ORIGINAL_ARTICLEResearch into lowering the temperature during the production and installation of hot asphalt has been going on for a long time. In addition to developing appropriate recipes, it is important to gain experience, particularly in asphalt paving technology, since in addition to the behavior of the different asphalt mixtures during their compaction, external factors such as the location of the project and weather conditions also play an important role. The testing of the low-temperature asphalt on the federal highway B 169 was carried out successfully. Due to the high pre-compaction by the paver screed, the required compaction values were achieved on the binder course with 4 rolling passes and on the surface course with 3 rolling passes. The viscosity-changing additives and the selected rolling technology had a positive influence on the results. However, precise measurement results for compaction could not be achieved using the PQI probe.https://acc-ern.tul.cz/archiv/PDF/ACC_2024_1_01.pdf2024-06-3071710.2478/acc-2024-0001Low-temperature asphaltBehavior of asphalt mixturesAsphalt-related emissionsPQI probeAlexanderSteinbachalexander.steinbach@cvb-sachsen.de1Chemnitzer Verkehrsbau GmbHAUTHORPeterRottib-pr@outlook.de2Ingenieurbüro Dr.-Ing. P. Rott,AUTHORBMDV. (2021). Allgemeines Rundschreiben Straßenbau (ARS) Nr. 09/2021: Durchführung von Erprobungsstrecken bei Baumaßnahmen an Bundesfernstraßen zum Einsatz von temperaturabgesenktem Walzasphalt in Verbindung mit Absaugeinrichtungen am Straßenfertiger. Bundesministerium für Digitales und Verkehr, Bonn. https://bmdv.bund.de/SharedDocs/DE/Anlage/StB/ars-aktuell/allgemeines-rundschreiben-strassenbau-2021-09.html1DAV. (2021). Technisches Informationspapier Niedrigtemperaturasphalt (NTA). Deutscher Asphaltverband (DAV) e.V., Bonn.2FGSV. (2021). Merkblatt für die Temperaturabsenkung von Asphalt. Forschungsgesellschaft für Straßen- und Verkehrswesen. Arbeitsgruppe Asphaltbauweisen. FGSV der Verlag.3Labtek. (2021). TransTech Non-Nuclear Asphalt Density Gauge. http://labtek.id/pages/content/product/data?ctg=C20210515190329ksK&id=P20210517 161506Zxp4

ORIGINAL_ARTICLEThe purpose of this article is to illustrate the intuitively understood links between the social and economic characteristics of an area and the waste production at a given location. These relationships have been investigated using statistical data from thirteen regions in the Czech Republic between 2019 and 2021. In order to evaluate the data, freely available tools such as Python 3.8.16 and a number of its libraries, e.g. matplotlib, plotly, sklearn, numpy and others, have been used.https://acc-ern.tul.cz/archiv/PDF/ACC_2024_1_02.pdf2024-06-30182310.2478/acc-2024-0002Waste productionSpatial distributionRegionsSocial characteristicsEconomic characteristicsLukášZedeklukas.zedek@tul.cz1Technical University of Liberec, Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Institute of New TechnologiAUTHORJanŠemberajan.sembera@tul.cz2Technical University of Liberec, Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Institute of New TechnologiAUTHORAl-Akel, S. (2023). Die abfallarme Baustelle als Beitrag zur Erhöhung der Verwertungsquote von Abfällen gemäß der Ersatzbaustoffverordnung. In: 19. Kreislaufwirtschaft- und Deponieworkshop Zittau-Liberec 2023, pp. 53–54. https://skladky.tul.cz/skladky23/files/ZittLiWo_sbornik_2023.pdf1ČSÚ. (2021a). Generation, Recovery and Disposal of Waste - 2021. https://csu.gov.cz/produkty/generation-recovery-and-disposal-of-waste-53pmfy4qu82ČSÚ. (2021b). Population - territorial comparison. https://vdb.czso.cz/vdbvo2/faces/en/index.jsf?page=vystup-objekt-parametry&sp=A&skupId=3829&pvokc=&katalog=33155&pvo=DEM13&z=T3ČSÚ. (2021c). Population