Improving Pedestrian Infrastructure Inventory in ...

Improving Pedestrian Infrastructure Inventory in

Massachusetts using Mobile LiDAR

Chengbo Ai University of Massachusetts Amherst

Jack MoranMassachusetts Department of Transportation

2019 Transportation Innovation Conference

Outline

• Background

• Objectives

• Methodology

• Preliminary Results

• Ongoing Process

• Summary

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Sources: MassDOT - DRAFT Massachusetts Pedestrian Transportation Plan


Background

• Pedestrian infrastructure is a vital transportation facilitator for safe and uninterrupted trips

• MassDOT seeks to provide equitable accommodation for all modes of transportation, and has been consistently invested in pedestrian infrastructures

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Background - Existing Effort (1)

• A current sidewalk inventory dataset resides within the GIS-based Road Inventory File (RIF)

• The two major drawbacks of the current dataset are the time-intensive nature of updates and the lack of condition information

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Sources: MassDOT - DRAFT Massachusetts Pedestrian Transportation Plan

Current infrastructure data may not be of a qualityor level of detail to make informed investment decisions for pedestrian infrastructures


Background - Existing Effort (2)

• The research team has identified 17 pedestrian infrastructure inventories– Coverage

– Technology

– Inventoried Information

– Cost and Productivity

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Limited coverage

Segway, pavement survey and video log image, aerial photo, manual

Lack of condition information in detail

Automated vs. manual


Background - Opportunity

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Mobile LiDAR data has been widely available and in many critical transportation applications. However, extensive manual interventions are still needed in most of the applications, and very little effort was focused on sidewalk facility inventory


Objective

• The goal of this research project is to demonstrate the feasibility of mobile LiDAR as an efficient tool to support inventory update and condition assessment of pedestrian infrastructures managed by MassDOT

• The objectives of this research project are to collect and process mobile LiDAR data, to verify and update the existing MassDOT’s sidewalk inventory, and to incorporate condition information into the inventory geodatabase

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• Verify sidewalk location

• Update 10-ft. sidewalk width

• Update 10-ft. cross slope

• Update visual condition

• Verify curb ramp locations

• Update curb ramp approach slope

State Route 9 - Critical Corridor


MethodologyOverview

• LiDAR point cloud filtering• Road cross section segmentation• Sidewalk candidates verification

Sidewalk Extraction

Curb Ramp Extraction• DPM-based curb ramp detection• Curb ramp candidates verification

Key Feature Measurement• Sidewalk width measurement• Sidewalk cross slope measurement• Sidewalk grade measurement• Curb ramps slope measurement

Sidewalk Polyline

Curb Ramp Point

Sidewalk Inventory• Sidewalk basemap comparison• Sidewalk key feature integration • Sidewalk basemap intersection update

using curb ramp slope constraint

LiDAR Point Cloud / GPS Trajectory

LiDAR Point Cloud / Video Log Image

Data Acquisition

Point Cloud Segmentation

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MethodologyData Acquisition

• Mobile LiDAR system at UMass Amherst– RIEGL VMZ-2000

• Data acquisition on State Route 9 covering more than 270 miles– Approximately 8 billion points

– Different sensor configurations

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Methodology Point Cloud Segmentation

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₋ Qi, C, Yi, L., Su, H., and Guibas, L. J. (2017). “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,” in

Advances in Neural Information Processing Systems, ed. I. Guyon et al., Curran Associates, Inc., 5099-5108.

⁻ Hou, Q., Ai, C., and Shah, Y. (2019). “Automated Roadway Scene Segmentation Incorporating LiDAR Reflectance in PointNet++,” in

submission to ASCE Journal of Computing in Civil Engineering.

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Methodology Sidewalk Extraction

• Cross section splitting– To partition the LiDAR points based on elevation, slope and homogeneity

using 1-D Canny edge detection

• Profile reconnection– To link the split profiles into a continuous boundary

16Ai, C., and Tsai, Y. (2016). “Automated Sidewalk Assessment Method for Americans with Disabilities Act Compliance Using Three-Dimensional Mobile Lidar.”

Transportation Research Record: Journal of the Transportation Research Board, 2542(1), 25-32

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• Sidewalk width, cross slope and grade measured at 10 ft. interval


Methodology Sidewalk Feature Measurement

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Methodology (Ongoing)Curb Ramp Identification and Measurement

• Deformable Part Model -based curb ramp detection (Hara et al, 2014)– Root filter - to describe the general shapes of curb ramps

– Part filter - to describe the individual components of curb ramps

– Non-maximum suppression - to eliminate overlapped detection results

• Interactive curb ramp detection validation tool (link)– To remove the false detections from the DPM

– To digitize the boundary of detected curb ramps

– To retrieve the corresponding LiDAR point cloud

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http://umass-its.com/LiDAR/T2_002.html


Preliminary Results

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(link)

http://umass-its.com/Sidewalk


Summary

✓ To collect and process mobile LiDAR data✓ Collected more than 8 billion LiDAR points (~250GB)

✓ To verify and update the existing MassDOT’s sidewalk inventory✓ Identified more than 85 miles of sidewalks locations and conducted the corresponding

measurements

✓ The processing time is approximately 10-15 min/mile

➢ To incorporate condition information into the inventory geodatabase➢ The extraction and measurement of curb ramps are undergoing

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• Mobile LiDAR is an effective and efficient tool for network-level pedestrian infrastructure inventory

• A complete point cloud processing pipeline (tools, algorithms, interfaces and procedures) has been developed for pedestrian infrastructure inventory and many other transportation assets

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• LiDAR point cloud filtering• Road cross section segmentation• Sidewalk candidates verification

Sidewalk Extraction

Curb Ramp Extraction• DPM-based curb ramp detection• Curb ramp candidates verification

Key Feature Measurement• Sidewalk width measurement• Sidewalk cross slope measurement• Sidewalk grade measurement• Curb ramps slope measurement

Sidewalk Polyline

Curb Ramp Point

Sidewalk Inventory• Sidewalk basemap comparison• Sidewalk key feature integration • Sidewalk basemap intersection update

using curb ramp slope constraint

LiDAR Point Cloud / GPS Trajectory

LiDAR Point Cloud / Video Log Image

Data Acquisition

Point Cloud Segmentation


Next StepA Larger Sidewalk Network

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▪ To apply the developed pipeline in this study to the existing LiDAR point cloud covering more than 6,000-mile highway

▪ To take advantage of the mobile LiDAR sensor at UMass Amherst to collect and process the updated data, and identify changes


Next StepOther Critical Assets

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http://aichengbo.com/research/




Next StepOther Applications

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• Applications beyond sidewalk infrastructure management– Network accessibility analysis

– Metabolic energy cost evaluation

• 101.9 ml/kg/min – Solid line route

• 21.0 ml/kg/min – Dash line route


Acknowledgement

• Jack Moran, Jeremy Mei and Luka Chong from the Highway Division

• Lily Oliver, Nicholas Zavolas, Quinn Molloy and Jose Simo from the Office of Transportation Planning

• Michael Knodler, Matt Mann, Kimberley Foster from the UMass Transportation Center

• Qing Hou, Yash Shah and Asako Tekeuchi from UMass Amherst

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Questions?

Chengbo AiOffice: 142A Marston Hall, UMass Amherst

Phone: 413.577.1273 (office)

912.660.4533 (cell)

website: https://cee.umass.edu/faculty/chengbo-ai


http://umass-its.com

Email: [email protected]

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https://cee.umass.edu/faculty/chengbo-ai


http://umass-its.com/

mailto:[email protected]

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