Using Probe-Based Speed Data and Interactive Maps for Long-term and COVID-era Congestion Monitoring in San Francisco
Probe data that provide roadway speeds and travel times are increasingly being used for a variety of purposes in the transportation domain. A key use of these datasets has been roadway performance monitoring by state and local transportation agencies that are mandated to measure and report performance of their transportation networks. The San Francisco County Transportation Authority (SFCTA)… read the rest of the article here.
Ride-Hail Data Suppression Strategies Can Lean to Biased Outcomes
The Chicago ride-hailing data set is one of the few data sets in the United States containing details of individual ride-hail trips. To protect privacy, locations and times are aggregated, and locations are further suppressed when the frequency of trips is low. Most researchers using this data remove the trips with suppressed locations or external destinations from their analysis… read the rest of the article here.
Estimating the Uncertainty of Traffic Forecasts from their Historical Accuracy
Traffic forecasters may find value in expressing the uncertainty of their forecasts as a range of expected outcomes. Traditional methods for estimating such uncertainty windows rely on assumptions about reasonable ranges of travel demand forecasting model inputs and parameters. Rather than relying on assumptions, we demonstrate how to use empirical measures of past forecast accuracy to estimate… read the rest of the article here.
Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco
Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger… read the rest of the article here.
The Changing Accuracy of Traffic Forecasts
Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe… read the rest of the article here.
How Far Are We From Transportation Equity? Measuring the Effect of Wheelchair Use on Daily Activity Patterns
The mobility needs of individuals with travel-limiting disabilities has been a transportation policy priority in the United States for more than thirty years, but efforts to model the behavioral implications of disability on travel have been limited. In this research, we present a daily activity pattern choice model for multiple person type segments including an individual’s wheelchair use as an explanatory variable… read the rest of the article here.
Understanding Ride-Hailing Sharing and Matching in Chicago: Travel Time, Cost, and Choice Models
Ride-hailing data is sparingly available throughout the U.S., which limits researchers’ understanding of the mode. Chicago is one of a few cities that have mandated ride-hailing companies to submit detailed trip data to their local transportation agency. The dataset is one of the few to contain trip-level attributes such as fare, travel time, and trip length. Most research using the Chicago dataset has focused on understanding why people use ride-hailing. This study focuses on why ride-hailing passengers choose shared over private trips and what influences the shared trips to be matched…. read the rest of the article here.
Publications Under Review
A model of ride-hailing driver participation: Shift duration, start time, and start location.
Heuristic Algorithms for Integrating Latent Demand in to the Design of Large-Scale On-Demand Multimodal Transit Systems
Path-Based Formulations for Design of On-demand Multimodal Transit Systems with Adoption Awareness
Comprehensive studies on On-demand Multimodal Transit Systems with Two Case Studies in San Francisco and Salt Lake City