Institute Associate Professor
Deputy Director of C2SMART University Transportation Center
Dr. Joseph Chow is an Institute Associate Professor in the Department of Civil & Urban Engineering and the Deputy Director at the C2SMART Tier-1 University Transportation Center at NYU, and heads BUILT@NYU: the Behavioral Urban Informatics, Logistics, and Transport Laboratory. His research expertise lies in transportation systems, with emphasis on multimodal networks, behavioral urban logistics, smart cities, and transport economics. He is an NSF CAREER award recipient; he is a former elected Chair of the Urban Transportation SIG and appointed TSL Cluster Chair at INFORMS Transportation Science & Logistics Society, chair of the TRB subcommittee on Route Choice and Spatiotemporal Behavior, and is an appointed Associate Editor for International Journal of Transportation Science & Technology and Transportation Research Record, the journal for the Transportation Research Board of the National Academies. At NYU he is an Associated Faculty at CUSP and Rudin Center. Prior to NYU, Dr. Chow was the Canada Research Chair in Transportation Systems Engineering at Ryerson University. From 2010 to 2012, he was a Lecturer at University of Southern California and a Postdoctoral Scholar at UC Irvine. He obtained a Ph.D. in Transportation Engineering from UC Irvine (‘10), and an M.Eng. (‘01) and B.S. (‘00) in Civil Engineering from Cornell University with a minor in Applied Math. Dr. Chow is a former Eisenhower and Eno Fellow and a licensed PE in NY.
Research Interests: Behavioral informatics, urban transportation systems
Bachelor of Science, Civil Engineering, 2000
Master of Engineering, Civil Engineering, 2001
University of California, Irvine
Doctor of Philosophy, Civil Engineering, 2010
University of Southern California
Epstein Department of Industrial and Systems Engineering Sol Price School of Public Policy
From: May 2010 to May 2012
University of California, Irvine
Institute of Transportation Studies
From: April 2010 to May 2012
Canada Research Chair and Assistant Professor
Department of Civil Engineering
From: June 2012 to August 2015
New York University
Department of Civil & Urban Engineering
From: September 2015 to present
1) Sayarshad, H.R., Chow, J.Y.J., 2017. Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem. Transportation Research Part E 106, 60-77.
2) Ma, Z., Urbanek, M., Pardo, M.A., Chow, J.Y.J., Lai, X., 2017. Spatial welfare effects of shared taxi operating policies for first mile airport access, International Journal of Transportation Science and Technology, in press, doi: 10.1016/j.ijtst.2017.07.001.
3) Djavadian, S., Chow, J.Y.J., 2017. An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ for a two-sided flexible transport market, Transportation Research Part B, 104, 36-57.
4) Guo, Q.W., Chow, J.Y.J., Schonfeld, P., 2017. Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options. Transportation Research Part C, Special issue on ISTTT 22, accepted for publication.
5) Mendes, L.M., Bennàssar, M.R., Chow, J.Y.J., 2017. Simulation experiment to compare light rail streetcar against shared autonomous vehicle fleet for Brooklyn Queens Connector. Transportation Research Record, in press, doi: 10.3141/2650-17.
1) Chow, J.Y.J., Jayakrishnan, R., Mahmassani, H.S., 2013. Is transport modeling education too multidisciplinary? A manifesto on the search for its evolving identity. Travel Behaviour Research: Current Foundations, Future Prospect, eds. E.J. Miller and M.J. Roorda, Lulu Publishing.
CAREER: Urban Transport Network Design with Privacy-Aware Agent Learning, (Principal Investigator)
National Science Foundation, 2017 - 2022
Stable Matching of Service Tours to Design Cooperative Policies for Transport Infrastructure Systems, (Principal Investigator)
NSF, 2016 - 2019
Design of Smarter Urban Logistics Systems, (Principal Investigator)
Canada Research Chairs Program, 2013 - 2016
Multimodal Systems Design with Network Interactions, (Principal Investigator)
NSERC, 2013 - 2016
Development of mobile device-based surrogate systems for connected and autonomous vehicle technologies, (Principal Investigator)
NSERC, OCE, 2015
Agent-based decision support system for a flexible transit service pilot, (Principal Investigator)
- Associated Faculty, NYU Center for Urban Science & Progress
- Vice Chair, INFORMS TSL Society, Urban Transportation SIG
- Member, Editorial Advisory Board for Transportation Research Part B from Elsevier
- Co-Chair, TRB Subcommittee on Freight Modeling
- Member, TRB Committee on Transportation Network Modeling
- Member, World Conference on Transport Research, Freight Transport Modelling SIG
Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit
The COVID-19 pandemic has raised new challenges for urban transportation — “back to the office” policies, staggered teleworking hours, and social distancing requirements on public transit may exacerbate traffic congestion and emissions due to shifts in travel modes and behaviors.
