Projects
Projects: Summary of Projects by RegionProjects in Region Scotland involving Technology Scotland : ES/S001875/1 |
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| Reference Number | ES/S001875/1 | |
| Title | Social and Economic Implications of Transport Sharing and Automation | |
| Status | Completed | |
| Energy Categories | Energy Efficiency(Transport) 25%; Not Energy Related 75%; |
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| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | SOCIAL SCIENCES (Sociology) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 50%; Systems Analysis related to energy R&D (Energy modelling) 25%; Sociological economical and environmental impact of energy (Consumer attitudes and behaviour) 25%; |
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| Principal Investigator |
Dr L Chen No email address given School of Social & Political Sciences University of Glasgow |
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| Award Type | Standard | |
| Funding Source | ESRC | |
| Start Date | 02 January 2018 | |
| End Date | 01 January 2021 | |
| Duration | 36 months | |
| Total Grant Value | £302,344 | |
| Industrial Sectors | ||
| Region | Scotland | |
| Programme | ESRC - Innovation Fellowships | |
| Investigators | Principal Investigator | Dr L Chen , School of Social & Political Sciences, University of Glasgow (100.000%) |
| Industrial Collaborator | Project Contact , PTV UK (0.000%) Project Contact , Peter Brett Associates (0.000%) Project Contact , Technology Scotland (0.000%) |
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| Web Site | ||
| Objectives | A recent study (Frey and Osborne, 2017) concluded that 47% of total US employment across all sectors of the economy has a very high risk (probability of 0.7 or higher) of being replaced by computer automation within next two decades. These include occupations in the transport and logistics sector (e.g. taxi, ambulance, public transport, delivery services, heavy truck drivers, chauffeurs, parking lot attendants, and traffic technicians). Yet smart cities companies and local councils and planning organisations are not taking account of these transformative changes into their planning and strategic processes. Many of the technologies along this line are still in their infancy, and obtaining the empirical (and often privately held) data for proper evaluation particularly as the technology changes rapidly, is challenging, if not impossible, making it difficult for such systems to become an integral part of the overall transport system in planned, strategic ways.The work undertaken will deliver on the social and urban implications of three of the eight core themes identified in the Industrial Strategy - (A) robotics and artificial intelligence, (B) transformative digital technologies, and (C) new energy technologies. As we will explain further, the work in these core areas will collectively address several pillars of the Industrial Strategy including innovations and commercialisation from investments in science, skills development, upgrading transport and digital infrastructure, support to industry growth across the country, delivering clean energy, and driving growth across the country. The research, training and engagement undertaken by Innovation fellows will support industry by addressing all four cross-cutting challenges: aside from cross-cutting skills and digital infrastructure, place - the research will yield critical insights into growth and regional variations across places finding patterns of business relevance in local economies, transport infrastructure technology uptake, and supply chains, and promote technologically transformative business practices - by helping business partners derive insights from data and analytics from advances in digital data infrastructure, automation and clean energy.In a nutshell, the main objectives of this project can be represented as the following research questions:1. What are the spatial and regional effects of automation and shared mobility technology?2. What business models and planning practices are needed to insert automation and sharing transport as integral components of regional policy making, infrastructure planning, and urban transport planning and policy-making?3. What framework is needed to evaluate regional variations and society-wide benefits and risks of automation?These research questions into the economic and infrastructural aspects of AI and automation have been co-produced with industry partners (including Peter Brett Associates, MaaS Scotland, and PTV UK) to further thegoals of the Industrial Strategy. | |
| Abstract | This study will link the changing nature of jobs due to automation and the platform economy to regional infrastructure planning and transport operations, and the role specifically of transport automation within this context. The patterns and forms of jobs are changing due to many different reasons, leading to non-traditional work schedules and differences in commuting patterns, non-standard work travel patterns, and even elimination of certain jobs and creation of new ones, with significant implications for regional infrastructure planning and transport operations. At the same time, there are enormous changes anticipated in infrastructure and operations, due to large-scale automation in the transport sector (eg autonomous and connected vehicles).This project will make estimates of the changing nature of jobs due to these considerations at the regional level towards the goal of deriving the transport and regional infrastructural planning consequences. The project will use labour market survey data as well as privately-held labour market data on jobs, skills and industry to estimate regional variations due to these trends, given regional industry-occupation mix. These changes will be linked to the Spatial Urban Data System (SUDS), which is a UK-wide geospatial data infrastructure under development within UBDC containing transport infrastructural and operational conditions. , and which has been recently used to identify areas of transport poverty throughout the UK and the extent to which and which we will expand through work with the project's industrial partners.Using these data sources, we will identify regional automation risks due to unique industry and skill concentrations and derive transport and infrastructure planning implications. Within this context, we will also evaluate the role of autonomous vehicles given potentially different commuting patterns using specialist transport simulation models. We will further develop specialist transport simulation models to ascertain which packages of "last-mile" transport solutions (low-energy station cars, autonomous vehicles, shared transport, active travel and demand-response services) are likely to bring about high-quality, sustainable and socially-equitable forms of transport accessibility in areas at risk of changing nature of jobs. We will then combine the results of our various model scenarios, using ensemble forecasting methods utilising Bayesian Model Averaging or related techniques to ascertain which packages are more likely to bring about high-quality transport accessibility in the selected areas. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 13/02/18 | |
