Predicting cycle-level traffic movements at signalized intersections using machine learning models

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

doi:10.1016/j.trc.2020.102930

Cited by 28 publications

(10 citation statements)

References 47 publications

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“…Short-term (51)(52)(53) and long-term prediction (54-56) has mostly been investigated. Detector data has also been used from camera and loop detectors to predict cycle volumes (57,58). Often advanced detectors can be used to predict speed profiles as well (36,37,39,59,60).…”

Section: Traffic-related Prediction Using Detector Datamentioning

confidence: 99%

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Islam

Abdel-Aty

Ugan

2023

Transportation Research Record: Journal of the Transportation R

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Signal phasing and timing can be adaptive and actuated in practice. This makes it challenging to understand what the cycle length and phase duration of the next few cycles will be. Many innovative applications can be designed based on the knowledge of future signal timing states such as dilemma zone warning, efficient route planning, and so forth. This work proposes a long short-term memory model capable of predicting both cycle length and phase duration prediction up to six cycles in the future. GPS information of several vehicles are merged with signal timing information of eight intersections. Several key features such as waiting time, approach speed and acceleration, departing speed and acceleration, are calculated based on the geolocation of individual journeys. The results show that cycle length prediction can reach mean absolute error (MAE) of about 7 s while phase prediction MAE is about 9 s.

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Section: Traffic-related Prediction Using Detector Datamentioning

confidence: 99%

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Islam

Abdel-Aty

Ugan

2023

Transportation Research Record: Journal of the Transportation R

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“…The XGBT model was used to determine the primary correlates of vehicle ownership and their complex relationships. XGBT was originally developed for data science [50], but it has also been used increasingly in urban planning and transportation science (e.g., [51][52][53]). The XGBT algorithm is a more regularized variant of the gradient boosting tree (GBT).…”

Section: Extreme Gradient Boosting (Xgbt)mentioning

confidence: 99%

Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm

Aghaabbasi

Ali

et al. 2022

Sustainability

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In the United States, several studies have looked at the association between automobile ownership and sociodemographic factors and built environment qualities, but few have looked at household travel characteristics. Their interactions and nonlinear linkages are frequently overlooked in existing studies. Utilizing the 2017 US National Household Travel Survey, the authors employed an extreme gradient boosting tree model to evaluate the nonlinear and interaction impacts of household travel characteristics and built environment factors on vehicle ownership in three states of the United States (California, Missouri, and Kansas) that are different in population size. To develop these models, three main XGBT parameters, including the number of trees, maximal depth, and minimum rows, were optimized using a grid search technique. In California, the predictability of vehicle ownership was driven by household travel characteristics (cumulative importance: 0.62). Predictions for vehicle ownership in Missouri and Kansas were dominantly influenced by sociodemographic factors (cumulative importance: 0.53 and 0.55, respectively). In all states, the authors found that the number of drivers in a household plays a vital role in the vehicle ownership decisions of households. Regarding the built environment attributes, deficiencies in cycling infrastructure were the most prominent attribute in predicting household vehicle ownership in California. This variable, however, has threshold connections with vehicle ownership, but the magnitude of these relationships is small. The outcomes imply that improving the condition of cycling infrastructure will help reduce the number of vehicles. In addition, incentives that encourage the households’ drivers not to buy new vehicles are helpful. The outcomes of this study might aid policymakers in developing policies that encourage sustainable vehicle ownership in the United States.

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“…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%

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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Predicting cycle-level traffic movements at signalized intersections using machine learning models

Cited by 28 publications

References 47 publications

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm

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

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