{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:38:40Z","timestamp":1769852320116,"version":"3.49.0"},"reference-count":73,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many factors that affect road safety. On the other hand, this issue is a dynamic problem, which means that it is always changing. So, there is a dire need for a thorough evaluation of road safety to deal with complex and uncertain problems. For this purpose, two machine learning methods called \u201cfeature selection algorithms\u201d are used. These algorithms include a combination of artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. In this study, two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic. When the performance and results of the two models were compared, the results showed that the two models made the same choices. In rural areas, the type of accident and the location were chosen as the highest and lowest priorities, respectively. According to the results, useful suggestions regarding the improvement of road safety on urban and rural roads were provided. The average speed and location were considered the highest and lowest priorities in urban areas, respectively. Finally, there was not a big difference between the results of the two algorithms in terms of how well the algorithm models worked, but the proposed PSO model converged more quickly than the proposed DE model.<\/jats:p>","DOI":"10.3390\/computers11100145","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T21:14:28Z","timestamp":1664140468000},"page":"145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Application of Feature Selection Approaches for Prioritizing and Evaluating the Potential Factors for Safety Management in Transportation Systems"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-4123","authenticated-orcid":false,"given":"Giuseppe","family":"Guido","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy"}]},{"given":"Sami","family":"Shaffiee Haghshenas","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2859-3920","authenticated-orcid":false,"given":"Sina","family":"Shaffiee Haghshenas","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7163-8114","authenticated-orcid":false,"given":"Alessandro","family":"Vitale","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3673-9814","authenticated-orcid":false,"given":"Vittorio","family":"Astarita","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3141\/1746-02","article-title":"Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections","volume":"1746","author":"Abdelwahab","year":"2001","journal-title":"Transp. Res. Rec."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3141\/1784-15","article-title":"Artificial neural networks and logit models for traffic safety analysis of toll plazas","volume":"1784","author":"Abdelwahab","year":"2002","journal-title":"Transp. Res. Rec."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/0001-4575(94)90021-3","article-title":"Simulation of traffic conflicts at unsignalized intersections with TSC-Sim","volume":"26","author":"Sayed","year":"1994","journal-title":"Accid. Anal. Prev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abduljabbar, R., Dia, H., Liyanage, S., and Bagloee, S.A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11.","DOI":"10.3390\/su11010189"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.aap.2017.02.022","article-title":"Crash data quality for road safety research: Current state and future directions","volume":"130","author":"Imprialou","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.aap.2017.04.007","article-title":"Analysis of factors affecting the severity of crashes in urban road intersections","volume":"103","author":"Mussone","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_7","first-page":"100142","article-title":"Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions","volume":"29","author":"Zheng","year":"2021","journal-title":"Anal. Meth. Accid. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105971","DOI":"10.1016\/j.aap.2021.105971","article-title":"Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters","volume":"152","author":"Yang","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.tra.2010.02.001","article-title":"The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives","volume":"44","author":"Lord","year":"2010","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.aap.2017.02.001","article-title":"Methodological considerations with data uncertainty in road safety analysis","volume":"130","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1177\/0361198119841555","article-title":"Application of Extreme Value Theory for Before-After Road Safety Analysis","volume":"2673","author":"Zheng","year":"2019","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/S0001-4575(99)00094-9","article-title":"Modeling traffic accident occurrence and involvement","volume":"32","author":"Radwan","year":"2000","journal-title":"Accid. Anal. Prev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1016\/j.aap.2010.03.013","article-title":"Multilevel data and Bayesian analysis in traffic safety","volume":"42","author":"Huang","year":"2010","journal-title":"Accid. Anal. Prev."