Eco-driving: the role of feedback in reducing emissions from everyday driving behaviours

ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management

Transport is Australia's third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants. The work reported in this paper aims to address these challenges by developing an IoT platform to measure and report on real-world last-mile delivery emissions. Such evaluations help to understand the factors contributing to freight emissions so that appropriate mitigation measures are implemented. Unlike previous research that was completed in controlled laboratory settings, the data collected in this research were from a delivery vehicle under real-world traffic and driving conditions. The IoT platform was tested to provide contextualised reporting by taking into account three main contexts including vehicle, environment and driving behaviours. This approach to data collection enabled the analysis of parcel level emissions and correlation of the vehicle characteristics, road conditions, ambient temperature and other environmental factors and driving behaviour that have an impact on emissions. The raw data collected from the sensors were analysed in real-time in the IoT platform, and the results showed a trade-off between parcel weight and total distance travelled which must be considered when selecting the best delivery order for reducing emissions. Overall, the study demonstrated the feasibility of the IoT platform in collecting the desired levels of data and providing detailed analysis of emissions at the parcel level. This type of micro-level understanding provides an important knowledge base for the enhancement of delivery processes and reduction of last-mile delivery emissions.

The Missing Link? Insights from an Innovative Feedback Exercise for Household Electricity and Travel Behaviour

The objective of this study was to explore how individuals perceive their different types of energy use and evaluate if this information could be useful for further understanding energy demand reduction. Based on a literature review, an innovative feedback exercise was developed that gave participants feedback on electricity use, travel, and then both electricity use and travel simultaneously. Through the use of an interdisciplinary mixed methods research approach, the findings indicate that electricity use and travel are viewed as being very different, there is an opportunity to improve travel-related feedback when compared to electricity, and most do not see a link between electricity and travel. A small number of participants did see a link between electricity and travel, and this was also explored. These findings, which until now have not been explored in the literature, take a first attempt at comparing how individuals perceive their different types of energy use and highlight the relevance of this type of research in order to further advance energy demand management. Implications for a low carbon energy transition and future areas of research were identified and discussed, including decreasing travel in a way that is perceived as being advantageous, improving feedback that illustrates the importance of individual choices, and the importance of examining multiple energy behaviours simultaneously.

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers’ socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers’ individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised.

The scope for adaptive capacity in emergency departments: modelling performance constraints using control task analysis and social organisational cooperation analysis

Patient flow between the emergency department (ED) and hospital wards becomes problematic when bed availability is limited. To better understand the constraints that shape patient flow and everyday work in the ED, we applied Control Task Analysis (i.e. Contextual Activities Template, CAT) and Social Organisational Cooperation Analysis (SOCA) phases from the Cognitive Work Analysis framework to identify ways in which to optimise patient flow. The model and analysis were created through observations in the ED of clinicians (e.g. nurses, doctors), and professional staff (e.g. ward personnel, clerks). The CAT and SOCA-CAT models illustrate workspaces, patient journey phases, and patient volume within the department that are heavily loaded with tasks and human and non-human agents performing these tasks, while others are underutilised. The findings suggest that an ED's adaptive capacity could be strengthened through the integration of additional human and non-human agents allowing the redistribution of clinical and non-clinical tasks. Practitioner Summary: Workflow in EDs is constrained by uneven geographical distribution of activities, insufficient adaptive support during critical patient journey phases and periods of high patient volume. Adaptive capacity could be strengthened by additional human and non-human agents in combination with a redistribution of tasks, supporting seamless successful structural and behavioural adaptation in ED.

Sustainable behavior in motion: designing mobile eco-driving feedback information systems

Emissions from road traffic contribute to climate change. One approach to reducing the carbon footprint is providing eco-driving feedback so that drivers adapt their driving style. Research about the impact of eco-feedback on energy consumption is the basis for designing a mobile eco-driving feedback information system that supports drivers in reducing fuel consumption. This work develops design knowledge from existing knowledge. Subsequently, we implement a prototypical instantiation based on the derived knowledge. Insights from a field study suggest that our design artifact allows most drivers to decrease fuel consumption by 4% on average. The paper’s theoretical contribution is a set of design principles and an architecture of the proposed mobile eco-driving feedback information system. One recommendation is to provide normative feedback that compares drivers with each other. This feedback appears to encourage drivers to decrease their fuel consumption additionally. The design knowledge may support researchers and practitioners in implementing efficient eco-driving feedback information systems.

Adjusting the need for speed: assessment of a visual interface to reduce fuel use

Previous research has identified that fuel consumption and emissions can be considerably reduced if drivers engage in eco-driving behaviours. However, the literature suggests that individuals struggle to maintain eco-driving behaviours without support. This paper evaluates an in-vehicle visual interface system designed to support eco-driving through recommendations based on both feedforward and feedback information. A simulator study explored participants' fuel usage, driving style, and cognitive workload driving normally, when eco-driving without assistance and when using a visual interface. Improvements in fuel-efficiency were observed for both assisted (8.5%) and unassisted eco-driving (11%), however unassisted eco-driving also induced a significantly greater rating of self-reported effort. In contrast, using the visual interface did not induce the same increase of reported effort compared to everyday driving, but itself did not differ from unassisted driving. Results hold positive implications for the use of feedforward in-vehicle interfaces to improve fuel efficiency. Accordingly, directions are suggested for future research. Practitioner Summary: Results from a simulator study comparing fuel usage from normal driving, engaging in unassisted eco-driving, or using a novel speed advisory interface, designed to reduce fuel use, are presented. Whilst both unassisted and assisted eco-driving reduced fuel use, assisted eco-driving did not induce workload changes, unlike unassisted eco-driving. Abbreviations: CO-2: carbon dioxide; NASA-TLX: NASA task load index; RMS: root-mean-square; MD: mean difference.

An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Driving

Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people’s health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption and emissions ranging from the 9.5% to the 31.5%, or even higher for drivers that are strongly engaged with the system. It was successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx ZynQ programmable system-on-a-chip (PSoC) family. This SOM-based system allows for real-time implementation, state-of-the-art timing performances, and low power consumption, which are suitable for developing advanced driving assistance systems (ADASs).

Sustainable Mobility: A Review of Possible Actions and Policies

In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socio-economic, and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.