Modeling and simulation of car accidents at a signalized intersection using cellular automata

Marzoug, Rachid; Lakouari, N.; Ez‐Zahraouy, H.; Téllez, Beatríz Castillo; Téllez, Margarita Castillo; Villalobos, L. Cisneros

doi:10.1016/j.physa.2021.126599

Cited by 18 publications

(11 citation statements)

References 40 publications

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“…The continuous growth of car ownership has led to an increasingly prominent traffic problem on freeways, resulting in frequent traffic accidents and long-term congestion. It is well known that congestion reduces the capacity of freeways, increases travel time for drivers, decreases traffic safety, and exacerbates environmental pollution [1][2][3]. Merge areas between mainlines and ramps are frequently congested.…”

Section: Introductionmentioning

confidence: 99%

Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout

Yang,

Ma,

2024

Sustainability

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Traffic exhaust pollution, especially in congested areas of freeways, is one of the main causes of air pollution. With the increase in the number of vehicles, traffic and environmental issues have become more prominent. In addition, traffic congestion leads to frequent starting and stopping of vehicles, further exacerbating environmental pollution. This article focuses on the problem of frequent starting and stopping of vehicles, using variable speed limit control to smooth traffic flow, reduce vehicle speed, and alleviate exhaust emissions caused by traffic congestion. At the same time, considering the traffic and environmental benefits of bottleneck areas on freeways, the VT-Micro model is used to calculate exhaust emissions, and a coordinated control method for the mainline and ramp of freeways is proposed. The simulation experiment results show that the total driving time of the mainline and ramp collaborative control method considering environmental benefits has been reduced by 24.69%, CO emissions have been reduced by 4.79%, HC emissions have been reduced by 7.65%, NOx emissions have been reduced by 2.48%, and fuel consumption has been reduced by 4.98%.

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Section: Introductionmentioning

confidence: 99%

Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout

Yang,

Ma,

2024

Sustainability

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“…Hence, they may not be feasible for executing maneuvers at hustling areas such as traffic intersections. This is important since a sizable fraction of vehicle collisions occur at traffic intersections [13] which also tend to be more severe [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information

Zhu¹,

Khan²,

Cremers³

2023

Preprint

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Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work rather relies only on having knowledge of the current state and intended direction to make predictions for multiple vehicles at intersections. Furthermore, message passing of this information between the vehicles provides each one of them a more holistic overview of the environment allowing for a more informed prediction. This is done by training a neural network which takes the state and intent of the multiple vehicles to predict their future trajectory. Using the intention as an input allows our approach to be extended to additionally control the multiple vehicles to drive towards desired paths. Experimental results demonstrate the robustness of our approach both in terms of trajectory prediction and vehicle control at intersections. The complete training and evaluation code for this work is available here: https:// github.com/Dekai21/Multi_Agent_Intersection.

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“…Numerous statistical models have been used to estimate the severity of traffic accident injuries. In the past, injury severity analysis and prediction have been dominated by statistical methods, such as the linear, nonlinear, generalised linear model (GLM), Poisson regression model (PRM), ordered probit model (OPM), mixed logit model (MLM), Bayesian ordered probit model (BOPM), random parameters ordered probit model (RPOPM), and cellular automata (CA) model, which were regarded as reasonable attempts at thoroughly formulating the relationship between the number of predicting variables [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Modelling overall accidents may not be as valuable as one might think in terms of creating safety countermeasures, as various types of accidents are frequently linked to distinct sets of primary variables.…”

Section: Introductionmentioning

confidence: 99%

Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations

Dong

Khattak

Ullah

et al. 2022

IJERPH

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Road traffic accidents are one of the world’s most serious problems, as they result in numerous fatalities and injuries, as well as economic losses each year. Assessing the factors that contribute to the severity of road traffic injuries has proven to be insightful. The findings may contribute to a better understanding of and potential mitigation of the risk of serious injuries associated with crashes. While ensemble learning approaches are capable of establishing complex and non-linear relationships between input risk variables and outcomes for the purpose of injury severity prediction and classification, most of them share a critical limitation: their “black-box” nature. To develop interpretable predictive models for road traffic injury severity, this paper proposes four boosting-based ensemble learning models, namely a novel Natural Gradient Boosting, Adaptive Gradient Boosting, Categorical Gradient Boosting, and Light Gradient Boosting Machine, and uses a recently developed SHapley Additive exPlanations analysis to rank the risk variables and explain the optimal model. Among four models, LightGBM achieved the highest classification accuracy (73.63%), precision (72.61%), and recall (70.09%), F1-scores (70.81%), and AUC (0.71) when tested on 2015–2019 Pakistan’s National Highway N-5 (Peshawar to Rahim Yar Khan Section) accident data. By incorporating the SHapley Additive exPlanations approach, we were able to interpret the model’s estimation results from both global and local perspectives. Following interpretation, it was determined that the Month_of_Year, Cause_of_Accident, Driver_Age and Collision_Type all played a significant role in the estimation process. According to the analysis, young drivers and pedestrians struck by a trailer have a higher risk of suffering fatal injuries. The combination of trailers and passenger vehicles, as well as driver at-fault, hitting pedestrians and rear-end collisions, significantly increases the risk of fatal injuries. This study suggests that combining LightGBM and SHAP has the potential to develop an interpretable model for predicting road traffic injury severity.

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Modeling and simulation of car accidents at a signalized intersection using cellular automata

Cited by 18 publications

References 40 publications

Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout

Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout

Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information

Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations

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