A team consisting of C2SMART members Ding Wang, Yueshuai Brian He, Jingqin Gao, Joseph Chow, Kaan Ozbay, and researchers from Cornell University, proposed a simulation tool for evaluating trade-offs between traffic congestion, emissions, and COVID-19 spread mitigation policies which impact travel behavior. Their research has been published in the November 2021 volume of the Transportation Research Part A journal.
Using New York City as a case study, the team used open-source agent-based simulation models to evaluate transportation system usage. Additionally, a Post Processing Software for Air Quality (PPS-AQ) estimation was used to evaluate air quality impacts associated with pandemic-related transportation changes.
The research also estimated system-wide contact exposure, finding that the social distancing requirement on public transit to be effective in reducing exposure but having negative impacts on congestion and emission within Manhattan and in neighborhoods at transit and commercial hubs.
The proposed integrated traffic simulation models and air quality estimation models may have the potential to help policymakers evaluate the impact of policies on traffic congestion and emissions and also to help identify transportation “hot spots,” both temporally and spatially.
The research was conducted with the support of the C2SMART University Transportation Center, and funded in part by the 55606–08-28 UTRC-September 11th grant as well as a grant from the U.S. Department of Transportation’s University Transportation Centers Program.
- Joseph Chow,
- Jingqin Gao,
- Kaan Ozbay
This research was performed under the direction of Joseph Chow, industry associate professor of civil and urban engineering and Deputy Director of the C2SMART transportation research center at NYU Tandon. Authors included former C2SMART graduate students Mina Lee and Gyugeun Yoon, and Brian Yueshuai He of the University of California, Los Angeles.
The e-scooter sharing ecosystem is now one of the fastest emerging micromobility services. As of 2018, such e-scooter sharing companies as Lime and Bird operate in over 100 cities around the world. This year New York City chose Lime, Bird, and VeoRide as the first participants in its inaugural electric scooter pilot.
Sector growth is being driven now by the low entry barrier, but also because e-scooters are potentially filling a mobility gap in cities that have weaker public transit infrastructure. This is due to the fact that they provide better access to transit access points and offer an economical means to travel short distances as part of a Mobility-as-a-Service (MaaS) system. They also reduce traffic congestion and fuel use, which can be a catalyst for adoption in cities where automobiles are the most common mode of transportation.
In a new predictive study, the researchers created forecast models for motorized stand-up scooters (e-scooters) in four U.S. cities based on user age, population, land area, and the number of scooters. Using data from Portland, Ore, Austin, Tex., Chicago, IL., and New York City, the model predicted 75,000 daily e-scooter trips in Manhattan for a deployment of 2000 scooters, which translates to $77 million in annual revenue. The investigators assessed the number of daily trips by the alternative modes of transportation that they would likely substitute based on statistical similarity.
The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. The study estimates that e-scooters could replace 32% of carpool; 13% of bikes; and 7.2% of taxi trips. E-scooters are likely to compete with the other modes at shorter distances than at longer distances. The results statistically support the hypothesis that e-scooters play distinct roles as direct trip-substituting at short distances and access trip-substituting at longer distances.
In the study, published in the Elsevier journal Transportation Research Part D: Transport and Environment, the investigators argued that their research results are valuable for government or city transportation planners as the growing popularity of environment-friendly-transportation make e-scooters a sustainable mode providing significantly less pollution. In particular, the confirmed hypothesis that e-scooters can play a role for access trips lends support to its value toward increasing the public’s accessibility to public transit.
“The economical demand analysis we proposed can promote the development of policy and infrastructure relating to e-scooter systems,” they write. “For example, e-scooters are driving much of the discussion on curb management practices, as well as mobility data sharing and as a core part of Mobility-as-a-Service platforms.
This research was conducted with support from the C2SMART University Transportation Center (USDOT #69A3551747124).
- Joseph Chow
Authors include Joseph Chow, Institute Associate Professor of civil and urban engineering and deputy director of the C2SMART transportation research center at NYU Tandon; principal author Theodoros P. Pantelidis, a former Ph.D. student under Chow’s direction; Saif Eddin G. Jabari, global network assistant professor of civil and urban engineering at NYU Abu Dhabi and NYU Tandon; Li Li, recently awarded her Ph.D. at NYU Abu Dhabi; and Tai-Yu Ma of the Luxembourg Institute of Socioeconomic research.
As the makeup of car-share fleets reflect the global shift to electric vehicles (EV) operators will need to address unique challenges to EV fleet scheduling. These include user time and distance requirements, time needed to recharge vehicles, and distribution of charging facilities — including limited availability of fast charging infrastructure (as of 2019 there are seven fast DC public charging stations in Manhattan including Tesla stations). Because of such factors, the viability of electric car-sharing operations depends on fleet rebalancing algorithms.
The stakes are high because potential customers may end up waiting or accessing a farther location, or even balk from using the service altogether if there is no available vehicle within a reasonable proximity (which may involve substantial access, e.g. taking a subway from downtown Manhattan to midtown to pick up a car) or no parking or return location available near the destination.
