Traffic demand and longer term forecasting from real-time observations
(2019) 6th International Conference on Time Series and Forecasting p.1247-1259- Abstract
- We optimize traffic signal timing sequences for a section of a traffic net-
work in order to reduce congestion based on anticipated demand. The system relies
on the accuracy of the predicted traffic demand in time and space which is carried
out by a neural network. Specifically, we design, train, and evaluate three different
neural network models and assert their capability to describe demand from traffic
cameras. To train these neural networks we create location specific time series data
by approximating vehicle densities from camera images. Each image passes through
a cascade of filtering methods and provides a traffic density estimate corresponding
to the camera location at that specific time. The system... (More) - We optimize traffic signal timing sequences for a section of a traffic net-
work in order to reduce congestion based on anticipated demand. The system relies
on the accuracy of the predicted traffic demand in time and space which is carried
out by a neural network. Specifically, we design, train, and evaluate three different
neural network models and assert their capability to describe demand from traffic
cameras. To train these neural networks we create location specific time series data
by approximating vehicle densities from camera images. Each image passes through
a cascade of filtering methods and provides a traffic density estimate corresponding
to the camera location at that specific time. The system is showcased using real-time
camera images from the traffic network of Goteborg. We specifically test this system
in reducing congestion for a small section of the traffic network. To facilitate the
learning and resulting prediction we collected images from cameras in that network
over a couple of months. We then use the neural network to produce forecasts of traffic
demand and adjust the traffic signals within that section. To simulate how congestion
will evolve once the traffic signals are adjusted we implement an advanced stochastic
model. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9960c8d7-c05b-4693-a540-8fcdb2a674ea
- author
- Sopasakis, Alexandros LU
- organization
- publishing date
- 2019-09-19
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- traffic demnad, forecasting, lstm, gru, saes, image processing, time series
- host publication
- ITISE 2019 International Conference on Time Series and Forecasting : Proceedings of Papers 25-27 September 2019 Granada (Spain) - Proceedings of Papers 25-27 September 2019 Granada (Spain)
- editor
- Valenzuela, Olga ; Rojas, Fernando ; Pomares, Hector and Rojas, Ignacio
- pages
- 1247 - 1259
- conference name
- 6th International Conference on Time Series and Forecasting
- conference location
- Granada, Spain
- conference dates
- 2019-09-25 - 2019-09-27
- ISBN
- 978-84-17970-78-9
- language
- English
- LU publication?
- yes
- id
- 9960c8d7-c05b-4693-a540-8fcdb2a674ea
- date added to LUP
- 2019-10-10 21:28:40
- date last changed
- 2022-12-08 14:06:17
@inproceedings{9960c8d7-c05b-4693-a540-8fcdb2a674ea, abstract = {{We optimize traffic signal timing sequences for a section of a traffic net-<br/>work in order to reduce congestion based on anticipated demand. The system relies<br/>on the accuracy of the predicted traffic demand in time and space which is carried<br/>out by a neural network. Specifically, we design, train, and evaluate three different<br/>neural network models and assert their capability to describe demand from traffic<br/>cameras. To train these neural networks we create location specific time series data<br/>by approximating vehicle densities from camera images. Each image passes through<br/>a cascade of filtering methods and provides a traffic density estimate corresponding<br/>to the camera location at that specific time. The system is showcased using real-time<br/>camera images from the traffic network of Goteborg. We specifically test this system<br/>in reducing congestion for a small section of the traffic network. To facilitate the<br/>learning and resulting prediction we collected images from cameras in that network<br/>over a couple of months. We then use the neural network to produce forecasts of traffic<br/>demand and adjust the traffic signals within that section. To simulate how congestion<br/>will evolve once the traffic signals are adjusted we implement an advanced stochastic<br/>model.}}, author = {{Sopasakis, Alexandros}}, booktitle = {{ITISE 2019 International Conference on Time Series and Forecasting : Proceedings of Papers 25-27 September 2019 Granada (Spain)}}, editor = {{Valenzuela, Olga and Rojas, Fernando and Pomares, Hector and Rojas, Ignacio}}, isbn = {{978-84-17970-78-9}}, keywords = {{traffic demnad; forecasting; lstm; gru; saes; image processing; time series}}, language = {{eng}}, month = {{09}}, pages = {{1247--1259}}, title = {{Traffic demand and longer term forecasting from real-time observations}}, year = {{2019}}, }