Name
Abstract | ||
|---|---|---|
For online traffic control at traffic control centers, there is a need for predictions of the traffic flow during a short horizon, for example, 30 min ahead. For this effort, predictions are needed of the traffic inflow into the network at motorways on the network boundary and at on-ramps. This paper presents an adaptive prediction algorithm for the inflows into the network in regular traffic situations based on stochastic control theory. The prediction algorithm is based on an adaptive prediction algorithm of T. Bohlin. The algorithm is designed and tested on traffic flow data of the ring road of Amsterdam. The results show that the algorithm provides robust predictions of traffic demand with relatively small errors for the next 30 min in a large-scale real-time environment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TITS.2013.2278192 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
traffic demand management,time-series model,short-term traffic flow forecasting,motorway network,large-scale real-time environment,online traffic control,stochastic systems,adaptive prediction algorithm,traffic control centers,stochastic control theory,adaptive prediction,amsterdam,traffic flow,adaptive control,interactive programming,large-scale systems,road traffic control,algorithms,real time information | Traffic generation model,Road traffic control,Traffic flow,Real-time data,Simulation,Engineering,Adaptive control,Traffic congestion reconstruction with Kerner's three-phase theory,Network traffic simulation,Stochastic control | Journal |
Volume | Issue | ISSN |
15 | 1 | 1524-9050 |
Citations | PageRank | References |
5 | 0.46 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yubin Wang | 1 | 11 | 1.30 |
| Jan H. van Schuppen | 2 | 358 | 45.36 |
| Jos Vrancken | 3 | 90 | 13.98 |