Operations & Analysis

The Transit Lab specializes in applying cutting-edge analytical and statistical methods to transit operations, scheduling and control. This includes operations planning and scheduling procedures used on a daily basis by practitioners and extends to techniques to ensure reliable field operations through effective management and control. A wide range of research projects have produced implementable tools and realistic recommendations in such areas as:

  • Effective scheduling for increasing network connectivity

  • Planning express and limited stop bus services

  • Rail service disruption recovery strategies

  • Determining appropriate corridor operational characteristics for BRT services

  • Utilizing automated data to develop bus route simulation models

Featured Projects

Public Transportation

Unplanned Incident Management for Urban Rail Systems

Agency:

CTA

This project aims to evaluate the impact of unplanned rail disruptions (incidents) on system performance, including passengers response to incidents and how demand and operations of transit services may change. Machine learning techniques are applied to provide decision support for central office and terminal dispatchers to mitigate the congestion caused by incidents.

Public Transportation

Bus Performance Visualization Dashboard

Agency:

MBTA, CTA

Transit Lab students have built a custom dashboard using automatically collected data (APC, AVL, AFC) to visualize the performance of the entire MBTA and CTA bus networks across dozen different performance metrics. Agency staff can filter by route, time period, direction and metrics for quick, detailed queries. In the future, this project will include an identification system to prioritize infrastructure upgrade locations based on performance and ridership.

Public Transportation

Simulation and Analysis of New Mobility

Agency:

TFL

At a strategic level, JTL is developing simulation and analysis platforms to understand and communicate the impact of new infrastructure and transformative technologies such as autonomous vehicles. Some of these methodologies and tools have been featured at the UK Science Museum’s Our Lives in Data exhibit. The strong record and commitment of JTL and TfL make this an ideal research partnership for understanding and shaping the future of urban transport.

Featured Publications

Train

Shuttle Planning for Link Closures in Urban Public Transport Networks

Authors:

Evelien van der Hurk, Haris N. Koutsopoulos, Nigel H.M. Wilson, Leo G. Kroon and Gabor Maroti

Journal:

Transportation Science

Date:

2017

Urban public transport systems must periodically close certain links for maintenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the closed links with a simple shuttle service. However, alternative shuttle services could reduce inconvenience at a lower operating cost. This paper proposes a model to select shuttle lines and frequencies under budget constraints. We propose a new formulation that allows a minimal frequency restriction on any line that is operated and minimizes passenger inconvenience cost, which includes transfers and frequency-dependent waiting time costs. This model is applied to a shuttle design problem based on a real-world case study of the Massachusetts Bay Transportation Authority network of Boston, Massachusetts. The results show that additional shuttle routes can reduce passenger delay compared to the standard industry practice, while also distributing delay more equally over passengers, at the same operating budget. The results are robust under different assumptions about passenger route choice behavior. Computational experiments show that the proposed formulation, coupled with a preprocessing step, can be solved faster than prior formulations.

Train

A Probabilistic Passenger-to-Train Assignment Model based on Automated Data

Authors:

Yiwen Zhu, Haris N. Koutsopoulos and Nigel H.M. Wilson

Journal:

Transportation Research Part B

Date:

2017

The paper presents a methodology for assigning passengers to individual trains using: (i) fare transaction records from Automatic Fare Collection (AFC) systems and (ii) Automatic Vehicle Location (AVL) data from train tracking systems. The proposed Passenger-to-Train Assignment Model (PTAM) is probabilistic and links each fare transaction to a set of feasible train itineraries. The method estimates the probability of the passenger boarding each feasible train, and the probability distribution of the number of trains a passenger is unable to board due to capacity constraints. The access/egress time distributions are important inputs to the model. The paper also suggests a maximum likelihood approach to estimate these distributions from AFC and AVL data. The methodology is applied in a case study with data from a major, congested, subway system during peak hours. Based on actual AFC and train tracking data, synthetic data was generated to validate the model. The results, both in terms of the trains passengers are assigned to and train loads, are similar to the "true" observations from the synthetic data. The probability of a passenger being left behind (due to capacity constraints) in the actual system is also estimated by time of day and compared with survey data collected by the agency at the same station. The left behind probabilities can be accurately estimated from the assignment results. Furthermore, it is shown that the PTAM output can also be used to estimate crowding metrics at transfer stations.

Train

Inferring Left Behind Passengers in Congested Metro Systems from Automated Data

Authors:

Yiwen Zhu, Haris N. Koutsopoulos and Nigel H.M. Wilson

Journal:

Transportation Research Part C

Date:

2018

With subway systems around the world experiencing increasing demand, measures such as passengers left behind are becoming increasingly important. This paper proposes a methodology for inferring the probability distribution of the number of times a passenger is left behind at stations in congested metro systems using automated data. Maximum likelihood estimation (MLE) and Bayesian inference methods are used to estimate the left behind probability mass function (LBPMF) for a given station and time period. The model is applied using actual and synthetic data. The results show that the model is able to estimate the probability of being left behind fairly accurately.

Train

Capacity-Constrained Network Performance Model for Urban Rail Systems

Authors:

Baichuan Mo, Zhengliang Ma, Haris N. Koutsopoulos and Jinhua Zhao

Journal:

Transportation Research Record

Date:

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 recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway (MTR) network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using left behind survey data and exit passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.