How predictable are macroscopic traffic states: a perspective of uncertainty quantification

Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleatoric uncertainty, and the additional distrust caused by data shortage...

Modeling the impact of lane-changing's anticipation on car-following behavior

Lane-changing (LC) in congested traffic has been identified as a trigger for the sudden deceleration behavior of the new follower in the target lane, leading to severe traffic disturbances. Thus, investigating the response of the new follower to an LC maneuver is an important research topic in the literature. To date, numerous efforts have...

Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling

Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical...

Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets...

How Gaps are Created during Anticipation of Lane Changes

The pre-insertion process called anticipation is an essential component of a lane-changing manoeuvre. There is little empirical research regarding the impact of anticipation. Thus, this paper aims to explore the behaviour of the new follower (NF) in the target lane when it encounters anticipation by using new trajectory datasets. The changing...

Estimate the limit of predictability in short-term traffic forecasting: An entropy-based approach

Accurate short-term traffic forecasting is the cornerstone for Intelligent Transportation Systems. In the past several decades, many models have been proposed to continuously improve the predictive accuracy. A key but unsolved question is whether there is a theoretical bound to the accuracy with which traffic can be predicted and whether that...

Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations

Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning...

Dynamic Graph Filters Networks: A Gray-box Model for Multistep Traffic Forecasting

Short-term traffic forecasting is one of the key functions in Intelligent Transportation System (ITS). Recently, deep learning is drawing more attention in this field. However, how to develop a deep learning based traffic forecasting model that can dynamically extract explainable spatial correlations from traffic data is still a challenging...

Discretionary lane-changing behavior: empirical validation for one realistic rule-based model

In this paper, we discuss the mechanisms for discretionary lane-changing behavior in traffic flow. NGSIM video data are used to check the validity of different lane-changing rules, and 373 lane changes at 4 locations in US-101 highway are analyzed. We find that the classical lane-changing rules of rule-based model cannot explain many cases in...

Macroscopic travel time reliability diagrams for freeway networks

Travel time reliability is considered to be one of the key indicators of transport system performance. Knowledge of the mechanisms of travel time unreliability enables the derivation of explanatory models with which travel time reliability can be predicted and utilized in traffic management. Inspired by the macroscopic fundamental diagram (MFD),...