Project Abstract

Every day, almost two million persons enter and leave the central business district of Manhattan using light-duty vehicles such as cars, taxis, vans, or trucks. Currently, around 1% of these vehicles are electric. This project aims to quantify the ramifications of a 100% electric commute in New York City. We will create a model that translates NYC’s transportation needs into electric charging demand, including emerging mobility trends (e.g., electric scooters) and remote work patterns. Interactive visualizations produced by the model will allow citizens, urban planners, and politicians to analyze the impact of mobility electrification and their policy decisions.

Project Description & Overview

TEC-NYC will offer unique insights on the practical challenges of a 100% electrified transportation sector in dense urban areas. This project focuses on New York City, but the methodology will be transferable. The project comprises three central milestones, each focusing on different skills of data processing.

First, we will create a comprehensive data set on the transportation needs between central Manhattan and the greater New York Metropolitan area. This data set will differentiate between the various modes of transportation and the commuted distance. Complementary, we gather data on NYC’s power system infrastructure and the technical specification individual electric transportation (e.g., e-vehicles, e-bikes, and e-scooters).

Second, we will combine the gathered data such that we can answer the following questions: If all transportation switches to e-mobility, what will be the charging demands in the city? How many commuters can switch between different modes of transportation? What would be the best combination of commuting modes for the needs of the commuters and the available infrastructure? This milestone requires data-analytics skills, including identifying cross-correlations and extrapolating data trends, e.g., to quantify the impact of remote working arrangements.

Finally, TEC-NYC will visualize the data in an interactive and engaging manner. The user will be able to change model parameters (e.g., how many commuters exchange their car for an e-bike) and observe the impact on the city’s power system. We will ask: Can you tune The Electric Commute to suit the city and its citizens?

Datasets

The data required for TEC-NYC is readily available through online resources and from our previous projects.

The transportation model will mainly be derived from the “Hub Bound Travel Data” published by the New York Metropolitan Transportation Council. Additional required data is publicly available from the NYC Open Data Platform, the NYC Department of Transportation and the Port Authority of New York and New Jersey. We will provide a detailed list of the relevant data sets and their sources.

Information on e-mobility technology and NYC’s power system infrastructure is available from our previous research. Data on distribution and transmission infrastructure will be “artificial but realistic” to accommodate security concerns related to the publication of real data on critical infrastructure. This data has been tested and used and will provide a realistic foundation for the planned analyses.

Competencies

Students should have fundamental knowledge on data analysis and processing, i.e., be at least comfortable with Excel and have some experience in a high-level programming language (Python or Julia are preferred). Further, students should be familiar with fundamental methods of data analyses such as regression models, correlation analyses and histograms.

Ideally, the students have advanced knowledge in Python, Julia, Matlab or similar data processing language, have experience in collaboration and version control tools such as Git, and are familiar with data-visualization packages such as Plotly or Bokeh.

Learning Outcomes & Deliverables

Each of the three milestones of TEC-NYC will address a central data analyses skill and will provide important insights. During the data collection, project members will learn how to access and handle large public data sets. They will learn the fundamentals of good data hygiene and database control. Students interested in power systems will gain additional insights through infrastructure data that is not publicly available. The initial data set will be the first deliverable.

For the second milestone, students will be required perform data anlysis tasks, e.g., suitable aggregation and de-aggregation, identifying trends and correlations. Depending on progress, more advanced data analytics methods, e.g., modelling traffic patterns using Markov decision processes are possible. The resulting second deliverable will be a numerical model that, at minimum, maps commuter numbers and modes of transportation, to electricity demands in the city.

Finally, the third milestone will strengthen the project member’s skills to design and implement an interactive data visualization tool. Depending on the student’s interests and previous experiences, such a tool can be created online of offline, with or without real-time calculation abilities. For this deliverable, we aim to focus on creating a visualization that not only makes a large data set accessible but is engaging to the user. We plan to achieve this by pursuing a carefully “gamified” approach, e.g., by asking provocative questions to the user or including exaggerated visuals if the user chooses infeasible input parameters.

Students

Amber Jiang, Sara Kou, Brian Newborn, Jingrong Zhang