TY - GEN
T1 - Big data and emerging transportation challenges
T2 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019
AU - Katrakazas, Christos
AU - Antoniou, Constantinos
AU - Vazquez, Natalia Sobrino
AU - Trochidis, Ilias
AU - Arampatzis, Stratos
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - In the last years many Big Data technologies have been applied to the transportation sector all over the world. Despite existing and future promising applications, critical factors which lead to a successful application and value generation from Big Data technologies in transport are largely unknown. The European Union (EU) Horizon 2020 (H2020) NOESIS project aims at identifying critical features leading to the successful implementation of Big Data technologies and services in the field of transport. In order to accomplish that aim, key challenges of Big Data utilization in the transport domain, need to be initially identified. The scope of this paper is to present the research findings on the major Big Data in Transportation challenges. The NOESIS challenges describe the major transportation areas and sub-problems that could benefit by Big Data. Firstly, a literature review was conducted in order to obtain the main areas (challenges) within the transportation domain which have the potential of greater exploitation through Big Data methods. 10 initial focus areas were identified from reviewing the state-of-The-Art in Big Data and transportation research. Secondly, findings from the literature review were discussed and validated during a workshop with experts on Big Data in Transportation, increasing those challenges to 13. For each of the focus areas, corresponding sub-problems have been also identified. The findings of this paper contribute to the exploitation of Big Data within transportation in two ways: i) it provides the necessary literature review and experts' discussion for identifying the transport domain areas in which big data technologies could be successfully applied and ii) it identifies sub-problems linked to each of the challenges that big data could help to improve transportation. As a result, it is believed that this work initiates a first step towards enhancing the socioeconomic impact of transportation investments using Big Data.
AB - In the last years many Big Data technologies have been applied to the transportation sector all over the world. Despite existing and future promising applications, critical factors which lead to a successful application and value generation from Big Data technologies in transport are largely unknown. The European Union (EU) Horizon 2020 (H2020) NOESIS project aims at identifying critical features leading to the successful implementation of Big Data technologies and services in the field of transport. In order to accomplish that aim, key challenges of Big Data utilization in the transport domain, need to be initially identified. The scope of this paper is to present the research findings on the major Big Data in Transportation challenges. The NOESIS challenges describe the major transportation areas and sub-problems that could benefit by Big Data. Firstly, a literature review was conducted in order to obtain the main areas (challenges) within the transportation domain which have the potential of greater exploitation through Big Data methods. 10 initial focus areas were identified from reviewing the state-of-The-Art in Big Data and transportation research. Secondly, findings from the literature review were discussed and validated during a workshop with experts on Big Data in Transportation, increasing those challenges to 13. For each of the focus areas, corresponding sub-problems have been also identified. The findings of this paper contribute to the exploitation of Big Data within transportation in two ways: i) it provides the necessary literature review and experts' discussion for identifying the transport domain areas in which big data technologies could be successfully applied and ii) it identifies sub-problems linked to each of the challenges that big data could help to improve transportation. As a result, it is believed that this work initiates a first step towards enhancing the socioeconomic impact of transportation investments using Big Data.
KW - Big Data
KW - Challenges
KW - Transportation
UR - http://www.scopus.com/inward/record.url?scp=85074905387&partnerID=8YFLogxK
U2 - 10.1109/MTITS.2019.8883308
DO - 10.1109/MTITS.2019.8883308
M3 - Conference contribution
AN - SCOPUS:85074905387
T3 - MT-ITS 2019 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems
BT - MT-ITS 2019 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 June 2019 through 7 June 2019
ER -