In order to calculate and estimate travel time with the data of video vehicle detectors, data of queue length is applied to the calculation of travel time and the roads are researched with the improved BP neural network algorithm and time series analysis. The decision coefficient is 93.36% when queue length is added to the calculation, which is improved by 41.03% compared with the neural network algorithm for the traffic data only, and 23.37% compared with the BPR algorithm. Using real-time travel time can been used to predict the follow-up travel time. And through the time series analysis, the relative error is 0.06. The average relative errors are 0.14 and 0.15 respectively for forecasting the travel time of the next period and next cycle. Results show that the queue length has higher accuracy for calculating travel time, which can be used to predict travel time of the urban road. The algorithm can provide ideas for calculation of index for other algorithms in the field of intelligent transportation and can also provide decision support for improving the traffic situation.