Batyrgareieva V.S., Shramko S.S., Samoilova O.M.: Mortality and injury in Ukraine as a result of traffic accidents in measuring of public health: to the analysis of social-legal and criminological problem. Wiadomości Lekarskie, 74, 11, 2021, 2870-2876
DOI: https://doi.org/10.36740/WLek202111202
Google Scholar
Makarova I., Khabibullin R., Pashkevich A., Shubenkova K.: Modeling as a method to improve road safety during mass events. Transportation Research Procedia, 20, 2017, 430-435, DOI: 10.1016/j.trpro.2017.01.070
DOI: https://doi.org/10.1016/j.trpro.2017.01.070
Google Scholar
Hughes R.L.: A Continuum Theory for the Flow of Pedestrians. Transportation Research Part B: Methodological, 36, 6, 2002, 507-535, DOI: 10.1016/S0191-2615(01)00015-7
DOI: https://doi.org/10.1016/S0191-2615(01)00015-7
Google Scholar
Huang L., Wong S.C., Zhang M., Shu C.W., Lam W.H.K.: Revisiting Hughes’ dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm. Transportation Research Part B: Methodological, 43, 1, 2009, 127-141, DOI: 10.1016/J.TRB.2008.06.003
DOI: https://doi.org/10.1016/j.trb.2008.06.003
Google Scholar
Di Francesco M., Markowich P.A., Pietschmann J F., Wolfram M.T.: On the Hughes’ model for pedestrian flow: the one-dimensional case. Journal of Differential Equations, 250, 3, 2011, 1334-1362, DOI: 10.1016/j.jde.2010.10.015
DOI: https://doi.org/10.1016/j.jde.2010.10.015
Google Scholar
Hänseler F.S., Lam W.H., Bierlaire M., Lederrey G., Nikolić M.: A dynamic network loading model for anisotropic and congested pedestrian flows. Transportation Research Part B: Methodological, 95, 2017, 149-168, DOI: 10.1016/j.trb.2016.10.017
DOI: https://doi.org/10.1016/j.trb.2016.10.017
Google Scholar
Cartenì A., De Guglielmo M.L., Pascale N.: Congested urban areas with high interactions between vehicular and pedestrian flows: A cost-benefit analysis for a sustainable transport policy in Naples, Italy. The Open Transportation Journal, 12, 2018, 273-288, DOI: 10.2174/1874447801812010273
DOI: https://doi.org/10.2174/1874447801812010273
Google Scholar
Gu Z., Osaragi T., Lu W.: Simulating pedestrians’ spatio- -temporal distribution in underground spaces. Sustainable Cities and Society, 48, 2019, ID article: 101552, DOI: 10.1016/j.scs.2019.101552
DOI: https://doi.org/10.1016/j.scs.2019.101552
Google Scholar
Fournier N.: Hybrid pedestrian and transit priority zoning policies in an urban street network: Evaluating network traffic flow impacts with analytical approximation. Transportation Research Part A: Policy and Practice, 152, 2021, 254-274, DOI: 10.1016/j.tra.2021.08.009
DOI: https://doi.org/10.1016/j.tra.2021.08.009
Google Scholar
Shafiei S., Gu Z., Saberi M.: Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network. Simulation Modelling Practice and Theory, 86, 2018, 169-186, DOI: 10.1016/j.simpat.2018.04.006
DOI: https://doi.org/10.1016/j.simpat.2018.04.006
Google Scholar
Zhou J., Wu Y., Mao X., Guo S., Zhang M.: Congestion evaluation of pedestrians in metro stations based on normal-cloud theory. Applied Sciences, 9, 17, 2019, ID article: 3624, DOI: 10.3390/app9173624
DOI: https://doi.org/10.3390/app9173624
Google Scholar
Wang J., Chen M., Yan W., Zhi Y., Wang Z.: A data-driven approach to estimate the probability of pedestrian flow congestion at transportation bottlenecks. KSCE Journal of Civil Engineering, 23, 1, 2019, 251-259, DOI: 10.1007/s12205-018-0063-1
DOI: https://doi.org/10.1007/s12205-018-0063-1
Google Scholar
