Intelligent collision risk detection in medium-sized cities of developing countries, using naturalistic driving: A review
Tài liệu tham khảo
Abdelrahman, 2018, Data-driven robust scoring approach for driver profiling applications, 2018
Amiri, 2020, Comparing the efficiency of different computation intelligence techniques in predicting accident frequency, IATSS Research, 44, 285, 10.1016/j.iatssr.2020.03.003
Bao, 2019, A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data, Accident Analysis & Prevention, 122, 239, 10.1016/j.aap.2018.10.015
Camlica, 2017, Feature abstraction for driver behaviour detection with stacked sparse auto-encoders
Chang, 2011, LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2, 1, 10.1145/1961189.1961199
Custer
Daptardar, 2015, Hidden Markov model based driving event detection and driver profiling from mobile inertial sensor data, 2015
Deisenroth, 2020
Deng, 2017, Driving style recognition method using braking characteristics based on hidden Markov model, PLoS ONE, 12, 10.1371/journal.pone.0182419
Ehsani, 2021, Naturalistic driving studies: an overview and international perspective, International Encyclopedia of Transportation, 7, 20, 10.1016/B978-0-08-102671-7.10651-7
Ferreira, 2017, Driver behavior profiling: an investigation with different smartphone sensors and machine learning, PLoS ONE, 12, 10.1371/journal.pone.0174959
Frank, 2017
Formosa, 2020, Predicting real-time traffic conflicts using deep learning, Accident Analysis & Prevention, 136, 10.1016/j.aap.2019.105429
Google Trends
Guo, 2010, Near crashes as crash surrogate for naturalistic driving studies, Transportation Research Record, 2147, 66, 10.3141/2147-09
Gutierrez-Osorio, 2020, Modern data sources and techniques for analysis and forecast of road accidents: a review, Journal of Traffic and Transportation Engineering (English Edition), 7, 432, 10.1016/j.jtte.2020.05.002
Hamzeie, 2017, Driver speed selection and crash risk: insights from the naturalistic driving study, Journal of Safety Research, 63, 187, 10.1016/j.jsr.2017.10.007
Huang, 2019, Objective and subjective analysis to quantify influence factors of driving risk, 2019
Liu, 2019, Brake maneuver prediction-an inference leveraging RNN focus on sensor confidence, 2019
Lotan, 2018
Mehdizadeh, 2020, A review of data analytic applications in road traffic safety. Part 1: descriptive and predictive modeling, Sensors, 20, 1107, 10.3390/s20041107
Moher, 2009, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, Annals of Internal Medicihe, 151, 264, 10.7326/0003-4819-151-4-200908180-00135
Monselise, 2019, Identifying important risk factors associated with vehicle injuries using driving behavior data and predictive analytics
Moreno Lozada, 2018
Muñoz, 2018
Najaf, 2018, Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models, International Journal of Injury Control and Safety Promotion, 25, 3, 10.1080/17457300.2017.1285789
Naji, 2018, Evaluating the driving risk of near-crash events using a mixed-ordered logit model, Sustainability, 10, 2868, 10.3390/su10082868
Nugroho, 2018, Utilization of onboard diagnostic Ⅱ (OBD-Ⅱ) on four-wheel vehicles for car data recorder prototype, 2018
Osman, 2019, Prediction of near-crashes from observed vehicle kinematics using machine learning, Transportation Research Record, 2673, 463, 10.1177/0361198119862629
Parsa, 2020, Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis, Accident Analysis & Prevention, 136, 10.1016/j.aap.2019.105405
Pedregosa, 2011, Scikit-learn: machine learning in Python, Journal of Machine Learning Research, 12, 2825
Peng, 2019, Rough set based method for vehicle collision risk assessment through inferring driver's braking actions in near-crash situations, IEEE Intelligent Transportation Systems Magazine, 11, 54, 10.1109/MITS.2019.2903539
Perez, 2017, Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data, Accident Analysis & Prevention, 103, 10, 10.1016/j.aap.2017.03.005
Salazar-Cabrera, 2019, Methodology for design of an intelligent transport system (ITS) architecture for intermediate Colombian City, Ingeniería Y Competitividad, 21, 49, 10.25100/iyc.v21i1.7654
Shrp2
Tao, 2015, The traffic accident hotspot prediction: based on the logistic regression method, 2015
Vlahogianni, 2017, Driving analytics using smartphones: algorithms, comparisons and challenges, Transportation Research Part C: Emerging Technologies, 79, 196, 10.1016/j.trc.2017.03.014
Wali, 2019, Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—concept of event-based driving volatility, Accident Analysis & Prevention, 132, 10.1016/j.aap.2019.105277
Wang, 2015, Driving risk assessment using near-crash database through data mining of tree-based model, Accident Analysis & Prevention, 84, 54, 10.1016/j.aap.2015.07.007
WHO
WHO
Wu, 2016, A novel model-based driving behavior recognition system using motion sensors, Sensors, 16, 1746, 10.3390/s16101746
Xiong, 2019, A forward collision avoidance algorithm based on driver braking behavior, Accident Analysis & Prevention, 129, 30, 10.1016/j.aap.2019.05.004
Ziakopoulos, 2019, A critical overview of driver recording tools, Journal of Safety Research, 72, 203, 10.1016/j.jsr.2019.12.021