Factors Contributing to Operating Speeds on Arterial Roads by Context ClassificationsSource: Journal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 147 ):;issue: 008::page 04021040-1DOI: 10.1061/JTEPBS.0000548Publisher: ASCE
Abstract: The aim of this study was to evaluate and identify the factors influencing operating speed considering context classification. The study focused on three context classifications: C3R–Suburban Residential, C3C–Suburban Commercial, and C4–Urban General. Tobit models were proposed and developed using big data, including traffic and roadway characteristics, land-use attributes, and sociodemographic information. Three years of INRIX speed data were obtained to calculate the 85th-percentile speed. The study proposed an approach to adjust the 85th-percentile speed from INRIX data, given that traffic flow on arterials could be disrupted by signalized intersections. Afterward, empirical analysis was conducted by developing three Tobit models: Generic, C3C/C3R, and C4 models using the adjusted 85th-percentile speed. For the three developed models, several variables [e.g., inside shoulder width, speed limit, and number of signalized intersections per mile (1.609 km)] were found to have significant influence on the 85th-percentile speed. The analysis also revealed potential speed management countermeasures that have significant impact on the 85th-percentile speed which, when implemented, could reduce speed-related crashes and enhance the safety of vulnerable road users.
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contributor author | Nada Mahmoud | |
contributor author | Mohamed Abdel-Aty | |
contributor author | Qing Cai | |
date accessioned | 2022-02-01T00:04:14Z | |
date available | 2022-02-01T00:04:14Z | |
date issued | 8/1/2021 | |
identifier other | JTEPBS.0000548.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4270860 | |
description abstract | The aim of this study was to evaluate and identify the factors influencing operating speed considering context classification. The study focused on three context classifications: C3R–Suburban Residential, C3C–Suburban Commercial, and C4–Urban General. Tobit models were proposed and developed using big data, including traffic and roadway characteristics, land-use attributes, and sociodemographic information. Three years of INRIX speed data were obtained to calculate the 85th-percentile speed. The study proposed an approach to adjust the 85th-percentile speed from INRIX data, given that traffic flow on arterials could be disrupted by signalized intersections. Afterward, empirical analysis was conducted by developing three Tobit models: Generic, C3C/C3R, and C4 models using the adjusted 85th-percentile speed. For the three developed models, several variables [e.g., inside shoulder width, speed limit, and number of signalized intersections per mile (1.609 km)] were found to have significant influence on the 85th-percentile speed. The analysis also revealed potential speed management countermeasures that have significant impact on the 85th-percentile speed which, when implemented, could reduce speed-related crashes and enhance the safety of vulnerable road users. | |
publisher | ASCE | |
title | Factors Contributing to Operating Speeds on Arterial Roads by Context Classifications | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 8 | |
journal title | Journal of Transportation Engineering, Part A: Systems | |
identifier doi | 10.1061/JTEPBS.0000548 | |
journal fristpage | 04021040-1 | |
journal lastpage | 04021040-11 | |
page | 11 | |
tree | Journal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 147 ):;issue: 008 | |
contenttype | Fulltext |