Research graph
References from Exploring Spatial Variations in Factors Influencing Cyclist Injury Severity in Traffic Crashes: A Comparison of Geographically Weighted Regression and Spatial Machine Learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Cycling during and after COVID: Has there been a boom in activity?
10.1016/j.trf.2023.09.017 · 2023 · External reference
COVID-19 and cycling: A review of the literature on changes in cycling levels and government policies from 2019 to 2022
10.1080/01441647.2023.2205178 · 2024 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
Bicyclist injury severities in bicycle-motor vehicle accidents
10.1016/j.aap.2006.07.002 · 2007 · External reference
Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations
10.1016/j.aap.2010.09.015 · 2011 · External reference
Cyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates
10.1016/j.jsr.2020.02.006 · 2020 · External reference
Factors impacting bike crash severity in urban areas
10.1016/j.jsr.2022.08.010 · 2022 · External reference
Analyzing severity of vehicle-bicycle crashes: An explainable boosting machine strategy
10.1080/19427867.2025.2500817 · 2025 · External reference
Unresolved reference
External reference
Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models
10.1016/j.aap.2018.12.009 · 2019 · External reference
Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model
10.1155/2019/8521649 · 2019 · External reference
10.1061/9780784483152.010
10.1061/9780784483152.010 · External reference
Bicyclists injury severities: An empirical assessment of temporal stability
10.1016/j.aap.2022.106616 · 2022 · External reference
Unobserved heterogeneity and the statistical analysis of highway accident data
2016 · External reference
Modeling crash spatial heterogeneity: Random parameter versus geographically weighting
10.1016/j.aap.2014.10.020 · 2015 · External reference
A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania
10.1016/j.aap.2020.105952 · 2021 · External reference
Localizing safety performance functions for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in Alabama: A geospatial modeling approach with clustering analysis
10.1016/j.aap.2022.106896 · 2023 · External reference
Implementing safety leading indicators in construction: Toward a proactive approach to safety management
10.1016/j.ssci.2022.105929 · 2023 · External reference
Using geographically weighted logistic regression (GWLR) for pedestrian crash severity modeling: Exploring spatially varying relationships with natural and built environment factors
10.1016/j.iatssr.2023.07.004 · 2023 · External reference
Collinearity: A review of methods to deal with it and a simulation study evaluating their performance
10.1111/j.1600-0587.2012.07348.x · 2013 · External reference
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
10.1016/j.tbs.2020.02.003 · 2020 · External reference
Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis
2020 · External reference
Comparison of four statistical and machine learning methods for crash severity prediction
10.1016/j.aap.2017.08.008 · 2017 · External reference
Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
10.1016/j.aap.2023.107378 · 2024 · External reference
Unresolved reference
External reference
Machine learning for occupational accident analysis: Applications, challenges, and future directions
2026 · External reference
Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach
10.1016/j.aap.2024.107697 · 2024 · External reference
Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest
10.1016/j.aap.2022.106880 · 2023 · External reference
Comparing built environment effects on bike-sharing and electric bike-sharing usage: A spatiotemporal machine learning approach
10.1016/j.tra.2025.104642 · 2025 · External reference
Multiscale geographical random forest: A novel spatial ML approach for traffic safety modeling integrating street-view semantic visual features
10.1016/j.trc.2025.105299 · 2025 · External reference
Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data: A spatial machine learning approach
10.1016/j.aap.2025.108180 · 2025 · External reference
Analysis and Prediction of Traffic Accidents Based on Interpretable Spatial Machine Learning: A Case Study in California
10.1155/atr/3184284 · 2025 · External reference
AI-based prediction of traffic crash severity for improving road safety and transportation efficiency
10.1038/s41598-025-10970-7 · 2025 · External reference
The effect of speed limit reductions in urban areas on cyclists’ injuries in collisions with cars
10.1080/15389588.2019.1680836 · 2019 · External reference
10.3390/su14010215
10.3390/su14010215 · External reference
The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities
