Articles (13)

Year:

2025

Year:

2025

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Collaborators (5)

Bochen Jia

University of Michigan

UNITED STATES

View details

Brian Lin

University of Michigan

UNITED STATES

View details

Anuj Pradhan

Assistant Professor

University of Massachusetts Amherst

UNITED STATES

View details

Heejin Jeong

Arizona State University

UNITED STATES

View details

Pei Li

University of Central Florida

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Shan Bao

Research Interests

Explore related searches

Contact this professor

About

Articles (13)

Investigating pedestrian crash injury patterns: A comparative study of children and non-children

Toward Designing Autonomous Shared Rides for People with Parkinson’s Disease: Barriers and User Needs Analysis

Learning from Moped Crash Data: Identifying Risk Factors Contributing to the Severity of Injuries Sustained by Moped Riders

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

Simulator evaluation of an intersection maneuver assist system with connected and automated vehicle technologies

Identifying User Needs and Current Challenges of External Interface Design for AV-VRU Communications: Insights from an Expert Survey Data Analysis

Driving Style Classification and the Effectiveness of Advanced Driving Assistance Systems: Differences between Teen and Adult Drivers

Identifying Factors Related to Crash Injury Levels Involving Bicyclists at Different Locations Through Crash Data Analysis

Modeling Pedestrian Injury Severity: A Case Study of Using Extreme Gradient Boosting Vs Random Forest in Feature Selection

Examination of Recent Pedestrian Safety Patterns at Intersections through Crash Data Analysis

Modeling drivers’ reaction when being tailgated: A Random Forests Method

A fallback mechanism or a commander? A discussion about the role and skill needs of future drivers within partially automated vehicles

An augmented warning system for pedestrians: User interface design and algorithm development

Collaborators (5)

Bochen Jia

Brian Lin

Anuj Pradhan

Heejin Jeong

Pei Li

Make your next move.