density (km2). https://vdb.czso.cz/vdbvo2/faces/en/index.jsf?page=vystup-objekt-parametry&sp=A&pvokc=&katalog=30829&pvo=RSO34&z=T4ČSÚ. (2021d). Average monthly gross wage and median of wages - comparison of regions. https://vdb.czso.cz/vdbvo2/faces/en/index.jsf?page=vystup-objekt-parametry&pvo=MZD07&sp=A&pvokc=&katalog=30852&z=T5ČSÚ. (2021e). Selected indicators by industry and household accounts. https://apl.czso.cz/pll/rocenka/rocenkavyber.volba?titul=Selected%20indicators%20by %20industry%20and%20household%20accounts&mypriznak=RD&typ=1&proc=rocen ka.presB&mylang=EN&jak=46Python Software Foundation. (2022). Python 3.8.16. https://www.python.org/downloads7

ORIGINAL_ARTICLEEnsuring public safety on our roads is a top priority, and the prevalence of road accidents is a major concern. Fortunately, advances in machine learning allow us to use data to predict and prevent such incidents. Our study delves into the development and implementation of machine learning techniques for predicting road accidents, using rich datasets from Catalonia and Toronto Fatal Collision. Our comprehensive research reveals that ensemble learning methods outperform other models in most prediction tasks, while Decision Tree and K-NN exhibit poor performance. Additionally, our findings highlight the complexity involved in predicting various aspects of crashes, as the Stacking Regressor shows variability in its performance across different target variables. Overall, our study provides valuable insights that can significantly contribute to ongoing efforts to reduce accidents and their consequences by enabling more accurate predictions.https://acc-ern.tul.cz/archiv/PDF/ACC_2024_1_03.pdf2024-06-30244910.2478/acc-2024-0003Accident preventionMachine learningTraffic safetyRoad safetyAccident forecastingRisk assessment.HananeZermaneh.zermane@univ-batna2.dz1Batna 2 University, Faculty of Technology, Laboratory of Automation and Manufacturing, Industrial Engineering DepartmentAUTHORAbderrahimZermanezermane.abderrahim@gmail.com2University of Putra Malaysia, Faculty of Engineering, Department of Chemical and Environmental Engineering, Safety Engineering IAUTHORMohd Zahirasri Mohd Tohirzahirasri@upm.edu.my3University of Navarra, Department of Construction, Installations and StructuresAUTHORAlkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity Prediction of Traffic Accident Using an Artificial Neural Network. Journal of Forecasting, 36(1), 100–108.110.1002/for.2425Augé, C. G., & Navarro, S. C. i. (2022). Fatal accidents in Catalonia between 2014 and 2022. https://zenodo.org/records/7316989/files/AccidentsMortals_2014-2022_Catalunya.csv?download=12Basagaña, X., & de la Peña-Ramirez, C. (2023). Ambient temperature and risk of motor vehicle crashes: A countrywide analysis in Spain. Environmental Research, 216(October 2022).310.1016/j.envres.2022.114599Behzadi Goodari, M., Sharifi, H., Dehesh, P., Mosleh-Shirazi, M. A., & Dehesh, T. (2023). Factors affecting the number of road traffic accidents in Kerman province, southeastern Iran (2015–2021). Scientific Reports, 13(1), 1–9.410.1038/s41598-023-33571-8Beirigo, B. A., Schulte, F., & Negenborn, R. R. (2018). Integrating People and Freight Transportation Using Shared Autonomous Vehicles with Compartments. IFAC-PapersOnLine, 51(9), 392–397.510.1016/j.ifacol.2018.07.064Bridgelall, R., & Tolliver, D. D. (2024). Railroad accident analysis by machine learning and natural language processing. Journal of Rail Transport Planning and Management, 29(December 2023), 100429.610.1016/j.jrtpm.2023.100429Castro, Y., & Kim, Y. J. (2016). Data mining on road safety: Factor assessment on vehicle accidents using classification models. International