This research seeks to investigate how additional data sources can be used within traffic control systems to reduce average delay and improve reliability of journey time. Current state of the art urban traffic control systems do not take full advantage of the improved granularity of data available as they use traditional, static detection methods such as inductive loops, infra-red and radar. Therefore further research was required to fully understand what new data sources are available, how they could be used and if there are any potential benefits for traffic control systems. The transport industry is moving into an era of data abundance as more people use smart phones, satellite navigation systems, Wi-Fi and Bluetooth devices. These richer data sources could provide additional information (vehicle location, speed and destination data) but it is currently unknown as to whether they can improve the performance of the road network. Much of the research in this thesis has been published through conference and journal papers. A novel traffic control algorithm called DEMA was developed during this research, which can significantly outperform MOVA (a leading industrial control algorithm) through reducing average delay by up to 34% when additional data sources are incorporated into the decision process. DEMA uses vehicle location, speed and turning intention information to select the most suitable stage for minimising delay. Also a study was conducted to determine if turning intention information could be predicted from outside of a vehicle, which is a previously un-researched area. The results demonstrated that people could correctly predict turning intention with a 70% median success rate when the vehicles were 50 metres from the junction. The outcomes of this research could have a significant impact on the future of urban traffic control systems as new data sources become more readily available in the transport industry.