Simulation analysis of school road traffic characteristics

Huasheng Liu; Haoran Deng; Jin Li; Yuqi Zhao; Sha Yang

doi:10.15446/dyna.v91n231.109853

Publicado

2024-01-31

Simulation analysis of school road traffic characteristics

Análisis de simulación de las características del tráfico escolar

DOI:

https://doi.org/10.15446/dyna.v91n231.109853

Palabras clave:

urban traffic; traffic characteristic; simulation analysis; sensitivity analysis; school commuting (en)
tráfico urbano; características del tráfico; análisis de simulación; análisis de sensibilidad; transporte escolar (es)

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Autores/as

Huasheng Liu Department of Transportation Planning and Management, Jilin University, Institute of Transportation, Chang Chun, China https://orcid.org/0000-0002-2635-999X
Haoran Deng Department of Transportation Planning and Management, Jilin University, Institute of Transportation, Chang Chun, China https://orcid.org/0009-0009-7463-830X
Jin Li Department of Transportation Planning and Management, Jilin University, Institute of Transportation, Chang Chun, China https://orcid.org/0000-0001-8783-4881
Yuqi Zhao Department of Transportation Planning and Management, Jilin University, Institute of Transportation, Chang Chun, China https://orcid.org/0009-0005-6952-8051
Sha Yang Department of Transportation Planning and Management, Jilin University, Institute of Transportation, Chang Chun, China https://orcid.org/0009-0006-9142-5435

Resumen (en)
Resumen (es)

This study aims to identify key factors and sensitive intervals affect the school road traffic characteristics. We collect traffic data from the parking area and the school road (400-700 meters). The simulation is calibrated to ensure the error of outputs are within 1.5%. A sensitivity analysis method is proposed, it makes the multifactor comparable. The sensitivity factors of vehicle delay, queue length, and average speed are 1.44, 2.03, and 0.28 in school road, and the bottleneck road are 3.07, 4.44, and 0.65. The traffic indicators change more concentrated in bottleneck road but greater in school road. 6 scenarios are created to analyze school road traffic characteristics. Traffic flow (TF), number of parking spaces (NPS), and stopping time (ST) are selected as variables. Scenarios 1-3 are univariate, and scenarios 4-6 are bivariate. TF is the key factor with a sensitivity interval of [1300,1700].

El objetivo de este estudio es identificar los factores clave y las zonas sensibles que afectan a las características del tráfico de las carreteras escolares. Recogemos datos de tráfico de la zona de aparcamiento y de la carretera escolar (400-700 metros). La simulación se calibró y el error de salida se situó dentro del 1,5%. Se propone un enfoque de análisis de sensibilidad multifactorial. Los coeficientes de sensibilidad para el retraso de los vehículos, la longitud de las colas y la velocidad media son 1,44, 2,03 y 0,28 para la sección de los colegios y 3,07, 4,44 y 0,65 para la sección de los cuellos de botella. Las variables son el flujo de tráfico (TF), el número de plazas de aparcamiento (NPS) y el tiempo de parada (ST). Los escenarios 1-3 y 4-6 se establecieron como univariantes y bivariantes.TF fue el factor clave con un intervalo de sensibilidad de [1300,1700].

Referencias

Müller, S., Tscharaktschiew, S., and Haase, K., Travel-to-school mode choice modeling and pat-terns of school choice in urban areas, Journal of Transport Geography, 16(5), pp. 342-357, 2008. DOI: https://doi.org/10.1016/j.jtrangeo.2007.12.004. DOI: https://doi.org/10.1016/j.jtrangeo.2007.12.004

Chen, J., Pang, M.B., and Yang, M., A cellular automaton model for the road in front of elementary and middle school gates during students going to school, Acta Physica Sinica, 63(9), art. 094502, 2014. DOI: https://doi.org/10.7498/aps.63.094502. DOI: https://doi.org/10.7498/aps.63.094502

Bina, M., Confalonieri, F., Abati, D., Villa, D., and Biassoni, F., Analysis of traffic upon school departure: environment, behaviour, well-being and risk factors for road crashes, Journal of Transport & Health. 22, art. 101119, 2021. DOI: https://doi.org/10.1016/j.jth.2021.101119. DOI: https://doi.org/10.1016/j.jth.2021.101119

Daganzo, C.F., and Geroliminis, N., An analytical approximation for the macroscopic fundamental diagram of urban traffic, Transportation Research Part B, 42(9), pp. 771-781, 2008. DOI: https://doi.org/10.1016/j.trb.2008.06.008. DOI: https://doi.org/10.1016/j.trb.2008.06.008

Jin, S., Luo, X., and Ma, D., Determining the breakpoints of fundamental diagrams, IEEE Intelligent Transportation Systems Magazine, 12(1), pp. 74-90, 2020. DOI: https://doi.org/10.1109/MITS.2018.2876576. DOI: https://doi.org/10.1109/MITS.2018.2876576

