Network, System and Operation

The Networks, Systems and Operations research cluster applies new analytical and simulation methods to the study of urban transportation systems. We seek out opportunities to understand and improve design and operation of transportation systems from a variety of different perspectives. Our research offers creative answers to important questions in urban transportation: How can autonomous vehicles be integrated into public transit operations? Is there a more efficient market structure for ridesharing? Where are transit networks most vulnerable to climate change?

Transportation networks are becoming more complex as real-time control, automation and mobility-on-demand systems proliferate. Transportation systems are generating more data than ever. We leverage that wealth of data and rise to the complexity challenge by using the latest statistical, optimization and simulation methods. Recent research projects include the design of a ridehailing dispatching algorithm using robust optimization and prediction of transit routes using automated ridership data combined with simulation-based optimization.

While many of our projects emphasize theoretical contributions, we ground our research in practical problems. Many of our studies are developed in partnership with some of the largest transit agencies in the world. JTL methods, including algorithms for real-time control of heavy rail transit systems and a system for passenger origin-destination inference, have been implemented in practice to improve operations and planning.

Evaluating the travel impacts of a shared mobility system for remote workers, Caros, Nicholas S., and Jinhua Zhao , Transportation Research Part D, (2023)

Given the rapid rise of remote work, there is an opportunity for new shared mobility services designed to meet the needs of passengers with multiple possible work locations. This paper develops a new optimization model to enable shared mobility systems to match drivers and passengers when passengers have flexible destinations. Constraints representing employer policies, such as mandatory co-location of colleagues and limited capacity of satellite offices are introduced in order to explore...

Robust Matching-Integrated Vehicle Rebalancing in Ride-hailing System with Uncertain Demand, Xiaotong Guo, Nicholas S. Caros, and Jinhua Zhao , Transportation Research Part B, (2021)

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver-customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing...

Competition between Shared Autonomous Vehicles and Public Transit: A Case Study in Singapore, Baichuan Mo, Zhejing Cao, Hongmou Zhang, Yu Shen, and Jinhua Zhao , Transportation Research Part C, (2021)

Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it. This study examines the competitive perspective where both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one...

Estimating the Potential for Shared Autonomous Scooters, Kondor, Dániel, Xiaohu Zhang, Meghjani Malika, Paolo Santi, Jinhua Zhao, and Carlo Ratti , IEEE Transactions on Intelligent Transportation Systems, (2020)

Recent technological developments have shown sig- nificant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the...

Capacity-Constrained Network Performance Model for Urban Rail Systems, Baichuan Mo, Zhenliang Ma, Haris Koutsopoulos, and Jinhua Zhao , Transportation Research Record, (2020)

This paper proposes a general Network Performance Model (NPM) for urban rail systems performance monitoring using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination (OD) demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly...

Modeling Epidemic Spreading through Public Transit using Time-Varying Encounter Network, Baichuan Mo, Feng Kairui, Yu Shen, Tam Clarence, Li Daqing, Yin Yafeng, and Jinhua Zhao , Transportation Research Part C, (2020)

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at...

Machine-learning-augmented analysis of textual data: application in transit disruption management, Peyman Noursalehi, Koutsopoulos Haris N., and Jinhua Zhao , IEEE Open Journal of Intelligent Transportation Systems, (2020)

Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions, underlying causes of each incident, impacts on the system, etc. In this paper, we develop a...

Dynamic Origin-Destination Prediction in Urban Rail Systems: A Multi-resolution Spatio-Temporal Deep Learning Approach, Peyman Noursalehi, Haris N. Koutsopoulos, and Jinhua Zhao , IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, (2020)

Short-term demand predictions, typically defined as less than an hour into the future, are essential for implementing dynamic control strategies and providing useful customer infor- mation in transit applications. Knowing the expected demand enables transit operators to deploy real-time control strategies in advance of the demand surge, and minimize the impact of abnormalities on the service quality and passenger experience. One of the most useful applications of demand prediction models in...

Integrating Shared Autonomous Vehicle in Public Transportation System: A Supply-Side Simulation of the First-Mile Service in Singapore, Yu Shen, Hongmou Zhang, and Jinhua Zhao , Transportation Research Part A, (2018)

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand...

Transit-Oriented Autonomous Vehicle Operation with Integrated Demand-Supply Interaction, Jian Wen, Chen Leo, Neema Nassir, and Jinhua Zhao , Transportation Research Part C, (2018)

Autonomous vehicles (AVs) represent potentially disruptive and innovative changes to public transportation (PT) systems. However, the exact interplay between AV and PT is understudied in existing research. This paper proposes a systematic approach to the design, simulation, and evaluation of integrated autonomous vehicle and public transportation (AV+PT) systems. Two features distinguish this research from the state of the art in the literature: the first is the transit-oriented AV operation...

Redesigning Subway Map to Mitigate Bottleneck Congestion: An Experiment in Washington DC Using Mechanical Turk, Zhan Guo, Jinhua Zhao, Chris Whong, Prachee Mishra, and Lance Wyman , Transportation Research Part A, Volume 106, p.158–169, (2017)

This paper explores the possibility of using subway maps as a planning tool to influence passenger route choice to mitigate congestion. Specifically, it tests whether extending the appearance of an overcrowded subway line on the Washington DC subway map would encourage passengers to use other underutilized lines. The experiment was conducted through the Mechanical Turk, a crowdsourcing platform, with 3056 participants, producing 21,240 route choice decisions on the official and six...

Team Members

Yu Shen's picture
Assistant Professor at Tongji Univ.
Shenhao Wang's picture
Postdoctoral Associate
Jian Wen's picture
MST Student
Hongmou Zhang's picture
Postdoctoral Associate
Nate Bailey's picture
PhD Candidate
Amelia Baum's picture
MST Student
Nick Caros's picture
PhD Candidate
Xiaotong Guo's picture
PhD Candidate
Baichuan Mo's picture
PhD Candidate
jmood's picture
PhD Candidate
Qing Yi Wang's picture
PhD Candidate