},{"key":"ref_14","unstructured":"Chong, M.M., Abraham, A., and Paprzycki, M. (2004). Traffic accident analysis using decision trees and neural networks. arXiv."},{"key":"ref_15","first-page":"2832","article-title":"A neural network (NN) model to predict intersection crashes based upon driver, vehicle and roadway surface characteristics","volume":"5","author":"Darcin","year":"2010","journal-title":"J. Sci. Res. Essays"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.sbspro.2011.08.028","article-title":"Investigating road safety issues through a microsimulation model","volume":"20","author":"Astarita","year":"2011","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.sbspro.2011.08.027","article-title":"Safety performance measures: A comparison between microsimulation and observational data","volume":"20","author":"Guido","year":"2011","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_18","first-page":"41","article-title":"Prediction of accident severity using artificial neural networks","volume":"9","author":"Moghaddam","year":"2011","journal-title":"Int. J. Civ. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.aap.2014.01.008","article-title":"Simulation of safety: A review of the state of the art in road safety simulation modelling","volume":"66","author":"Young","year":"2014","journal-title":"Accid. Anal. Prev."},{"key":"ref_20","first-page":"175","article-title":"Effects of calibration process on the simulation of rear-end conflicts at roundabouts","volume":"6","author":"Gallelli","year":"2019","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/0001-4575(96)00009-7","article-title":"Statistical analysis of accident severity on rural freeways","volume":"28","author":"Shankar","year":"1996","journal-title":"Accid. Anal. Prev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.trpro.2018.12.186","article-title":"Surrogate Safety Measures from Traffic Simulation Models a Comparison of different Models for Intersection Safety Evaluation","volume":"37","author":"Astarita","year":"2019","journal-title":"Transp. Res. Procedia"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105487","DOI":"10.1016\/j.aap.2020.105487","article-title":"Investigation into passing behavior at passing zones to validate and extend the use of driving simulators in two-lane roads safety analysis","volume":"139","author":"Karimi","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_24","first-page":"12","article-title":"Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks","volume":"10","author":"Zeng","year":"2016","journal-title":"Anal. Meth. Accid. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10462-016-9467-9","article-title":"Artificial intelligence techniques for driving safety and vehicle crash prediction","volume":"46","author":"Halim","year":"2016","journal-title":"Artif. Intell. Rev."},{"key":"ref_26","first-page":"1","article-title":"An improved approach for association rule mining using a multi-criteria decision support system: A case study in road safety","volume":"9","author":"Gharnati","year":"2017","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fernandes, B., Vicente, H., Ribeiro, J., Analide, C., and Neves, J. (2018, January 20\u201322). Evolutionary Computation on Road Safety. Proceedings of the International Conference on Hybrid Artificial Intelligence Systems, Oviedo, Spain.","DOI":"10.1007\/978-3-319-92639-1_54"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/15389588.2018.1471599","article-title":"Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network","volume":"19","author":"Xu","year":"2018","journal-title":"Traffic Inj. Prev."},{"key":"ref_29","first-page":"775","article-title":"Machine learning applied to road safety modeling: A systematic literature review","volume":"7","author":"Silva","year":"2020","journal-title":"J. Transp. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107484","DOI":"10.1016\/j.comnet.2020.107484","article-title":"Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems","volume":"182","author":"Boukerche","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Afework, A., and Sipos, T. (2020, January 21\u201323). Modelling of accidents for four lane non-urban highways using artificial neural networks technique. Proceedings of the 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.","DOI":"10.1109\/SACI49304.2020.9118819"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guido, G., Haghshenas, S.S., Haghshenas, S.S., Vitale, A., Astarita, V., and Haghshenas, A.S. (2020). Feasibility of stochastic models for evaluation of potential factors for safety: A case study in Southern Italy. Sustainability, 12.","DOI":"10.3390\/su12187541"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guido, G., Haghshenas, S.S., Haghshenas, S.S., Vitale, A., Gallelli, V., and Astarita, V. (2020). Development of a binary classification model to assess safety in transportation systems using GMDH-type neural network algorithm. Sustainability, 12.","DOI":"10.3390\/su12176735"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tonni, S.I., Aka, T.A., Antik, M.M., Taher, K.A., Mahmud, M., and Kaiser, M.S. (2021, January 27\u201328). Artificial intelligence based driver vigilance system for accident prevention. Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh.","DOI":"10.1109\/ICICT4SD50815.2021.9396916"},{"key":"ref_35","unstructured":"Ministero delle Infrastrutture e dei Trasporti (2012). Studio di Valutazione dei Costi Sociali dell\u2019incidentalit\u00e0 Stradale."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106332","DOI":"10.1016\/j.aap.2021.106332","article-title":"Correlated mixed logit modeling with heterogeneity in means for crash severity and surrogate measure with temporal instability","volume":"160","author":"Wang","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-020-04081-3","article-title":"Prediction and analysis of the severity and number of suburban accidents using logit model, factor analysis and machine learning: A case study in a developing country","volume":"3","author":"Ghasedi","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106034","DOI":"10.1016\/j.aap.2021.106034","article-title":"Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models","volume":"153","author":"Yan","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Haghshenas, S.S., Haghshenas, S.S., Geem, Z.W., Kim, T.H., Mikaeil, R., Pugliese, L., and Troncone, A. (2021). Application of harmony search algorithm to slope stability analysis. Land, 10.","DOI":"10.3390\/land10111250"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guido, G., Haghshenas, S.S., Vitale, A., and Astarita, V. (2022, January 17\u201320). Challenges and Opportunities of Using Data Fusion Methods for Travel Time Estimation. Proceedings of the 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), Istanbul, Turkey.","DOI":"10.1109\/CoDIT55151.2022.9804014"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Guido, G., Haghshenas, S.S., Haghshenas, S.S., Vitale, A., Gallelli, V., and Astarita, V. (2022). Prioritizing the Potential Smartification Measures by Using an Integrated Decision Support System with Sustainable Development Goals (a Case Study in Southern Italy). Safety, 8.","DOI":"10.3390\/safety8020035"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guido, G., Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., Vitale, A., Astarita, V., Park, Y., and Geem, Z.W. (2022). Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy. Safety, 8.","DOI":"10.3390\/safety8020028"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4685","DOI":"10.1007\/s10706-022-02178-7","article-title":"Developing the Rule of Thumb for Evaluating Penetration Rate of TBM, Using Binary Classification","volume":"40","author":"Akbarzadeh","year":"2022","journal-title":"Geotech. Geol. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105468","DOI":"10.1016\/j.aap.2020.105468","article-title":"A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed object crashes among elderly drivers","volume":"138","author":"Amiri","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1080\/15389588.2020.1770238","article-title":"Forecasting deaths of road traffic injuries in China using an artificial neural network","volume":"21","author":"Qian","year":"2020","journal-title":"Traffic Inj. Prev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"117266","DOI":"10.1016\/j.conbuildmat.2019.117266","article-title":"Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer","volume":"232","author":"Golafshani","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.jobe.2018.01.007","article-title":"Compressive strength prediction of environmentally friendly concrete using artificial neural networks","volume":"16","author":"Naderpour","year":"2018","journal-title":"J. Build. Eng."},{"key":"ref_48","first-page":"20","article-title":"Predicting the torsional strength of reinforced concrete beams strengthened with FRP sheets in terms of artificial neural networks","volume":"5","author":"Naderpour","year":"2018","journal-title":"J. Struct. Constr. Eng."},{"key":"ref_49","first-page":"52","article-title":"Development of intelligent systems to predict diamond wire saw performance","volume":"1","author":"Mikaeil","year":"2017","journal-title":"J. Soft Comput. Civ. Eng."},{"key":"ref_50","unstructured":"Eidgahee, D.R., Jahangir, H., Solatifar, N., Fakharian, P., and Rezaeemanesh, M. (2022). Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches. Neural Comput. Appl., 1\u201326."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Se, C., Champahom, T., Jomnonkwao, S., and Ratanavaraha, V. (2022). Motorcyclist injury severity analysis: A comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances. Int. J. Inj. Contr. Saf. Promot., 1\u201316.","DOI":"10.1080\/17457300.2022.2081985"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1260\/2046-0430.1.4.305","article-title":"Characteristics and prediction of traffic accident casualties in Sudan using statistical modeling and artificial neural networks","volume":"1","author":"Ali","year":"2012","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sanayei, R., Vafaeinejad, A., Karami, J., and Zanjirabad, H.A. (2021). A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway. Geocarto Int., 1\u201317.","DOI":"10.1080\/10106049.2021.1871669"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105302","DOI":"10.1016\/j.ssci.2021.105302","article-title":"Road