In a new study, published in the journal Transportation Science, the authors present an algorithmic technique based on graph theory that allows electric mobility services like carshares to reduce operating expenses, in part because the algorithm operates in real time, and anticipates future costs, which could make it easier for fleets to switch to EV operations in the future.
The common practice for carshare scheduling is for users to book specific time slots and reserve a vehicle from a specific location. The return location is required to be the same for “two-way” systems but is relaxed for “one-way” systems. Examples of free-floating systems were the BMW ReachNow car sharing system in Brooklyn (until 2018) and Car2Go in New York City. These two systems recently merged to become ShareNow, which is no longer in the North American market.
Rebalancing involves having either the system staff or users (through incentives) periodically drop off vehicles at locations that would better match supply to demand. While there is an abundant literature on methods to handle carshare rebalancing, research on rebalancing EVs to optimize access to charging stations is limited: there is a lack of models formulated for one-way EV carsharing rebalancing that captures all the following: 1) the stochastic dynamic nature of rebalancing with stochastic demand; 2) incorporating users’ access cost to vehicles; and 3) capacities at EV charging stations.
The researchers offer an innovative rebalancing policy based on cost function approximation (CFA) that uses a novel graph structure that allows the three challenges to be addressed. The team’s rebalancing policy uses cost function approximation in which the cost function is modeled as a relocation problem on a node-charge graph structure.
The researchers validated the algorithm in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, 303 traffic analysis zones) and charging station location data (18 charging stations with 4 port capacities). The proposed non-myopic rebalancing heuristic reduces the cost increase compared to myopic rebalancing by 38%. Other managerial insights are further discussed.
The researchers reported that their formulation allowed them to explicitly consider a customer’s charging demand profile and optimize rebalancing operations of idle vehicles accordingly in an online system. They also reported that their approach solved the relocation problem in 15% – 89% of the computational time of commercial solvers, with only 7 – 35% optimality gaps in a single rebalancing decision time period.
The study’s authors say future research directions include dynamic demand (function of time, price and other factors), data-driven (machine learning) algorithms for updating, more realistic/ commercial simulation environment using data from larger operations, and detailed cost-benefit analysis on the tradeoffs of EV’s and regular vehicles.
This research was supported by the C2SMART University Transportation Center, the Luxembourg National Research Fund, the New York University Abu Dhabi (NYUAD) Center for Interacting Urban Networks (CITIES), Tamkeen, the Swiss Re Institute through the Quantum CitiesTM Initiative. Data was provided by BMW ReachNow car-sharing operations in Brooklyn, New York, USA.
- Joseph Chow
This research was led by Joseph Chow, assistant professor of civil and urban engineering and deputy director of the C2SMART transportation research center at NYU Tandon, with Kaan Ozbay, director of C2SMART, and lead author Diego Correa, a former Ph.D. student, now General Director of Mobility of the City of Cuenca, Ecuador.
With the rapidly changing landscape for taxis, ride-hailing, and ride-sourcing services, public agencies have an urgent need to understand how such new services impact social welfare, as well as how customers are matched to service providers, and how ride-sourcing operations, surge pricing policy and more are evaluated.
The researchers conducted an empirical study to understand these problems specifically for ride-sharing service Uber in New York City (NYC). Since key data is not readily available for the service, the team deployed a dynamic spatial equilibrium model using data on distribution, service, and revenue for NYC taxi fleets, data that is readily available from the city. Specifically, they performed spatial distribution analyses using data on demand activities, service coverage, fleet sizes, matches (rider pickups), and social welfare (the social compensation or detriment to riders of pricing and availability of service) by zone and time of day. They tied that to Uber pickup data for a specific time period in New York City (NYC).
They found, for example, that the NYC taxi industry generates $495,900 in consumer surplus and $1,022,000 in Taxi profits representing the aggregate surplus of 16,400 taxi-passenger matches. For the Uber market, welfare estimates indicate that $73,300 in consumer surplus and $151,300 in Uber profits, representing the aggregate surplus of 2,250 Uber-passenger matches in the 4-hour analysis period.
Additionally, taxi demand over the study period is 20,949, while full matches are 16,433, implying that 4,516 demanded customer trips are unmet each hour, or an average of 452 every 6 min. This contrasts with the 5,537 Taxis that are vacant at any one time. The externalities of this inefficiency are not directly captured by the model. However, the consumer surplus of the other mobility options reflects the level of congestion in the roadways due to the Taxi and Uber fleet scenarios. It can guide policy for improving lower externality options. For the congestion charging scenario for Uber, a $5 charge should be accompanied by at least a 1.20% increase in consumer surplus in lower externality modes like public transit. This can be achieved by ensuring that enough of the congestion charge is diverted to improving the transit for that difference.
Future research will inevitably consider collaborating with local agencies to evaluate different Uber policies.
This research was partially supported by the National Science Foundation Grant No. CMMI-1634973, the C2SMART Tier-1 University Transportation Center and the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) Ecuador.
- Joseph Chow