An improved model for predicting trip mode distribution using convolution deep learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F21%3A00120713" target="_blank" >RIV/00216224:14610/21:00120713 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11227-020-03394-9" target="_blank" >https://link.springer.com/article/10.1007/s11227-020-03394-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11227-020-03394-9" target="_blank" >10.1007/s11227-020-03394-9</a>
Alternative languages
Result language
angličtina
Original language name
An improved model for predicting trip mode distribution using convolution deep learning
Original language description
Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic, and air pollution. The majority of existing trip mode inference models operate based on human-selected features and traditional machine learning algorithms. However, human-selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
The Journal of Supercomputing
ISSN
0920-8542
e-ISSN
1573-0484
Volume of the periodical
77
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
3638-3652
UT code for WoS article
000565025000001
EID of the result in the Scopus database
2-s2.0-85089899533