Real-time sidewalk slope calculation through integration of GPS trajectory and image data to assist people with disabilities in navigation

People with disabilities face many obstacles in everyday outdoor travels. One of the most notable obstacles is steep slope on sidewalk segments. Current navigation systems/services do not all support map databases with slope attributes and cannot calculate sidewalk slope in real time. In this paper, we present a technique for calculating slopes of sidewalk segments by image data and predict the most suitable route for each individual user through integration with GPS trajectory. In our technique we make use of GPS trajectory data, to identify the sidewalk segment on which the traveler will most probably pass, and images of the identified sidewalk segment. Through edge detection techniques we detect edges of objects, such as buildings, billboards, and walls, in the background. Slope of the segment is then calculated by comparing its line representation in the map with the detected edges. Our experiment result indicates effective calculation of sidewalk slopes

Automating Intersection Marking Data Collection and Condition Assessment at Scale With An Artificial Intelligence-Powered System

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance

How Do Neighbourhood Definitions Influence the Associations between Built Environment and Physical Activity?

Researchers investigating relationships between the neighbourhood environment and health first need to decide on the spatial extent of the neighbourhood they are interested in. This decision is an important and ongoing methodological challenge since different methods of defining and delineating neighbourhood boundaries can produce different results. This paper explores this issue in the context of a New Zealand-based study of the relationship between the built environment and multiple measures of physical activity. Geographic information systems were used to measure three built environment attributes—dwelling density, street connectivity, and neighbourhood destination accessibility—using seven different neighbourhood definitions (three administrative unit boundaries, and 500, 800, 1000- and 1500-m road network buffers). The associations between the three built environment measures and five measures of physical activity (mean accelerometer counts per hour, percentage time in moderate–vigorous physical activity, self-reported walking for transport, self-reported walking for recreation and self-reported walking for all purposes) were modelled for each neighbourhood definition. The combination of the choice of neighbourhood definition, built environment measure, and physical activity measure determined whether evidence of an association was detected or not. Results demonstrated that, while there was no single ideal neighbourhood definition, the built environment was most consistently associated with a range of physical activity measures when the 800-m and 1000-m road network buffers were used. For the street connectivity and destination accessibility measures, associations with physical activity were less likely to be detected at smaller scales (less than 800 m). In line with some previous research, this study demonstrated that the choice of neighbourhood definition can influence whether or not an association between the built environment and adults’ physical activity is detected or not. This study additionally highlighted the importance of the choice of built environment attribute and physical activity measures. While we identified the 800-m and 1000-m road network buffers as the neighbourhood definitions most consistently associated with a range of physical activity measures, it is important that researchers carefully consider the most appropriate type of neighbourhood definition and scale for the particular aim and participants, especially at smaller scales

Data Collection and Machine Learning Methods for Automated Pedestrian Facility Detection and Mensuration

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view imagery. We test data from these two viewpoints individually and with an ensemble method that we refer to as our “dual-perspective prediction model”. In order to obtain this data, we developed a data collection pipeline that combines crowdsourced pedestrian facility location data with aerial and street-view imagery from Bing Maps. In addition to the Convolutional Neural Network used to perform pedestrian facility detection using this data, we also trained a segmentation network to measure the length and width of crosswalks from aerial images. In our tests with a dual-perspective image dataset that was heavily occluded in the aerial view but relatively clear in the street view, our dual-perspective prediction model was able to increase prediction accuracy, recall, and precision by 49%, 383%, and 15%, respectively (compared to using a single perspective model based on only aerial view images). In our tests with satellite imagery provided by the Mississippi Department of Transportation, we were able to achieve accuracies as high as 99.23%, 91.26%, and 93.7% for aerial crosswalk detection, aerial sidewalk detection, and aerial crosswalk mensuration, respectively. The final system that we developed packages all of our machine learning models into an easy-to-use system that enables users to process large batches of imagery or examine individual images in a directory using a graphical interface. Our data collection and filtering guidelines can also be used to guide future research in this area by establishing standards for data quality and labelling

Evaluation Of Pedestrian Sidewalk Utilisation In Residential Areas Of Bloemfontein City, South Africa

The necessity of pedestrian-friendly environments is evident when looking at the multitude of benefits that it offers. These benefits include improved social integration, stimulating economic growth, and accessibility. The safety of pedestrians is not guaranteed, with a third of all road fatalities on South African roads being pedestrian fatalities. With the increased urbanisation among people from rural areas, there is a need for the development of safer non-motorised transport, especially because two-thirds of the population rely on walking as a mode of transport. In central areas of cities, effort has been done to enhance the walkability of the area, however, residential areas are often last on the list when it comes to the implementation of appropriate sidewalk infrastructure. It is observed that, although dangerous, pedestrians in residential areas increasingly use the roadway for walking. Sidewalks form an integral part of efforts to facilitate pedestrian access, which, in turn, support an effective and successful transportation network. This study examined the most essential attributes that contribute to the walkability of residential areas. More specifically, this study evaluated the factors contributing to the use or avoidance of sidewalks in residential areas. For this purpose, a case-study was performed in a residential area where the problem of pedestrians using the roadway was identified to be quite severe. To this end, the residential area of Universitas in Bloemfontein, Free State, South Africa was selected. A survey research methodology was followed, where data was collected through questionnaires and physical surveys. This study also employed a Conjoint Analysis technique, which is a multivariate technique used to understand an individual’s preference, in order to identify the levels of importance with regards to sidewalk attributes. The Conjoint Analysis was used to objectively identify and categorise sidewalk attributes (walkable width, number of obstacles, walking surface, and changes in elevation) that contribute to the use or avoidance of sidewalks. The findings revealed that attributes such as walkable width and the number of obstacles are significant parameters which influence the use of sidewalks in residential areas. Furthermore, the results revealed the relative importance of each evaluated attribute, which provided valuable insight into the prioritisation and possible budget allocation towards these attributes when it comes to the development of walkability. Finally, the Conjoint Analysis results were evaluated against pedestrians’ genuine willingness to make use of selected sidewalks within the study area. The evaluation revealed that the utility values produced by the Conjoint Analysis could be used to predict how likely it is that a pedestrian would use a specific sidewalk. Additionally, other significant concerns influencing neighbourhood walkability, such as personal safety and conflict with motorised traffic, were also identified by respondents. The results and findings of this study were used to recommend alternative planning and design guidelines that contribute to the development of walkability in residential areas. It is envisaged that, if the plausible recommended planning and design guidelines are implemented, the walkability of the study area will improve substantially