2024 · External reference
Unresolved reference
External reference
Are gates at rail grade crossings always safe? Examining motorist gate-violation behaviors using path analysis
10.1016/j.trf.2018.03.014 · 2018 · External reference
Cyclist crash severity modeling: A hybrid approach of XGBoost-SHAP and random parameters logit with heterogeneity in means and variances
10.1016/j.jsr.2025.04.003 · 2025 · External reference
Revisiting Hit-and-Run Crashes: A Geo-Spatial Modeling Method
10.1177/0361198118773889 · 2018 · External reference
Unresolved reference
External reference
Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes
10.1016/j.aap.2022.106622 · 2022 · External reference
Influence of Type of Traffic Control on Injury Severity in Bicycle-Motor Vehicle Crashes at Intersections
10.1177/0361198118773576 · 2018 · External reference
Individual and contextual factors associated with bicyclist injury severity in traffic incidents between bicyclists and motorists in Chile
10.1016/j.aap.2021.106077 · 2021 · External reference
Crash risk: How cycling flow can help explain crash data
10.1016/j.aap.2016.04.033 · 2017 · External reference
Mixed logit model based diagnostic analysis of bicycle-vehicle crashes at daytime and nighttime
10.1016/j.ijtst.2021.10.001 · 2022 · External reference
Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes
10.1016/j.jsr.2021.09.005 · 2021 · External reference
10.1186/s12889-022-14402-3
10.1186/s12889-022-14402-3 · External reference
10.1371/journal.pone.0315692
10.1371/journal.pone.0315692 · External reference
Unresolved reference
External reference
Predicting injury-severity for cyclist crashes using natural language processing and neural network modelling
10.1016/j.ssci.2023.106153 · 2023 · External reference
The impact of weather, road surface, time-of-day, and light conditions on severity of bicycle-motor vehicle crash injuries
10.1002/ajim.22849 · 2018 · External reference
Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment
10.1016/j.aap.2022.106681 · 2022 · External reference
Critical factors in fatal collisions of adult cyclists with automobiles
10.1016/j.aap.2010.04.001 · 2010 · External reference
Modeling spatial nonstationary and overdispersed crash data: Development and comparative analysis of global and geographically weighted regression models applied to macrolevel injury crash data
2021 · External reference
A note on modeling vehicle accident frequencies with random-parameters count models
10.1016/j.aap.2008.10.005 · 2009 · External reference
Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities
10.1016/j.aap.2018.01.035 · 2018 · External reference
An analysis of bicycle accidents with respect to spatial heterogeneity
10.1038/s41598-023-49143-9 · 2023 · External reference
Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis
10.1068/a301905 · 1998 · External reference
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost
10.1016/j.compenvurbsys.2022.101845 · 2022 · External reference
Unresolved reference
External reference
Injury severity prediction of cyclist crashes using random forests and random parameters logit models
10.1016/j.aap.2023.107275 · 2023 · External reference
Factors affecting injury severity at pedestrian crossing locations with Rectangular RAPID Flashing Beacons (RRFB) using XGBoost and random parameters discrete outcome models
10.1016/j.aap.2022.106937 · 2023 · External reference
Machine learning for next-generation intelligent transportation systems: A survey
10.1002/ett.4427 · 2022 · External reference
Analyzing freeway safety influencing factors using the CatBoost model and interpretable machine-learning framework, SHAP
10.1177/03611981231208903 · 2024 · External reference
GeoShapley-based interpretation of older adult pedestrian fatal vs injury crash frequency in dense urban environments
10.1016/j.aap.2026.108450 · 2026 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
10.3390/app12189156
10.3390/app12189156 · External reference
A Review of Neural Networks Applied to Transport
10.1016/0968-090x(95)00009-8 · 1995 · External reference
An overview of neural network
2019 · External reference
10.20944/preprints202503.0643.v1
10.20944/preprints202503.0643.v1 · External reference
Unresolved reference
External reference
A stable and optimized neural network model for crash injury severity prediction
10.1016/j.aap.2014.09.006 · 2014 · External reference
Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks
2016 · External reference
10.3390/ijerph17207466
10.3390/ijerph17207466 · External reference
Severity analysis of road transport accidents of hazardous materials with machine learning
10.1080/15389588.2021.1900569 · 2021 · External reference
Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data
