Science and Technology

The relationship between vehicle telemetry and sustainability

vehicle telemetry

The objective of this PhD project is to analyse the relationship between transportation telemetry and its impact on commercially sustainable transportation solutions and networks.

In a transportation paradigm shift, Connected Autonomous Vehicles (CAV) offer a diverse and vast array of opportunities to businesses, governments, military, and domestic end-users (Yang, 2021).

A significant next-generation disruptor is the magnitude of potential new metrics for data analysis and how this information may be harvested, mined, interpreted, protected and utilised to foster a competitive advantage through the application of sustainable commercial practices (Gonzalez et al., 2020; Kane & Whitehead, 2017).

With the onset of big data streams from vehicles becoming a reality, the application of data analytics to understand behaviour together with the perception of consumers when utilising contemporary technologies offer the potential to better understand future travel demands and technology uptake (Choi & Ji, 2015; Deb et al., 2018; Wadud et al., 2016). This deeper analysis is possible as consumer future behaviour can be better realised by exploring past or current behaviour (Bamberg et al., 2003; Keszey, 2020).

Moreover, many Logistics Service Providers (LSPs) have rarely fully utilised the previous capabilities (Evangelista & Sweeney, 2014) which illustrates the need to better understand consumer-technology engagement.


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However, complications and periphery opportunities around CAVs are not always obvious as this new era dawns (Kuru & Khan, 2021; Society of Automotive Engineers, 2021). Moreover, sub-optimal outcomes are a potential threat from the poor application of new technology (Kane & Whitehead, 2017). Furthermore, studies are calling for further holistic transport research employing empirical data (Keszey, 2020; Kopelias et al., 2020; Sen et al., 2020; Sovacool et al., 2018) as existing research primarily remains focussed on theoretical performance goals which may have limited long term value. Additionally, the understanding of transport-related sustainability fosters a systems thinking mindset whereby complex interconnected elements and relationships can be viewed as a whole (Naumann et al., 2020).

Due to the interconnected nature of transportation networks and their role within everyday lives, the findings from this PhD project will advance and realise the consequential impact on social, economic, and environmental aspects from commercial sustainability practices.

Project background

At a United Nations conference in 2012, world leaders unanimously agreed that transport is critical and central to the future of sustainable development (United Nations, n.d.). As a result, and for the first time at this magnitude, the spotlight has fallen heavily on transport. In recent years, transport has been credited with the creation of 23% of all greenhouse gas emissions and 18% of all human-made emissions within the global economy (Kopelias et al., 2020; Sustainable Mobility for All, 2017).

Highlighting the significance and concerns, the first of an ongoing series of biennial Global Mobility Reports was published in 2017 to assess the current performance of the global transport sector and its relationship with a sustainable future (Sustainable Mobility for All, 2017). How transport is considered has started to change by fostering greater alignment with sustainable practices and concepts.
In recent years, this has been recognised through the reality of technological developments such as connected autonomous vehicle capabilities which continue to accelerate through technological maturity (Kehal & Zhang, 2020; Kuru & Khan, 2021).

However, this journey has only just begun and requires considerable research and development if it is to be successfully managed and deployed (Keszey, 2020; Kopelias et al., 2020). Transport systems and networks are complex by design (Novack et al., 2018), this is, in part, due to the interrelated and interoperability of systems surrounding transport networks along with their dynamic nature to ever-changing demand drivers.

Therefore, understanding these interrelated dynamics offers new opportunities to explore the relationships between how technological capabilities may be linked to sustainability through augmented practices and application.

Project objectives

  • Examine contemporary literature to collate and understand the existing body of knowledge regarding vehicle telemetry and sustainability.
  • Explore and understand the role technological advancements may play in the successful implementation and deployment of vehicle and transport networks.
  • Understand and disseminate the collaboration between the connected technological capabilities and how they impact sustainability within transport networks.


Bamberg, S., Ajzen, I., & Schmidt, P. (2003). Choice of travel mode in the theory of planned behavior: The roles of past behavior, habit, and reasoned action. Basic and Applied Social Psychology, 25(3), 175-187.
Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human – Computer Interaction, 31(10), 692.
Deb, S., Rahman, M. M., Strawderman, L. J., & Garrison, T. M. (2018). Pedestrians’ receptivity toward fully automated vehicles: Research review and roadmap for future research. IEEE Transactions on Human-Machine Systems, 48(3).
Gonzalez, J. G., Casado-Mansilla, D., & López-de-Ipiña, D. (2020). Analysis of Driver’s Reaction Behavior Using a Persuasion-Based IT Artefact. Sustainability, 12(17), 6857.
Kane, M., & Whitehead, J. (2017). How to ride transport disruption – a sustainable framework for future urban mobility. Australian Planner, 54(3), 177-185.
Kehal, M., & Zhang, Z. (2020). Social internet of vehicles: An epistemological and systematic perspective. Library Hi Tech, 38(1), 221-231.
Keszey, T. (2020). Behavioural intention to use autonomous vehicles: Systematic review and empirical extension. Transportation Research Part C, 119.
Kopelias, P., Demiridi, E., Vogiatzis, K., Skabardonis, A., & Zafiropoulou, V. (2020). Connected & autonomous vehicles – environmental impacts – a review. Science of the Total Environment, 712.
Kuru, K., & Khan, W. (2021). A framework for the synergistic integration of fully autonomous ground vehicles with smart city. IEEE Access, 9, 923-948.
Naumann, R. B., Sandt, L., Kumfer, W., LaJeunesse, S., Heiny, S., & Lich, K. H. (2020). Systems thinking in the context of road safety: Can systems tools help us realize a true “Safe Systems” approach? Current epidemiology reports, 7(4), 343-351.
Novack, R. A., Gibson, B. J., Suzuki, Y., & Coyle, J. J. (2018). Transportation: A global supply chain perspective (Ninth ed.). Cengage.
Sen, B., Kucukvar, M., Onat, N. C., & Tatari, O. (2020). Life cycle sustainability assessment of autonomous heavy‐duty trucks. Journal of Industrial Ecology, 24(1), 149-164.
Society of Automotive Engineers. (2021). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. (Retrieved 16th May 2021) SAE International.
Sovacool, B. K., Noel, L., Axsen, J., & Kempton, W. (2018). The neglected social dimensions to a vehicle-to-grid (V2G) transition: A critical and systematic review. Environmental Research Letters, 13(1), 013001.
Sustainable Mobility for All. (2017). Global mobility report 2017: Tracking sector performance. Sustainable Mobility for All.
United Nations. (n.d.). Sustainable transport: Department of economic and social affairs. (Retrieved 28th March 2021)
Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A, 86, 1-18.
Yang, F. (2021). Research on the course, form and strategy of autonomous driving competition. Journal of Physics: Conference Series, 1948(1).

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