Trajectory-based real-time pedestrian crash prediction at intersections: A novel non-linear link function for block maxima led Bayesian GEV framework addressing heterogeneous traffic condition
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918161504272384 |
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| author | Anowar, Parvez Haque, Nazmul Raihan, Md Asif Hadiuzzaman, Md |
| author_facet | Anowar, Parvez Haque, Nazmul Raihan, Md Asif Hadiuzzaman, Md |
| contents | This study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous, non-lane-based traffic. Existing approaches often assume linear relationships between covariates and parameters, oversimplifying the complex, non-monotonic interactions among different road users. To overcome this, the framework introduces a non-linear link function within a Bayesian generalized extreme value (GEV) structure to capture traffic variability more accurately. The framework applies extreme value theory through the block maxima approach using post-encroachment time as a surrogate safety measure. A hierarchical Bayesian model incorporating both linear and non-linear link functions into GEV parameters is estimated using Markov Chain Monte Carlo simulation. It also introduces a behavior-normalized Modified Crash Risk (MRC) formula to account for pedestrians' habitual risk-taking behavior. Seven Bayesian hierarchical models were developed and compared using deviance information criterion. Models employing non-linear link functions for the location and scale parameters significantly outperformed their linear counterparts. The results revealed that pedestrian speed has a negative relationship with crash risk, while flow and speed of motorized vehicles, pedestrian flow, and non-motorized vehicles conflicting speed contribute positively. The MRC formulation reduced overestimation and provided crash predictions with 93% confidence. The integration of non-linear link functions enhances model flexibility, capturing the non-linear nature of traffic extremes. The proposed MRC metric aligns crash risk estimates with real-world pedestrian behavior in mixed-traffic environments. This framework offers a practical analytical tool for traffic engineers and planners to design adaptive signal control and pedestrian safety interventions before crashes occur. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trajectory-based real-time pedestrian crash prediction at intersections: A novel non-linear link function for block maxima led Bayesian GEV framework addressing heterogeneous traffic condition Anowar, Parvez Haque, Nazmul Raihan, Md Asif Hadiuzzaman, Md Applications Physics and Society Methodology 60G70 (Primary) 62F15, 62G32, 62P30 (Secondary) I.6.4; J.2; G.3; J.7 This study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous, non-lane-based traffic. Existing approaches often assume linear relationships between covariates and parameters, oversimplifying the complex, non-monotonic interactions among different road users. To overcome this, the framework introduces a non-linear link function within a Bayesian generalized extreme value (GEV) structure to capture traffic variability more accurately. The framework applies extreme value theory through the block maxima approach using post-encroachment time as a surrogate safety measure. A hierarchical Bayesian model incorporating both linear and non-linear link functions into GEV parameters is estimated using Markov Chain Monte Carlo simulation. It also introduces a behavior-normalized Modified Crash Risk (MRC) formula to account for pedestrians' habitual risk-taking behavior. Seven Bayesian hierarchical models were developed and compared using deviance information criterion. Models employing non-linear link functions for the location and scale parameters significantly outperformed their linear counterparts. The results revealed that pedestrian speed has a negative relationship with crash risk, while flow and speed of motorized vehicles, pedestrian flow, and non-motorized vehicles conflicting speed contribute positively. The MRC formulation reduced overestimation and provided crash predictions with 93% confidence. The integration of non-linear link functions enhances model flexibility, capturing the non-linear nature of traffic extremes. The proposed MRC metric aligns crash risk estimates with real-world pedestrian behavior in mixed-traffic environments. This framework offers a practical analytical tool for traffic engineers and planners to design adaptive signal control and pedestrian safety interventions before crashes occur. |
| title | Trajectory-based real-time pedestrian crash prediction at intersections: A novel non-linear link function for block maxima led Bayesian GEV framework addressing heterogeneous traffic condition |
| topic | Applications Physics and Society Methodology 60G70 (Primary) 62F15, 62G32, 62P30 (Secondary) I.6.4; J.2; G.3; J.7 |
| url | https://arxiv.org/abs/2510.12963 |