The Concept of Assessing the Ergonomic Stability of the Traffic Flow of Large Places with the Balance of the Dynamics of Changes in Flow Factors
DOI:
https://doi.org/10.32515/2664-262X.2024.9(40).2.256-272Keywords:
traffic flow, forecasting, dynamic model, traffic flow density, traffic speed, traffic flow robustness criterion, oscillation amplitude, oscillation period, traffic flow stability, traffic jamAbstract
The methodological approach of forecasting the congestion of the streets of large cities, taking into account the fluctuations in the density of traffic flows and the speed of movement of cars in the flow, received further development. The methodical approach takes into account fluctuations in the dynamics of the traffic flow in the form of changes in the acceleration of the movement of cars in the flow and fluctuations in changes in the infrastructure of the road environment, which is associated with the number of traffic lights, pedestrian crossings and the number of lanes for the movement of vehicles.
Through modeling, it was found that increasing the acceleration values of cars in the stream significantly increases the range of robustness. At low values of acceleration of cars, the reserve of resistance to the formation of traffic jams decreases, which negatively affects the traffic flow, there is a probability of the formation of traffic jams. The presence of fluctuations in the density of the traffic flow and the speed of movement of cars in the flow, due to changes in the acceleration of cars, allows making adjustments to the value of the robustness criterion. Adjustments can be made for different clusters of the road network, for different times of the day, and take into account the period of fluctuations. The dependences of the change in the robustness range of the traffic flow upon changing the infrastructure of the road network are given. It has been established that increasing the number of lanes and simultaneously reducing the number of traffic lights and pedestrian crossings on the controlled cluster significantly increases the stability of the movement of vehicles in the flow. Conversely, reducing the number of lanes, increasing the number of traffic lights and pedestrian crossings on the controlled cluster significantly reduces the stability of the movement of vehicles in the flow. Adjustment of density and movement speed can be performed taking into account the amplitude of oscillations.
The concept of modeling and forecasting the stability of traffic flows of large cities to the formation of traffic jams is formulated. The main components of the concept, according to which such an assessment is performed step by step, taking into account the dynamics of changes in influencing factors, are substantiated. The proposed concept differs from the known ones in that it takes into account fluctuations in traffic flow parameters - the density and speed of movement of vehicles over time. Such changes are characteristic of the city's road network during "peak hours".
References
Список літератури
1. Оцінка ергономічної стійкості транспортного потоку на дільницях дорожньої мережі. Ідентифікація математичної моделі / Войтов В.А. та ін. Центральноукраїнський науковий вісник. Технічні науки. 2023. Вип. 7(38), ч.І. С. 236-245. https://doi.org/10.32515/2664-262X.2023.7(38).1.236-245
2. Обгрунтування критерію стійкості транспортного потоку на дільницях дорожньої мережі / Кравцов А.Г. та ін. Центральноукраїнський науковий вісник. Технічні науки. 2023. Вип. 7(38), ч.ІІ. С. 222-230. https://doi.org/10.32515/2664-262X.2023.7(38).2.222-230
3. Дослідження математичної моделі стійкості транспортного потоку на дільницях дорожньої мережі міста / Горяїнов О.М. та ін. Центральноукраїнський науковий вісник. Технічні науки. 2023. Вип. 8(39), ч.І. С. 183-195. https://doi.org/10.32515/2664-262X.2023.8(39).1.183-195
4. Прогнозування завантаженості вулиць великих міст з урахуванням коливань щільності та швидкості руху транспортних потоків / Войтов В.А. та ін. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 9(40), ч.І. С. 165-177. https://doi.org/10.32515/2664-262X.2024.9(40).1.165-177