10.1080/15472450.2022.2106564 · 2024 · External reference
The safety risks of roadside trees: An analysis of injury outcomes in vehicle-tree collisions
10.1016/j.aap.2026.108751 · 2026 · External reference
Gate-violation behavior at highway-rail grade crossings and the consequences: Using geo-Spatial modeling integrated with path analysis
10.1016/j.aap.2017.10.010 · 2017 · External reference
Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities
10.1016/j.jsr.2021.02.015 · 2021 · External reference
Injury severity of police officers involved in traffic crashes: A spatial analysis of Alabama
10.1016/j.ssci.2023.106406 · 2024 · External reference
Tobler’s first law and spatial analysis
10.1111/j.1467-8306.2004.09402005.x · 2004 · External reference
10.4135/9780857020130.n13
10.4135/9780857020130.n13 · External reference
Crashes involving cyclists aged 50 and over in the Netherlands: An in-depth study
10.1016/j.aap.2016.07.016 · 2017 · External reference
Factors influencing injury severity of cyclists involved in crashes with motor vehicles: Bike lanes, alcohol, lighting, speed, and helmet use
10.14423/smj.0000000000000665 · 2017 · External reference
Determinants of cyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances
2017 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Statistical analysis of cyclists’ injury severity at unsignalized intersections
10.1080/15389588.2014.969802 · 2015 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
Comparative Analysis of Injury Severity Resulting from Pedestrian-Motor Vehicle and Bicycle-Motor Vehicle Crashes on Roadways in Alabama
10.3141/2514-09 · 2015 · External reference
The impact of transportation infrastructure on bicycling injuries and crashes: A review of the literature
10.1186/1476-069x-8-47 · 2009 · External reference
Street lighting for preventing road traffic injuries
2009 · External reference
Safety evaluation of protected bike lane treatments at intersections: A causal framework
10.1016/j.aap.2025.108132 · 2025 · External reference
Unresolved reference
External reference
Safe system approach to preventing cyclist fatalities: Safety by design for urban and rural environments
10.1186/s40621-025-00621-w · 2025 · External reference
Unresolved reference
External reference
Safer Cycling Through Improved Infrastructure
10.2105/ajph.2016.303507 · 2016 · External reference
Cyclist crash comparison of mixing zone and fully split phase signal treatments at intersections with protected bicycle lanes in New York City
10.1177/0361198119859301 · 2019 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
10.3390/su16177280
10.3390/su16177280 · External reference
The impact of weather, road surface, time-of-day, and light conditions on severity of bicycle-motor vehicle crash injuries
10.1002/ajim.22849 · ExternalCitation · doi-reference
Machine learning for next-generation intelligent transportation systems: A survey
10.1002/ett.4427 · ExternalCitation · doi-reference
A Review of Neural Networks Applied to Transport
10.1016/0968-090x(95)00009-8 · ExternalCitation · doi-reference
Bicyclist injury severities in bicycle-motor vehicle accidents
10.1016/j.aap.2006.07.002 · ExternalCitation · doi-reference
A note on modeling vehicle accident frequencies with random-parameters count models
10.1016/j.aap.2008.10.005 · ExternalCitation · doi-reference
Critical factors in fatal collisions of adult cyclists with automobiles
10.1016/j.aap.2010.04.001 · ExternalCitation · doi-reference
Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations
10.1016/j.aap.2010.09.015 · ExternalCitation · doi-reference
A stable and optimized neural network model for crash injury severity prediction
10.1016/j.aap.2014.09.006 · ExternalCitation · doi-reference
Modeling crash spatial heterogeneity: Random parameter versus geographically weighting
10.1016/j.aap.2014.10.020 · ExternalCitation · doi-reference
Crash risk: How cycling flow can help explain crash data
10.1016/j.aap.2016.04.033 · ExternalCitation · doi-reference
Crashes involving cyclists aged 50 and over in the Netherlands: An in-depth study
10.1016/j.aap.2016.07.016 · ExternalCitation · doi-reference
Comparison of four statistical and machine learning methods for crash severity prediction
10.1016/j.aap.2017.08.008 · ExternalCitation · doi-reference
Gate-violation behavior at highway-rail grade crossings and the consequences: Using geo-Spatial modeling integrated with path analysis
10.1016/j.aap.2017.10.010 · ExternalCitation · doi-reference
Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities
10.1016/j.aap.2018.01.035 · ExternalCitation · doi-reference
Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models
10.1016/j.aap.2018.12.009 · ExternalCitation · doi-reference