Huang C., Zhang F., Xu Z., Wei J.: The Diverse Gait Dataset: Gait segmentation using inertial sensors for pedestrian localization with different genders, heights and walking speeds. Sensors, 22, 4, 2022, ID article: 1678, DOI: 10.3390/s22041678
DOI: https://doi.org/10.3390/s22041678
Google Scholar
Shen L., Weng W.: Experimental study on movement characteristics of pedestrians with different speeds. Journal of Statistical Mechanics: Theory and Experiment, 2022, 8, 2022, ID article: 083404, DOI: 10.1088/1742-5468/ac8420
DOI: https://doi.org/10.1088/1742-5468/ac8420
Google Scholar
Korjagin S., Klachek P.: Innovative development of intelligent transport systems based on biocybernetical vehicle control systems. Transportation Research Procedia, 20, 2017, 326-333, DOI: 10.1016/j.trpro.2017.01.038
DOI: https://doi.org/10.1016/j.trpro.2017.01.038
Google Scholar
Fornalchyk Y., Kernytskyy I., Hrytsun O., Royko Y.: Choice of the rational regimes of traffic light control for traffic and pedestrian flows. Scientific Review Engineering and Environmental Sciences, 30, 1, 2021, 38-50, DOI: 10.22630/PNIKS.2021.30.1.4
DOI: https://doi.org/10.22630/PNIKS.2021.30.1.4
Google Scholar
Horbachov P.F., Makarichev O.V., Atamanyuk H.V.: Model of determining the pedestrians’ delay in the transition of streets and roads outside the pedestrian crossing. Automobile Transport, 41, 2017, 82-91
DOI: https://doi.org/10.30977/AT.2219-8342.2017.41.0.82
Google Scholar
Hilevych V.V., Mohyla I.A., Mikhotskyi O.S.: Vyznachennia hranychnykh mezh vlashtuvannia nerehulovanykh pishokhidnykh perekhodiv za kryteriiem zatrymky transportnykh zasobiv. Visnyk Natsionalnoho universytetu Lvivska Politekhnika, Dynamika, mitsnist ta proektuvannia mashyn i pryladiv, 838, 2016, 146-152 (in Ukrainian)
Google Scholar
Lin Z.Y., Zhang P., Hang H.L.: A dynamic continuum route choice model for pedestrian flow with mixed crowds. Transportmetrica A: Transport Science, 19, 1, 2022, ID article: 2075951, DOI: 10.1080/23249935.2022.2075951
DOI: https://doi.org/10.1080/23249935.2022.2075951
Google Scholar
Jiang Y., Zhang Y., Lin C., Wu D., Lin C.T.: EEG-based driver drowsiness estimation using an online multi-view and transfer TSK fuzzy system. IEEE Transactions on Intelligent Transportation Systems, 22, 3, 2020, 1752-1764, DOI: 10.1109/TITS.2020.2973673
DOI: https://doi.org/10.1109/TITS.2020.2973673
Google Scholar
Weijermars W., Bos N., Schoeters A., Meunier J.C., Nuyttens N., Dupont E., Machata K., Bauer R., Perez K., Martin J.L., Johansson H., Filtness A., Brown L., Thomas P.: Serious road traffic injuries in Europe, lessons from the EU research project SafetyCube. Transportation Research Record, 2672, 32, 2018, 1-9, DOI: 10.1177/0361198118758055
DOI: https://doi.org/10.1177/0361198118758055
Google Scholar
Wang Y., Shen B., Wu H., Wang C., Su Q., Chen W.: Modeling illegal pedestrian crossing behaviors at unmarked mid-block roadway based on extended decision field theory. Physica A: Statistical Mechanics and its Applications, 562, 2021, ID article: 125327, DOI: 10.1016/j.physa.2020.125327
DOI: https://doi.org/10.1016/j.physa.2020.125327
Google Scholar
Hu L., Ou J., Huang J., Wang F., Wang Y., Ren B., Peng H., Zhou L.: Safety evaluation of pedestrian-vehicle interaction at signalized intersections in Changsha, China. Journal of Transportation Safety & Security, 14, 10, 2022, 1750-1775, DOI: 10.1080/19439962.2021.1960662
DOI: https://doi.org/10.1080/19439962.2021.1960662
Google Scholar