5. Wang, S., Chen, C., Zhang, J., Gu, X., & Huang, X. Vulnerability assessment of urban road traffic systems based on traffic flow. International Journal of Critical Infrastructure Protection. 2022. 38, 100536. https://doi.org/10.1016/j.ijcip.2022.100536
6. Romanowska, A., & Jamroz, K. Comparison of traffic flow models with real traffic data based on a quantitative assessment. Applied Sciences. 2021. 11(21), 9914. https://doi.org/10.3390/app11219914
7. Gore, N., Chauhan, R., Easa, S., & Arkatkar, S. Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications. Accident Analysis & Prevention. 2023. 185, 107020. https://doi.org/10.1016/j.aap.2023.107020
8. Mohammadian, S., Haque, M. M., Zheng, Z., & Bhaskar, A. Integrating safety into the fundamental relations of freeway traffic flows: A conflict-based safety assessment framework. Analytic methods in accident research. 2021. 32, 100187. https://doi.org/10.1016/j.amar.2021.100187
9. Lan, C. J., & Davis, G. A. Empirical assessment of a Markovian traffic flow model. Transportation research record. 1997. 1591(1), P. 31-37. https://doi.org/10.3141/1591-05
10. Juran, I., Prashker, J. N., Bekhor, S., & Ishai, I. A dynamic traffic assignment model for the assessment of moving bottlenecks. Transportation research part C: emerging technologies. 2009. 17(3), P. 240-258. https://doi.org/10.1016/j.trc.2008.10.003
11. Treiber, M., & Kesting, A. Validation of traffic flow models with respect to the spatiotemporal evolution of congested traffic patterns. Transportation research part C: emerging technologies. 2012. 21(1), P. 31-41. https://doi.org/10.1016/j.trc.2011.09.002
12. Mei, Y., Wang, S., Gong, M., & Chen, J. Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai. Sustainability. 2024. 16(12), 4956. https://doi.org/10.3390/su16124956
13. Pompigna, A., & Mauro, R. A Statistical Simulation Model for the Analysis of the Traffic Flow Reliability and the Probabilistic Assessment of the Circulation Quality on a Freeway Segment. Sustainability. 2022. 14(23), 16019. https://doi.org/10.3390/su142316019
14. Goh, Y. M., & Love, P. E. Methodological application of system dynamics for evaluating traffic safety policy. Safety science. 2012. 50(7), P. 1594-1605. https://doi.org/10.1016/j.ssci.2012.03.002
15. Zeng, J., Qian, Y., Wang, B., Wang, T., & Wei, X. The impact of traffic crashes on urban network traffic flow. Sustainability. 2019. 11(14), 3956. https://doi.org/10.3390/su11143956
16. Xiao, D., Ding, H., Sze, N. N., & Zheng, N. Investigating built environment and traffic flow impact on crash frequency in urban road networks. Accident Analysis & Prevention. 2024. 201, 107561. https://doi.org/10.1016/j.aap.2024.107561
17. Ognjenovic, S., Donceva, R., & Vatin, N. Dynamic homogeneity and functional dependence on the number of traffic accidents, the role in urban planning. Procedia Engineering, 2015. 117. P. 551-558. https://doi.org/10.1016/j.proeng.2015.08.212
18. Theofilatos, A., & Yannis, G. A review of the effect of traffic and weather characteristics on road safety. Accident Analysis & Prevention. 2014. 72, P. 244-256. https://doi.org/10.1016/j.aap.2014.06.017
19. Cheng, Z., Lu, J., & Li, Y. Freeway crash risks evaluation by variable speed limit strategy using real-world traffic flow data. Accident Analysis & Prevention. 2018. 119, P. 176-187. https://doi.org/10.1016/j.aap.2018.07.009
20. Golob, T. F., Recker, W. W., & Alvarez, V. M. Freeway safety as a function of traffic flow. Accident Analysis & Prevention. 2004. 36(6), P. 933-946. https://doi.org/10.1016/j.aap.2003.09.006
21. Shi, A., Tao, Z., Xinming, Z., & Jian, W. Evolution of traffic flow analysis under accidents on highways using temporal data mining. In 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications. 2014. P. 454-457. https://doi.org/10.1109/ISDEA.2014.109
22. Jiang, R., Jin, C. J., Zhang, H. M., Huang, Y. X., Tian, J. F., Wang, W., ... & Jia, B. Experimental and empirical investigations of traffic flow instability. Transportation research part C: emerging technologies. 2018. 94, P. 83-98. https://doi.org/10.1016/j.trc.2017.08.024
