Name
Title | ||
|---|---|---|
Random Process Model for Urban Traffic Flow Using a Wavelet-Bayesian Hierarchical Technique |
Abstract | ||
|---|---|---|
The existing well-known short-term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily "trend" of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time-of-the-day. A case study has been performed at two busy junctions at the city-centre of Dublin to validate the effectiveness of the strategy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1111/j.1467-8667.2010.00681.x | COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING |
Keywords | Field | DocType |
wavelets,traffic volume,random process,traffic flow,stochastic processes,algorithms,bayes theorem | Data mining,Traffic generation model,Data set,Mathematical optimization,Traffic flow,Operations research,Stochastic process,Engineering,Traffic volume,Bayes' theorem,Bayesian probability,Wavelet | Journal |
Volume | Issue | ISSN |
25.0 | 8.0 | 1093-9687 |
Citations | PageRank | References |
6 | 0.58 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bidisha Ghosh | 1 | 85 | 9.13 |
| Biswajit Basu | 2 | 101 | 8.28 |
| Margaret O'Mahony | 3 | 123 | 10.41 |