This article deals with the processing of data on the entrances
and exits of passengers for railway stations in Moscow and the
suburbs. Smart transport cards are used worldwide in transport applications
as a payment tool. So, for railways (cities) its usage creates the
big and constantly updated collections of transactions data from cards
validation equipment. The deployment model for railways in Moscow region
allows us to know exactly the starting and ending points of the each
route. This detailed information allows us to obtain generalized information
on the modes (models) of the actual use of the railway transport.
The detected travel patterns could be mapped to the model of the social
and economic behavior of residents of the capital region. And vice
versa, we can use known artifacts of the behavior of the inhabitants of
the region as the search patterns for transport data.The conclusion that
mobility is one of the main characteristics and one of the key components
of a smart city is a well-known fact.
Friday, December 14, 2018
Data Mining on the Use of Railway Stations
Thursday, October 25, 2018
Tuesday, September 04, 2018
On Proximity-Based Information Delivery
Friday, August 10, 2018
On transport models
In this paper, we discuss the questions, associated with the forecast for passenger traffic for urban railways. The aim of the study is to select and verify the model for predicting passenger traffic of new urban railways. The article is based on the practical tasks implemented during the project phase for new urban railways in Moscow, Russia. We are considering data sources for building the forecast, as well as practical models that can be used to obtain numerical estimates. Among the discussed data sources, we target migration data that can be collected with the help of telecommunications operators, and information on the use of public transport, obtained from the validation of transport cards. Also, in this paper, we investigate the metrics for traffic along the new city rail line, which can be determined on the basis of the projected passenger traffic. The result of the work was the constructed model of the transport behavior of passengers, taking into account the availability of new urban railways and a set of metrics for assessing the functioning of this transport tool.
Monday, August 06, 2018
Use cases for time series database
See more about time series databases
Thursday, July 26, 2018
On machine learning speculations
Failure to distinguish between explanation and speculation.
Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.
from here
Monday, June 18, 2018
Monday, May 21, 2018
5G and Digital Economy
On 5G projects for urban railways and Russian Digital Economy program
On Digital Economy Issues Looking From the Information Systems Viewpoint