23. Shen, J., & Yang, G. Crash risk assessment for heterogeneity traffic and different vehicle-following patterns using microscopic traffic flow data. Sustainability. 2020. 12(23), 9888. https://doi.org/10.3390/su12239888
24. Cascetta, E., Punzo, V., & Montanino, M. Empirical analysis of effects of automated section speed enforcement system on traffic flow at freeway bottlenecks. Transportation research record. 2011. 2260(1), P. 83-93. https://doi.org/10.3141/2260-10
25. Hafram, S. M., & Asrib, A. R. Traffic Conditions and Characteristics: Investigation of Road Segment Performance. International Journal of Environment, Engineering and Education. 2022. 4(3), P. 108-114. http://ijeedu.com/index.php/ijeedu/article/view/77
26. Sugiyama, Y., Fukui, M., Kikuchi, M., Hasebe, K., Nakayama, A., Nishinari, K., ... & Yukawa, S. Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam. New journal of physics. 2008. 10(3), 033001. DOI https://doi.org/10.1088/1367-2630/10/3/033001
27. Feng, X., Zhang, Y., Qian, S., & Sun, L. The traffic capacity variation of urban road network due to the policy of unblocking community. Complexity. 2021. 9292389. https://doi.org/10.1155/2021/9292389
28. Almatar, K. M. Traffic congestion patterns in the urban road network: (Dammam metropolitan area). Ain Shams engineering journal. 2023. 14(3), 101886. https://doi.org/10.1016/j.asej.2022.101886
29. Khattak, M. W., De Backer, H., De Winne, P., Brijs, T., & Pirdavani, A. Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models. Infrastructures. 2024. 9(3), 47. https://doi.org/10.3390/infrastructures9030047
30. Ernazarov, A. Efficiency of functioning of intersections with high-intensity traffic and pedestrian flows. Technical science and innovation. 2022. P. 192-197. https://doi.org/10.51346/tstu-01.22.1-77-0162
31. Zhao, H. T., Yang, S., & Chen, X. X. Cellular automata model for urban road traffic flow considering pedestrian crossing street. Physica A: statistical mechanics and its applications. 2016. 462, P. 1301-1313. https://doi.org/10.1016/j.physa.2016.06.146
32. Nagatani, T. The physics of traffic jams. Reports on progress in physics. 2002. 65(9), 1331. DOI: https://doi.org/10.1088/0034-4885/65/9/203
33. Fei, L., Zhu, H. B., & Han, X. L. Analysis of traffic congestion induced by the work zone. Physica A: Statistical Mechanics and its Applications. 2016. 450, P. 497-505. https://doi.org/10.1016/j.physa.2016.01.036
34. Rodriguez, E., Ferreira, N., & Poco, J. JamVis: exploration and visualization of traffic jams. The European Physical Journal Special Topics. 2022. 231 (9), P. 1673-1687. https://doi.org/10.1140/epjs/s11734-021-00424-2
References
1. Vojtov, V.A. et al. (2023). Otsinka erhonomichnoi stijkosti transportnoho potoku na dil'nytsiakh dorozhn'oi merezhi. Identyfikatsiia matematychnoi modeli [Assessment of ergonomic sustainability of traffic flow at road network sections. Identification of a mathematical model]. Tsentral'noukrains'kyj naukovyj visnyk. Tekhnichni nauky – Central Ukrainian scientific bulletin. Technical Sciences, 7(38), 236-245. https://doi.org/10.32515/2664-262X.2023.7(38).1.236-245 [in Ukrainian].
2. Kravtsov, A.H. et al. (2023). Obhruntuvannia kryteriiu stijkosti transportnoho potoku na dil'nytsiakh dorozhn'oi merezhi [Justification of the traffic flow stability criterion at the sections of the road network]. Tsentral'noukrains'kyj naukovyj visnyk. Tekhnichni nauky – Central Ukrainian scientific bulletin. Technical Sciences, 7(38), 222-230. https://doi.org/10.32515/2664-262X.2023.7(38).2.222-230 [in Ukrainian].
3. Horiainov, O.M. et al. (2023). Doslidzhennia matematychnoi modeli stijkosti transportnoho potoku na dil'nytsiakh dorozhn'oi merezhi mista [Study of the mathematical model of the stability of the traffic flow in the sections of the city's road network]. Tsentral'noukrains'kyj naukovyj visnyk. Tekhnichni nauky – Central Ukrainian scientific bulletin. Technical Sciences, 8(39), I, 183-195 https://doi.org/10.32515/2664-262X.2023.8(39).1.183-195 [in Ukrainian].
