A Generic Predictive Model for On-Street Parking Availability

Unlu, Eren; Delfau, Jean-Baptiste; Nguyen, Bich Quyen; Chau, Eric; Chouiten, Mehdi

doi:10.1007/978-3-030-38822-5_4

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

2020

DOI: 10.1007/978-3-030-38822-5_4

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A Generic Predictive Model for On-Street Parking Availability

Eren Unlu

Jean-Baptiste Delfau

Bich Quyen Nguyen

et al.

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Cited by 2 publications

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“…Treebased ensemble algorithms proved their robustness in handling datasets with high multicollinearity and high dimensionality which makes the CatBoost a well-fit algorithm for extracting patterns within the SHRP2-NDS data (14,19,20). The CatBoost algorithm was implemented in several studies related to driving style recognition and on-street parking availability prediction and demonstrated its high performance (21,22). However, the literature indicates that CatBoost has been overlooked in traffic safety research.…”

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confidence: 99%

“…Higher scores reflect a greater number of errors and more impairment. A score of ø 3 represents a cognitive deficit, while a score of 1 Higher values indicate greater tendency: (0-9),(10)(11)(12)(13)(14)(15)(16)(17)(18),(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) 29 LOS Level of safety (LOS) of the event, either a safety-critical one (SCE) or normal baseline event (BLE)…”

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Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

Mousa

Fadaly

et al. 2021

Transportation Research Record: Journal of the Transportation R

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According to the National Highway Traffic Safety Administration (NHTSA), about seven million traffic accidents claimed more than 36,560 human lives in the U.S. in 2018 . These statistics have prompted researchers to investigate the driver characteristics associated with safety-critical events (SCE). This paper presents a hybrid CatBoost algorithm for identifying the feature levels associated with SCE. The model accounts for numerous difficulties and drawbacks reported in the literature. The model was trained and validated using the entire set of the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2-NDS) events (crash/near-crash and normal/baseline). Results indicate that secondary tasks (interacting with object in-vehicle, reaching for objects in the vehicle, pet interaction, cellphone/tablet use, and writing/texting), intersection influence (parking lots/driveway/entrance/exit, uncontrolled intersections, traffic signals, interchanges, and stop signs), income (under $29,000 and $100,000–$149,000), age (16–19 and 20–24), traffic density (level of service C, D, and E/F), high sensation-seeking tendency (scoring 18–35 on a scale of 35), low driving knowledge (scoring 0–8.9 on a 19-point scoring system questionnaire), and gender = female are the feature levels having an association with SCE with a probability varying between 51% and 87%. Results also revealed that passenger interactions, eating/drinking, driving away from intersections or interchanges, being age 70—79, or driving in traffic density = A are more related to safe driving. Consideration of these results can contribute to reducing roadway crashes and improve traffic safety.

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confidence: 99%

mentioning

confidence: 99%

Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

Mousa

Fadaly

et al. 2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

Prediction of On-Street Parking Level of Service Based on Random Undersampling Decision Trees

Pozo

González

Wilby

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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A Generic Predictive Model for On-Street Parking Availability

Cited by 2 publications

References 15 publications

Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

Prediction of On-Street Parking Level of Service Based on Random Undersampling Decision Trees

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