The surface roughness of roads is an essential road characteristic. Due to the employed carrying platforms (which are often cars), existing measuring methods can only be used for motorable roads. Until now, there has been no effective method for measuring the surface roughness of un-motorable roads, such as pedestrian and bicycle lanes. This hinders many applications related to pedestrians, cyclists and wheelchair users. In recognizing these research gaps, this paper proposes a method for measuring the surface roughness of pedestrian and bicycle lanes based on Global Positioning System (GPS) and accelerometer sensors on bicycle-mounted smartphones. We focus on the International Roughness Index (IRI), as it is the most widely used index for measuring road surface roughness. Specifically, we analyzed a computing model of road surface roughness, derived its parameters with GPS and accelerometers on bicycle-mounted smartphones, and proposed an algorithm to recognize potholes/humps on roads. As a proof of concept, we implemented the proposed method in a mobile application. Three experiments were designed to evaluate the proposed method. The results of the experiments show that the IRI values measured by the proposed method were strongly and positively correlated with those measured by professional instruments. Meanwhile, the proposed algorithm was able to recognize the potholes/humps that the bicycle passed. The proposed method is useful for measuring the surface roughness of roads that are not accessible for professional instruments, such as pedestrian and cycle lanes. This work enables us to further study the feasibility of crowdsourcing road surface roughness with bicycle-mounted smartphones.

With the acceleration of the urbanization process, the problems caused by extreme weather such as heavy rainstorm events have become more and more serious. During such events, the road and its auxiliary facilities may be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a space–time dimension, and as rainfall data are obtained through discrete monitoring stations, the acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate users in understanding the impact of urban waterlogging on traffic, the visualization of waterlogging information needs to be displayed under different spatial and temporal granularity. Therefore, the appropriateness of the visualization granularity directly affects the user’s cognition of the road waterlogging map. To solve this problem, this paper established a spatial granularity and temporal granularity computing quantitative model for spatio-temporal visualization of road waterlogging and the evaluation method of the model was based on the cognition experiment. The minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading method and two color schemes for road waterlogging visualization based on the user’s cognition. To verify the feasibility of the method, we developed a prototype system and implemented a dynamic spatio-temporal visualization of the waterlogging process in the main urban area of Nanjing, China. The user cognition experiment showed that most participants thought that the segmentation of road was helpful to the local visual expression of waterlogging, and the color schemes of waterlogging depth were also helpful to display the road waterlogging information more effectively.

ABSTRACT:At the present, OpenStreetMap (OSM) is considered as one of the most successful and popular VGI (Volunteered Geographic Information) projects. It provides a platform that all the registered members coming from different areas in the world can cooperate with each other to mapping our world. Besides, OSM attracts more and more people, companies and even the governmental agencies because of its free and open source. Studies have proofed that both the quantity and quality of OSM data in several western countries, i.e. Germany, France and the Netherland are even better than the authority data. In recent years, the quantity of the OSM data and the number of contributors in China increased rapidly, but the overall distribution of OSM data is very fit with the distribution of population and the economic development and it displays an uneven development in different provinces and cities in China. Besides, the state of the OSM in China is just similar to that in Germany in 2010 in terms of data quantity and quality, although China is about 25 times to Germany regarding land area and the smartphone penetration in China and Germany does not have a large distance (51.7% to 68.8%). Why is the development of OSM in China so poor and backward when comparing that with western countries, although the environment in hardware and software in China are similar to the western countries? Attempting to answer this question, this paper presents a user survey in China. Mainly, knowledges and experiences about OSM and OSM contribution were asked in the user survey. The user survey was conducted both by paper and pen and by using online platform. Totally, over 1200 participants with the age range from 15 to 80 and a huge diverse of background took part in the user survey. In this paper, we would like to describe the design of the questions for the user survey at first. Then we will demonstrate the results of the user survey, as well as the analysis and conclusions, which can be drawn from the user survey.

A scene understanding method of combining semantic segmentation and ontology description is proposed in this paper. The method not only obtains the object of each part of the scene but also gets the relationship between the parts of the scene. Firstly, to realize the semantic segmentation of indoor scenes for 3D point clouds, PointNet is used in this paper as a tool for processing point clouds, and the S3DIS as PointNet datasets for training and testing. Secondly, in order to combine semantic segmentation and ontology, an ontology to describe the relationship between objects and scenes of the semantic segmentation result is used in this paper. Finally, we build indoor scenes ontology based on IndoorGML and use Protégé to display the spatial position ontology of the indoor scene.