Journal of Transportation Technologies

Journal of Transportation Technologies

ISSN Print: 2160-0473
ISSN Online: 2160-0481
www.scirp.org/journal/jtts
E-mail: jtts@scirp.org
"Crash Frequency Analysis"
written by Azad Abdulhafedh,
published by Journal of Transportation Technologies, Vol.6 No.4, 2016
has been cited by the following article(s):
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[1] Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest
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[2] Combining the Deep Neural Network with the K-Means for Traffic Accident Prediction
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[3] Modeling and evaluation of micromobility in a multi-modal transportation system.
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[4] A Delphi-FMEA model to assess county-level speeding crash risk in North Dakota
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[6] Comparison between Common Statistical Modeling Techniques Used in Research, Including: Discriminant Analysis vs Logistic Regression, Ridge …
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[7] Modeling Vehicle Crash Frequency When Multicollinearity Exists in Vehicle Crash Data: Ridge Regression versus Ordinary Least Squares Linear Regression
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[8] Addressing Traffic Congestion by Using of the Counter-Intuitive Phenomenon of Braess' Paradox in Transportation Networks
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[9] Session: Road user behaviour and risk perception, part
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[10] Developing transferable real-time crash prediction models for highly imbalanced data
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[11] CRASH PREDICTION ON ROAD SEGMENTS USING MACHINE LEARNING METHODS
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[12] Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads
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[13] Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects
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[14] Safety analytics at a granular level using a Gaussian process modulated renewal model: a case study of the COVID-19 pandemic
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[15] Vehicle Crash Frequency Analysis Using Ridge Regression
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[16] Incorporating K-means, Hierarchical Clustering and PCA in Customer Segmentation
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[17] Data-driven urban traffic accident analysis and prediction using logit and machine learning-based pattern recognition models
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[18] Safety Effects of Geometric Design Consistency on Two-lane Rural Highways: The Case of Ethiopia
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[19] A review on neural network techniques for the prediction of road traffic accident severity
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[20] Understanding how relationships between crash frequency and correlates vary for multilane rural highways: Estimating geographically and temporally weighted …
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[21] Evaluating the impact of socio-economic contributing factors of cities in California on their traffic safety condition
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[22] Predicting Critical Near-Crashes at Signalized Intersections
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[23] 개별 차량 궤적 자료를 활용한 동적 운전 피로 및 과속 위험도 융합 분석
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[24] Legal analysis of implementation of the traffic accident monitoring system
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[25] A Comparative Study of Machine Learning Algorithms to Predict Road Accident Severity
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[26] التنبؤ بشدة خطورة الحوادث المرورية في مدينة اللاذقية باستخدام تقنيات الذكاء الصنعي‎
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[27] A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey
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[28] Prediction of Fatality Crashes with Multilayer Perceptron of Crash Record Information System Datasets
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[29] A Parametric Model for Accident Prediction Along Ado Ekiti–Ikole Ekiti Road, Ekiti State, Nigeria
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[30] Predicting critical bicycle-vehicle conflicts at signalized intersections
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[31] Analysis of crash frequency model: Study case of Indonesia toll roads
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[32] Highway Stopping Sight Distance, Decision Sight Distance, and Passing Sight Distance Based on AASHTO Models
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[33] Backpropagation–Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom
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[34] ANALYSIS AND FORECASTING OF ROAD TRAFFIC ACCIDENTS IN YANGON MUNICIPAL AREA (2014-2018)
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[35] Development of Crash Prediction Model for Hill Roads: A Case Study of NH06, Nepal
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[36] Application of GIS for Urban Traffic Accidents: A Critical Review
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[37] Micro-level safety risk assessment model for a two-lane heterogeneous traffic environment in a developing country: A comparative crash probability modeling …
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[38] Metro transit system resilience: Understanding the impacts of outdoor tracks and weather conditions on metro system interruptions
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[39] How Vehicle Types and Operator's Legal Status Affect Safety of Interprovincial Buses in Thailand
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[40] Feasibility Analysis of an Improved Machine Learning Approach for the Prediction of Road Traffic Accidents
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[41] Development of Crash Frequency Prediction Model and Identification of Hazardous site locations: A case study of BP highway
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[42] Bayesian Statistical Modeling of Claim Frequency for Auto Insurance
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[43] Applications of Data Mining Techniques in Transportation Safety Study
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[44] Application of Data Mining Techniques in Transportation Safety Study
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[45] Investigating the link between the built environment and the incidence of pedestrian crashes in Cape Town, South Africa
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[46] How Operators' Legal Status Affects Safety of Intercity Buses in Thailand
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[47] Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams
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[48] A SYSTEMIC PEDESTRIAN SAFETY PLANNING TOOL FOR RURAL AND SMALL URBAN AREAS
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[49] Statistische Analyse von Unfallzahlen
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[50] A comparative evaluation of the effects of categorical factors on the safety of multi-lane Interstate highways
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[51] Development of Safety Performance Measures for Urban Mid-Blocks
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[52] Road crash prediction models: different statistical modeling approaches
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[53] Road traffic crash data: an overview on sources, problems, and collection methods
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[54] Predicting Highway Crash Frequency: A Review of Statistical Regression Models
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[55] Crash severity modeling in transportation systems
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[56] Asian Transport Studies
[57] CRASH ANALYSIS AND PREDICTION FRAMEWORK: ENHANCING SAFETY THROUGH ROADWAY INFRASTRUCTURE AUGMENTATION
[58] Applying The K-Means, The Hierarchical Clustering, and the Principal Component Analysis (PCA) in Customer Segmentation of a Credit Card Company Based …
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