TY - CHAP
T1 - Data processing and harmonization for intelligent transportation systems: an application scenario on highway traffic flows
AU - Figueiras, Paulo
AU - Guerreiro, Guilherme
AU - Silva, Ricardo Almeida
AU - Costa, Ruben
AU - Jardim-Gonçalves, Ricardo
N1 - Sem PDF
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In the advent of ITS (Intelligent Transportation Systems) the transportation sector generates large volumes of real-time data that needs to be collected, harmonized, interpreted, aggregated, and analysed. To this end, innovative big data processing and mining, together with optimization techniques, need to be developed and applied to better support real-time decision-making capabilities. This chapter presents an ETL (Extract-Transform-Load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. It relies on the adoption of “big data” technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators, and is capable of handling real-time and historical data. The DATEX-II data model is adopted, enabling harmonization of traffic data provided by the highway operators.
AB - In the advent of ITS (Intelligent Transportation Systems) the transportation sector generates large volumes of real-time data that needs to be collected, harmonized, interpreted, aggregated, and analysed. To this end, innovative big data processing and mining, together with optimization techniques, need to be developed and applied to better support real-time decision-making capabilities. This chapter presents an ETL (Extract-Transform-Load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. It relies on the adoption of “big data” technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators, and is capable of handling real-time and historical data. The DATEX-II data model is adopted, enabling harmonization of traffic data provided by the highway operators.
KW - Data mining
KW - Industry
KW - Predictive analytics
UR - http://www.scopus.com/inward/record.url?scp=85045232650&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75181-8_14
DO - 10.1007/978-3-319-75181-8_14
M3 - Chapter
AN - SCOPUS:85045232650
SN - 978-3-319-75180-1
T3 - Studies in Computational Intelligence
SP - 281
EP - 301
BT - Learning Systems: From Theory to Practice
A2 - Sgurev, V.
A2 - Piuri, V.
A2 - Jotsov , V.
PB - Springer Verlag
CY - Cham
ER -