Algorithms for clustering data
Algorithms for clustering data
Traffic signal timing at isolated intersections using simulation optimization
WSC '86 Proceedings of the 18th conference on Winter simulation
Using cooperative mediation to coordinate traffic lights: a case study
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques
Expert Systems with Applications: An International Journal
Ant colony algorithm for traffic signal timing optimization
Advances in Engineering Software
| Hi-index | 12.05 |
Recent advances in Intelligent Transportation Systems (ITS) provide large quantities of data that can be exploited for the development of improved traffic management systems. However, mining this data to extract useful patterns and information also presents significant challenges. In an earlier presentation we presented a novel method for optimising traffic timing plans via the use of clustering algorithms to automatically generate Time-Of-Day (TOD) intervals. By detecting time intervals which share common traffic conditions, it was possible to obtain TOD intervals which better reflect the underlying generators of traffic patterns. However, the above-mentioned procedure suffers from a problem: the data that is used to find the clusters was generated using the original traffic patterns, which means that the estimates of the true underlying TOD intervals could be inaccurate. In this paper, we present a refinement of the method which is based on iteratively re-estimating the TOD intervals. Experiments have been conducted to test the usefulness of this additional step and the results are presented and discussed in detail.