Meriton_20160415_CityTransport_4thSub_V4.pdf (1.57 MB)
Exploring the influence of big data on city transport operations: a Markovian approach
journal contribution
posted on 2017-05-12, 12:41 authored by Rashid Mehmood, Roy Meriton, Gary Graham, Patrick Hennelly, Mukesh KumarPurpose – The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart
city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks
of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.
Design/methodology/approach – A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.
Findings – This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing
and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.
Research limitations/implications – The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work
complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.
Practical implications – The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms
optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).
Social implications – The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory,
knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/value – Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity
contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.
History
School
- Loughborough University London
Published in
International Journal of Operations & Production ManagementVolume
37Issue
1Pages
75 - 104Citation
MEHMOOD, R. ...et al., 2017. Exploring the influence of big data on city transport operations: a Markovian approach. International Journal of Operations & Production Management, 37(1), pp. 75-104.Publisher
© Emerald PublishingVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2016-04-19Publication date
2017-01-03Notes
This paper was published in the journal International Journal of Operations & Production Management and the definitive published version is available at https://doi.org/10.1108/IJOPM-03-2015-0179.ISSN
0144-3577Publisher version
Language
- en
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