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...

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...

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...

Dynamica van verkeersnetwerken: Individuele oorzaken, collectieve gevolgen

In het Nederlandse wegverkeer is veel vertraging. Een vermindering van het verkeer zorgt ook voor een vermindering van die vertraging. Dit kan bereikt worden door maatregelen als beprijzing of spitsrechten. Het verkeerssysteem is één samenhangend systeem over grote afstanden. Om de effecten van deze maatregelen op netwerkschaal te beoordelen,...

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...

Departure rates optimization and perimeter control: Comparison and cooperation in a multi-region urban network

With the renewed interest in the Macroscopic Fundamental Diagram (MFD) in the last decade, studies on network-level urban traffic control have increased in popularity. A popular urban traffic control approach is perimeter control, in which vehicle accumulation is kept below some critical accumulation value. An alternative control strategy is to...

Traffic dynamics: Its impact on the macroscopic fundamental diagram

Literature shows that–under specific conditions–the Macroscopic Fundamental Diagram (MFD) describes a crisp relationship between the average flow (production) and the average density in an entire network. The limiting condition is that traffic conditions must be homogeneous over the whole network. Recent works describe hysteresis effects:...