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
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
On the design of an optimal flexible bus dispatching system with modular bus units: Using the three-dimensional macroscopic fundamental diagram
This research was led by Monica Menendez, Global Network professor of civil and urban engineering, and Joseph Chow, deputy director of the C2SMART University Transportation Center at NYU Tandon.
This project proposes a flexible bus dispatching system using automated modular vehicle technology, and considers multimodal interactions and congestion propagation dynamics.
This study proposes a novel flexible bus dispatching system in which a fleet of fully automated modular bus units, together with conventional buses, serves the passenger demand. These modular bus units can either operate individually or combined (forming larger modular buses with a higher passenger capacity). This provides enormous flexibility to manage the service frequencies and vehicle allocation, reducing thereby the operating cost and improving passenger mobility.
The investigators developed an optimization model to determine the optimal composition of modular bus units and the optimal service frequency at which the buses (both conventional and modular) should be dispatched across each bus line. They explicitly accounted for the dynamics of traffic congestion and complex interactions between the modes at the network level, based on a recently proposed three-dimensional macroscopic fundamental diagram (3D-MFD). To the best of Chow and Menendez' knowledge, this is the first application of the 3D-MFD and modular bus units for the frequency setting problem in the domain of bus operations.
Using this system of analysis, the researchers were able to show improved costs across the system by adjusting the number of combined modular bus units and their dispatching frequencies to changes in car and bus passenger demand. A comparison with the commonly used approach that considers only the bus system (neglecting the complex multimodal interactions and congestion propagation) reveals the value of the proposed modeling framework.
- Joseph Chow,
- Monica Menendez
This research was led by Joseph Chow, deputy director of the C2SMART University Transportation Center at NYU Tandon. Co-authors included Kaan Ozbay, Director, and Shri Iyer, Managing Director of C2SMART. Chow and Ozbay are professors in the department of Civil and Urban Engineering.
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and raised new challenges for public transit. Cities are grappling with what policies can be effective for a phased reopening shaped by social distancing.
The C2SMART researchers used a baseline model for pre-COVID conditions to create a new model representing travel behavior during the COVID-19 pandemic. They achieved this both by recalibrating the population agendas to include work-from-home, and by re-estimating the mode choice model (to fit observed traffic and transit ridership data) for the Center’s MATsim-NYC platform, a multi-agent simulation test bed for evaluating emerging transportation technologies and policies. They then analyzed the increase in car traffic due to the phased reopen plan guided by the state government of New York.
Analyzing four reopening phases and two reopening scenarios (with and without transit capacity restrictions), they found that a reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. They also discovered that limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels.
They noted that, while the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility.
The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.
- Joseph Chow,
- Kaan Ozbay,
- Shri Iyer
A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City
This research was led by Joseph Chow, deputy director of the C2SMART University Transportation Center at NYU Tandon and professor of civil and urban engineering, with researchers Brian Yueshuai He, Jinkai Zhu, Ziyi Ma, and Ding Wang.
Evaluation of the demand for emerging transportation technologies and policies can vary by time of day due to spillbacks on roadways, rescheduling of travelers’ activity patterns, and shifting to other modes that affect the level of congestion. These effects are not well-captured with static travel demand models.
Chow and his team calibrated and validated the first open-source multi-agent simulation model for New York City, called MATSim-NYC, to support agencies in evaluating policies such as congestion pricing. The simulation-based virtual test bed is loaded with a “synthetic” 2016 population of over eight million people, calibrated in a prior study. Model validation using transit stations and road links is comparable to NYPBM.
In a study published in Transport Policy the researchers used the model to evaluate a congestion pricing plan proposed by the Regional Plan Association, and found a much higher (127K) car trip reduction compared to the RPA report (59K). The team discovered that the Association’s pricing policy would impact the population segment making trips within Manhattan differently from the population segment of trips outside Manhattan: benefits from congestion reduction benefit the former by about 110%+ more than the latter.
The simulation can show that 37.3% of the Manhattan segment would be negatively impacted by the pricing compared to 39.9% of the non-Manhattan segment, which has implications for redistribution of congestion pricing revenues. The citywide travel consumer surplus decreases when the congestion pricing goes up from $9.18 to $14 both ways even as it increases for the Charging-related population segment. This implies that increasing pricing from $9.18 to $14 benefits Manhattanites at the expense of the rest of the city.
RPA congestion pricing policy would have net increase in consumer surplus. The results suggest toll revenue redistribution should focus on outer boroughs.
- Kaan Ozbay,
- Joseph Chow