Detecting road intersections from GPS traces using longest common subsequence algorithm

Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users’ turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score

Road intersection detection through finding common sub-tracks between pairwise GNSS traces

This paper proposes a novel approach to detect road intersections from GNSS traces. Different from the existing methods of detecting intersections directly from the road users’ turning behaviors, the proposed method detects intersections indirectly from common sub-tracks shared by different traces. We first compute the local distance matrix for each pair of traces. Second, we apply image processing techniques to find all “sub-paths” in the matrix, which represents good alignment between local common sub-tracks. Lastly, we identify the intersections from the endpoints of the common sub-tracks through Kernel Density Estimation (KDE). Experimental results show that the proposed method outperforms the traditional turning point-based methods in terms of the F-score, and our previous connecting point-based method in terms of computational efficiency

Automatic extraction of relevant road infrastructure using connected vehicle data and deep learning model

This thesis presents a novel approach for extracting road infrastructure information from connected vehicle trajectory data, employing geohashing and image classification techniques. The methodology involves segmenting trajectories using geohash boxes and generating image representations of road segments. These images are then processed using YOLOv5 to accurately classify straight roads and intersections. Experimental results demonstrate a high level of accuracy, with an overall classification accuracy of 95%. Straight roads achieve a 97% F1 score, while intersections achieve a F1 score of 90%. These results validate the effectiveness of the proposed approach in accurately identifying and classifying road segments. The integration of geohashing and image classification techniques offers numerous benefits for road network analysis, traffic management, and autonomous vehicle navigation systems. By extracting road infrastructure information from connected vehicle data, a comprehensive understanding of road networks is achieved, facilitating optimization of traffic flow and infrastructure maintenance. The scalability and adaptability of the approach make it well-suited for large-scale datasets and urban areas. The combination of geohashing and image classification provides a robust framework for extracting valuable insights from connected vehicle data, thereby contributing to the advancement of smart transportation systems. The results emphasize the potential of the proposed approach in enhancing road network analysis, traffic management, and autonomous vehicle navigation, thereby expanding the knowledge in this field and inspiring further research

Mobile localization : approach and applications

textLocalization is critical to a number of wireless network applications. In many situations GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., understanding shoppers' behavior within retail spaces by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses. More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we demonstrate the accuracy and robustness of our new schemes. Second, we focus on localizing a single mobile node, which may not be connected with multiple nodes (e.g., without network connectivity or only connected with an access point). We propose trajectory-based localization using Wi-Fi or magnetic field measurements. We show that these measurements have the potential to uniquely identify a trajectory. We then develop a novel approach that leverages multi-level wavelet coefficients to first identify the trajectory and then localize to a point on the trajectory. We show that this approach is highly accurate and power efficient using indoor and outdoor experiments. Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.Computer Science

Privacy Leakage through Sensory Data on Smart Devices

Mobile devices are becoming more and more indispensable in people’s daily life. They bring variety of conveniences. However, many privacy issues also arise along with the ubiquitous usage of smart devices. Nowadays, people rely on smart devices for business and work, thus much sensitive information is released. Although smart device manufactures spend much effort to provide system level strategies for privacy preservation, lots of studies have shown that these strategies are far from perfect. In this dissertation, many privacy risks are explored. Smart devices are becoming more and more powerful as more and more sensors are embedded into smart devices. In this thesis, the relationship between sensory data and a user’s location information is analyzed first. A novel inference model and a corresponding algorithm are proposed to infer a user’s location information solely based on sensory data. The proposed approach is validated towards real-world sensory data. Another privacy issue investigated in this thesis is the inference of user behaviors based on sensory data. From extensive experiment results, it is observed that there is a strong correlation between sensory data and the tap position on a smart device’s screen. A sensory data collection app is developed to collect sensory data from more than 100 volunteers. A conventional neural network based method is proposed to infer a user’s input on a smart phone. The proposed inference model and algorithm are compared with several previous methods through extensive experiments. The results show that our method has much better accuracy. Furthermore, based on this inference model, several possible ways to steal private information are illustrated