4. Vojtov V.A. at al. (2024). Prohnozuvannya zavantazhenosti vulytsʹ velykykh mist z urakhuvannyam kolyvanʹ shchilʹnosti ta shvydkosti rukhu transportnykh potokiv [Forecasting the congestion of the streets of large cities, taking into account fluctuations in the density and speed of traffic flows]. Tsentral'noukrains'kyj naukovyj visnyk. Tekhnichni nauky – Central Ukrainian scientific bulletin. Technical Sciences, 9(40),1 pp. 165-177. https://doi.org/10.32515/2664-262X.2024.9(40).1.165-177 [in Ukrainian].
5. Wang, S., Chen, C., Zhang, J., Gu, X., & Huang, X. (2022). Vulnerability assessment of urban road traffic systems based on traffic flow. International Journal of Critical Infrastructure Protection. 38, 100536. https://doi.org/10.1016/j.ijcip.2022.100536 [in English].
6. Romanowska, A., & Jamroz, K. (2021). Comparison of traffic flow models with real traffic data based on a quantitative assessment. Applied Sciences. 11(21), 9914. https://doi.org/10.3390/app11219914 [in English].
7. Gore, N., Chauhan, R., Easa, S., & Arkatkar, S. (2023). Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications. Accident Analysis & Prevention. 185, 107020. https://doi.org/10.1016/j.aap.2023.107020 [in English].
8. Mohammadian, S., Haque, M. M., Zheng, Z., & Bhaskar, A. (2021). Integrating safety into the fundamental relations of freeway traffic flows: A conflict-based safety assessment framework. Analytic methods in accident research. 32, 100187. https://doi.org/10.1016/j.amar.2021.100187 [in English].
9. Lan, C. J., & Davis, G. A. (1997). Empirical assessment of a Markovian traffic flow model. Transportation research record. 1591(1), P.31-37. https://doi.org/10.3141/1591-05 [in English].
10. Juran, I., Prashker, J. N., Bekhor, S., & Ishai, I. (2009). A dynamic traffic assignment model for the assessment of moving bottlenecks. Transportation research part C: emerging technologies. 17(3), P. 240-258. https://doi.org/10.1016/j.trc.2008.10.003 [in English].
11. Treiber, M., & Kesting, A. (2012). Validation of traffic flow models with respect to the spatiotemporal evolution of congested traffic patterns. Transportation research part C: emerging technologies. 21(1), P. 31-41. https://doi.org/10.1016/j.trc.2011.09.002 [in English].
12. Mei, Y., Wang, S., Gong, M., & Chen, J. (2024). Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai. Sustainability. 16(12), 4956. https://doi.org/10.3390/su16124956 [in English].
13. Pompigna, A., & Mauro, R. A (2022). Statistical Simulation Model for the Analysis of the Traffic Flow Reliability and the Probabilistic Assessment of the Circulation Quality on a Freeway Segment. Sustainability. 14(23), 16019. https://doi.org/10.3390/su142316019 [in English].
14. Goh, Y. M., & Love, P. E. (2012). Methodological application of system dynamics for evaluating traffic safety policy. Safety science. 50(7), P. 1594-1605. https://doi.org/10.1016/j.ssci.2012.03.002 [in English].
15. Zeng, J., Qian, Y., Wang, B., Wang, T., & Wei, X. (2019). The impact of traffic crashes on urban network traffic flow. Sustainability. 11(14), 3956. https://doi.org/10.3390/su11143956 [in English].
16. Xiao, D., Ding, H., Sze, N. N., & Zheng, N. (2024). Investigating built environment and traffic flow impact on crash frequency in urban road networks. Accident Analysis & Prevention. 201, 107561. https://doi.org/10.1016/j.aap.2024.107561 [in English].
17. Ognjenovic, S., Donceva, R., & Vatin, N. (2015). Dynamic homogeneity and functional dependence on the number of traffic accidents, the role in urban planning. Procedia Engineering, 117. P. 551-558. https://doi.org/10.1016/j.proeng.2015.08.212 [in English].
18. Theofilatos, A., & Yannis, G. (2014). A review of the effect of traffic and weather characteristics on road safety. Accident Analysis & Prevention. 72, P. 244-256. https://doi.org/10.1016/j.aap.2014.06.017 [in English].
