Work

Traffic Data Collection and Representation as National‐Level Fundamental Diagrams for England

Year: 2026

Type: article

Abstract: ABSTRACT Traffic congestion significantly affects speed, and thus energy consumption of heavy goods vehicles (HGVs). One of the ways of correlating traffic state with vehicle speed is fundamental diagrams (FDs). This study develops a methodology to collect national‐level traffic data for England, integrate it with vehicle data, and use the data to construct FDs by type of road in England. Traffic counts and time‐averaged traffic speed are obtained from the National Highways database and Road Traffic dataset, and space‐averaged traffic speed data is obtained from HERE Maps. Missing entries are added using the temporal pattern of traffic flow, and outliers in the count data are filtered using spline‐regression and unsupervised k‐means clustering. Traffic data is classified by road types using information from HERE Maps. FDs are constructed for each type of road and validated using a separate test dataset from the National Highways database. The correlation between macroscopic traffic flow data and microscopic vehicle data is verified by validating the FDs with HGV speed data collected from on‐board telematics systems. The results can be used to predict vehicle speed directly from traffic density using universal HGV FDs for England, that is useful for estimating energy consumption.

Source: IET Intelligent Transport Systems

DOI: 10.1049/itr2.70137

Authors: Zixuan Chai, Parth Deshpande, Xiaoxiang Na, David Cebon

Institution: University of Cambridge

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,790