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With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework. 

Spot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destination (e.g., an exit of the parking lot) of a single driver in a parking lot to generate the probability profile that contains the probability of each spot being occupied in a parking lot. To deal with conflicting estimation results generated from different drivers, due to the variance in different drivers’ parking behaviors, a novel aggregation approach SpotE-TD is proposed. The proposed aggregation method is based on truth discovery techniques and can handle the variety in Quality of Information of different vehicles. We evaluate our proposed method through a real-life deployment study. Results show that SpotE-TD can efficiently provide spot-level parking availability information with a 20% higher accuracy than the state-of-the-art. 

Accurately profiling potholes on road surfaces not only helps eliminate safety related concerns and improve commuting efficiency for drivers, but also reduces unnecessary maintenance cost for transportation agencies. In this paper, we propose a smartphone-based system that is capable of precisely estimating the length and depth of potholes, and introduce a holistic design on pothole data collection, profile aggregation and pothole warning and reporting. The proposed system relies on the built-in inertial sensors of vehicle-carried smartphones to estimate pothole profiles, and warn the driver about incoming potholes. Because of the difference in driving behaviors and vehicle suspension systems, a major challenge in building such system is how to aggregate conflicting sensory reports from multiple participating vehicles. To tackle this challenge, we propose a novel reliability-aware data aggregation algorithm called Reliability Adaptive Truth Discovery (RATD). It infers the reliability for each data source and aggregates pothole profiles in an unsupervised fashion. Our field test shows that the proposed system can effectively estimate pothole profiles, and the RATD algorithm significantly improves the profiling accuracy compared with popular data aggregation methods.