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Intelligent transportation systems aim to develop scientific and intelligent transportation, which will make urban traffic fluent and promise citizens easy and secure travel experience. Based on the collected sensing data, urban mobile sensing is proposed to extract real-time and accurate information in transportation system, i.e., digital map, traffic flow, traffic light state, accident. In this paper, we propose urban mobile sensing system based on probe vehicles, which are GPS equipped and generate GPS reports periodically. Moreover, we introduce two urban sensing algorithms, i.e., road recognition and traffic light state estimation.Related urban sensing researches mostly employ GPS trace data with high sampling rate and high accuracy. However, through analysis we find that GPS trace data generated by our probe vehicles are coarse-grained in terms of sampling rate, GPS position, heading direction, and so on. This introduces considerable challenges in designing urban sensing algorithms.Road recognition with probe vehicle aims to recognize city roads of various types. In our designed algorithm there are mainly three steps, i.e., pruning coarse-grained GPS trace data, clustering GPS data on the same road segment, and applying shape-aware B-spline fitting to generate roads. We conduct experiments and find that the coverage of arterial roads recognized reaches 93% with GPS data generated by 2,000 vehicles in 1.5 hours.In traffic light state estimation, we aim to detect continuous states of road traffic lights. Through observation and study, we find that there is strong relationship between traffic light state and vehicle mobility. Thus, we introduce the strategy containing snapshot state estimation and pervasive state estimation, and design heuristic algorithms to solve the problem. We conduct experimental study and find that the estimation error rate of 60% of urban traffic lights is as low as 19%.
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