Factors Contributing to Operating Speeds on Arterial Roads by Context Classifications

Mahmoud, Nada; Abdel-Aty, Mohamed; Cai, Qing

doi:10.1061/jtepbs.0000548

Cited by 4 publications

(6 citation statements)

References 35 publications

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“…In contrast, implementing curb and gutter outside shoulder type was found to be associated with higher odds of the lower classes of the response variable. The same conclusions were reached in previous studies ( 14, 42 ). Further, an expected significant positive increase in the odds that the operating speed exceeds the target speed was encountered when having better pavement conditions or increasing the average block length.…”

Section: Resultssupporting

confidence: 91%

“…It could be explained as increasing the number of vehicles, buses, or trucks makes the roadway segment more congested, therefore, the operating speed decreases. Similar results that concluded the negative influence of traffic exposure on operating speed were found in previous research ( 1, 14, 39 ).…”

Section: Resultssupporting

confidence: 91%

“…Aiming to improve geometric design, safety, and traffic operations of roadway segments, previous research focused on analyzing various traffic parameters such as: traffic volume ( 9, 10 ), travel time ( 11, 12 ), and vehicle speed ( 13, 14 ). Particularly, speed behavior was considered a critical factor that significantly affects the severity of crashes ( 15 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results concluded that several traffic and geometric characteristics significantly affect the modeled 85th percentile speed, including inside shoulder width, speed limit, and the number of signalized intersections per mile (1.6 km). Further, the study suggested some potential speed management countermeasures to reduce speed-related crashes when implemented, such as lane narrowing, short blocks, and curb extension ( 14 ). Generally, previous studies concluded that road geometric factors (i.e., shoulder type, section grade, segment length, lane width, and curve length), traffic factors (i.e., annual average daily traffic [AADT], posted speed limit, intersection density), and land use characteristics (i.e., proportion of industrial, residential, and commercial land uses) are the most common significant variables associated with higher speed behavior of drivers ( 26–29 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The calculated 85th percentile speed was utilized to represent the drivers’ operating speed along roadway segments ( 2, 16, 22 ). It was adjusted to avoid traffic flow disruption caused by the existence of intersections along the segments, following the same approach as in the latest research by Mahmoud et al ( 14 ). The approach used the calculated marginal effects of the independent variable “number of intersections” from their developed Tobit model to modify the 85th percentile speed.…”

Section: Data Collection and Processingmentioning

confidence: 99%

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Analyzing the Difference Between Operating Speed and Target Speed Using Mixed-Effect Ordered Logit Model

Mahmoud

Abdel-Aty

Cai

et al. 2022

Transportation Research Record

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Desired operating speed (target speed) plays an important role in enhancing traffic operations and providing safe mobility to road users. Understanding the difference between vehicles’ operating speed and target speed on arterial roads is important for achieving safer speed that is consistent with the activity generated in the context classified roadways. This paper proposes a mixed-effect ordered logit model to examine the significant exogenous factors that affect the difference between the two speeds. To the best of the authors’ knowledge, no existing research has adopted the concept of target speed. Three years of probe vehicle-based data (INRIX speed data) and exogenous variables including traffic and roadway characteristics, land use attributes, and sociodemographic information are utilized in the models. The data include information for around 1,600 roadway segments in Central Florida. The results conclude that 16 variables are significantly associated with the difference between target speed and operating speed including speed limit, volume exposure, shoulder width information, sidewalk and shared path proportions, block length, number of signals, pavement conditions, residential and mixed land use, population density, and percentage of poverty. The results also indicate the effect of different time periods on the response variable. Therefore, different posted speed limits are recommended based on the time of day. Further, the study suggests the roadway measures that should be followed to achieve the desired target speed.

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Section: Resultssupporting

confidence: 91%