Joseph Chow ,

PhD, PE

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.

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).