Micro-level safety risk assessment model for a two-lane heterogeneous traffic environment in a developing country: A comparative crash probability modeling approach

Mahmud, Shoaib; Ferreira, Luis; Hoque, Sanaul; Tavassoli, Ahmad

doi:10.1016/j.jsr.2019.03.008

Cited by 11 publications

(9 citation statements)

References 33 publications

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“…Therefore, the most suitable model for the dual state is the zero-inflated model. It is called the zero-inflated negative binomial (ZINB) when established on the NB model [31]. Vuong statistics were used to compare between the ZINB and the NB model with f 1 (.)…”

Section: Crash Frequency Modelsmentioning

confidence: 99%

“…These results were inconsistent with those of Joon-Ki et al [8], which suggested that roads with narrow shoulders led to more frequency of trailing. However, for developing countries, Mahmud et al [31] found that shoulders did not contribute to more frequent crashes. As for the relationship of these two factors with traffic volume, it was found that the two factors positively correlated with traffic volume (Figure 4).…”

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confidence: 97%

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Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

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Section: Crash Frequency Modelsmentioning

confidence: 99%

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confidence: 97%

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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“…Traffic characteristics • Average Annual Daily Traffic (AADT) (Alarifi et al, 2018;Huang et al, 2016;Liu et al, 2017;Mahmud et al, 2019;Xiong et al, 2023) • Vehicle Miles Traveled (VMT) (Xu et al, 2019) • Running red lights (Retting et al, 1999) • Street level (Alarifi et al, 2018) • Zonal level (Huang et al, 2016;Liu et al, 2017;Mahmud et al, 2019;Retting et al, 1999;Xiong et al, 2023;Xu et al, 2019) Road characteristics • One-way streets, bus and bike lanes, road quality (WHO, 2018) • Speed limit (Almasi & Behnood, 2022;Huang et al, 2016;Liu et al, 2017;Ma et al, 2017;Mahmud et al, 2019;Rahman et al, 2023) (Alarifi et al, 2018;Huang et al, 2016;Liu et al, 2017;Xie & Yan, 2008) • Proximity to intersections (Li et al, 2019) • Number of intersections (Hasan et al, 2022;Shariat-Mohaymany et al, 2015) • Road type (Hasan et al, 2022;Huang et al, 2016;Li et al, 2019;Wu et al, 2024) • Number of road lanes (Alarifi et al, 2018;Huang et al, 2016;Ma et al, 2017) • Presence of a median on roads (Alarifi et al, 2018;Huang et al, 2016) • Vertical grade, curvature of roads (Wen et al, 2019) • Pavement condition (Huang et al, 20...…”

Section: Feature Category Features Author(s) and Publication Year Scalementioning

confidence: 99%

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

Mohammadi,

Taleai,

Otto

et al. 2024

Transactions in GIS

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Contemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.

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“…In addition, modeling results suggested that specific combinations of traffic characteristics increase the likelihood of crashes. Mahmud et al [3] investigated the factors affecting crash frequency on a two-lane two-way highway in a heterogeneous traffic environment using micro-level traffic flow data; accordingly, he identified the safety risk locations of a particular road section. It was concluded that speeding was the primary influential factor in crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In China and many other developing countries, it has been a common phenomenon that passenger car (PC) driving is mixed with heavy vehicles (HVs), i.e., trucks and buses, on both freeways and arterials [1][2][3], which is defined as heterogeneous traffic flow. This is mainly because public transport, such as inner-city buses and intercity coaches, can carry more passengers in comparison with PCs and thus has the potential to relieve traffic congestion problems [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

Shen

Yang

2020

Sustainability

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This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.

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Micro-level safety risk assessment model for a two-lane heterogeneous traffic environment in a developing country: A comparative crash probability modeling approach

Cited by 11 publications

References 33 publications

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

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