Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections
Tài liệu tham khảo
Aksjonov, 2018, A novel driver performance model based on machine learning, IFAC-PapersOnLine, 51, 267, 10.1016/j.ifacol.2018.07.044
Aksjonov, 2018, Detection and evaluation of driver distraction using machine learning and fuzzy logic, IEEE Trans. Intell. Transp. Syst., 20, 2048, 10.1109/TITS.2018.2857222
Alibabai, 2008, Dynamic origin-destination demand estimation using turning movement counts, Transp. Res. Rec., 2085, 39, 10.3141/2085-05
Breiman, 2001, Random forests, Machine Learn., 45, 5, 10.1023/A:1010933404324
Cai, 2019, Applying a deep learning approach for transportation safety planning by using high-resolution transportation and land use data, Transport. Res. Part A: Policy Practice, 127, 71
Cascetta, 1993, Dynamic estimators of origin-destination matrices using traffic counts, Transport. Sci., 27, 363, 10.1287/trsc.27.4.363
Charouh, 2019, Improved background subtraction-based moving vehicle detection by optimizing morphological operations using machine learning
Chicco, 2021, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 7, 10.7717/peerj-cs.623
Elith, 2008, A working guide to boosted regression trees, J. Anim. Ecol., 77, 802, 10.1111/j.1365-2656.2008.01390.x
Falamarzi, 2018, Development of a random forests regression model to predict track degradation index: Melbourne case study
Fang, 2018, Driver risk assessment using traffic violation and accident data by machine learning approaches
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189
Friedman, 2003, Multiple additive regression trees with application in epidemiology, Stat. Med., 22, 1365, 10.1002/sim.1501
Genuer, 2010, Variable selection using random forests, Pattern Recogn. Lett., 31, 2225, 10.1016/j.patrec.2010.03.014
Geurts, 2006, Extremely randomized trees, Machine Learn., 63, 3, 10.1007/s10994-006-6226-1
Ghanim, 2018, Estimating turning movements at signalized intersections using artificial neural networks, IEEE Trans. Intelligent Transport. Syst., 99, 1
Ghanim, 2019, A Case study for surrogate safety assessment model in predicting real-life conflicts, Arabian J. Sci. Eng., 44, 4225, 10.1007/s13369-018-3326-8
Gholami, 2016, Using stop bar detector information to determine turning movement proportions in shared lanes, J. Adv. Transport., 50, 802, 10.1002/atr.1376
Gwak, 2018, Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance
Heyns, 2019, Predicting traffic phases from car sensor data using machine learning, Procedia Computer Sci., 151, 92, 10.1016/j.procs.2019.04.016
Hua, 2016, We can track you if you take the metro: tracking metro riders using accelerometers on smartphones, IEEE Trans. Inf. Forensics Secur., 12, 286, 10.1109/TIFS.2016.2611489
Iranitalab, 2017, Comparison of four statistical and machine learning methods for crash severity prediction, Accid. Anal. Prev., 108, 27, 10.1016/j.aap.2017.08.008
Jahangiri, 2015, Applying machine learning techniques to transportation mode recognition using mobile phone sensor data, IEEE Trans. Intell. Transp. Syst., 16, 2406, 10.1109/TITS.2015.2405759
Julio, 2016, Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms, Res. Transport. Econ., 59, 250, 10.1016/j.retrec.2016.07.019
Lai, 2018, Understanding drivers' route choice behaviours in the urban network with machine learning models, IET Intel. Transport Syst., 13, 427, 10.1049/iet-its.2018.5190
Lin, 1983, 673
Liu, 2019, A tailored machine learning approach for urban transport network flow estimation, Transport. Res. Part C, 108, 130, 10.1016/j.trc.2019.09.006
Martin, 1992, Network programming to derive turning movements from link flows, Transp. Res.
Mason, 1999, Boosting algorithms as gradient descent, Adv. Neural Inf. Process. Syst., 12, 512
Müller, 2018, Real-time crash severity estimation with machine learning and 2D mass-spring-damper model
Nagao, 2018, Estimation of crowd density applying wavelet transform and machine learning, Physica A: Statist. Mech. Appl., 510, 145, 10.1016/j.physa.2018.06.078
PBC. (2021). Palm Beach County, Engineering and Public Works, Traffic Division - Hand Turning Movement Counts. Retrieved from: https://discover.pbcgov.org/engineering/traffic/Pages/default.aspx.
Pedregosa, 2011, Scikit-learn: machine learning in python, J. Machine Learn. Res., 12, 2825
Rodriguez-Galiano, 2012, An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens., 67, 93, 10.1016/j.isprsjprs.2011.11.002
Santur, 2016, Random forest based diagnosis approach for rail fault inspection in railways
Schaefer, 1988, Estimation of intersection turning movements from approach counts, ITE J., 58, 41
Shaaban, 2018, Evaluation of transit signal priority implementation for bus transit along a major arterial using microsimulation, Procedia Comp. Sci., 130, 82, 10.1016/j.procs.2018.04.015
Shaaban, 2019, A Strategy for Emergency Vehicle Preemption and Route Selection, Arabian J. Sci. Eng., 44, 8905, 10.1007/s13369-019-03913-8
Sharma, 2018, Data-driven optimization of railway maintenance for track geometry, Transport. Res. Part C: Emerg. Technol., 90, 34, 10.1016/j.trc.2018.02.019
Shirazi, 2016, Vision-based turning movement monitoring: count, speed & waiting time estimation, IEEE Intell. Transp. Syst. Mag., 8, 23, 10.1109/MITS.2015.2477474
Tageldin, 2015, Automated analysis and validation of right-turn merging behavior, J. Transport. Safety Security, 7, 138, 10.1080/19439962.2014.942019
Toran Pour, 2017, Modelling pedestrian crash severity at mid-blocks, Transportmetrica A: Transp. Sci., 13, 273, 10.1080/23249935.2016.1256355
Tsiligkaridis, 2017, Anomaly detection in transportation networks using machine learning techniques
Wu, 2001, An O-D based method for estimating link and turning volume based on counts
Yang, 2018, A novel car-following control model combining machine learning and kinematics models for automated vehicles, IEEE Trans. Intell. Transp. Syst., 20, 1991, 10.1109/TITS.2018.2854827
Zaki, 2014, Use of drivers' jerk profiles in computer vision-based traffic safety evaluations, Transp. Res. Rec., 2434, 103, 10.3141/2434-13
Zhou, 2019, Bike-sharing or taxi? modeling the choices of travel mode in Chicago using machine learning, J. Transp. Geogr., 79, 102479, 10.1016/j.jtrangeo.2019.102479