Journal of Crashworthiness, 21(2), 104–111.710.1080/13588265.2015.1122278Catalan Traffic Service. (2024). Comunicats d’accidents mortals. https://transit.gencat.cat/ca/el_servei/premsa_i_comunicacio/comunicats_d_accidents_mortals/8Chang, L. Y., & Chien, J. T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17–22.910.1016/j.ssci.2012.06.017Comi, A., Polimeni, A., & Balsamo, C. (2022). Road Accident Analysis with Data Mining Approach: Evidence from Rome. Transportation Research Procedia, 62(Ewgt 2021), 798–805.1010.1016/j.trpro.2022.02.099de Oña, J., de Oña, R., Eboli, L., Forciniti, C., Machado, J. L., & Mazzulla, G. (2014). Analysing the Relationship Among Accident Severity, Drivers’ Behaviour and Their Socio-economic Characteristics in Different Territorial Contexts. Procedia - Social and Behavioral Sciences, 160(Cit), 74–83.1110.1016/j.sbspro.2014.12.118Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444.1210.1016/j.aap.2005.06.024Duarte Monedero, B., A. Gil-Alana, L., & Valbuena Martínez, M. C. (2021). Road accidents in Spain: Are they persistent? IATSS Research, 45(3), 317–325.1310.1016/j.iatssr.2021.01.002Gatera, A., Kuradusenge, M., Bajpai, G., Mikeka, C., & Shrivastava, S. (2023). Comparison of random forest and support vector machine regression models for forecasting road accidents. Scientific African, 21, e01739.1410.1016/j.sciaf.2023.e01739Hashmienejad, S. H.-A., & Hasheminejad, S. M. H. (2017). Traffic accident severity prediction using a novel multi-objective genetic algorithm. International Journal of Crashworthiness, 22(4), 425–440.1510.1080/13588265.2016.1275431Hu, N., Zhang, D., Xie, K., Liang, W., & Hsieh, M. Y. (2022). Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting. Connection Science, 34(1), 429–448.1610.1080/09540091.2021.2006607Insurance Institute for Highway Safety. (2024). Fatality Facts 2021 Yearly snapshot. U.S. Department of Transportation’s. https://www.iihs.org/topics/fatality-statistics/detail/yearly-snapshot17Kang, K., & Ryu, H. (2019). Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science, 120(June), 226–236.1810.1016/j.ssci.2019.06.034Kaplan, S., & Prato, C. G. (2012). Risk factors associated with bus accident severity in the United States: A generalized ordered logit model. Journal of Safety Research, 43(3), 171–180.1910.1016/j.jsr.2012.05.003Kashyap, A. A., Raviraj, S., Devarakonda, A., Nayak K, S. R., Santhosh, K. V., & Bhat, S. J. (2022). Traffic flow prediction models–A review of deep learning techniques. Cogent Engineering, 9(1).2010.1080/23311916.2021.2010510Kaye, S. A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis and Prevention, 137(May 2019), 105441.2110.1016/j.aap.2020.105441Kunt, M. M., Aghayan, I., & Noii, N. (2011). Prediction for traffic accident severity: Comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26(4), 353–366.2210.3846/16484142.2011.635465Lee, H., Kang, M., Hwang, K., & Yoon, Y. (2024). Heliyon The typical AV accident scenarios in the urban area obtained by clustering and association rule mining of real-world accident reports. Heliyon, 10(3), e25000.2310.1016/j.heliyon.2024.e25000Li, R., Pereira, F. C., & Ben-Akiva, M. E. (2018). Overview of traffic incident duration analysis and prediction. European Transport Research Review, 10(2), 1–13.2410.1186/s12544-018-0300-1Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41–49.25Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37.2610.1016/j.ssci.2012.11.001Moriano, P., Berres, A., Xu, H., & Sanyal, J. (2024). Spatiotemporal features of traffic help reduce automatic accident detection time. Expert Systems with Applications, 244(November 2023), 122813.2710.1016/j.eswa.2023.122813Noy, I. Y., Shinar, D., & Horrey, W. J. (2018). Automated driving: Safety blind spots. Safety Science, 102(March 2017), 68–78.2810.1016/j.ssci.2017.07.018Papadoulis, A., Quddus, M., & Imprialou, M. (2019). Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis and Prevention, 124(December 2018), 12–22.2910.1016/j.aap.2018.12.019Rezaei, A., & Caulfield, B. (2020). Examining public acceptance of autonomous mobility. Travel Behaviour and Society, 21(November 2019), 235–246.3010.1016/j.tbs.2020.07.002Rezapour, M., Mehrara Molan, A., & Ksaibati, K. (2020). Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. International Journal of Transportation Science and Technology,3110.1016/j.ijtst.2019.10.002Rubio-Romero, J. C., Carmen Rubio Gámez, M., & Carrillo-Castrillo, J. A. (2013). Analysis of the safety conditions of scaffolding on construction sites. Safety Science, 55, 160–164.3210.1016/j.ssci.2013.01.006Schoettle, B., & Sivak, M. (2014). Public Opinion About Self-Driving Vehicles in China, India, Japan, The U.S., The U.K., and Australia. In UMTRI (UMTRI-2014-3). The University of Michigan Transportation Research Institute, Ann Arbor, Michigan 48109-2150, U.S.A.33Taamneh, M., Alkheder, S., & Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety and Security, 9(2), 146–166.3410.1080/19439962.2016.1152338Toronto Police Service. (2022). Fatal Collisions: Toronto Police Service Public Safety Data Portal. https://data.torontopolice.on.ca/pages/fatalities35WHO. (2018). Global status report on road safety 2018. Licence: CC BYNC- SA 3.0 IGO. https://www.who.int/publications/i/item/978924156568436WHO. (2021). Global Plan Decade of actıon for road safety 2021-2030. https://www.who.int/publications/m/item/global-plan-for-the-decade-of-action-for-road-safety-2021-203037WHO. (2024). WHO: Death on the roads. Global Status Report on Road Safety. https://extranet.who.int/roadsafety/death-on-the-roads/%0Ahttps://extranet.who.int/roadsafety/death-on-the-roads/#trends38WHO Regional Office for Europe. (2009). European status report on road safety: towards safer roads and healthier transport choices. WHO Regional Office for Europe, Copenhagen. https://iris.who.int/handle/10665/10726639Yan, X., Radwan, E., & Abdel-Aty, M. (2005). Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accident Analysis and Prevention, 37(6), 983–995.4010.1016/j.aap.2005.05.001Yannis, G., Dragomanovits, A., Laiou, A., La Torre, F., Domenichini, L., Richter, T., Ruhl, S., Graham, D., & Karathodorou, N. (2017). Road traffic accident prediction modelling: a literature review. Proceedings of the Institution of Civil Engineers: Transport, 170(5), 245–254.4110.1680/jtran.16.00067Yokoyama, A., & Yamaguchi, N. (2020). Comparison between ANN and random forest for leakage current alarm prediction. Energy Reports, 6, 150–157.4210.1016/j.egyr.2020.11.271Zermane, A., Mohd Tohir, M. Z., Zermane, H., Baharudin, M. R., & Mohamed Yusoff, H. (2023). Predicting fatal fall from heights accidents using random forest classification machine learning model. Safety Science, 159(November 2022), 106023.4310.1016/j.ssci.2022.106023Zermane, H., & Drardja, A. (2022). Development of an efficient cement production monitoring system based on the improved random forest algorithm. International Journal of Advanced Manufacturing Technology, 120(3–4), 1853–1866.4410.1007/s00170-022-08884-zZheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., Cui, Z., & Wang, Z. (2019). Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 7, 39897–39910.4510.1109/ACCESS.2019.2903319Zhou, X., Lu, P., Zheng, Z., Tolliver, D., & Keramati, A. (2020). Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree. Reliability Engineering and System Safety, 200, 106931.4610.1016/j.ress.2020.106931