Data

A Data-Centric Weak Supervised Learning for Highway Traffic Incident Detection

Sun, Yixuan
Mallick, Tanwi
Balaprakash, Prasanna
Macfarlane, Jane
2022

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of...

A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data

Ugirumurera, Juliette
Severino, Joseph
Bensen, Erik A.
Wang, Qichao
Macfarlane, Jane
2023

Traffic signals play an important role in transportation by enabling traffic flow management, and ensuring safety at intersections. In addition, knowing the traffic signal phase and timing data can allow optimal vehicle routing for time and energy efficiency, eco-driving, and the accurate simulation of signalized road networks. In this paper, we present a machine learning (ML) method for estimating traffic signal timing information from vehicle probe data. To the authors best knowledge, very few works have presented ML techniques for determining traffic signal timing parameters from...

Data-Driven Energy Use Estimation in Large Scale Transportation Networks

Wang, Bin
Chan, Cy
Somasi, Divya
Macfarlane, Jane
Rask, Eric
2019

Energy consumption in the transportation sector accounts for 28.8% of the total value among all the industry sectors in the United States, reaching 28.2 quadrillion btu in 2017. Having an accurate evaluation of the vehicle fuel and energy consumption values is a challenging task due to numerous implicit influential factors, such as the variety of powertrain configurations, time-varying traffic and congestion patterns, and emerging new technologies, such as regenerative braking. In this paper, we propose to present a data-driven computational framework to evaluate the energy impact on the...

Differential Privacy in Aggregated Mobility Networks: Balancing Privacy and Utility

Haydari, Ammar
Chuah, Chen-Nee
Zhang, Michael
Macfarlane, Jane
Peisert, Sean
2024

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This paper presents a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated...

A Data-Driven Method for Determining Hotspots in Airport Movement Areas

Davis, Jon
Makdisi, Sinan
Holman, Cal
Madrigal, Anahi
Zada, Mohammad
Rakas, Jasenka
2025

As aviation demand continues to increase in the post-COVID-19 era, the risk of incidents and accidents in aircraft operations at airport airfields has increased. Large hub airports often contain multiple runways connected to the apron area using a complex system of taxiways. As a result, an increased risk of incidents has been appearing in certain parts of these complex taxiway systems, which are defined as airport taxiway hotspots. While a list of taxiway hotspots at U.S. airports exists to support and alleviate concerns for pilots and ground personnel, the methodology for determining...

Data Communications Availability and Operations

Rakas, Jasenka
Bauranov, Aleksandar
2017

Next Generation Data Communication System (Data Comm) is the key element within the FAA’s program of modernization of the Nation Airspace System.

Data link Technology for Contingency Management in Super Density Airspace Operations

Rakas, Jasenka
Bauranov, Aleksandar
Cheng, Kevin
Monsalud, Andrew
Hayashi, Miwa
2014

Contingency management in SDO is achieved by combining: 1. existing technologies, concepts and software (ERAM, ADS-B, AAR, Biomimicry), and 2. newly developed models (DFDI, Conflict Resolution and Mixed-mode communications for Flight Formation). The proposed concept is demonstrated in 2D and 3D environments in strategic and tactical situations. Improvements in efficiency, safety and aviation operations are achieved by enhancing usage of DataComm in the NextGen environment.

Background Paper: The General Transit Feed Specification (GTFS) Makes Trip-Planning Easier — Especially During a Pandemic — Yet its Use by California Agencies is Uneven

Frick, Karen Trapenberg
Kumar, Tanu
Post, Alison
2020

The General Transit Feed Specification (GTFS) is an open source data format public transportation agency use to share information about routes and vehicle arrival and departure times. A variety of trip-planning applications, including Google Maps, rely on GTFS feeds to incorporate public transit information. In April 2020, the California Integrated Travel Project conducted a Feasibility Study that called for the widespread adoption of GTFS-static (GTFS-s) and GTFS-realtime (GTFS-r) to make transit simpler for California residents; however, there is little research on patterns of...

The General Transit Feed Specification Makes Trip-Planning Easier — Especially During a Pandemic — Yet its Use by California Agencies is Uneven

Frick, Karen Trapenberg
Kumar, Tanu
Li, Ruyin
Patil, Atharva
Post, Alison
2020

Developed in 2005, the General Transit Feed Specification (GTFS) is making transit trip planning easier by allowing public transportation agencies to share transit schedules in an electronic format that can be used by a variety of trip-planning applications, such as Google Maps. The GTFS can be used to share static transit schedules (GTFS-s) or provide real-time information on transit vehicle arrivals and departures (GTFS-r). Providing real-time updates has proven to be exceptionally valuable during the COVID-19 pandemic. For example, between January 13th and April 25th of this year Apple...

Commuters' Normal and Shift Decisions in Unexpected Congestion: En Route Responses to Advanced Traveler Information Systems Volume 2

Polydoropoulou, A.
Ben-akiva, M.
Khattak, A.
Lauprete, G.
1996

Advanced Traveler Information Systems (ATIS) are being developed to provide travelers with real-time information about traffic conditions. To evaluate the benefits of ATIS products and services, questions concerning potential market, usage, and travel response must be addressed. This paper focuses on en-route travel response to ATIS. The main objective is to explore how travelers deal with unexpected congestion and how they might respond to qualitative, quantitative, prescriptive and predictive information. Data on travelers’ route switching decisions are obtained through a survey of...