A Review of Research on Driving Styles and Road Safety

Objective: To outline a conceptual framework for understanding driving style and, based on this, review the state-of-the-art research on driving styles in relation to road safety.</br></br> Background: Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking. </br></br> Method: A systematic literature search was conducted, including empirical, theoretical and methodological research on driving styles related to road safety. </br></br> Results: A conceptual framework was proposed where driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorising and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and socio-cultural factors influencing driving style were reviewed. </br></br> Conclusion: Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesise these results and make useful generalisations. There is a considerable potential for increasing road safety by means of behaviour modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles. </br></br> Application: Knowledge about driving styles can be applied in programmes for modifying driver behaviour and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems

How many parameters to model states of mind ?

A series of examples of computational models is provided, where the model aim is to interpret numerical results in terms of internal states of agents minds. Two opposite strategies or research can be distinguished in the literature. First is to reproduce the richness and complexity of real world as faithfully as possible, second is to apply simple assumptions and check the results in depth. As a rule, the results of the latter method agree only qualitatively with some stylized facts. The price we pay for more detailed predictions within the former method is that consequences of the rich set of underlying assumptions remain unchecked. Here we argue that for computational reasons, complex models with many parameters are less suitable.Comment: 5 pages, no figures; Proceedings 27th European Conference on Modelling and Simulation ECMS Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang (Editors), 201

Assessing the sustainability performance of inter-urban intelligent transport

The implementation of ITS to increase the efficiency of saturated highways has become increasingly prevalent. It is a high level objective for many international governments and operators that highways should be managed in a way that is both sustainable i.e. environmental, social and economically sound and supportive of a Low-Carbon-Energy Future. Some clarity is therefore needed to understand how Intelligent Transport Systems perform within the constraints of that objective. This thesis describes the development of performance criteria that reflect the contributions of Information Communication Technology (ICT) emissions, vehicle emissions and the embedded carbon within the physical transport infrastructure that typically comprises three types of Intelligent Transport System. Active Traffic Management, Intelligent Speed Adaptation and the Automated Highway System are a collection of systems designed to transform the road network into a highly efficient and congestion free transport solution and all possess varying levels of uncertainty in terms of sustainability performance. The performance criteria form part of a new framework methodology ‘EnvFUSION’ (Environmental Fusion for ITS) outlined here. An attributional LCA and c-LCA (consequential lifecycle assessment) are both undertaken which forms part of a data fusion process using data from various sources. The models forecast improvements for the three ITS technologies in-line with social acceptability, economic profitability and major carbon reduction scenarios up to 2050 on one of the UK's most congested highways. Analytical Hierarchy Process and Dempster-Shafer theory are used to weight criteria which form part of an Intelligent Transport Sustainability Index. Overall performance is then synthesized. Results indicate that there will be a substantial increase in socio-economic and emissions benefits, provided that the policies are in place and targets are reached which would otherwise delay their realisation. To conclude, an integrated strategic performance management framework is proposed which performs socio-technical comparisons of four key performance areas between ITS schemes in order to identify energy and emission hotspots

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

The resilience of road transport networks redundancy, vulnerability and mobility characteristics

This thesis is concerned with the development of a composite resilience index for road transport networks. The index employs three characteristics, namely redundancy, vulnerability and mobility, measuring resilience at network junction, link and origin-destination levels, respectively. Various techniques have been adopted to quantify each characteristic and the composite resilience index as summarised below. The redundancy indicator for road transport network junctions is based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and relative link speed were examined. The developed redundancy indicator covers the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The vulnerability indicator for road transport network links is developed by combining vulnerability attributes (e.g. link capacity, flow, length, free flow and traffic congestion density) with different weights using a new methodology based on fuzzy logic and exhaustive search optimisation techniques. Furthermore, the network vulnerability indicators are calculated using two different aggregations: an aggregated vulnerability indicator based on physical characteristics and the other based on operational characteristics. The mobility indicator for road transport networks is formulated from two mobility attributes reflecting the physical connectivity and level of service. The combination of the two mobility attributes into a single mobility indicator is achieved by a fuzzy logic approach. Finally, the interdependence of the proposed characteristics is explored and the composite resilience index is estimated from the aggregation of the three characteristics indicators using two different approaches, namely equal weighting and principal component analysis methods. Moreover, the impact of real-time travel information on the proposed resilience characteristics and the composite resilience index has been investigated. The application of the proposed methodology on a synthetic road transport network of Delft city (Netherlands) and other real life case studies shows that the developed indicators for the three characteristics and the composite resilience index responded well to traffic load change and supply variations. The developed composite resilience index will be of use in various ways; first, helping decision makers in understanding the dynamic nature of resilience under different disruptive events, highlighting weaknesses in the network and future planning to mitigate the impact of disruptive events. Furthermore, each developed indicator for the three characteristics considered can be used as a tool to assess the effectiveness of different management policies or technologies to improve the overall network performance or the daily operation of road transport networks

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

Road traffic noise and its prediction by computer simulation with particular reference to signalised intersections

Interrupted or congested traffic flow situations increase the number of possibly relevant variables over the free flow case and it becomes virtually impossible to obtain enough uncorrelated measured data to establish the regression coefficients for all these variables. [Continues.