- Our first application deadline for Fall 2020 entry is December 15! Learn more about our graduate programs in Applied Urban Science and Informatics using the following links:
- Congratulations to Professor Maurizio Porfiri, whose research on “From firearms to fish — following patterns to discover causality” made the cover of Chaos! Chaos is an interdisciplinary journal of nonlinear science.
- The BUILT Lab, led by Professor Joseph Chow, had 9 papers accepted for presentation at the upcoming 99th Transportation Research Board Annual Meeting in Washington, DC. The research topics revolve around MaaS, optimal learning for fleet management, and emerging technologies like e-scooters, modular autonomous vehicles, and air taxis. The papers include:
- Flexible Bus Dispatching System with Modular and Fully Automated Bus Units (Igor Dakic, Kaidi Yang, Monica Menendez, Joseph Chow)
- Forecasting e-Scooter Competition with Direct and Access Trips by Mode and Distance in New York City (Mina Lee, Joseph Chow, Gyugeun Yoon, Yueshuai He)
- Air Taxi Skyport Location Problem for Airport Access (Srushti Rath, Joseph Chow)
- Reinforcement Learning-based Sequential Transit Route Design under Demand Uncertainty (Gyugeun Yoon, Joseph Chow)
- Day-to-Day Market Evaluation of Last-Mile Transit Operations using Modular Autonomous Vehicles with En-Route Transfers (Nicholas Caros, Joseph Chow)
- A Many-to-Many Assignment Game Method to Evaluate Cost Allocations of Link Operators in a Mobility-as-a-Service Market without Route Enumeration (Saeid Rasulkhani, Theodoros Pantelidis, Joseph Chow)
- Online Route Choice Modeling for Mobility-as-a-Service Networks with Non-Separable, Congestible Link Capacity Effects (Jia Xu, Joseph Chow, Song Gao)
- A Path-Based Many-to-Many Assignment Game to Model Mobility-as-a-Service Networks (Theodoros Pantelidis, Saeid Rasulkhani, Joseph Chow)
- Empirical Validation of Network Learning with Taxi GPS Data from Wuhan, China (Jia Xu, Qian Xie, Joseph Chow, Xintao Liu)
- In late November, The GovLab launched the Responsible Data for Children (RD4C) Initiative in partnership with UNICEF. In our increasingly datafied environment, there is a clear need to develop and disseminate responsible approaches for handling data for and about children and upholding the United Nations General Assembly’s Convention on the Rights of the Child (adopted 30 years ago) in our data age. RD4C seeks to build awareness regarding the need for special attention to data issues affecting children—especially in this age of changing technology and data linkage; and to engage with governments, communities, and development actors to put the best interests of children and a child rights approach at the centre of our data activities. Learn more about RD4C:
- Assistant Professor Yury Dvorkin was invited to organize a webinar at the TU Berlin’s Infrastructure Research and Policy Autumn School in October, titled “Stochastic Electricity Market Modeling with Applications to Peer-to-Peer Trading”.
- Assistant Professor Yury Dvorkin was also invited to give an IEEE Webinar in November, titled “Battery Energy Storage Applications in Power Systems”.
- Alain Boldini, Mert Karakaya, Manuel Ruiz Marín, Maurizio Porfiri. Application of symbolic recurrence to experimental data, from firearm prevalence to fish swimming. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019; 29 (11): 113128 DOI: 10.1063/1.5119883
- Kun Qin, Yuanquan Xu, Chaogui Kang, Stanislav Sobolevsky, Mei-Po Kwan. Modeling Spatio-Temporal Evolution of Urban Crowd Flows. ISPRS International Journal of Geo-Information.
- Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestions, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, and yet very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix computation based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps as (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification and correlation into a unified and efficient analytical framework. We validate the proposed methodology under both synthetic and real world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity and spatial constraint are also discussed.
- Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara. Fair Predictors under Distribution Shift. arXiv preprint arXiv:1911.00677 (2019).
- Zhengbo Zou, Xinran Yu, and Semiha Ergan. Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network. Building and Environment, Volume 168, 2020.
- Russell J.Funk, Britta Glennon, Julia Lane, Raviv Murciano-Goroff, and Matthew B. Ross. Money for Something: Braided Funding and the Structure and Output of Research Groups. 2019. IZA Discussion Papers 12762, Institute of Labor Economics (IZA).
- Frauke Kreuter, Rayid Ghani, and Julia Lane. Change Through Data: A Data Analytics Training Program for Government Employees. 2019. Harvard Data Science Review, 1(2).
- Roberto C.S.N.P. Souza, Renato M. Assunção, Daniel B. Neill, and Wagner Meira, Jr. Detecting Spatial Clusters of Disease Infection Risk Using Sparsely Sampled Social Media Mobility Patterns. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’19).
- Pietro De Lellis, Shinnosuke Nakayama, and Maurizio Porfiri. Using demographics toward efficient data classification in citizen science: a Bayesian approach. PeerJ Computer Science 5:e239.