A Post-Scan Point Cloud Colorization Method for Cultural Heritage Documentation

The 3D laser scanning technique is important for cultural heritage documentation. The laser itself normally does not carry any color information, so it usually requires an embedded camera system to colorize the point cloud. However, when the embedded camera system fails to perform properly under some external interferences, a post-scan colorization method is always desired to improve the point cloud visuality. This paper presents a simple but efficient point cloud colorization method based on a point-to-pixel orthogonal projection under an assumption that the orthogonal and perspective projections can produce similar effects for a planar feature as long as the target-to-camera distance is relatively short (within several meters). This assumption was verified by a simulation experiment, and the results show that only approximately 5% of colorization error was found at a target-to-camera distance of 3 m. The method was further verified with two real datasets collected for the cultural heritage documentation. The results showed that the visuality of the point clouds for two giant historical buildings had been greatly improved after applying the proposed method

Investigating impacts of environmental factors on the cycling behavior of bicycle-sharing users

As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the cycling behavior of users of bicycle-sharing systems, as users of bicycle-sharing systems tend to be different from regular cyclists. Although earlier studies have examined effects of safety and convenience on the cycling behavior of regular riders, they rarely explored effects of safety and convenience on the cycling behavior of BSS riders. Therefore, in this study, we aimed to investigate how road safety, convenience, and public safety affect the cycling behavior of BSS riders by controlling for other environmental factors. Specifically, in this study, we investigated the impacts of environmental characteristics, including population density, employment density, land use mix, accessibility to point-of-interests (schools, shops, parks and gyms), road infrastructure, public transit accessibility, road safety, convenience, and public safety on the usage of BSS. Additionally, for a more accurate measure of public transit accessibility, road safety, convenience, and public safety, we used spatiotemporally varying measurements instead of spatially varying measurements, which have been widely used in earlier studies. We conducted an empirical investigation in Chicago with cycling data from a BSS called Divvy. In this study, we particularly attempted to answer the following questions: (1) how traffic accidents and congestion influence the usage of BSS; (2) how violent crime influences the usage of BSS; and (3) how public transit accessibility influences the usage of BSS. Moreover, we tried to offer implications for policies aiming to increase the usage of BSS or for the site selection of new docking stations. Empirical results demonstrate that density of bicycle lanes, public transit accessibility, and public safety influence the usage of BSS, which provides answers for our research questions. Empirical results also suggest policy implications that improving bicycle facilities and reducing the rate of violent crime rates tend to increase the usage of BSS. Moreover, some environmental factors could be considered in selecting a site for a new docking station

Are crowdsourced datasets suitable for specialized routing services? Case study of Openstreetmap for routing of people with limited mobility

Nowadays, Volunteered Geographic Information (VGI) has increasingly gained attractiveness to both amateur users and professionals. Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most famous crowdsourced mapping platforms, we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation of people with limited mobility. We assess the completeness of OSM data regarding sidewalk information. Relevant attributes for sidewalk information such as sidewalk width, incline, surface texture, etc. are considered, and through both extrinsic and intrinsic quality analysis methods, we present the results of fitness for use of OSM data for routing services of disabled persons. Based on empirical results, it is concluded that OSM data of relatively large spatial extents inside all studied cities could be an acceptable region of interest to test and evaluate wheelchair routing and navigation services, as long as other data quality parameters such as positional accuracy and logical consistency are checked and proved to be acceptable. We present an extended version of OSMatrix web service and explore how it is employed to perform spatial and temporal analysis of sidewalk data completeness in OSM. The tool is beneficial for piloting activities, whereas the pilot site planners can query OpenStreetMap and visualize the degree of sidewalk data availability in a certain region of interest. This would allow identifying the areas that data are mostly missing and plan for data collection events. Furthermore, empirical results of data completeness for several OSM data indicators and their potential relation to sidewalk data completeness are presented and discussed. Finally, the article ends with an outlook for future research study in this area

Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data

With the development of information and communications technology, user-generated content and crowdsourced data are playing a large role in studies of transport and public health. Recently, Strava, a popular website and mobile app dedicated to tracking athletic activity (cycling and running), began offering a data service called Strava Metro, designed to help transportation researchers and urban planners to improve infrastructure for cyclists and pedestrians. Strava Metro data has the potential to promote studies of cycling and health by indicating where commuting and non-commuting cycling activities are at a large spatial scale (street level and intersection level). The assessment of spatially varying effects of air pollution during active travel (cycling or walking) might benefit from Strava Metro data, as a variation in air pollution levels within a city would be expected. In this paper, to explore the potential of Strava Metro data in research of active travel and health, we investigate spatial patterns of non-commuting cycling activities and associations between cycling purpose (commuting and non-commuting) and air pollution exposure at a large scale. Additionally, we attempt to estimate the number of non-commuting cycling trips according to environmental characteristics that may help identify cycling behavior. Researchers who are undertaking studies relating to cycling purpose could benefit from this approach in their use of cycling trip data sets that lack trip purpose. We use the Strava Metro Nodes data from Glasgow, United Kingdom in an empirical study. Empirical results reveal some findings that (1) when compared with commuting cycling activities, non-commuting cycling activities are more likely to be located in outskirts of the city; (2) spatially speaking, cyclists riding for recreation and other purposes are more likely to be exposed to relatively low levels of air pollution than cyclists riding for commuting; and (3) the method for estimating of the number of non-commuting cycling activities works well in this study. The results highlight: (1) a need for policymakers to consider how to improve cycling infrastructure and road safety in outskirts of cities; and (2) a possible way of estimating the number of non-commuting cycling activities when the trip purpose of cycling data is unknown

Investigation of Travel and Activity Patterns Using Location-based Social Network Data : A Case Study of Active Mobile Social Media Users

Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel behavior and activity analysis. Illustrating this situation, recently, a number of studies attempted to use LBSN data (e.g., Foursquare check-ins) to investigate patterns of human travel and activity. Of particular note is that compared to other individual-level characteristics of users, such as age, profession, education, income and so forth, gender is relatively highly available in the profiles of Foursquare users. Moreover, considering gender differences in travel and activity analysis is a popular research topic and is helpful in better understanding the changes in women’s roles in family, labor force participation, society and so forth. Therefore, this paper empirically investigates how gender influences the travel and activity patterns of active local Foursquare users in New York City. Empirical investigations of gender differences in travel and activity patterns are conducted at both the individual and aggregate level. The empirical results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. Finally, the results of the empirical study and the extent to which LBSN data can be exploited to produce travel diary data are discussed

Examining Associations of Environmental Characteristics with Recreational Cycling Behaviour by Street-Level Strava Data

Policymakers pay much attention to effectively increasing frequency of people’s cycling in the context of developing sustainable and green cities. Investigating associations of environmental characteristics and cycling behaviour could offer implications for changing urban infrastructure aiming at encouraging active travel. However, earlier examinations of associations between environmental characteristics and active travel behaviour are limited by low spatial granularity and coverage of traditional data. Crowdsourced geographic information offers an opportunity to determine the fine-grained travel patterns of people. Particularly, Strava Metro data offer a good opportunity for studies of recreational cycling behaviour as they can offer hourly, daily or annual cycling volumes with different purposes (commuting or recreational) in each street across a city. Therefore, in this study, we utilised Strava Metro data for investigating associations between environmental characteristics and recreational cycling behaviour at a large spatial scale (street level). In this study, we took account of population density, employment density, road length, road connectivity, proximity to public transit services, land use mix, proximity to green space, volume of motor vehicles and traffic accidents in an empirical investigation over Glasgow. Empirical results reveal that Strava cyclists are more likely to cycle for recreation on streets with short length, large connectivity or low volume of motor vehicles or on streets surrounded by residential land