Paths to ADA-Compliance: The Performance and Cost Efficiency of Measurement Technologies that Support ADA-Mandated, Self-Evaluations of Pedestrian Rights of Way
This study used terrestrial laser scanner and open source processing algorithms to develop an approach to automate the evaluation of transportation infrastructure in public rights of way. We estimated compliance or noncompliance of specific roadway features with the design standards adopted by the US Access Board and required under the Americans with Disabilities Act (ADA) such as minimum sidewalk width, maximum cross slopes and presence/absence of pedestrian connectivity automatically with extracting roadway features from point cloud data (PCD). We then compared the accuracy and cost efficiency of the automated with more conventional evaluative techniques to identify the potential risks, gains and the overall efficacy of these approaches. The collected raw data were processed through a sequential process including simplification, optimization, segmentation, and road feature categorization. Finally, the road elements were detected as the road feature objects. By developing a more thorough assessment process, this research provided communities with the information necessary to strategically plan transportation infrastructure improvements for people with limited mobility.
The Americans with Disabilities Act (ADA) advocated for the rights of individuals with disabilities, provided satisfying services for individuals with disabilities to live within the community. The ADA of 1990 is a civil rights statute that prohibits discrimination against people who have disabilities. As a necessary step to providing accessibility under the ADA, local public entities are required to perform inventories of their current facilities. Stakeholders are responsible for providing accessibility for individuals with disabilities, particularly in using route facilities like sidewalks and curbs. New constructions need to consider ADA, or ADA Application Guideline (ADAAG) and the Uniform Federal Accessibility Standards (UFAS) (as long as it is applicable) in the design and construction phases. For the existing facilities, however, ADA requirements’ checklist needs to be observed. ADAAG provides a list of the vital features to be checked in numerous steps --from planning to construction-- in the new and existing transportation and construction projects. However, roadway facilities’ conventional way of checking is usually time-consuming and costly; it is impossible to maintain the facilities in a convenient timeframe. The foremost important guideline on accessible design is that the UFAS must be observed in all steps of construction. While the incentives for completing an ADA transition plan are many, completion of such a plan and the inventory of existing physical barriers, in particular, can be a daunting task. Lack of budget and lack of staffing combined, make the inventory process extremely challenging to complete. As a result, many transition plans tend to stall in the inventory phase, either awaiting a full completion of self-evaluation activities or unable to take the data collected and develop priorities for remediation. Therefore, there is a need for developing cost efficiency and automated measurement system to assist ADA-compliance. The primary aim of this study is to develop a method and corresponding algorithms which use manipulated standard algorithm to automatically evaluate the compliance of transportation and construction infrastructure/facilities (i.e., ramps, curbs, and alternate routes) with ADA based on analyzing PCD.
This study is one of the few in using a comprehensively automated algorithm to assess the compliance of roadway features with ADA. The primary motivation of our research is to prepare a comprehensive evaluation method of public infrastructure facilities (such as curb ramps and sidewalks) with regard to ADA requirements, using PCD collected by laser scanning. ADA maintains design criteria for safe and accessible roadways to people with disabilities. Data acquisition and 3D modeling can facilitate the evaluation process of local facilities about ADA requirements. Point cloud data is a high-quality 3D modeling data collected by laser scanners. The following four steps describe the fully automated segmentation and tracking of transportation infrastructure:
- Data acquisition and preprocessing: Scanning the environment with the laser scanner. Collected PCD data from the laser scanner is preprocessed by importing it into PCD platforms (e.g., Microsoft Visual Studio, Cloud Compare, and Cyclone). PCD will be thus cleaned up and organized,
- Data processing: In this step, PCD data is filtered based on points coordinates information, outlier removal (K-nearest point), and normal estimation,
- Classification: A rich amount of the information (such as Points coordinates and colors) was captured in PCD by the laser scanner. After the preprocessing and processing steps, PCD still has random objects which need to be classified based on their features. In this step, we use plane normal vector information to extract different surfaces such as roads, sidewalks, and ramps,
- Feature Extraction: PCD is classified based on the plane vectors. Each surface has some unique information which is used to classify objects. After this classification, each object has associated geometric information such as slope, width, and length. We use this information to analyze ADA Requirements such as sidewalk width or ramp slope.
Various techniques were utilized: normal estimation, surface fitting, segmentation, and road feature categorization. An experimental test was conducted to evaluate our proposed method on Ross Street in Kalamazoo, Michigan. The results show that the mean absolute error of the selected curb ramps data is 0.22% and the sidewalks are 0.13% when comparing with other manual measurements. Our proposed method and algorithms are expected to help local authorities assess their infrastructure facilities and identify accessibility problems with regards to ADA requirements.
PCD before data processing
Sidewalk and ramp detection flow in automated PCD
(top view, front view, full top PCD view, surface top PCD view)
In addition, a 270 fields of view mobile platform were setup on the Segway. The Segway was equipped with LiDAR, three cameras and IMU to detect static object such as sidewalks and ramps as well as vehicles. Like laser scanners, the LIDAR produces 3D point cloud data. However, LiDAR data is suitable for mobile data collection thank to faster data collection speed than laser scanners. The LiDAR results shows X, Y, Z as well as distance that can be used in an automated object detection.
Camera fields of view
LiDAR fields of view
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