![](bg7.png)
For easy understanding, it can be drawn an anal-
ogy with the terms of relational databases: a key space
corresponds to the concept database schema in a re-
lational model. This key space can contain multiple
column families, which corresponds to the concept of
a relational table. In turn, column families contain
columns, which are combined using the row key in
the row. A column consists of three parts: a column
name, a timestamp, and a value. The columns within
the record are ordered. Unlike a relational database,
there are no restrictions on the fact that records (and
in terms of database these are rows) contain columns
with the same names as in other records.
This generalized model description should be spe-
cialized for each type of data source by changing col-
umn names and reorganizing column families.
4 CONCLUSION
In this paper the complex study on the regional intel-
ligent transportation system module is presented. The
conceptual scheme and algorithms which are used to
control traffic flows in high-speed transport corridors
are described. To solve the module’s tasks it is nec-
essary to collect and aggregate information from het-
erogeneous data sources.
The information infrastructure of intelligent trans-
portation system is considered. It allows aggregation,
storage and receiving information from a data ware-
house effectively. The presented model is based on
the decomposition of the information data warehouse
model into data models corresponding to the degree
of data structuring and amount of data.
An important question on traffic safety has to be
implemented in the ITS regional module. In case of
car accidents the total travel time increases signifi-
cantly, that’s why there should be a developed sys-
tem to recognize accidents immediately and to man-
age road servises for eliminating consequences of ac-
cidents. Based on on-line video streams from high-
way corridors and approaches to them the pre-trained
reccurent neural network could identify accident with
a high accuracy and send to emergency services GPS
cam location to find out the fastest way to the scene
of the car accident.
ACKNOWLEDGEMENTS
The reported study was supported by the Russian Sci-
ence Foundation within the project 18-71-10034.
REFERENCES
Chowdhury, M., Apon, A., and Dey, K. (2017). Data Ana-
lytics for Intelligent Transportation Systems. Elsevier,
Amsterdam, 1st edition.
Galkin, A. and Sysoev, A. (2019). Formalizing crite-
ria of intelligent transportation and logistics systems
functioning. In Proceedings of the 2nd International
Congress on Transport Infrastructure and Systems in
a changing world (AIIT), in press.
Hasegawa, T. (2014). Intelligent transport system. URL:
http://www.iatss.or.jp/common/pdf/en/publication/
commemorativepublication/iatss40 theory 05.pdf.
ITS-America (2019). Its-america. the official website.
URL: https://www.itsa.org.
Khabibullina, E. and Pogodaev, A. (2019). Data warehouse
model of regional intelligent transportation system in
the concept of its-russia. In Proceedings of the 1st
International Conference on Control Systems, Mathe-
matical Modelling, Automation and Energy Efficiency
(SUMMA). IEEE Xplore.
Khabibullina, E., Sysoev, A., Kadasev, D., and Voronin, N.
(2019a). Data aggregation schemes to determine traf-
fic flow parameters in regional intelligent transporta-
tion systems. In Proceedings of the 2nd International
Congress on Transport Infrastructure and Systems in
a changing world (AIIT), in press.
Khabibullina, E., Sysoev, A., and Voronin, N. (2019b). Ag-
gregation of heterogeneous data for determination of
transportation flow parameters. Transport of the Rus-
sian Federation, 2(81).
Korchagin, V., Tursunov, A., and Rizaeva, Y. (2011).
Sistemnoe predstavlenie struktury adaptivnogo up-
ravleniya predpriyatiem transporta [systematic repre-
sentation of the structure of transportation company
control]. Vestnik Tadjikskogo tehnicheskogo univer-
siteta, 2.
LTA and ITSS (2014). Smart mobility 2030:
Its strategic plan for singapore. URL:
https://www.lta.gov.sg/content/dam/ltaweb/corp/
RoadsMotoring/files/SmartMobility2030.pdf.
NTI (2011). National technological initiative (NTI): Au-
tonet. URL: https://nti2035.ru/markets/autonet.
Sysoev, A., Anikienko, T., and Blyumin, S. (2019). High-
way capacity estimation: International regulation and
neurostructural remodeling approach. Periodica Poly-
technica Transportation Engineering.
Sysoev, A., Hohmann, S., and Geistefeldt, J. (2017). Dif-
ferential evolution approach to calculate optimal ramp
metering rates. International Journal for Traffic and
Transport Engineering (IJTTE), 7(1).
Sysoev, A. and Voronin, N. (2019). Approach to sensi-
tivity analysis of stochastic freeway capacity model
based on applying analysis of finite fluctuations. In
Proceedings of the 1st International Conference on
Control Systems, Mathematical Modelling, Automa-
tion and Energy Efficiency (SUMMA). IEEE Xplore.
TRB (2010). Highway capacity manual — HCM 2010.
URL: http://www.trb.org/Main/Blurbs/164718.aspx.
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