A Novel Data-driven Algorithm for the Automated Detection of Unexpectedly High Traffic Flow in Uncongested Traffic States

TL;DR

This paper introduces a data-driven algorithm to detect days with high traffic flow and no traffic jams, aiming to identify high-performance days that could inform traffic jam reduction strategies.

Contribution

The paper presents a novel three-step algorithm that uses empirical traffic data to identify high-performance days characterized by high flow and absence of congestion.

Findings

The algorithm successfully identified high-performance days in the Dutch A15 motorway case study.

High-performance days are characterized by a high number of unperturbed moments.

The method provides a new way to analyze traffic flow and congestion patterns.

Abstract

We present an algorithm to identify days that exhibit the seemingly paradoxical behaviour of high traffic flow and, simultaneously, a striking absence of traffic jams. We introduce the notion of high-performance days to refer to these days. The developed algorithm consists of three steps: step 1, based on the fundamental diagram (i.e. an empirical relation between the traffic flow and traffic density), we estimate the critical speed by using robust regression as a tool for labelling congested and uncongested data points; step 2, based on this labelling of the data, the breakdown probability can be estimated (i.e. the probability that the average speed drops below the critical speed); step 3, we identify unperturbed moments (i.e. moments when a breakdown is expected, but does not occur) and subsequently identify the high-performance days based on the number of unperturbed moments. Identifying high-performance days could be a building block in the quest for traffic jam reduction; using more detailed data one might be able to identify specific characteristics of high-performance days. The algorithm is applied to a case study featuring the highly congested A15 motorway in the Netherlands.