Widespread technological development and deployment have created an abundance of data sources for traffic monitoring. A database that integrates data from all these technologies would maximize coverage of the network, given the available data. Sometimes, however, there are multiple independent measurements of the current traffic conditions for a particular portion of the network. In these cases, a variety of data fusion techniques can be used to achieve better estimates while helping to overcome information overload. This paper discusses several techniques for fusing data from competitive sensor configurations, describes the analytical foundation of these techniques, and interprets how each technique might be used most appropriately. In addition, these data fusion techniques are implemented and compared relative to their ability to accurately and reliably estimate traffic speeds. A real-world case study in Toronto, Ontario, Canada, demonstrates that estimates from data fusion techniques that pull loop detector data and probe vehicle data from an integrated database are more accurate and reliable than estimates based on individual data sources. Consequently, these data fusion–based estimates can be taken with greater certainty and confidence.