Constructing the fundamental diagrams of traffic flow from large-scale vehicle trajectory data

TL;DR

This paper introduces an automated method and open-source tool for constructing fundamental traffic flow diagrams from large-scale vehicle trajectory data by identifying stationary traffic states within specific regions.

Contribution

It presents a systematic approach based on Edie's definitions for measuring traffic variables and constructing fundamental diagrams using parallelogram-shaped aggregation regions.

Findings

Automated identification of stationary traffic states.

Open-source tool for constructing fundamental diagrams.

Applicable to real-world and simulated trajectory data.

Abstract

For decades, researchers and practitioners typically measure macroscopic traffic flow variables, i.e., density, flow, and speed, using time or space cuts, and then construct the fundamental diagrams of traffic flow. With the advent of large-scale vehicle trajectory datasets, often capturing 100 % of vehicle dynamics, Edie's generalized definitions have become widely recognized as the most appropriate framework for measuring these variables. However, while Edie's formulation explicitly requires the traffic state within the measurement region to be both stationary and homogeneous, there is little guidance on how to systematically identify such qualified regions and construct the corresponding fundamental diagrams. To address this gap, this paper proposes an Edie's definition-based method for measuring traffic variables and constructing the fundamental diagrams of traffic flow by automatically identifying stationary traffic states using parallelogram-shaped aggregation regions. An open-source tool is developed and released to support both researchers and practitioners. From now on, we have an automated tool that can generate fundamental diagrams directly from any large-scale time-space diagram of vehicle trajectories, either collected from the real world or generated by simulation (such as testing car-following models).