Using Big Data Analytics and Psychological factors to Understand Heavy Goods Vehicle Driver Behaviour in Real-Time

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science


The aim of this research is to investigate Big Data fusion and mining techniques for merging and analysing multi-level Heavy Goods Vehicle (HGV) driver data, and identifying psychological factors affecting HGV driving behaviour. Additionally, this research aims at exploring predictive analytics of data streams for real-time detection of risky driving behaviour. Determining driving styles and the factors causing incidents or accidents in real time, assist stakeholders to promote actions and develop feedback systems to reduce risks, costs and increase safety in roads.


Heavy Goods Vehicles (HGVs) are at the forefront of trade and commerce in the United Kingdom. Both private and public sectors rely on HGVs road transport for the delivery of goods and services. For instance, 1.4 billion tonnes of freight were transported by road between 2016 and 2017 in the UK. Over the same period, a total of 7.8 million tonnes of freight was moved to or from the UK by HGVs [1]. As a result of the importance of HGVs to a nation's economy, there are great efforts being employed to reduce the number of road accidents caused by HGVs as well as the costs associated with HGVs such as fuel and maintenance costs. These issues are due to one or more of the following factors: vehicle characteristics, weather conditions, company policies and driver behaviour, with driver behaviour being by far the leading determinant [3].

In psychology, risky or dangerous driving behaviour is classified into intentional (deliberate) and unintentional behaviours [2]. The intentional behaviours consist of violations and mistakes which involve inappropriate actions while unintentional behaviours consists of slips and lapses which involve errors due to memory or attention failures [4]. The intentional behaviours can be captured from vehicle operation data (Telematics) such as over speeding, harsh braking, harsh cornering etc. However, capturing the unintentional behaviours is very complex because they can't be directly measured and need to be extracted from other measurements such as physiological measures and images. A greater constraint exists in fusing and analysing these heterogeneous data sources in order to obtain a holistic view of risky driver behaviour.

Academic Contributions

The contributions of this research will regard novel algorithms or frameworks to tackle Data fusion of multi-level data sources and identifying the psychological factors affecting HGV driving behaviour.

Research Questions

This research plans on answering the following research questions:

How do we develop a viable and extensible big data fusion and mining algorithm or framework taking into consideration the heterogeneity complexities of big data?
How can we accurately understand and predict HGV driver behaviour?
How can we extract psychological factors that affect HGV driver behaviour from video streams?
How does monitoring drivers via cameras affect their driving performance or behaviour?
What is the relationship between in-vehicle driver behaviour and the external environment?
How are traffic violations or intentional driver behaviour affected by different visibility conditions or weather conditions (e.g. day, night, and rainy) ?

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S515528/1 01/10/2018 30/09/2022
2115674 Studentship EP/S515528/1 01/10/2018 30/09/2022 Jimiama Mosima Mafeni Mase