19. Cheng, Z., Lu, J., & Li, Y. (2018). Freeway crash risks evaluation by variable speed limit strategy using real-world traffic flow data. Accident Analysis & Prevention. 119, P. 176-187. https://doi.org/10.1016/j.aap.2018.07.009 [in English].
20. Golob, T. F., Recker, W. W., & Alvarez, V. M. (2004). Freeway safety as a function of traffic flow. Accident Analysis & Prevention. 36(6), P. 933-946. https://doi.org/10.1016/j.aap.2003.09.006 [in English].
21. Shi, A., Tao, Z., Xinming, Z., & Jian, W. (2014). Evolution of traffic flow analysis under accidents on highways using temporal data mining. In 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications. P. 454-457. https://doi.org/10.1109/ISDEA.2014.109 [in English].
22. Jiang, R., Jin, C. J., Zhang, H. M., Huang, Y. X., Tian, J. F., Wang, W., ... & Jia, B. (2018). Experimental and empirical investigations of traffic flow instability. Transportation research part C: emerging technologies. 94, P. 83-98. https://doi.org/10.1016/j.trc.2017.08.024 [in English].
23. Shen, J., & Yang, G. (2020). Crash risk assessment for heterogeneity traffic and different vehicle-following patterns using microscopic traffic flow data. Sustainability. 12(23), 9888. https://doi.org/10.3390/su12239888 [in English].
24. Cascetta, E., Punzo, V., & Montanino, M. (2011). Empirical analysis of effects of automated section speed enforcement system on traffic flow at freeway bottlenecks. Transportation research record. 2260(1), P. 83-93. https://doi.org/10.3141/2260-10 [in English].
25. Hafram, S. M., & Asrib, A. R. (2022.) Traffic Conditions and Characteristics: Investigation of Road Segment Performance. International Journal of Environment, Engineering and Education. 4(3), P. 108-114. http://ijeedu.com/index.php/ijeedu/article/view/77 [in English].
26. Sugiyama, Y., Fukui, M., Kikuchi, M., Hasebe, K., Nakayama, A., Nishinari, K., ... & Yukawa, S. (2008). Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam. New journal of physics. 10(3), 033001. DOI 10.1088/1367-2630/10/3/033001 [in English].
27. Feng, X., Zhang, Y., Qian, S., & Sun, L. (2021). The traffic capacity variation of urban road network due to the policy of unblocking community. Complexity. 9292389. https://doi.org/10.1155/2021/9292389 [in English].
28. Almatar, K. M. (2023). Traffic congestion patterns in the urban road network: (Dammam metropolitan area). Ain Shams engineering journal. 14(3), 101886. https://doi.org/10.1016/j.asej.2022.101886 [in English].
29. Khattak, M. W., De Backer, H., De Winne, P., Brijs, T., & Pirdavani, A. (2024). Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models. Infrastructures. 9(3), 47. https://doi.org/10.3390/infrastructures9030047 [in English].
30. Ernazarov, A. (2022). Efficiency of functioning of intersections with high-intensity traffic and pedestrian flows. Technical science and innovation. P. 192-197. https://doi.org/10.51346/tstu-01.22.1-77-0162 [in English].
31. Zhao, H. T., Yang, S., & Chen, X. X. (2016). Cellular automata model for urban road traffic flow considering pedestrian crossing street. Physica A: statistical mechanics and its applications. 462, P. 1301-1313. https://doi.org/10.1016/j.physa.2016.06.146 [in English].
32. Nagatani, T. (2002). The physics of traffic jams. Reports on progress in physics. 65(9), 1331. DOI 10.1088/0034-4885/65/9/203 [in English].
33. Fei, L., Zhu, H. B., & Han, X. L. (2016). Analysis of traffic congestion induced by the work zone. Physica A: Statistical Mechanics and its Applications. 450, P. 497-505. https://doi.org/10.1016/j.physa.2016.01.036 [in English].
34. Rodriguez, E., Ferreira, N., & Poco, J. (2022). JamVis: exploration and visualization of traffic jams. The European Physical Journal Special Topics. 231 (9), P. 1673-1687. https://doi.org/10.1140/epjs/s11734-021-00424-2 [in English].
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Copyright (c) 2024 Viktor Vojtov, Andriy Kravtsov, Anton Voitov, Natalija Berezhna, Igor Sysenko, Leonid Kryvenko, Ihor Babaryka
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