Joseph Chow ,

PhD, PE

Researchers at NYU Tandon School of Engineering’s C2SMART Center have developed an algorithm to plan the most efficient routes for modular vehicle (MV) fleets — specially-designed vehicles that attach and detach from one another as they move people around cities — removing a significant obstacle to making this type of transportation system a reality. 

In a paper published in Transportation Research Part C: Emerging Technologies, the researchers employ a mathematical model called MILP (Mixed Integer Linear Programming) to optimize the service time for the passengers and the travel cost for the vehicles in an MV system. The model factors in passenger pickups and deliveries, en-route transfers, and variable capacity of the MVs to identify the best routes and schedules for the attachments and separations of the vehicles. 

Conventional mass transit and demand-responsive transportation systems can face challenges accommodating fluctuations in traveler demand, leading to long travel times, energy inefficiencies, traffic congestion and financial waste.  

Low-capacity vehicles like vans may be slow and overcrowded in peak times. High-capacity vehicles like buses may be largely unoccupied when demand is low. On-demand services like paratransit often deliver only one passenger at a time, making them expensive to operate.

MVs offer a flexible and efficient alternative. The independent vehicles in MV fleets can connect while in motion, creating platoons that travel as one unit until the vehicles detach. According to research lead Joseph Chow,  Institute Associate Professor in the Department of Civil & Urban Engineering and the Deputy Director of C2SMART, MVs can move people faster, with less energy consumption and operational expenses than many conventional systems.

“MVs offer a promising alternative to move people more efficiently in certain situations,” said Chow, who collaborated on the research with NYU Tandon Ph.D. student Zhexi Fu. “Imagine, for instance, employees at the same company. The individual vehicles could pick up people who live within similar enclaves, and join together in a platoon to deliver the entire group to its workplace. MVs also have significant potential to improve on-demand transportation that delivers people door-to-door, including those that serve people with disabilities.”

Currently, no city has an MV system in use, although Next Transportation Systems is piloting a MV test in Dubai now. According to Chow, the inability to track and route MV fleets has been a significant roadblock to potential real-world adoption. To build its routing model, the C2SMART team used the Anaheim network, a traffic simulation of Anaheim, California.

The research on MV routing is the latest in a long series of studies Chow has conducted around urban mobility. Among his previous studies include examinations of Dial-a-Ride programs,  e-scooter usage, and urban bus networks. Chow’s new research also advances the mission of C2SMART, a U.S. Department of Transportation (US DOT) Tier 1 University Transportation Center (UTC) designated to address the US DOT priority area of Congestion Reduction. 

The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where users’ preferences and revenue management are often overlooked.

Researchers at NYU Tandon, including Joseph Chow, professor of civil and urban engineering and Deputy Director of the C2SMART Tier 1 University Transportation Center, have designed innovative solutions. In a paper in Elsevier’s Transportation Research, they present a mechanism for accepting and rejecting user requests in a Demand Responsive Transportation (DRT) context based on the representative utilities of alternative transportation modes. They consider utility-maximising users and propose a mixed-integer programming formulation for a Chance Constrained DARP (CC-DARP), that captures users’ preferences.

They further introduce class-based user groups and consider various pricing structures for DRT services, and develop a local search-based heuristic and a matheuristic to solve the proposed CC-DARP. The study includes numerical results for both DARP benchmarking instances and a realistic case study based on New York City yellow taxi trip data. They found, with computational experiments performed on 105 benchmarking instances with up to 96 nodes yielded average profit gaps of 2.59% and 0.17% using the proposed local search heuristic and matheuristic, respectively.

The based on the case study the work suggests that a zonal fare structure is the best strategy in terms of optimizing revenue and ridership. Their CC-DARP formulation provides a new decision-support tool to inform revenue and fleet management for DRT systems on a strategic planning level.

Co-authors of the study are Xiaotong Dong and Travis Waller of the School of Civil and Environmental Engineering, University of New South Wales, Australia; and David Rey of the SKEMA Business School, Université Côte d’Azur, France.

Despite the emergence of many new mobility options in cities around the world, fixed route transit is still the most efficient means of mass transport. Bus operations are subject to vicious and virtuous cycles. Evidence of this can be seen in New York City. 

Since 2007, travel speed reductions and increased congestion as a result of more mobility options competing for road space have led to a vicious cycle of ridership reduction and further increased congestion as former transit passengers take to other less congestion-efficient modes. In Brooklyn, bus ridership has declined by 21% during this period. While the decrease in ridership has been steady throughout this period, there is an emerging concern that it will only get worse as for-hire vehicle services like Uber and Lyft add more trips to the road network 

Intervention in the form of network redesign is required to promote a virtuous cycle and make the bus more competitive, especially in the face of increased competition from ride-hail services. This can be done by redesigning the bus network in a way that reduces operating and user costs while increasing accessibility for more riders.

Now, new research from Joseph Chow, professor of computer science and engineering at the NYU Tandon School of Engineering and a member of the C2SMART Tier 1 University Transportation Center at NYU Tandon; and doctoral student Ziyi Ma are proposing a simulation-based transit network design model for bus frequency planning in large-scale transportation networks with activity-based behavioral responses. The model is applied to evaluate the existing Brooklyn bus network, other proposed network redesign, and used to develop an alternative design based on the researcher’s methodology.

The MATSim-NYC model designed by Chow and Ma is able to simulate patterns similar to the existing bus network in Brooklyn with some calibration. This model was used to confirm a plan from another group to increase ridership, but was also able to refine it even further. The increased ridership draws primarily from passenger car use (nearly 75%), with a small 2.5% drawn from ride-hail services and another 5% from taxis. This suggests the redesigns should be effective in moving people away from less efficient transportation modes.

The researchers are now looking to further refine their model so that it can become a key tool for policymakers planning the future of transportation in the city.

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.