Floating Car Data (FCD) has been analyzed for various purposes in past years. However, limited research about the behaviors of taking long-distance taxi rides has been made available. In this paper, we used data from over 12,000 taxis during a six-month period in Shanghai to analyze the spatiotemporal patterns of long-distance taxi trips. We investigated these spatiotemporal patterns by comparing them with metro usage in Shanghai, in order to determine the extent and how the suburban trains divert the passenger flow from taxis. The results identified 12 pick-up and six drop-off hotspots in Shanghai. Overall, the pick-up locations were relatively more concentrated than the drop-off locations. Temporal patterns were also revealed. Passengers on long-distance taxi rides were observed to avoid the rush hours on the street as their first priority and tried to avoid the inconvenience of interchanges on the metro lines as their second priority.

Knowing how people interact with urban environments is fundamental for a variety of fields, ranging from transportation to social science. Despite the fact that human mobility patterns have been a major topic of study in recent years, a challenge to understand large-scale human behavior when a certain event occurs remains due to a lack of either relevant data or suitable approaches. Psychological crowd refers to a group of people who are usually located at different places and show different behaviors, but who are very sensitively driven to take the same act (gather together) by a certain event, which has been theoretically studied by social psychologists since the 19th century. This study aims to propose a computational approach using a machine learning method to model psychological crowds, contributing to the better understanding of human activity patterns under events. Psychological features and mental unity of the crowd are computed to detect the involved individuals. A national event happening across the USA in April, 2015 is analyzed using geotagged tweets as a case study to test our approach. The result shows that 81% of individuals in the crowd can be successfully detected. Through investigating the geospatial pattern of the involved users, not only can the event related users be identified but also those unobserved users before the event can be uncovered. The proposed approach can effectively represent the psychological feature and measure the mental unity of the psychological crowd, which sheds light on the study of large-scale psychological crowd and provides an innovative way to understanding human behavior under events.

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.

The authors wish to make a correction to their paper