Nick Caros

Nick Caros

PhD Candidate, MIT

Publications

ROVE: An integrated transit performance and passenger journey visualization engine

Published in Transportation Research Record, 2022

Transit agencies collect a vast amount of data on vehicle positions, passenger loading and, increasingly, origin-destination flows. Collecting and synthesizing this data to support operations and planning is a significant challenge and can be constrained by information silos within transit agencies. In this paper, an open-source bus performance and journey visualization dashboard, Ridership and Operations Visualization Engine (ROVE), is presented that integrates multiple disparate data sources into a flexible and iterative analysis tool. It differs from existing commercial products by including origin-destination flows along with standard performance metrics, and is designed to be adaptable and relevant to any transit agency. Two case studies are presented to demonstrate the functionality of the dashboard: planning transit priority infrastructure and evaluating network design changes. The dashboard was developed in partnership with Chicago Transit Authority and Massachusetts Bay Transportation Authority, and practical details from the installation and maintenance procedures are included for prospective users.

Recommended citation: Caros, N.S., Guo, X., Stewart, A.F., Attanucci, J., Smith, N., Nioras, D., Gartsman, A. and Zimmer, A., (2022). "ROVE: An integrated transit performance and passenger journey visualization engine." Transportation Research Record. 2677, pp.1082-1097. https://doi.org/10.1177/03611981221103232

Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand

Published in Transportation Research Part B: Methodological, 2021

There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver-customer matching into vehicle rebalancing problems to produce better rebalancing strategies. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model.The MIVR model is shown to have better performance by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model.

Recommended citation: Guo, X., Caros, N.S. and Zhao, J., (2021). "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand." Transportation Research Part B: Methodological. 150, pp.161-189. https://www.sciencedirect.com/science/article/abs/pii/S0191261521001004

Effects of violent crime and vehicular crashes on active mode choice decisions in New York City

Published in Travel Behaviour and Society, 2020

Substantial research has explored the effects of different variables on mode choice with the intent of understanding this behavior so that active modes can be encouraged. This study furthers that effort by investigating the impact of perceived danger from crime on the probability of choosing an active mode of transportation among travellers in New York City from 2009 to 2011.

Recommended citation: Caros, N.S. and Chow, J.Y.J., (2020). "Effects of violent crime and vehicular crashes on active mode choice decisions in New York City." Travel Behaviour and Society. 18, pp.37-45. https://www.sciencedirect.com/science/article/abs/pii/S2214367X18302060

Dynamic market evaluation of last-mile transit operations with en-route transfers

Published in Transportmetrica B: Transport Dynamics, 2020

This study extends the two-sided day-to-day learning framework to simulate the performance of a mobility service using modular autonomous vehicles (MAVs) capable of en-route passenger transfers.

Recommended citation: Caros, N.S. and Chow, J.Y.J. (2020). "Dynamic market evaluation of last-mile transit operations with en-route transfers." Transportmetrica B: Transport Dynamics. 22, pp.1-25. https://www.tandfonline.com/doi/abs/10.1080/21680566.2020.1809549