The important issue for intra-city truck dispatching system is to measure and store actual travel speeds between customer locations. Travel speeds(and times) in nearly all metropolitan areas change drastically during the day because of congestion in certain parts of the city road network. We propose a back-propagation neural network model to recognize the pattern of intra-city vehicle travel speeds between locations that relieve much burden for the data collection and computer storage requirements. On a real-world study using the travel speed data[1] collected in Seoul, we evaluate performance of neural network model and compare with Park & Song model[2] that employs the least square method.