Calls for papers for two journal special issues

Prof. Chow is co-editing special issues for two top journals in the transportation field.
The first is in IEEE ITS Magazine (recent impact factor of 3.654), with Xiaopeng Li at USF, Monica Menendez at ETHZ, Sean Qian at CMU, and Xiaobo Qu at UT Sydney. The issue focuses more on the technological advances in emerging mobility systems:
The second is in TR Part C (recent impact factor 3.805), with Feixiong Liao and Harry Timmermans at Eindhoven, and Song Gao at UM Amherst. This issue focuses more on user and operator behavior and decision-making:


ARISE 2017 Colloquium

The NYU Tandon School of Engineering conducts the Applied Research Innovations in Science and Engineering (ARISE) program since summer 2013. We had two ARISE participants this summer, Harpreet Kaur and Alexander Leon, who have spent the past five weeks to make practical contributions to our lab’s research objectives.  Congratulations to Harpreet and Alex. They both gave brilliant presentations at the program’s concluding colloquium.

Research on smart repositioning of idle vehicles published in TR Part E

One of the biggest challenges in operating on-demand mobility services is the need to dynamically reposition idle vehicles, whether they are taxis, shared vehicles/bikes, or empty shuttles. This latest research with Dr. Hamid R. Sayarshad at Cornell University proposes new models and algorithms to anticipate future demand for the problem by approximating future opportunity costs with queue delay. In addition, we formulated a lower bound of the queueing-based location model from Marianov & Serra that can be solved much more computationally efficiently. Simulation tests in a controlled study area with NYC taxi data suggests the feasibility of nearly 30% improvement over myopic positioning techniques that do not use data to look ahead.

This work was initially undertaken when Hamid was a PhD student with funding support from the Canada Research Chairs program. Resources from C2SMART are also acknowledged.

NSF-funded publication quantifies taxi sharing consumer benefits

Recently, TLC announced using Via’s software to enable yellow taxi sharing ( in favor of a taxi sharing policy. Our latest NSF-funded paper with researchers from NYU Tandon, CUSP, and Courant (Ziyi Ma, Matthew Urbanek, Maria Alejandra Pardo Baquero, Xuebo Lai), now in press, quantifies this benefit for riders that use taxi to access the airport (~10% improvement in consumer surplus) and demonstrate how different matching policies can significantly affect the spatial distribution of that benefit.

Ziyi Ma was supported by the NYU Undergraduate Summer Research Program. Joseph Chow was partially supported by National Science Foundation grant CMMI-1634973. The JFK airport taxi mode choice survey was shared by PANYNJ, which is gratefully acknowledged.

The open access paper can be found here:

NSF-funded paper accepted for presentation at IEEE ITSC 2017 in Yokohama

The latest paper, a joint effort between Yueshuai Brian He, Prof. Chow, and U. Toronto researcher Dr. Mehdi Nourinejad, has been accepted for presentation in the IEEE Intelligent Transportation Systems Conference held in Yokohama in fall 2017. The paper topic, “A Privacy Design Problem for Sharing Transport Service Tour Data”, investigates a method to protect the privacy of a private transport operator’s tour data by anonymizing it under the constraint of providing sufficiently accurate user performance metrics for public use. This work should be increasingly important as public agencies and private operators like Via and Lyft seek out data sharing arrangements to support smart cities.

This research is supported by NSF CAREER grant CMMI-1652735.

A preprint of the paper can be found here:

New publication in Transportation Research Part B on MaaS as part of a two-sided market

Our work on simulation-based evaluation of Mobility as a Service as a “2-sided market” is now published in TR Part B. The goal in this work is to develop a tool that can compare “apples to oranges” where the MaaS operator’s decisions and policies are also dependent on user interaction–whether at “within day” level or at “day-to-day” level of dependency. For example, this would allow a city agency to compare the welfare effects of a TNC with a particular surge pricing policy (within day dependency) with another scenario where an EV car sharing company may alter fleet size, composition, pricing policies over time (day-to-day).

Dr. Djavadian was supported by the Canada Research Chairs program and an NSERC Discovery Grant. Prof. Chow was partly supported by the C2SMART University Transportation Center. An early version of this work was presented at IATBR 2015 in Windsor, UK.