Current Status of Transportation Data Analytics and Pilot Case Studies Using Artificial Intelligence (AI)

Xie, Yuanchang; Liu, Ruifeng; Bhuyan, Zubin; Chen, Danjue; Ge, Tingjian; Shirazi, Mohammadali; Vergara, Eduardo; Aviles-Ordonez, Juan; University of Maine at Orono. Department of Civil and Environmental Engineering; University of Massachusetts at Lowell

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Current Status of Transportation Data Analytics and Pilot Case Studies Using Artificial Intelligence (AI)

2023-12-01
By Xie, Yuanchang ; Liu, Ruifeng ; Bhuyan, Zubin ; ...

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Details:

Creators:

Xie, Yuanchang ; Liu, Ruifeng ; Bhuyan, Zubin ; Chen, Danjue ; Ge, Tingjian ; ... More +

Xie, Yuanchang ; Liu, Ruifeng ; Bhuyan, Zubin ; Chen, Danjue ; Ge, Tingjian ; Shirazi, Mohammadali ; Vergara, Eduardo ; Aviles-Ordonez, Juan Less -
Corporate Creators:

University of Maine at Orono. Department of Civil and Environmental Engineering ; University of Massachusetts at Lowell

University of Maine at Orono. Department of Civil and Environmental Engineering ; University of Massachusetts at Lowell Less -
Corporate Contributors:

New England Transportation Consortium ; Maine. Dept. of Transportation

New England Transportation Consortium ; Maine. Dept. of Transportation Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Case Studies Computer Vision Data Analysis Deep Learning Microwave Devices Probe Data Radar Ramps Thermal Transportation Transportation Data Analytics Work Zone Safety
Publication/ Report Number:

NETCR124
Resource Type:

Tech Report
Geographical Coverage:

New England ; United States

New England ; United States Less -
Edition:

Final Report (05/2021-12/2023)
Corporate Publisher:

New England Transportation Consortium
Abstract:

Data is becoming increasingly important for state Departments of Transportation (DOTs) in making strategic and day to- day decisions. This research offers a comprehensive review of both existing and emerging data sources for Transportation Systems Management and Operations (TSMO). It discusses and summarizes the pros and cons of each data source. Additionally, interviews were conducted with federal and state DOT employees to gather their insights on future data sources and data needs, data integration and analysis, data archiving, sharing, security, and privacy, as well as stakeholders and workforce development. Based on the review and interview results, recommendations are provided regarding future data needs, emerging data sources, data processing and analytics, etc. The research also conducts three case studies to showcase the potential of using emerging data and AI technologies to address TSMO needs. These studies utilize advanced radar and thermal camera sensors, along with probe data, to model vehicle speed when approaching highway horizontal curves, examine how traffic signs may impact vehicle speed and lane-changing behaviors at highway work zones, and explore factors influencing speeding at highway horizontal curves and ramps. These studies demonstrate the benefits and necessity of combining data from different sources to meet TSMO needs and highlight the potential of AI.
Format:

PDF
Funding:

NETC 20-2
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:0ac5e12ededce854c883a0b6325325c0a6faf114b47e37d14cdd91bf33589b3c
Download URL:

https://rosap.ntl.bts.gov/view/dot/75610/dot_75610_DS1.pdf
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Current Status of Transportation Data Analytics and Pilot Case Studies Using Artificial Intelligence (AI)

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