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Automatic concrete spalling detection has become an important issue for metro tunnel examinations and maintenance. This paper focuses on concrete spalling detection research with surface roughness analysis based on point clouds produced by 3D mobile laser scanning (MLS) system. In the proposed method, at first, the points on ancillary facilities attached to tunnel surface are considered as outliers and removed via circular scan-line fitting and large residual error filtering. Then, a roughness descriptor for the metro tunnel surface is designed based on the triangulated grid derived from point clouds. The roughness descriptor is generally defined as the ratio of surface area to the projected area for a unit, which works well in identifying high rough areas on the tunnel surface, such as bolt holes, segment seams, and spalling patches. Finally, rough area classification based on Hough transformation and similarity analysis is performed on the identified areas to accurately label patches belonging to segment seams and bolt holes. After removing the patches of bolt holes and segment seams, the remaining patches are considered as belonging to concrete spalling. The experiment was conducted on a real tunnel interval in Shanghai. The result of concrete spalling detection revealed the validity and feasibility of the proposed method.

With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

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Massive Global Positioning System (GPS) trajectory datasets are being produced owing to the advances in mobile sensors, the Internet, and GPS devices. Accurately inferring travel modes from GPS trajectory data can be helpful in transportation planning and modeling, infrastructure design, etc. However, adverse factors such as data noise, differences in sampling rate, and inadequate features have a negative impact on the results of travel mode classification. In this paper, to address such issues, we first propose a preprocessing workflow, which includes data cleaning, segmentation, and resampling, to preprocess raw trajectories. Then, we add new features related to the road and bus stop information for travel mode classification using an XGBoost (eXtreme Gradient Boosting) classifier, along with various basic features of the trajectories. We conducted a set of experiments on the GeoLife dataset using a group of state-of-the-art methods. The results showed that the proposed methods can improve the classification accuracy by using all the classifiers we compared and the classification accuracy using the XGBoost classifier can reach a maximum of 90.41%.

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