Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network

Zhang, Shile; Abdel-Aty, Mohamed; Cai, Qing; Pei, Li; Ugan, Jorge

doi:10.1016/j.aap.2020.105799

Cited by 35 publications

(15 citation statements)

References 29 publications

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“…Pedestrians' and vehicles' features are extracted from video data using detection and tracking techniques in computer vision. Zhang et al proposed a long short-term memory neural network to predict the pedestrian-vehicle conflicts 2s ahead [11]. Farag introduced a real-time road-object detection and tracking (LR_ODT) method for autonomous driving based on the fusion of LiDAR and RADAR data [12].…”

Section: Related Workmentioning

confidence: 99%

Tracking Pedestrians Under Occlusion in Parking Space

Zhou¹,

Yamada²,

Watanabe³

et al. 2023

Computer Systems Science and Engineering

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Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians' sudden appearance in the vehicle's path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision digital map of the parking lot, pedestrians' smart device's sensing data, and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot. However, this subject has not been studied and explored in existing studies. To fill this void, this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces. We also evaluate the proposed method through real-world experiments. The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy. It can also be used for pedestrian tracking in parking spaces.

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Section: Related Workmentioning

confidence: 99%

Tracking Pedestrians Under Occlusion in Parking Space

Zhou¹,

Yamada²,

Watanabe³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…Zhang et al [9] used detection and tracking techniques in computer vision to extract pedestrian and vehicle features from video data. An LSTM (Long short Memory) neural network is proposed to predict pedestrian-vehicle collisions 2 s ago.…”

Section: Research Status At Home and Abroadmentioning

confidence: 99%

A Review of Research on Pedestrian-vehicle Conflict in Non-signal-controlled Road Sections

Yang¹

2022

AJST

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Pedestrian crossings, as the main traffic facilities for pedestrians crossing the street, play an important role in ensuring the safety of pedestrians crossing the street. However, there are many places on domestic crosswalks without signal control, which leads to the interaction between vehicles and pedestrians on the crosswalk, which will not only cause traffic congestion, affect the normal traffic order, but also cause traffic accidents. It is of great significance to improve the traffic efficiency to strengthen the understanding of the seriousness of the conflict between people and vehicles in the non-signal control section, and to prevent the accident of drivers and pedestrians.

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“…With the development of information technology, high solution traffic data were easy to access and apply in transportation systems. Recently, many studies applied ambient sensing-based driving data in proactive safety studies [3,[23][24][25]. For example, Zhang et al [23] predicted the pedestrian-vehicle conflicts at the signalized intersections using the trajectories of pedestrians and vehicles.…”

Section: Proactive Safety Studies On Pedestriansmentioning

confidence: 99%

“…Recently, many studies applied ambient sensing-based driving data in proactive safety studies [3,[23][24][25]. For example, Zhang et al [23] predicted the pedestrian-vehicle conflicts at the signalized intersections using the trajectories of pedestrians and vehicles. Li et al [24] presented a surrogate safety measure for pedestrian crashes based on GPS trajectory data.…”

Section: Proactive Safety Studies On Pedestriansmentioning

confidence: 99%

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

Yin

2021

Sustainability

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The traditional way to evaluate pedestrian safety is a reactive approach using the data at an aggregate level. The objective of this study is to develop real-time safety models for pedestrian red-light running using the signal cycle level traffic data. Traffic data for 464 signal cycles during 16 h were collected at eight crosswalks on two intersections in the city of Nanjing, China. Various real-time safety models of pedestrian red-light running were developed based on the different combination of explanatory variables using the Bayesian Poisson-lognormal (PLN) model. The Bayesian estimation approach based on Markov chain Monte Carlo simulation is utilized for the real-time safety models estimates. The models’ comparison results show that the model incorporated exposure, pedestrians’ characteristics and crossing maneuver, and traffic control and crosswalk design outperforms the model incorporated exposure and the model incorporated exposure, pedestrians’ characteristics, and crossing maneuver. The result indicates that including more variables in the real-time safety model could improve the model fit. The model estimation results show that pedestrian volume, ratio of males, ratio of pedestrians on phone talking, pedestrian waiting time, green ratio, signal type, and length of crosswalk are statistically significantly associated with the pedestrians’ red-light running. The findings from this study could be useful in real-time pedestrian safety evaluation as well as in crosswalk design and pedestrian signal optimization.

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Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network

Cited by 35 publications

References 29 publications

Tracking Pedestrians Under Occlusion in Parking Space

Tracking Pedestrians Under Occlusion in Parking Space

A Review of Research on Pedestrian-vehicle Conflict in Non-signal-controlled Road Sections

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

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