With the development of intelligent transportation, more and more surveillance cameras are deployed at the intersections of urban roads, which makes it possible to use the urban traffic surveillance data to estimate the vehicle travel time and query the route. Aiming at the problem of urban travel time estimation, a travel time estimation method based on the urban traffic surveillance data was proposed, which is called UTSD. Firstly, the traffic surveillance cameras were mapped into the urban road network, and a directed weighted urban road network graph was constructed based on traffic monitoring data recording. Secondly, a spatio-temporal index and a reverse index structure were built for travel time estimation, the former was used to quick search the camera records of all vehicles, and the latter was used to fast obtain the travel time and the passing camera trajectory of each vehicle. These two indexes significantly improved the efficiency of data query and travel time estimation. Finally, based on the constructed indexing structures, an effective travel time estimation and path query method was given. According to the departure time, origin and destination, the vehicles with the same origin and destination were matched on the spatio-temporal index structure, and then the reverse index was used to quickly obtain the travel time estimate and vehicle route. Using the real traffic monitoring big data of a provincial capital city for experimental evaluation, compared with Dijkstra shortest path algorithm based on directed graph and Baidu algorithm, the accuracy rate of the proposed method UTSD is improved by 65.02% and 40.94%, respectively. In addition, the average query time of UTSD is less than 0.3 s when the 7-day monitoring data is used as historical data, which verifies the effectiveness and efficiency of the proposed method.
