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 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.
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.
| Original language | English |
|---|---|
| Title of host publication | ITISE 2019 International Conference on Time Series and Forecasting |
| Subtitle of host publication | Proceedings of Papers 25-27 September 2019 Granada (Spain) |
| Editors | Olga Valenzuela, Fernando Rojas, Hector Pomares, Ignacio Rojas |
| Pages | 1247-1259 |
| ISBN (Electronic) | 978-84-17970-78-9 |
| Publication status | Published - 2019 Sept 19 |
| Event | 6th International Conference on Time Series and Forecasting - Granada, Spain Duration: 2019 Sept 25 → 2019 Sept 27 Conference number: 6 |
Conference
| Conference | 6th International Conference on Time Series and Forecasting |
|---|---|
| Abbreviated title | ITISE 2019 |
| Country/Territory | Spain |
| City | Granada |
| Period | 2019/09/25 → 2019/09/27 |
Subject classification (UKÄ)
- Transport Systems and Logistics
Free keywords
- traffic demnad
- forecasting
- lstm
- gru
- saes
- image processing
- time series