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Annual Average Daily Traffic (AADT) is an important parameter used in traffic engineering analysis. Departments of Transportation (DOTs) continually collect traffic count using both permanent count stations (i.e., Automatic Traffic…

The Federal Highway Administration (FHWA) mandates that state Departments of Transportation (DOTs) collect reliable Annual Average Daily Traffic (AADT) data. However, many U.S. DOTs struggle to obtain accurate AADT, especially for…

Accurate annual average daily traffic (AADT) data are vital for transport planning and infrastructure management. However, automatic traffic detectors across national road networks often provide incomplete coverage, leading to…

The prediction of high-resolution hourly traffic volumes of a given roadway is essential for transportation planning. Traditionally, Automatic Traffic Recorders (ATR) are used to collect this hourly volume data. These large datasets are…

Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary across time-of-day patterns. This study is conducted on the…

Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the…

This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k.a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban roads. Time series of such raw data exhibit heavy…

The Adaptive Smoothing Method (ASM) is a data-driven approach for traffic state estimation. It interpolates unobserved traffic quantities by smoothing measurements along spatio-temporal directions defined by characteristic traffic wave…

Transportation agencies monitor freeway performance using various measures such as VMT (Vehicle Miles Traveled), VHD (Vehicle Hours of Delay), and VHT (Vehicle Hours Traveled). Public transportation agencies typically rely on point detector…

Insight into individual driving behavior and habits is essential in traffic operation, safety, and energy management. With Connected Vehicle (CV) technology aiming to address all three of these, the identification of driving patterns is a…

Conventional Public Transport (PT) cannot support the mobility needs in weak demand areas. Such areas could be better served by integrating, within PT, Demand-Responsive Transport (DRT), in which bus routes dynamically adapt to user demand.…

Autonomous driving vehicles aim to free the hands of vehicle operators, helping them to drive easier and faster, meanwhile, improving the safety of driving on the highway or in complex scenarios. Automated driving systems (ADS) are…

The research examined predicting short-duration traffic flow counts with the Kalman filtering technique (KFT), a computational filtering method. Short-term traffic prediction is an important tool for operation in traffic management and…

Given the efficiency and equity concerns of a cordon toll, this paper proposes a few alternative distance-dependent area-based pricing models for a large-scale dynamic traffic network. We use the Network Fundamental Diagram (NFD) to monitor…

Simulation systems have become an essential component in the development and validation of autonomous driving technologies. The prevailing state-of-the-art approach for simulation is to use game engines or high-fidelity computer graphics…

Conventional direction of arrival (DOA) estimation algorithms suffer from performance degradation due to antenna pattern distortion and substantial computational complexity in real-time execution. The support vector regression (SVR)…

The validation and verification of automated driving functions (ADFs) is a challenging task on the journey of making those functions available to the public beyond the current research context. Simulation is a valuable building block for…

Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a…

We develop a constructive approach for $\ell_0$-penalized estimation in the sparse accelerated failure time (AFT) model with high-dimensional covariates. Our proposed method is based on Stute's weighted least squares criterion combined with…

Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performance is supported by well-developed estimation theory,…