404-734-2376

Applications:
Behavioral Science, Civic Infrastructure, Complex Systems, Economics, Energy Research, Human Subjects Trials and Intervention Studies, Marketing and Consumer Behavior Research, Mobile Devices, Transportation Research, Urban Planning
Methodologies:
Artificial Intelligence, Causal Inference, Classification, Computational Tools for Data Science, Data Collection Design, Decision Science, Deep Learning, Econometrics, Longitudinal Data Analysis, Machine Learning, Missing Data and Imputation, Ontology, Pattern Analysis and Classification, Spatio-Temporal Data Analysis, Statistical Analysis and Simulation, Statistical Inference, Statistical Modeling, Survey Methodology
Relevant Projects:

NSF-RAISE; NSF-HDR; NCHRP; Mineta Transportation Institute; Michigan Department of Transportation; DiDi Chuxing; Toyota Research Institute; Ford Mobility Research


Connections:

University of Michigan; Transportation Research Board Standing Committees on Highway Performance Monitoring, Women’s Issues in Transportation, Transportation Planning Applications; Fellow, Data Science for Social Good, Atlanta

Aditi Misra

Assistant Research Scientist

University of Michigan Transportation Research Institute


Affiliation(s):

Department of Civil and Environmental Engineering

Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.

This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.