Anthony Vanky

By |

Anthony Vanky develops and applies data science and computational methods to design, plan, evaluate cities, emphasizing their applications to urban planning and design. Broadly, his work focuses on the domains of transportation and human mobility; social behaviors and urban space; policy evaluation; quantitative social sciences; and the evaluation of urban form. Through this work, he has extensively collaborated with public and private partners. In addition, he considers creative approaches toward data visualization, public engagement and advocacy, and research methods.

 

Anthony Vanky’s Cityways project analyzed 2.2 million trips from 135,000 people over one year to understand the factors that influence outdoor pedestrian path choice. Factors considered included weather, urban morphology, businesses, topography, traffic, the presence of green spaces, among others.

Robert Manduca

By |

Professor Manduca’s research focuses on urban and regional economic development, asking why some cities and regions prosper while others decline, how federal policy influences urban fortunes, and how neighborhood social and economic conditions shape life outcomes. He studies these topics using computer simulations, spatial clustering methods, network analysis, and data visualization.

In other work he explores the consequences of rising income inequality for various aspects of life in the United States, using descriptive methods and simulations applied to Census microdata. This research has shown how rising inequality has lead directly to lower rates of upward mobility and increases in the racial income gap.

9.9.2020 MIDAS Faculty Research Pitch Video.

Screenshot from “Where Are The Jobs?” visualization mapping every job in the United States based on the unemployment insurance records from the Census LODES data. http://robertmanduca.com/projects/jobs.html

Joshua P Newell

By |

I work in the area of urban sustainability, with research questions at multiple scales and environmental and socio-economic systems. My work uses spatial analysis (esp. GIS and remote sensing) and mass-balance accounting (life cycle assessment, material flow analysis). My lab is starting to use big data from a range of sources (Zillow, Twitter, etc) and I am interested in collaborating with data sciences of various stripes on sustainability and equity challenges.

Robert Hampshire

By |

He develops and applies operations research, data science, and systems approaches to public and private service industries. His research focuses on the management and policy analysis of emerging networked industries and innovative mobility services such as smart parking, connected vehicles, autonomous vehicles, ride-hailing, bike sharing, and car sharing. He has worked extensively with both public and private sector partners worldwide. He is a queueing theorist that uses statistics, stochastic modeling, simulation and dynamic optimization.

Aditi Misra

By |

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.

Victoria Morckel

By |

Dr. Morckel uses spatial and statistical methods to examine ways to improve quality of life for people living in shrinking, deindustrialized cities in the Midwestern United States. She is especially interested in the causes and consequences of population loss, including issues of vacancy, blight, and neighborhood change.

Suitability Analysis Results: Map of Potential Properties to Naturalize in the City of Flint, Michigan.

Raed Al Kontar

By |

My research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.

 

Greg Rybarczyk

By |

Dr. Greg Rybarczyk is an Associate Professor of Geography at the University of Michigan-Flint. He is also a Fellow at the Urban Design/Mental Health Institute (UK) and Director of the GIS program. He received his Ph.D. from the University of Wisconsin-Milwaukee and has over a decade of experience utilizing geospatial and empirical approaches to examine active transportation, mobility, travel sentiment, urban design, accessibility, food systems, and public health.

Recent works:

Platt, L., and G. Rybarczyk. (2020) “Skateboarder and scooter rider perceptions of the urban environment: A qualitative analysis of user generated content,” Urban Geography, DOI: 10.1080/02723638.2020.1811554.

Rybarczyk, G., A. Ozbil, E. Andresen, and Z. Hayes. (2020) “Physiological responses to urban design during bicycling: A naturalistic investigation,” Transportation Research Part F: Psychology and Behaviour, 68: 79-93; https://doi.org/10.1016/j.trf.2019.12.001

Rybarczyk, G. and S. Banerjee. (2015) Visualizing active travel sentiment in an urban context, Journal of Transport and Health, 2(2): 30

Rybarczyk, G., S. Banerjee, M. Starking-Szymanski, and R. Shaker. (2018) “Travel and us: The impact of mode share on sentiment using geosocial media data and GIS,” Journal of Location-Based Services 12(1): 40-62

9.9.2020 MIDAS Faculty Research Pitch Video.

Jerome P. Lynch

By |

Jerome P. Lynch, PhD, is Professor and Donald Malloure Department Chair of the Civil and Environmental Engineering Department in the College of Engineering in the University of Michigan, Ann Arbor.

Prof. Lynch’s group works at the forefront of deploying large-scale sensor networks to the built environment for monitoring and control of civil infrastructure systems including bridges, roads, rail networks, and pipelines; this research portfolio falls within the broader class of cyber-physical systems (CPS). To maximize the benefit of the massive data sets, they collect from operational infrastructure systems, and undertake research in the area of relational and NoSQL database systems, cloud-based analytics, and data visualization technologies. In addition, their algorithmic work is focused on the use of statistical signal processing, pattern classification, machine learning, and model inversion/updating techniques to automate the interrogation sensor data collected. The ultimate aim of Prof. Lynch’s work is to harness the full potential of data science to provide system users with real-time, actionable information obtained from the raw sensor data collected.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.