A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania
10.1016/j.aap.2020.105952 · ExternalCitation · doi-reference
Individual and contextual factors associated with bicyclist injury severity in traffic incidents between bicyclists and motorists in Chile
10.1016/j.aap.2021.106077 · ExternalCitation · doi-reference
Bicyclists injury severities: An empirical assessment of temporal stability
10.1016/j.aap.2022.106616 · ExternalCitation · doi-reference
Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes
10.1016/j.aap.2022.106622 · ExternalCitation · doi-reference
Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment
10.1016/j.aap.2022.106681 · ExternalCitation · doi-reference
Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest
10.1016/j.aap.2022.106880 · ExternalCitation · doi-reference
Localizing safety performance functions for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in Alabama: A geospatial modeling approach with clustering analysis
10.1016/j.aap.2022.106896 · ExternalCitation · doi-reference
Factors affecting injury severity at pedestrian crossing locations with Rectangular RAPID Flashing Beacons (RRFB) using XGBoost and random parameters discrete outcome models
10.1016/j.aap.2022.106937 · ExternalCitation · doi-reference
Injury severity prediction of cyclist crashes using random forests and random parameters logit models
10.1016/j.aap.2023.107275 · ExternalCitation · doi-reference
Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
10.1016/j.aap.2023.107378 · ExternalCitation · doi-reference
Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach
10.1016/j.aap.2024.107697 · ExternalCitation · doi-reference
Safety evaluation of protected bike lane treatments at intersections: A causal framework
10.1016/j.aap.2025.108132 · ExternalCitation · doi-reference
Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data: A spatial machine learning approach
10.1016/j.aap.2025.108180 · ExternalCitation · doi-reference
GeoShapley-based interpretation of older adult pedestrian fatal vs injury crash frequency in dense urban environments
10.1016/j.aap.2026.108450 · ExternalCitation · doi-reference
The safety risks of roadside trees: An analysis of injury outcomes in vehicle-tree collisions
10.1016/j.aap.2026.108751 · ExternalCitation · doi-reference
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost
10.1016/j.compenvurbsys.2022.101845 · ExternalCitation · doi-reference
Using geographically weighted logistic regression (GWLR) for pedestrian crash severity modeling: Exploring spatially varying relationships with natural and built environment factors
10.1016/j.iatssr.2023.07.004 · ExternalCitation · doi-reference
Mixed logit model based diagnostic analysis of bicycle-vehicle crashes at daytime and nighttime
10.1016/j.ijtst.2021.10.001 · ExternalCitation · doi-reference
Cyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates
10.1016/j.jsr.2020.02.006 · ExternalCitation · doi-reference
Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities
10.1016/j.jsr.2021.02.015 · ExternalCitation · doi-reference
Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes
10.1016/j.jsr.2021.09.005 · ExternalCitation · doi-reference
Factors impacting bike crash severity in urban areas
10.1016/j.jsr.2022.08.010 · ExternalCitation · doi-reference
Cyclist crash severity modeling: A hybrid approach of XGBoost-SHAP and random parameters logit with heterogeneity in means and variances
10.1016/j.jsr.2025.04.003 · ExternalCitation · doi-reference
Implementing safety leading indicators in construction: Toward a proactive approach to safety management
10.1016/j.ssci.2022.105929 · ExternalCitation · doi-reference
Predicting injury-severity for cyclist crashes using natural language processing and neural network modelling
10.1016/j.ssci.2023.106153 · ExternalCitation · doi-reference
Injury severity of police officers involved in traffic crashes: A spatial analysis of Alabama
10.1016/j.ssci.2023.106406 · ExternalCitation · doi-reference
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
10.1016/j.tbs.2020.02.003 · ExternalCitation · doi-reference
Comparing built environment effects on bike-sharing and electric bike-sharing usage: A spatiotemporal machine learning approach
10.1016/j.tra.2025.104642 · ExternalCitation · doi-reference
Multiscale geographical random forest: A novel spatial ML approach for traffic safety modeling integrating street-view semantic visual features
10.1016/j.trc.2025.105299 · ExternalCitation · doi-reference
Are gates at rail grade crossings always safe? Examining motorist gate-violation behaviors using path analysis
10.1016/j.trf.2018.03.014 · ExternalCitation · doi-reference
Cycling during and after COVID: Has there been a boom in activity?
