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Designing a deep-learning network for traffic density and volume prediction

Sjögren, Simon LU (2019) In Bachelor's Theses in Mathematical Sciences NUMK01 20182
Mathematics (Faculty of Engineering)
Popular Abstract
It is relatively easy to know the day to day traffic flow on a highway without
taking into account on which lane the cars are driving on. It is more difficult
to understand how traffic over time evolves while looking at the lanes, or more
specifically, by looking at traffic volume on the specific lanes on a highway,
can we have a good guess for where the vehicles should be on a different lane
of the same highway in the future? And could it also be possible to predict
future congestion in one juncture by looking at incoming traffic from the past
at another or multiple junctions somewhere else?

This project serves as a precursor for possible future projects by looking at
how traffic volume on a given road with 5 lanes evolves over... (More)
It is relatively easy to know the day to day traffic flow on a highway without
taking into account on which lane the cars are driving on. It is more difficult
to understand how traffic over time evolves while looking at the lanes, or more
specifically, by looking at traffic volume on the specific lanes on a highway,
can we have a good guess for where the vehicles should be on a different lane
of the same highway in the future? And could it also be possible to predict
future congestion in one juncture by looking at incoming traffic from the past
at another or multiple junctions somewhere else?

This project serves as a precursor for possible future projects by looking at
how traffic volume on a given road with 5 lanes evolves over time. The idea is
to use past data of traffic volume and then make a model to predict how traffic
volume looks in the future specifically for these lanes at certain hours.

The historical traffic data was fed into something called an artificial neural
network. The process of how an artificial neural network is operating is inspired
from how neurons in the brain are operating. A signal is sent from one neuron to another, if this signal is strong enough it will be passed on to the next
neuron. However, if the signal is too weak, it will not be passed to the next
neuron. These neurons can also receive multiple signals from different neurons.
Signals sent between neurons will strengthen the connection between these two.
The more signals being sent, the stronger the connections. An artificial neural
network works in a similar manner. Between each artificial neuron there is a
connection, also known as weight. These weights must be adjusted in such a
way so that the network will give good enough results.

The traffic data is split up in three parts, one part is the traffic volume at different times, the other part is the corresponding future traffic volume on the
same lane and the final part is the test data. Feeding the network the data
and adjusting the weights with respect to this information, is called supervised
learning. Supervised learning is when the input to the network and its corresponding output are both known. What is unknown, however, are the weights.
When training has been completed, the third part of the data is used to test
the accuracy of the network. (Less)
Please use this url to cite or link to this publication:
author
Sjögren, Simon LU
supervisor
organization
alternative title
Konstruera ett djupinlärning neuralt nätverk för trafikvolym förutsägning
course
NUMK01 20182
year
type
M2 - Bachelor Degree
subject
publication/series
Bachelor's Theses in Mathematical Sciences
report number
LUNFNA-4027-2019
ISSN
1654-6229
other publication id
2019:K13
language
English
id
8995908
date added to LUP
2019-10-10 13:53:16
date last changed
2019-10-13 15:34:00
@misc{8995908,
  author       = {Sjögren, Simon},
  issn         = {1654-6229},
  language     = {eng},
  note         = {Student Paper},
  series       = {Bachelor's Theses in Mathematical Sciences},
  title        = {Designing a deep-learning network for traffic density and volume prediction},
  year         = {2019},
}