Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.

Due to their wide coverage, low acquisition cost and large data quantity, the mobile phone signaling data are suitable for fine-grained and large-scale estimation of traffic conditions. However, the relatively high level of data noise makes it difficult for the estimation to achieve sufficient accuracy. According to the characteristics of mobile phone data noise, this paper proposed an improved density peak clustering algorithm (DPCA) to filter data noise. In addition, on the basis of the long short-term memory model (LSTM), a traffic state estimation model based on mobile phone feature data was established with the use of denoising data to realize the estimation of the expressway traffic state with high precision, fine granules, and wide coverage. The Shanghai–Nanjing Expressway was used as a case study area for method and model verification, the results of which showed that the denoising method proposed in this paper can effectively filter data noise, reduce the impact of extreme noise data, significantly improve the estimation accuracy of the traffic state, and reflect the actual traffic situation in a fairly satisfactory manner.