10.1016/j.trf.2023.09.017 · ExternalCitation · doi-reference
An analysis of bicycle accidents with respect to spatial heterogeneity
10.1038/s41598-023-49143-9 · ExternalCitation · doi-reference
AI-based prediction of traffic crash severity for improving road safety and transportation efficiency
10.1038/s41598-025-10970-7 · ExternalCitation · doi-reference
10.1061/9780784483152.010
10.1061/9780784483152.010 · ExternalCitation · doi-reference
Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis
10.1068/a301905 · ExternalCitation · doi-reference
COVID-19 and cycling: A review of the literature on changes in cycling levels and government policies from 2019 to 2022
10.1080/01441647.2023.2205178 · ExternalCitation · doi-reference
Statistical analysis of cyclists’ injury severity at unsignalized intersections
10.1080/15389588.2014.969802 · ExternalCitation · doi-reference
The effect of speed limit reductions in urban areas on cyclists’ injuries in collisions with cars
10.1080/15389588.2019.1680836 · ExternalCitation · doi-reference
Severity analysis of road transport accidents of hazardous materials with machine learning
10.1080/15389588.2021.1900569 · ExternalCitation · doi-reference
Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data
10.1080/15472450.2022.2106564 · ExternalCitation · doi-reference
Analyzing severity of vehicle-bicycle crashes: An explainable boosting machine strategy
10.1080/19427867.2025.2500817 · ExternalCitation · doi-reference
Tobler’s first law and spatial analysis
10.1111/j.1467-8306.2004.09402005.x · ExternalCitation · doi-reference
Collinearity: A review of methods to deal with it and a simulation study evaluating their performance
10.1111/j.1600-0587.2012.07348.x · ExternalCitation · doi-reference
Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model
10.1155/2019/8521649 · ExternalCitation · doi-reference
Analysis and Prediction of Traffic Accidents Based on Interpretable Spatial Machine Learning: A Case Study in California
10.1155/atr/3184284 · ExternalCitation · doi-reference
Influence of Type of Traffic Control on Injury Severity in Bicycle-Motor Vehicle Crashes at Intersections
10.1177/0361198118773576 · ExternalCitation · doi-reference
Revisiting Hit-and-Run Crashes: A Geo-Spatial Modeling Method
10.1177/0361198118773889 · ExternalCitation · doi-reference
Cyclist crash comparison of mixing zone and fully split phase signal treatments at intersections with protected bicycle lanes in New York City
10.1177/0361198119859301 · ExternalCitation · doi-reference
Analyzing freeway safety influencing factors using the CatBoost model and interpretable machine-learning framework, SHAP
10.1177/03611981231208903 · ExternalCitation · doi-reference
The impact of transportation infrastructure on bicycling injuries and crashes: A review of the literature
10.1186/1476-069x-8-47 · ExternalCitation · doi-reference
10.1186/s12889-022-14402-3
10.1186/s12889-022-14402-3 · ExternalCitation · doi-reference
Safe system approach to preventing cyclist fatalities: Safety by design for urban and rural environments
10.1186/s40621-025-00621-w · ExternalCitation · doi-reference
10.1371/journal.pone.0315692
10.1371/journal.pone.0315692 · ExternalCitation · doi-reference
Factors influencing injury severity of cyclists involved in crashes with motor vehicles: Bike lanes, alcohol, lighting, speed, and helmet use
10.14423/smj.0000000000000665 · ExternalCitation · doi-reference
10.20944/preprints202503.0643.v1
10.20944/preprints202503.0643.v1 · ExternalCitation · doi-reference
Safer Cycling Through Improved Infrastructure
10.2105/ajph.2016.303507 · ExternalCitation · doi-reference
Comparative Analysis of Injury Severity Resulting from Pedestrian-Motor Vehicle and Bicycle-Motor Vehicle Crashes on Roadways in Alabama
10.3141/2514-09 · ExternalCitation · doi-reference
10.3390/app12189156
10.3390/app12189156 · ExternalCitation · doi-reference
10.3390/ijerph17207466
10.3390/ijerph17207466 · ExternalCitation · doi-reference
10.3390/su14010215
10.3390/su14010215 · ExternalCitation · doi-reference
10.3390/su16177280
10.3390/su16177280 · ExternalCitation · doi-reference
10.4135/9780857020130.n13
10.4135/9780857020130.n13 · ExternalCitation · doi-reference
