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			"accessLevel": "public",
			"bureauCode": [
				"021:04"
			],
			"contactPoint": {
				"fn": "National Transportation Library Data Curator",
				"hasEmail": "mailto:ntldatacurator@dot.gov"
			},
			"dataQuality": true,
			"description": "This project aims to understand how we can link and harness new data sources along with machine-learning based optimization techniques to improve driver-pedestrian interactions at intersections. With a strong emphasis on safe mobility, this study will address this critical issue in a real-life context with data links from and actuation feedback to 8 monitored intersections in the Chattanooga Shallowford road corridor between the Lee Highway and Gunbarrel Road intersections. The study reflects strong collaborations between UTK, UNC, ORNL, and the City of Chattanooga, TN. This project will contribute by incorporating safety into optimization of traffic signals, collect and link data from traffic signals (cameras) and analyze behaviors of pedestrians and drivers at intersections, and provides an opportunity for students to engage in multiple aspects of safety analysis including data linking and new AI techniques. The impact of this project can be substantial in terms of enhancing safety and efficiency of intersections. Working closely with Chattanooga and ORNL, the project team will impact the safety as well as performance of traffic signals in a testbed corridor through: 1) data linking, 2) pedestrian detection and 3) optimization. (2023-09-30)",
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					"@type": "dcat:Distribution",
					"accessURL": "https://doi.org/10.15139/S3/C6DHKY",
					"mediaType": "application/pdf",
					"title": "CSCRS_FinalReport_R43.pdf"
				},
				{
					"@type": "dcat:Distribution",
					"accessURL": "https://doi.org/10.15139/S3/C6DHKY",
					"mediaType": "application/pdf",
					"title": "CSCRS_SlideDeck_R43 Final.pdf"
				},
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					"@type": "dcat:Distribution",
					"accessURL": "https://doi.org/10.15139/S3/C6DHKY",
					"mediaType": "application/vnd.ms-powerpoint",
					"title": "CSCRS_SlideDeck_R43 Final.pptx"
				},
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					"accessURL": "https://doi.org/10.15139/S3/C6DHKY",
					"mediaType": "application/vnd.ms-excel",
					"title": "FARS2020-intersection-ped crashes.xlsx"
				}
			],
			"format": "ZIP",
			"identifier": "https://doi.org/10.15139/S3/C6DHKY",
			"issued": "2024-09-12",
			"keyword": [
				"Intersections",
				"Crashes",
				"Drivers",
				"Pedestrians",
				"Artificial intelligence",
				"Nighttime crashes",
				"Driving behavior",
				"Traffic signals",
				"Fatalities",
				"Machine learning",
				"Traffic signal control systems",
				"Behavior",
				"Optimization",
				"Pedestrian detectors",
				"Pedestrian vehicle interface",
				"Traffic safety",
				"Human factors",
				"Highways"
			],
			"landingPage": "https://doi.org/10.15139/S3/C6DHKY",
			"language": [
				"en-US"
			],
			"license": "https://creativecommons.org/publicdomain/zero/1.0/",
			"modified": "2024-09-17",
			"policyStatement": "This dataset was made public under the requirements enumerated in the U.S. Department of Transportation's 'Plan to Increase Public Access to the Results of Federally-Funded Scientific Research' Version 1.1 << https://doi.org/10.21949/1520559 >> and guidelines suggested by the DOT Public Access website << https://doi.org/10.21949/1503647  >>, in effect and current as of December 03, 2020.",
			"policyURL": "https://doi.org/10.21949/1520559 , https://doi.org/10.21949/1503647",
			"programCode": [
				"021:053"
			],
			"publisher": {
				"@type": "org:Organization",
				"name": "Collaborative Sciences Center for Road Safety (CRCRS)",
				"subOrganizationOf": {
					"@type": "org:Organization",
					"name": "University of North Carolina at Chapel Hill",
					"subOrganizationOf": {
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						"name": "University Transportation Centers (UTC) Program",
						"subOrganizationOf": {
							"@type": "org:Organization",
							"name": "Office of the Assistant Secretary for Research and Technology",
							"subOrganizationOf": {
								"@type": "org:Organization",
								"name": "Department of Transportation",
								"subOrganizationOf": {
									"@type": "org:Organization",
									"name": "U.S. Government"
								}
							}
						}
					}
				}
			},
			"references": [
				"https://rosap.ntl.bts.gov/view/dot/77356",
				"https://rosap.ntl.bts.gov/view/dot/77358"
			],
			"spatial": "United States",
			"title": "Applying AI to Data Sources to Improve Driver-Pedestrian Interactions at Intersections [supporting dataset]",
			"webService": null
		}
	]
}