TY - GEN
T1 - Modeling historical traffic data using artificial neural networks
AU - Ghanim, Mohammad S.
AU - Abu-Lebdeh, Ghassan
AU - Ahmed, Kamran
PY - 2013/8/19
Y1 - 2013/8/19
N2 - The Design-Hour Volume (DHV), which is defined as the 30th highest hour volume in a year, is a significant concept in transportation engineering and planning. Finding the DHV requires hourly traffic counts for an entire year. However, this becomes a challengeable task when part of the data is not collected because of different reasons, such as construction activities or hardware failure. In this paper, an Artificial Neural Network (ANN) approach is used to develop a DHV prediction model based on historical traffic counts. The model takes into account the correlation between DHV and other variables such as AADT, functional classification, and number of lanes. Results show that the ANN model is capable of providing accurate and reliable DHV estimates.
AB - The Design-Hour Volume (DHV), which is defined as the 30th highest hour volume in a year, is a significant concept in transportation engineering and planning. Finding the DHV requires hourly traffic counts for an entire year. However, this becomes a challengeable task when part of the data is not collected because of different reasons, such as construction activities or hardware failure. In this paper, an Artificial Neural Network (ANN) approach is used to develop a DHV prediction model based on historical traffic counts. The model takes into account the correlation between DHV and other variables such as AADT, functional classification, and number of lanes. Results show that the ANN model is capable of providing accurate and reliable DHV estimates.
KW - Artificial Neural Networks
KW - Design Hour Volume
KW - Historical Traffic Modeling
KW - Traffic Forecast
UR - http://www.scopus.com/inward/record.url?scp=84881452247&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881452247&partnerID=8YFLogxK
U2 - 10.1109/ICMSAO.2013.6552717
DO - 10.1109/ICMSAO.2013.6552717
M3 - Conference contribution
AN - SCOPUS:84881452247
SN - 9781467358149
T3 - 2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013
BT - 2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013
T2 - 2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013
Y2 - 28 April 2013 through 30 April 2013
ER -