surface conditions forecasting in rainy weather using artificial neural networks","volume":"140","author":"Kim","year":"2021","journal-title":"Saf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"19397","DOI":"10.1038\/s41598-020-76569-2","article-title":"A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting","volume":"10","author":"Huang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s11069-019-03688-z","article-title":"Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: The second part of Emamzade Hashem tunnel)","volume":"97","author":"Mikaeil","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/3852194","article-title":"The Importance of Exercise and General Mental Health on Prediction of Property-Damage-Only Accidents among Taxi Drivers in Tehran: A Study Using ANFIS-PSO and Regression Models","volume":"2019","author":"Abbasi","year":"2019","journal-title":"J. Adv. Transp."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1007\/s10706-020-01213-9","article-title":"Feasibility of intelligent models for prediction of utilization factor of TBM","volume":"38","author":"Noori","year":"2020","journal-title":"Geotech. Geol. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.iatssr.2020.03.003","article-title":"Comparing the efficiency of different computation intelligence techniques in predicting accident frequency","volume":"44","author":"Amiri","year":"2020","journal-title":"IATSS Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Shaffiee Haghshenas, S., Pirouz, B., Shaffiee Haghshenas, S., Pirouz, B., Piro, P., Na, K.S., and Geem, Z.W. (2020). Prioritizing and analyzing the role of climate and urban parameters in the confirmed cases of COVID-19 based on artificial intelligence applications. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17103730"},{"key":"ref_61","unstructured":"Kennedy, J., and Eberhart, R. (1995, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-international conference on neural networks, Perth, WA, Australia."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","article-title":"Particle swarm optimization","volume":"1","author":"Poli","year":"2007","journal-title":"Swarm Intell."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Xu, X., Zeng, Z., Wang, Y., and Ash, J. (2019, January 5\u20138). Crash Density and Severity Prediction Using Recurrent Neural Networks Combined with Particle Swarm Optimization. Proceedings of the International Conference on Management Science and Engineering Management, Ontario, ON, Canada.","DOI":"10.1007\/978-3-030-21248-3_41"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.jrmge.2019.01.002","article-title":"Application of several optimization techniques for estimating TBM advance rate in granitic rocks","volume":"11","author":"Armaghani","year":"2019","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_65","unstructured":"Storn, R., and Price, K. (1996, January 20\u201322). Minimizing the real functions of the ICEC\u201996 contest by differential evolution. Proceedings of the IEEE International Conference on Evolutionary Computation, Nagoya, Japan."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"55807","DOI":"10.1109\/ACCESS.2019.2913017","article-title":"Prediction of network traffic of smart cities based on DE-BP neural network","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_68","first-page":"990","article-title":"Evaluation of gang saws\u2019 performance in the carbonate rock cutting process using feasibility of intelligent approaches","volume":"22","author":"Dormishi","year":"2019","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.measurement.2018.03.056","article-title":"Application of metaheuristic algorithms to optimal clustering of sawing machine vibration","volume":"124","author":"Aryafar","year":"2018","journal-title":"Measurement"},{"key":"ref_70","unstructured":"World Health Organization (2018). Global Status Report on Road Safety, World Health Organization."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.aap.2019.01.033","article-title":"A comprehensive and unified framework for analysing the effects on injuries of measures influencing speed","volume":"125","author":"Elvik","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_72","first-page":"2138","article-title":"Experimental relationships between operating speeds of successive road design elements in two-lane rural highways","volume":"32","author":"Eboli","year":"2017","journal-title":"Transport"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.aap.2012.10.001","article-title":"New geometric design consistency model based on operating speed profiles for road safety evaluation","volume":"61","year":"2013","journal-title":"Accid. Anal. Prev."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/10\/145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:09Z","timestamp":1760143089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/10\/145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":73,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["computers11100145"],"URL":"https:\/\/doi.org\/10.3390\/computers11100145","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,23]]}}}