Wireless sensor networks are expected to be deployed on urban roadways to monitor the traffic continuously. One of the requirements of traffic monitoring is displaying the traffic states of the front roadways, which can guide the drivers to choose the right way and avoid potential traffic congestions. In this scenario, the information of traffic state changes should be refreshed as early as possible. We propose an adaptive segmentation of the traffic flow based on discrete Fourier transform, which responses timely to traffic state changes without inducing large error. On the other hand, considering the limited power of wireless sensor networks, we propose a novel algorithm for in-network aggregation of the traffic flow time-series, which reduces the communication cost between the sensor nodes and base station significantly. The proposed algorithm scales well with the size of the sensor networks. Our methods are computationally efficient and suitable to be implemented on sensor nodes. The primary experiments on PeMS data demonstrate the effectiveness and energy efficiency of our approach.