Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom

Although driving behavior has been largely studied amongst private motor vehicles drivers, the literature addressing heavy goods vehicle (HGV) drivers is scarce. Identifying the existing groups of driving stereotypes and their proportions enables researchers, companies, and policy makers to establish group-specific strategies to improve safety and economy. In addition, insight into driving styles can help predict drivers' reactions and therefore enable the modeling of interactions between vehicles and the possible obstacles encountered on a journey. Consequently, there are also contributions to the research and development of autonomous vehicles and smart roads. In this paper, our interest lies in investigating driving behavior within the HGV community in the United Kingdom (U.K.). We conduct analysis of a telematics dataset containing the incident information on 21 193 HGV drivers across the U.K. We are interested in answering two research questions: 1) What groups of behavior are we able to uncover? 2) How do these groups complement current findings in the literature? To answer these questions, we apply a two-stage data analysis methodology involving consensus clustering and ensemble classification to the dataset. Through the analysis, eight patterns of behavior are uncovered. It is also observed that although our findings have similarities to those from previous work on driving behavior, further knowledge is obtained, such as extra patterns and driving traits arising from vehicle and road characteristics

Achieving genuinely dynamic road user charging : issues with a GNSS-based approach

Peer reviewedPostprin

Recommendations for safety and sustainability measures of the EU FP7 Project UDRIVE

The aim of Task 5.1 is to identify and select, among the outcomes of SP4, the results that are relevant to infer recommendations for measures improving road safety and sustainability. Due to time constraint, the analyses and the recommendations have been done in less time that it was planned at the beginning of the project. The key outcomes of the SP4 work with particular reference to crash risk, unsafe driving, and eco-driving will be studied and organized in terms of relevance to safety and sustainability policies and potential actions towards road users, vehicle and road. Recommendations have been developed to propose actions to stakeholders that can be implemented in the near future to increase safety and sustainability of road transport. This work integrates several reviews of different measures implemented previously in France, Germany, Netherlands and United Kingdom in terms of road safety measures. Then, the recommendations consider possible updates of existing measures and the development of new measures. They will include four kinds of areas: • Recommendations in terms of regulation and enforcement measures; • Recommendations for awareness campaigns and training; • Recommendations for design of road infrastructure; • Recommendations for vehicle safety. Looking at road fatalities statistics, we have identified vulnerable road users as a topic which is important to create recommendations for. We have also identified factors that can have an influence on fatality occurrence like age and infrastructure. A report by the World Health Organization in 2015 (WHO, 2015) identified some area’s wherein there is a need for recommendations to improve road safety. We have selected from the by WHO recommended topics, 3 topics which could be explored by naturalistic studies: seat belt, speed, distraction. Another topic that we are looking into is critical situations. The difficulties with investigating critical situations with road fatalities data bases, is that these databases often do not provide fully detailed information about the dynamic of the accident. Naturalistic studies have the ability to explore incidents more in-depth. Another objective of UDRIVE is to improve sustainability by looking into eco-driving. We will look at recommendations for this topic in this report as well

Towards real-time heavy goods vehicle driving behaviour classification in the United Kingdom

Determining the driving styles and the factors causing incidents in real time could assist stakeholders to promote actions and develop feedback systems to reduce risks, costs and to increase safety in roads. This paper presents a classification system for Heavy Goods Vehicles (HGVs) drivers, using a core set of driving pattern stereotypes which were uncovered from driving incidents across three years i.e. 2014, 2015 and 2016. To achieve that, the driving stereotypes are established by employing a 2-stage ensemble classification framework followed by a profile labelling algorithm to define the set of driving stereotypes. Very similar stereotypes are later merged to form the core driving stereotypes for UK HGV drivers. Upon establishing core driving stereotypes across these three years, a decision tree classifier learns the classification rules to identify the driving stereotypes for the HGV drivers in a new dataset. High accuracy is achieved, indicating that the core driving patterns uncovered in this work could potentially be employed to identify UK HGV driving patterns in real-time.Postprin

Verification of the HDM-4 fuel consumption model using a Big data approach: a UK case study

This paper presents an assessment of the accuracy of the HDM-4 fuel consumption model calibrated for the United Kingdom and evaluates the need for further calibration of the model. The study focuses on HGVs and compares estimates made by HDM-4 to measurements from a large fleet of vehicles driving on motorways in England. The data was obtained from the telematic database of truck fleet managers (SAE J1939) and includes three types of HGVs: light, medium and heavy trucks. Some 19,991 records from 1,645 trucks are available in total. These represent records of trucks driving at constant speed along part of the M1 and the M18, two motorways in England

Transformative versus conservative automotive innovation styles: contrasting the electric vehicle manufacturing strategies for the BMW i3 and Fiat 500e

The automotive industry is a critically important stakeholder influencing the sustainability of passenger transport. How traditional car manufacturers respond to carbon reduction and vehicle targets, alongside other selection pressures, can greatly influence the availability and affordability of new innovations such as electric vehicles. In this paper, we explore the automotive innovation styles surrounding two electric vehicles: the BMW i3, and the Fiat 500e. To do so, we tie together ideas from technological innovation systems and corporate product innovation style. Our results illustrate a case of a “compliance car,” the Fiat 500e, vs. the first mass production EV by a major German car manufacturer, the BMW i3. BMW adheres to a transformative change-shaping innovation style that attempts to promote in-house learning that can create value. Fiat adheres to a conservative sustaining innovation style that attempts to outsource innovation, promotes limited learning, and focuses on maintaining value. Both styles interestingly result in converging product development patterns over time

Context-aware intelligent decisions: online assessment of heavy goods vehicle driving risk

There is a growing interest in assessing the impact of drivers' actions and behaviours on road safety due to the numerous road fatalities and costs attributed to them. For Heavy Goods Vehicle (HGV) drivers, assessing the road safety risks of their behaviours is a subject of interest for researchers, governments and transport companies, as nations rely on HGVs for the delivery of goods and services. However, HGV driving is a complex, dynamic, uncertain and multifaceted task, mostly influenced by individual traits and external contextual factors. Advanced computational and artificial intelligence (AI) methods have provided promising solutions to automatically characterise the manner by which drivers operate vehicle controls and assess their impact on road safety. However, several challenges and limitations are faced by the current intelligence-supported driving risk assessment approaches proposed by researchers, such as: (1) the lack of comprehensive driving risk datasets; (2) information about the impact of inevitable contextual factors on HGV drivers' responses is not considered, such as drivers' physical and mental states, weather conditions, traffic conditions, road geometry, road types, and work schedules; (3) ambiguity in the definition of driving behaviours is not considered; and (4) imprecision of AI models, and variability in experts' subjective views are not considered. To overcome the aforementioned challenges and limitations, this multidisciplinary research aims at exploring multiple sources of data including information about the impact of contextual factors captured from crucial stakeholders in the HGV sector to develop a reliable context-aware driving risk assessment framework. To achieve this aim, AI methods are explored to accurately detect drivers' driving styles, affective states and driving postures using telematics data, facial images, and driver posture images respectively. Subsequently, due to the lack of comprehensive driving risk datasets, fuzzy expert systems (FESs) are explored to fuse detected driving behaviours and perceived external factors using knowledge from domain experts. The key findings of this research are: (1) recurrent neural networks are effective in capturing the temporal dynamics and differences between the different types of driver distraction postures and affective states; (2) there is a trade-off between efficiency and privacy in processing facial images using AI approaches; (3) the fusion of driver behaviours and external factors using FESs produces realistic, reliable and fair driving risk assessments; and (4) a hierarchical representation of a decision-making process simplifies reasoning compared to flat representations