Evaluating the Pedestrian Gap Acceptance in Semicontrolled Midblock Crosswalks with an Integrated AHP-FL Approach

Alver, Yalçın; Katanalp, Burak Yiğit

doi:10.1061/jtepbs.0000720

Cited by 2 publications

(2 citation statements)

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“…Their study did not consider the exact number of dispatchers required for certain aspects of deep learning and the data collection process. Alver et al [ 21 ] proposed a comprehensive AHP-FL (analytic hierarchy process–fuzzy logic) method to address the issue of how pedestrians assess the safety of available gaps. However, this method is susceptible to individual subjective biases.…”

Section: Introductionmentioning

confidence: 99%

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning

Cai,

Wang,

2024

Sensors

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Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

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Section: Introductionmentioning

confidence: 99%

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning

Cai,

Wang,

2024

Sensors

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“…1) The main contributing factors : In numerous studies on pedestrian behaviors, environmental factors such as road width and traffic volume, road light situation, or weather conditions such as rainfall, have been reported to affect pedestrian crossing decisions ( 6 ). Vehicle speed, distance to the pedestrian, and time to collision (TTC) also influence pedestrian crossing decisions ( 7 ).…”

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confidence: 99%

Pedestrian Crossing Intention Prediction Model Considering Social Interaction between Multi-Pedestrians and Multi-Vehicles

Zhou

Lijun

Liu

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Accurate pedestrian crossing intention prediction is critical for autonomous vehicles and advanced driver assistance systems. In multi-pedestrian and multi-vehicle interaction scenes, the social interaction between pedestrians and other traffic participants is ubiquitous, which affects pedestrian crossing decisions and the accuracy of prediction. However, previous studies on pedestrian crossing intention lack comprehensive consideration and mathematical modeling of the social interaction. We propose a “social interaction force” (SIF) to identify and quantify social interaction behaviors and combine the hidden Markov model (HMM) to predict pedestrian crossing intentions 1.0 s ahead. Firstly, a large dataset of pedestrian-vehicle interaction samples is collected from two views, and high-dimensional features are extracted for pedestrian intention prediction. Next, the concept of SIF is proposed to quantitatively characterize the influence of other pedestrians and vehicles on pedestrian crossing decisions, including “pedestrian interaction force” and “pedestrian-vehicle interaction force.” Finally, SIF, pedestrian features, and road structure features are input into HMM. Sliding time windows are applied to the HMM to achieve dynamic prediction of pedestrian intention sequences. Experimental results show that the recognition accuracy of the proposed model is 0.976, and the accuracy of 1.0 s ahead prediction is 0.932 with guaranteed prediction speed. The proposed model performance is superior to that of the most prevalent models developed thus far.

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Evaluating the Pedestrian Gap Acceptance in Semicontrolled Midblock Crosswalks with an Integrated AHP-FL Approach

Cited by 2 publications

References 68 publications

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning

Pedestrian Crossing Intention Prediction Model Considering Social Interaction between Multi-Pedestrians and Multi-Vehicles

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