Big Data in Transport Modelling and Planning

Iliashenko, Oksana; Iliashenko, Victoria; Lukianchenko, Ekaterina

doi:10.1016/j.trpro.2021.02.145

Cited by 27 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, new technologies, such as the Internet of Things (IoT) and Machine Learning, have arisen, and many of them can be applied to improve solutions to longstanding problems. It is shown in [33], whose authors gathered real applications around the world and described the problems and how each technology was employed to solve them. As examples, one can cite the use of big data and IoT to solve problems with road irregularities in Boston (USA) and how big data, IoT, and Machine Learning were applied to identify road sections with a high risk of road transportation accidents in Moscow (Russia).…”

Section: Case Studiesmentioning

confidence: 99%

Data collection and analysis applied to intelligent transportation systems: a case study on public transportation

Oliveira

Iano

Vaz

et al. 2023

Discov Artif Intell

View full text Add to dashboard Cite

The big data concept has been gaining strength over the last few years. With the arise and dissemination of social media and high access easiness to information through applications, there is a necessity for all kinds of service providers to collect and analyze data, improving the quality of their services and products. In this regard, the relevance and coverage of this niche of study are notorious. It is not a coincidence that governments, supported by companies and startups, are investing in platforms to collect and analyze data, aiming at the better efficiency of the services provided to the citizens. Considering the aforementioned aspects, this work makes contextualization of the Big Data and ITS (Intelligent Transportation System) concepts by gathering recently published articles, from 2017 to 2021, considering a survey and case studies to demonstrate the importance of those themes in current days. Within the scope of big data applied to ITS, this study proposes a database for public transportation in the city of Campinas (Brazil), enabling its improvement according to the population demands. Finally, this study tries to present clearly and objectively the methodology employed with the maximum number of characteristics, applying statistical analyses (box-and-whisker diagrams and Pearson correlation), highlighting the limitations, and expanding the studied concepts to describe the application of an Advanced Traveler Information System (ATIS), a branch of Intelligent Transportation System (ITS), in a real situation. Therefore, besides the survey of the applied concepts, this work develops a specific case study, highlighting the identified deficiencies and proposing solutions. Future works are also contemplated to expand this study and improve the accuracy of the achieved results.

show abstract

Section: Case Studiesmentioning

confidence: 99%

Data collection and analysis applied to intelligent transportation systems: a case study on public transportation

Oliveira

Iano

Vaz

et al. 2023

Discov Artif Intell

View full text Add to dashboard Cite

show abstract

“…In recent years, new technologies, such as the Internet of Things (IoT) and Machine Learning, have arisen, and many of them can be applied to improve solutions of longstanding problems. It is shown by Iliashenko et al (2021), who gathered real applications around the world and described the problems and how each technology was employed to solve them. As examples, one can cite the use of big data and IoT to solve problems with road irregularities in Boston (USA) and how big data, IoT, and Machine Learning were applied to identify road sections with a high risk of road transportation accidents in Moscow (Russia).…”

Section: Case Studiesmentioning

confidence: 99%

Data Collection and Analysis Applied to Intelligent Transportation Systems A Case Study on Public Transportation

Oliveira

Iano

Vaz

et al. 2022

Preprint

View full text Add to dashboard Cite

The big data concept has been gaining strength over the last years. With the arise and dissemination of social media and high access easiness to information through applications, there is a necessity for all kinds of service providers to collect and analyze data, improving the quality of their services and products. In this regard, the relevance and coverage of this niche of study is notorious. It is not a coincidence that governments, supported by companies and startups, are investing in platforms to collect and analyze data, aiming at the better efficiency of the services provided to the citizens. Considering the aforementioned aspects, this work makes a contextualization of the Big Data and ITS (Intelligent Transportation System) concepts by gathering recently published articles, from 2017 to 2021, taking survey and case studies into consideration with the objective of demonstrating the importance of those themes in current days. Within the scope of big data applied to ITS, this study proposes a database for the public transportation in the city of Campinas (Brazil), enabling its improvement according to the population demands. Finally, this study tries to present clearly and objectively the methodology employed with the maximum number of characteristics, applying statistical analyses (box-and-whisker diagrams and Pearson correlation), highlighting the limitations, and expanding the studied concepts to describe the application of an Advanced Traveler Information System (ATIS), a branch of Intelligent Transportation System (ITS), in a real situation. Therefore, besides the survey of the applied concepts, this work develops a specific case study, highlighting the identified deficiencies and proposing solutions. Future works are also contemplated with the objective of expand this study and improve the accuracy of the achieved results.

show abstract

“…Big data analytics allow for the processing of voluminous data to create meaningful insights on operational performance, forecasting, inventory and transportation management. It has applications in geolocation analysis, in the optimization of transport logistics and in vehicle maintenance techniques for combating fraud through wear analysis (Iliashenko et al, 2021). It has been utilized to optimize crew and vehicle routing and warehousing layout networks, to predict consumer demands and to estimate delivery times.…”

Section: The Opportunities and Challenges Of Big Data For Logistics 40mentioning

confidence: 99%

Illustrating scholar–practitioner collaboration for data-driven decision-making in the optimization of logistics facility location and implications for increasing the adoption of AR and VR practices

Rohani

Peerally

Moghavvemi

et al. 2021

TQM

View full text Add to dashboard Cite

PurposeThis study illustrates the experience of scholar–practitioner collaboration for data-driven decision-making through the problematic of optimizing facility locations and minimizing logistics costs for La Palette Rouge (LPR) of Portugal.Design/methodology/approachThe authors used a mixed mixed-method approach involving (1) a quantitative exploratory analysis of big data, which applied analytics and mathematical modeling to optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner collaboration for data-driven decision-making.FindingsThe quantitative analysis compared more than 20 million possible configurations and proposed the optimal logistics structures. The proposed optimization model minimizes the logistics costs by 22%. Another optimal configuration revealed that LPR can minimize logistics costs by 12% through closing one of its facilities. The illustrative description demonstrates that well-established resource-rich multinational enterprises do not necessarily have the in-house capabilities and competencies to handle and analyze big data.Practical implicationsThe mathematical modeling for optimizing logistics networks demonstrates that outcomes are readily actionable for practitioners and can be extended to other country and industry contexts with logistics operations. The case illustrates that synergistic relationships can be created, and the opportunities exist between scholars and practitioners in the field of Logistics 4.0 and that scientific researcher is necessary for solving problems and issues that arise in practice while advancing knowledge.Originality/valueThe study illustrates that several Logistics 4.0 challenges highlighted in the literature can be collectively addressed through scholar–practitioner collaborations. The authors discuss the implications of such collaborations for adopting virtual and augmented reality (AR) technologies and to develop the capabilities for maximizing their benefits in mature low-medium technology industries, such as the food logistics industry.

show abstract

Big Data in Transport Modelling and Planning

Cited by 27 publications

References 5 publications

Data collection and analysis applied to intelligent transportation systems: a case study on public transportation

Data collection and analysis applied to intelligent transportation systems: a case study on public transportation

Data Collection and Analysis Applied to Intelligent Transportation Systems A Case Study on Public Transportation

Illustrating scholar–practitioner collaboration for data-driven decision-making in the optimization of logistics facility location and implications for increasing the adoption of AR and VR practices

Contact Info

Product

Resources

About