Transportation network companies (TNCs) like Uber and Lyft launched with the goals of not only increasing convenience and lowering costs for riders, but also solving congestion and energy use concerns caused by low utilization of public transportation and excessive private vehicle ownership. However, we have found that in fact, these TNCs are adding to congestion, energy usage, and emissions.
TNCs connect travelers with drivers on app-based platforms and have expanded rapidly; it’s been reported that such private-ride TNC services as Uber and Lyft have resulted in 180 percent more traffic on urban streets and added billions of vehicle miles traveled in large metropolitan areas. Anyone living in a big city can attest to these effects, based on witnessing traffic flow.
We collected data from TNC platforms in New York City over parts of two years. Our data included a GPS record of online Uber drivers sampled every few seconds so we could visualize and quantify traffic changes. We segregated the data into moving and stationary activities, and used energy models to calculate changes in fuel consumption and emissions during those years. Analyzing this data from the four largest boroughs — Manhattan, Brooklyn, Bronx and Queens — we found that TNCs were the largest contributing factor to changes in road traffic conditions.
For example, in New York City, from 2017–2019, while both the population and the number of registered vehicles decreased slightly, the number of for-hire vehicles grew by almost 50 percent, adding 90 percent more daily trips for users. Average citywide speeds declined by more than 20 percent on weekdays, and the heavier traffic led to 136 percent more NOx, 152 percent more CO2, and 150 percent more hydrocarbon emissions per kilometer traveled by these for-hire vehicles.
It is increasingly critical that transportation planners and city policymakers in large urban areas understand how TNCs affect traffic conditions, and how they can best be regulated and integrated into legacy transportation systems. These plans and decisions will affect not only the mobility requirements of millions of urbanites, but also the employment and standard of living of hundreds of thousands of drivers working in this marketplace.
None of this should be viewed as shooting down the potential of TNCs for improving the efficiency and sustainability of urban mobility. Instead, the impacts of these for-hire vehicles highlight the need for a deeper data dive and analytics to understand how to optimally match passengers and drivers, and what types of vehicles should be used — not only on city streets, but also at major transportation hubs.
These hubs — like railway terminals and airports — suffer most from the inefficiencies of wasted vehicle capacity, heavier congestion and emissions, and unneeded energy consumption. The inefficiencies are compounded because the hubs bring together a large number of commuters from multiple transit modes in short time spans and don’t optimally leverage multi-modal mobility services.
We are proposing, in collaboration with stakeholders in New York City, a disruptive, next-generation mobility solution built around automated electric vehicles (AEVs) to optimize the energy consumption of travel demand at transportation hubs. Layered on top of this AEV fleet will be a Multi-modal, Energy-optimal Trip Scheduling in Real time (METS-R) solution — an integrated, data-driven system that supports the planning and real-time operation of the fleet with data acquisition, trip analysis, operation algorithms, energy estimation, and high-performance simulation.
We’re leading a $1.2 million Department of Energy Vehicle Technology Office project to demonstrate the performance of our METS-R system with real-world case studies of three hubs in New York City: Penn Station in Manhattan and LaGuardia and JFK airports in Queens. The overall objective of the study is threefold:
● Design an efficient approach for a multi-modal transportation system at the major hubs by supplementing existing transportation solutions with a shared autonomous vehicle fleet.
● Develop a high-performance, agent-based simulation platform to model anomalous events.
● Understand the overall energy consumption at transportation hubs in the present system and improve energy flow and energy efficiency with the METS-R system during real-time operations.
Next-generation, high-capacity, mobility-on-demand solutions will address these data science issues to determine how to best pair AEVs with transportation demand for optimal energy consumption and sustainability. The optimized routing of AEV fleets with limited battery capacity requires identifying real-time, minimal-electricity-consumption paths via online routing algorithms that outperform conventional, shortest-travel-time approaches. The solutions can then be used to generate the most efficient and sustainable routes from the hub to connect to existing transit systems.
Satish V. Ukkusuri, PhD
Reilly Professor of Civil Engineering
Transportation and Infrastructure Systems Engineering Group
Lyles School of Civil Engineering
College of Engineering, Purdue University