Szagała P., Olszewski P., Czajewski W., Dąbkowski P.: Active Signage of Pedestrian Crossings as a Tool in Road Safety Management. Sustainability, 13, 16, 2021, ID article: 9405, DOI: 10.3390/su13169405
DOI: https://doi.org/10.3390/su13169405
Google Scholar
Olszewski P., Dąbkowski P., Szagała P., Czajewski W., Buttler I.: Surrogate safety indicator for unsignalised pedestrian crossings. Transportation research part F: traffic psychology and behaviour, 70, 2020, 25-36, DOI: 10.1016/j.trf.2020.02.011
DOI: https://doi.org/10.1016/j.trf.2020.02.011
Google Scholar
Zhang C., Chen F., Wei Y.: Evaluation of pedestrian crossing behavior and safety at uncontrolled mid-block crosswalks with different numbers of lanes in China. Accident Analysis & Prevention, 123, 2019, 263-273, DOI: 10.1016/j.aap.2018.12.002
DOI: https://doi.org/10.1016/j.aap.2018.12.002
Google Scholar
Forde A., Daniel J.: Pedestrian walking speed at un-signalized midblock crosswalk and its impact on urban street segment performance. Journal of traffic and transportation engineering (English edition), 8, 1, 2021, 57-69, DOI: 10.1016/j.jtte.2019.03.007
DOI: https://doi.org/10.1016/j.jtte.2019.03.007
Google Scholar
Song J., Qiu Z., Ren G., Li X.: Prediction of pedestrian exposure to traffic particulate matters (PMs) at urban signalized intersection. Sustainable Cities and Society, 60, 2020, 102153, DOI: 10.1016/j.scs.2020.102153
DOI: https://doi.org/10.1016/j.scs.2020.102153
Google Scholar
Stipancic J., Miranda-Moreno L., Strauss J., Labbe A.: Pedestrian safety at signalized intersections: Modelling spatial effects of exposure, geometry and signalization on a large urban network. Accident Analysis & Prevention, 134, 2020, ID article: 105265, DOI: 10.1016/j.aap.2019.105265
DOI: https://doi.org/10.1016/j.aap.2019.105265
Google Scholar
Sun X., Lin K., Wang Y., Ma S., Lu H.: A study on pedestrian-vehicle conflict at unsignalized crosswalks based on game theory. Sustainability, 14, 13, 2022, ID article: 7652, DOI: 10.3390/su14137652
DOI: https://doi.org/10.3390/su14137652
Google Scholar
Santhosh A., Sam E., Bindhu B.K.: Pedestrian accident prediction modelling – A case study in Thiruvananthapuram City. In: Mathew T.V., Joshi G.J., Velaga N.R., Arkatkar S. (eds): Transportation Research, Lecture Notes in Civil Engineering, 45, 2019, 637-645, Springer, Singapore, DOI: 10.1007/978-981-32-9042-6_50
DOI: https://doi.org/10.1007/978-981-32-9042-6_50
Google Scholar
Zhao P., Ma J., Xu C., Zhao C., Ni Z.: Research on the safety of the left hard shoulder in a multi-lane highway based on safety performance function. Sustainability, 14, 22, 2022, ID article: 15114, DOI: 10.3390/su142215114
DOI: https://doi.org/10.3390/su142215114
Google Scholar
Chowdhury T.U., Park P.Y., Gingerich K.: Estimation of appropriate acceleration lane length for safe and efficient truck platooning operation on freeway merge areas. Sustainability, 14, 19, 2022, ID article: 12946, DOI: 10.3390/su141912946
DOI: https://doi.org/10.3390/su141912946
Google Scholar
Beza A.D., Maghrour Zefreh M., Torok A.: Impacts of different types of automated vehicles on traffic flow characteristics and emissions: a microscopic traffic simulation of different freeway segments. Energies, 15, 18, 2022, ID articles: 6669, DOI: 10.3390/en15186669
DOI: https://doi.org/10.3390/en15186669
Google Scholar
Li L.: MATLAB User Manual. The MathWorks, Natick, 2001
Google Scholar
Gasz K., Kruszyna M.: Analyses of pedestrian entry – process to pedestrians crossing. Roads and Bridges - Drogi i Mosty, 3, 2, 2004, 41-64 (in Polish)
Google Scholar