Modeling

Impact of Transit Signal Priority on Level of Service at Signalized Intersections

Skabardonis, Alexander
Christofa, Eleni
2011

The assessment of Transit Signal Priority (TSP) impacts at traffic signals is typically based on simulation and field studies. There is a need for macroscopic procedures for analysis of TSP as part of the Highway Capacity Manual (HCM) analysis methodology for signalized intersections. This capability will allow prediction of TSP impacts (and related control strategies) at a planning and operations level without the complexity of simulation modeling. The paper presents a technique of estimating the average green times for each lane group, and modifications to the HCM formula for estimating...

TRANSYT Display: A Graphics Package for Signal Timing

Loubal, P S
Skabardonis, A
Wingerd, L
May, A D
Torregrosa, L
STUDIES
1985

This report describes the development and application of transyt display, an interactive microcomputer program to produce plots of a signalized network and its input and output values from the widely used transyt simulation and optimization model. The program can display on a monitor screen both input data and measures of traffic performance for each link and node of the particular network. Any display can be printed, and the user has considerable flexibility in selecting plot size, and including captions to the plot. The report presents several applications of the program. Future program...

Urban Arterial Speed–Flow Equations for Travel Demand Models

Dowling, Richard G
Skabardonis, Alexander
2008

This paper describes the effort to improve the speed–flow relationships for urban arterial streets that are contained in the Southern California Association of Governments (SCAG) metropolitan area travel demand model. Intersection traffic counts and floating car runs were made over 4-h-long periods on 1-mi-long sections of eight different arterial streets within the city of Los Angeles. The field data were then filtered to identify which speed measurements were taken during below-capacity conditions and which measurements were made during congested conditions when demand exceeded the...

A Pedestrian Exposure Model for the California State Highway System

Griswold, Julia B.
Medury, Aditya
Schneider, Robert J.
Amos, Dave
Li, Ang
Grembek, Offer
2019

For this study, we developed one of the first statewide pedestrian exposure models, using log-linear regression to estimate annual pedestrian crossing volumes at intersections on the California State Highway System. We compiled a database of more than 1,200 count locations, one of the largest ever used to create a pedestrian volume mode. We initially evaluated 75 explanatory variables for the model. The final model is based on the three land-use variables (employment density, population density, number of schools), four roadway network variables (number of street segments, intersections...

(PDF) Quantifying the Impact of Air Travel on Growth of COVID-19 Pandemic in the United States

Dai, Lu
Tereshchenko, Ivan
Hansen, Mark
2021

This paper develops models to quantify the dynamics of the impact of air travel on the spread of the COVID-19 pandemic, using a wide range of datasets covering the period from March to December 2020. With the help of flight operation data, we first develop a novel approach to estimate the county-level daily air passenger traffic, which combines passenger load factor estimates and information about the air traffic distribution. Cross-sectional models using aggregated county-level variables are estimated. While this study focuses on air travel variables, we also control for potential spatial...

Modeling Go-around Occurrence Using Principal Component Logistic Regression

Dai, Lu
Liu, Yulin
Hansen, Mark
2021

A go-around is an aborted approach of an aircraft. We model go-around occurrence using Principal Component Logistic Regression (PCLR). This entails go-around detection, feature engineering, and model estimation. As a case study, we consider John F. Kennedy (JFK) International Airport arrivals, and model go-around occurrence based on information available when the subject flight is five nautical miles from its landing runway threshold. The PCLR model is based on Principal Component Analysis (PCA) for analyzing data that suffer from multi-collinearity. The model provides a representation of...

Using Flight Shifting to Mitigate Delay in Multiple Airport Regions

Li, Ang
Hansen, Mark
2021

This study aims to improve operational performance of a multiple airport region (MAR) by analyzing interdependent capacity scenarios of that MAR airports and redistributing airport traffic to make more efficient use of the available capacity. We propose to shift flights between MAR airports in order to reduce flight delays. Both the deterministic and stochastic versions of a flight shift model are formulated as a mixed-integer linear program (MILP). The proposed methodology is applied to New York MAR, which includes five airports, using data for the year 2015. The deterministic model is...

Relative trajectory cost estimation for CTOP applications using multivariate nonparametric finite mixture logit

Tereshchenko, Ivan
Hansen, Mark
2020

We study airline decision-making in response to the Federal Aviation Administration’s Airspace Flow Program (AFP) to empirically describe policy-relevant behavioral strategies and preference structure of air carriers. Using observed responses made by airlines, we infer utility functions of different route options in AFP with respect to flight time and arrival delay using a finite mixture latent class choice model. We empirically describe the trade-off that airlines face between flight time and departure or arrival delay. The average estimated cost of airborne delay is 24 times greater than...

Modeling Ground Delay Program Incidence using Convective and Local Weather Information

Liu, Yi
Hansen, Mark
Zhang, Danqing
Liu, Yulin
2019

In this work, we model the impact of weather condition on ground delay program (GDP) incidence using support vector machine (SVM) and logistic regression. We use SVM to analyze how spatial patterns of convective weather affect GDP occurrence and produce heatmaps to visualize the impact. Additionally, the SVM results are combined with local airport weather variables and airport traffic level indicator to yield a logistic model that considers both local conditions at the airport and convective weather in the surrounding area. We apply our methods to five airports: Newark Liberty...

Sequential Prediction of Go-Around Occurrence

Dai, Lu
Liu, Yulin
Hansen, Mark
2022

A go-around is an aborted landing event of an aircraft that is on final approach. Go-arounds are costly and detrimental to safety. Building upon our previous work in go-around detection and analysis of feature contributions, we investigate different learning models and prediction regimes for making sequential predictions of go-around probabilities based on realized trajectory data and environment factors as the aircraft proceeds on its approach. This paper develops and compares the performance of different learning algorithms and prediction strategies for the sequential go-around...