References

Local indicators of spatial association—LISA

10.1111/j.1538-4632.1995.tb00338.x · 1995 · External reference

Macro-level vulnerable road users crash analysis: A Bayesian joint modeling approach of frequency and proportion

10.1016/j.aap.2017.07.020 · 2017 · External reference

Unresolved reference

External reference

Quantifying spatial inequities in traffic injury rates through the integration of urban road network measures and social vulnerability

10.1016/j.aap.2025.107916 · 2025 · External reference

Incomplete accident reporting: meta-analysis of studies made in 13 countries

10.3141/1665-18 · 1999 · External reference

10.1201/9781003435464

10.1201/9781003435464 · External reference

Multiscale Geographically Weighted Regression (MGWR)

2017 · External reference

The analysis of spatial association by use of distance statistics

10.1111/j.1538-4632.1992.tb00261.x · 1992 · External reference

Unresolved reference

External reference

Application of risky driving behavior in crash detection and analysis

10.1016/j.physa.2021.126808 · 2022 · External reference

The frequency-severity indeterminacy

10.1016/j.aap.2005.07.001 · 2006 · External reference

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

10.1016/j.aap.2009.10.016 · 2010 · External reference

Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach

10.1016/j.jtrangeo.2018.04.027 · 2018 · External reference

Aggression of electric bike riders in China: comparisons of the Cycling Anger Expression Inventory toward drivers, pedestrians, and other riders

10.1080/03081060.2024.2386327 · 2024 · External reference

Exploring the spatial relationship of e-bike and motorcycle crashes: Implications for risk reduction

10.1016/j.jsr.2023.11.007 · 2024 · External reference

Macro-level analysis of bicycle safety: Focusing on the characteristics of both crash location and residence

10.1080/15568318.2017.1407973 · 2018 · External reference

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis

10.1016/j.aap.2023.107375 · 2024 · External reference

Exploring the heterogeneous effects of riding behaviours and road conditions on delivery rider severities in scooter-style electric bicycle crashes involving vehicles

10.1080/17457300.2023.2279960 · 2024 · External reference

Exploring the effect of road network, demographic, and land use characteristics on teen crash frequency using geographically weighted negative binomial regression

10.1016/j.aap.2022.106615 · 2022 · External reference

A caution regarding rules of thumb for variance inflation factors

10.1007/s11135-006-9018-6 · 2007 · External reference

mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale

10.3390/ijgi8060269 · 2019 · External reference

Using spatial analysis and variable selection of traffic violations to determine crash frequency

2024 · External reference

Comparison of injury severity between E-bikes-related and other two-wheelers-related accidents: Based on an accident dataset

10.1016/j.aap.2023.107189 · 2023 · External reference

An observational study on the risk behaviors of electric bicycle riders performing meal delivery at urban intersections in China

10.1016/j.trf.2021.04.010 · 2021 · External reference

Exploring the influences of point-of-interest on traffic crashes during weekdays and weekends via multi-scale geographically weighted regression

10.3390/ijgi10110791 · 2021 · External reference

Detecting multicollinearity in regression analysis

10.12691/ajams-8-2-1 · 2020 · External reference

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

2014 · External reference

Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression

10.1016/j.aap.2020.105833 · 2020 · External reference

Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

10.1016/j.aap.2023.107123 · 2023 · External reference

Multicollinearity

10.1016/j.ajodo.2021.02.005 · 2021 · External reference

Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model

10.1080/17457300.2024.2385102 · 2024 · External reference

Analyzing safety concerns of (e-) bikes and cycling behaviors at intersections in urban area

10.3390/su14074231 · 2022 · External reference

Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US

10.1016/j.aap.2024.107528 · 2024 · External reference

Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

10.1016/j.jsr.2020.12.009 · 2021 · External reference

Analysis of factors affecting the severity of injuries in electric two‐wheeled vehicle crashes with or without violation: a random parametric logit model considering heterogeneity of means and variances

10.1155/2024/7861707 · 2024 · External reference

Geographically weighted random forests for macro-level crash frequency prediction

10.1016/j.aap.2023.107370 · 2024 · External reference

Exploring key spatio-temporal features of crash risk hot spots on urban road network: A machine learning approach

10.1016/j.tra.2023.103717 · 2023 · External reference

Analyzing rider-injury severity in electric bicycle jaywalking accidents

2023 · External reference

Exploring the impact of right-turn safety measures on E-bike-heavy vehicle conflicts at signalized intersections

10.1016/j.aap.2024.107722 · 2024 · External reference

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

10.1016/j.heliyon.2024.e29961 · 2024 · External reference

Unresolved reference

External reference