Traffic Operations and Management

Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction

Huang, Zongyuan
Wang, Weipeng
Huang, Shaoyu
Gonzalez, Marta C.
Jin, Yaohui
Xu, Yanyan
2025

Predicting individual mobility patterns is crucial across various applications. While current methods mainly focus on predicting the next location for personalized services like recommendations, they often fall short in supporting broader applications such as traffic management and epidemic control, which require longer period forecasts of human mobility. This study addresses mid-term mobility prediction, aiming to capture daily travel patterns and forecast trajectories for the upcoming day or week. We propose a novel Multi-scale Spatial-Temporal Decoupled Predictor (MSTDP) designed to...

Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction

Lu, Jiajian
Grembek, Offer
Hansen, Mark
2022

Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer...

Identifying similar days for air traffic management

Gorripaty, Sreeta
Liu, Yi
Hansen, Mark
Pozdnukhov, Alexey
2017

Air traffic managers face challenging decisions due to uncertainity in weather and air traffic. One way to support their decisions is to identify similar historical days, the traffic management actions taken on those days, and the resulting outcomes. We develop similarity measures based on quarter-hourly capacity and demand data at four case study airports—EWR, SFO, ORD and JFK. We find that dimensionality reduction is feasible for capacity data, and base similarity on principal components. Dimensionality reduction cannot be efficiently performed on demand data, consequently similarity is...

Airborne flight time: A comparative analysis between the U.S. and China

Liu, Ke
Zheng, Zhe
Zou, Bo
Hansen, Mark
2023

Actual airborne time (AAT) is the time between actual wheels-off and actual wheels-on of a flight. Given the ever-growing demand for air travel and growing flight delays, understanding the behavior of AAT is increasingly important for on time performance and delay propagation. Of particular interest is the comparison on AAT in different countries with varying air route structures, air traffic management systems, weather, and geography. This paper performs the first comparative empirical analysis of AAT behavior, focusing on the U.S. and China. The focus is on how AAT is affected by origin-...

Traffic Management and Resource Allocation for UAV-based Parcel Delivery in Low-altitude Urban Space

Li, Ang
Hansen, Mark
Zou, Bo
2022

This research proposes a framework of Unmanned Aircraft Vehicles (UAV) system traffic management in the context of parcel delivery in low-altitude urban airspace, including clustering-based UAV path planning, Unmanned Aircraft System Traffic Management (UTM) with conflict detection and resolution (CD&R), and mechanism design for airspace resource allocation. For UAV path planning, we develop a procedure by first clustering a large variety of obstacles that arise from building heights and terrain topology and can impede UAV flying. Based on the clustered obstacles, Saturated Fast-...

Fifteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2023) STL combining LSTM for long-term predicting airport traffic flow

Wang, Ziming
Wang, Yanjun
Zhao, Yaoshuai
Hansen, Mark
Delahaye, Daniel
2023

Airport traffic flow exhibits significant periodicity on a daily scale, few studies have given attention to periodicity when predicting airport traffic flow. In this article, we propose a novel model that combines long short-term memory (LSTM) and seasonal-trend decomposition procedure based on loess (STL) to predict the arrival/departure traffic flow at the airport. A sinusoidal template-matching method based on Fréchet distance is used to restack the periodic input variables. A time series decomposition algorithm STL is used to decompose the traffic flow time series into trend, seasonal...

Flight Time and Flight Traffic Before, During, and After the Pandemic: What Has Changed?

Xu, Jing
Dai, Lu
Hansen, Mark
2024

This paper develops a model for quantifying the relationship between flight volume and its operational performance at the macro level and investigating whether there are any changes before, during, and after the pandemic. Inspired by the market basket concept from economics, we first calculate macro-level effective flight time (EFT) for the U.S. domestic flight market by constructing a flight basket. Semi-log-linear models are developed to formulate the relationship between the total number of flights and macro-level EFT and its components. The estimation results indicate that the total...

Excess Delay from GDP: Measurement and Causal Analysis

Liu, Ke
Hansen, Mark
2025

Ground Delay Programs (GDPs) have been widely used to resolve excessive demand-capacity imbalances at arrival airports by shifting foreseen airborne delay to pre-departure ground delay. While offering clear safety and efficiency benefits, GDPs may also create additional delay because of imperfect execution and uncertainty in predicting arrival airport capacity. This paper presents a methodology for measuring excess delay resulting from individual GDPs and investigates factors that influence excess delay using regularized regression models. We measured excess delay for 1210 GDPs from 33 U.S...

The Los Angeles Freeway Service Patrol (FSP) Evaluation: Study Methodology and Preliminary Findings

Petty, Karl
Bertini, Robert L
2025

This paper presents and discusses a methodology to evaluate the effectiveness, in terms of benefit-to-cost ratio, of the Freeway Service Patrol (FSP) in a Los Angeles freeway section. The methodology addresses the process of estimating incident delay using probe vehicles, and the lack of "before" field data. The difficulties that these problems present are discussed. Also, some preliminary findings are reported.

The I-880 field Experiment : Analysis of the Incident Data

Skabardonis, Alexander
University of California, Berkeley
1997

This paper reports on the I-880 field experiment in which field data on incidents were collected through observations of probe vehicle drivers before and after the implementation of freeway service patrols (FSP) over a freeway section. The paper describes the incident patterns and identifies the major factors affecting incident frequency and duration. FSP significantly reduced the response times but did not have a significant effect on the durations of all incidents.