Abstract
The prediction of traffic flow is a challenge. There are many factors that can affect traffic flow. One of the factors is an inter path relationship between neighbouring roads. For example, an individual incidents (such as accidents) may cause ripple effects (a cascading failure) which then spreads and creates a sustained traffic jam the neighbouring area. To know the relationship between road segments we propose multiple regression method to predict the traffic based on the nearby surrounding roads. The prediction factor is chosen from a high-relation road with the path to be searched. To know the relationship between roads we calculate their correlation among neighbouring roads. The results are then displayed on the map for further observation. From this study, we demonstrate that multiple regression method can be used to predict impact of speed of vehicles on neighbouring roads on traffic flows.
| Original language | English |
|---|---|
| Title of host publication | Advances in Visual Informatics - 5th International Visual Informatics Conference, IVIC 2017, Proceedings |
| Publisher | Springer Verlag |
| Pages | 309-318 |
| Number of pages | 10 |
| Volume | 10645 LNCS |
| ISBN (Print) | 9783319700090 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
| Event | 5th International Visual Informatics Conference, IVIC 2017 - Bangi, Malaysia Duration: 28 Nov 2017 → 30 Nov 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10645 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 5th International Visual Informatics Conference, IVIC 2017 |
|---|---|
| Country | Malaysia |
| City | Bangi |
| Period | 28/11/17 → 30/11/17 |
Fingerprint
Keywords
- Multiple regressions
- Traffic flow prediction
- Traffic flow propagation
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Predicting traffic flow based on average speed of neighbouring road using multiple regression. / Priambodo, Bagus; Ahmad, Azlina.
Advances in Visual Informatics - 5th International Visual Informatics Conference, IVIC 2017, Proceedings. Vol. 10645 LNCS Springer Verlag, 2017. p. 309-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10645 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Predicting traffic flow based on average speed of neighbouring road using multiple regression
AU - Priambodo, Bagus
AU - Ahmad, Azlina
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The prediction of traffic flow is a challenge. There are many factors that can affect traffic flow. One of the factors is an inter path relationship between neighbouring roads. For example, an individual incidents (such as accidents) may cause ripple effects (a cascading failure) which then spreads and creates a sustained traffic jam the neighbouring area. To know the relationship between road segments we propose multiple regression method to predict the traffic based on the nearby surrounding roads. The prediction factor is chosen from a high-relation road with the path to be searched. To know the relationship between roads we calculate their correlation among neighbouring roads. The results are then displayed on the map for further observation. From this study, we demonstrate that multiple regression method can be used to predict impact of speed of vehicles on neighbouring roads on traffic flows.
AB - The prediction of traffic flow is a challenge. There are many factors that can affect traffic flow. One of the factors is an inter path relationship between neighbouring roads. For example, an individual incidents (such as accidents) may cause ripple effects (a cascading failure) which then spreads and creates a sustained traffic jam the neighbouring area. To know the relationship between road segments we propose multiple regression method to predict the traffic based on the nearby surrounding roads. The prediction factor is chosen from a high-relation road with the path to be searched. To know the relationship between roads we calculate their correlation among neighbouring roads. The results are then displayed on the map for further observation. From this study, we demonstrate that multiple regression method can be used to predict impact of speed of vehicles on neighbouring roads on traffic flows.
KW - Multiple regressions
KW - Traffic flow prediction
KW - Traffic flow propagation
UR - http://www.scopus.com/inward/record.url?scp=85035119056&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035119056&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70010-6_29
DO - 10.1007/978-3-319-70010-6_29
M3 - Conference contribution
AN - SCOPUS:85035119056
SN - 9783319700090
VL - 10645 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 309
EP - 318
BT - Advances in Visual Informatics - 5th International Visual Informatics Conference, IVIC 2017, Proceedings
PB - Springer Verlag
ER -