Qu, X., Wang, S., and Zhang, J., On the fundamental diagram for freeway traffic: a novel calibration approach for single-regime model, Transportation Research Part B, 73, pp. 91-102, 2015. DOI: https://doi.org/10.1016/j.trb.2015.01.001. DOI: https://doi.org/10.1016/j.trb.2015.01.001

Peng, G., Wang, W., and Tan, H., Chaotic jam and phase transitions in heterogeneous lattice model integrating the delay characteristics difference with passing effect under autonomous and human-driven vehicles environment, Chaos Solitons and Fractals, 177, art. 114252, 2023. DOI: https://doi.org/10.1016/j.chaos.2023.114252. DOI: https://doi.org/10.1016/j.chaos.2023.114252

Zhao, Y., Zheng, J., Wong, W., Wang, X., Meng, Y., and Liu, H., Various methods for queue length and traffic volume estimation using probe vehicle trajectories, Transportation Research part C, 107, pp. 70-91, 2019. DOI: https://doi.org/10.1016/j.trc.2019.07.008. DOI: https://doi.org/10.1016/j.trc.2019.07.008

Bie, Y., Mao, C., and Yang, M., Development of vehicle delay and queue Length Models for adaptive traffic control at signalized roundabout, Procedia Engineering, 137, pp. 141-150, 2016. DOI: https://doi.org/10.1016/j.proeng.2016.01.244. DOI: https://doi.org/10.1016/j.proeng.2016.01.244

Chen, J., Jiang, R., Li, X., Hu, M., Jia, B., and Gao, Z., Morning commute problem with queue-length-dependent bottleneck capacity, Transportation Research Part B, 121, pp. 184-215, 2019. DOI: https://doi.org/10.1016/j.trb.2019.01.009. DOI: https://doi.org/10.1016/j.trb.2019.01.009

Jiang, X., and Adeli, H., Freeway work zone traffic delay and cost optimization model, Journal of Transportation Engineering, 129(3), pp. 230-241. 2003. DOI: https://doi.org/10.1061/(ASCE)0733-947X(2003)129:3(230). DOI: https://doi.org/10.1061/(ASCE)0733-947X(2003)129:3(230)

Chung, K., Rudjanakanoknad, J., and Cassidy, M.J., Relation between traffic density and capacity drop at three freeway bottlenecks, Transportation Research Part B-Methodological, 41(1), pp. 82-95, 2007. DOI: https://doi.org/10.1016/j.trb.2006.02.011. DOI: https://doi.org/10.1016/j.trb.2006.02.011

Lee, J., and Lee, J., Preventing capacity drop at isolated merging bottleneck through variable speed limit control, Sensors and Materials, 31(10), pp. 3397-3407, 2019. DOI: https://doi.org/10.18494/sam.2019.2567. DOI: https://doi.org/10.18494/SAM.2019.2567

Qi, H., Chen, M., and Wang, D., Recurrent and non-recurrent bottleneck analysis based on traffic state rank distribution, Transportmetrica B-Transport Dynamics, 7(1), pp. 275-294, 2019. DOI: https://doi.org/10.1080/21680566.2017.1401496. DOI: https://doi.org/10.1080/21680566.2017.1401496

Raju, N., Arkatkar, S., and Joshi, G., Examining effect of bottleneck on multi-lane roads at midblock sections using simulation, in: Proceedings of the American-Society-of-Civil-Engineers (ASCE) India Conference on Urbanization Challenges in Emerging Economies, New Delhi, D.C., pp. 697-705, 2018. DOI: https://doi.org/10.1061/9780784482025.071. DOI: https://doi.org/10.1061/9780784482025.071

Wan, Q., Peng, G., Li, Z., and Inomata, F.H.T., Spatiotemporal trajectory characteristic analysis for traffic state transition prediction near expressway merge bottleneck, Transportation Research Part C, 117, art. 102682, 2020. DOI: https://doi.org/10.1016/j.trc.2020.102682. DOI: https://doi.org/10.1016/j.trc.2020.102682

Park, B., and Schneeberger, J., Microscopic simulation model calibration and validation: case study of VISSIM simulation model for a coordinated actuated signal system, Transportation Research Record: Journal of the Transportation Research Board, 1856, pp. 185-192, 2003. DOI: https://doi.org/10.3141/1856-20. DOI: https://doi.org/10.3141/1856-20

Pourmoradnasseri, M., Khoshkhah, K., and Hadachi, A., Real-time calibration of disaggregated traffic demand, arXiv, 2210, art. 17315, 2022. DOI: https://doi.org/10.48550/arXiv.2210.17315. DOI: https://doi.org/10.21203/rs.3.rs-2238497/v1

Pourmoradnasseri, M., Khoshkhah, K., and Hadachi, A., Leveraging IoT data stream for near-real-time calibration of city-scale microscopic traffic simulation, IET Smart Cites, 5(4), pp. 269-290, 2022. DOI: https://doi.org/10.5281/zenodo.8125656. DOI: https://doi.org/10.1049/smc2.12071

Hollander, Y., and Liu, R., The principles of calibrating traffic microsimulation models, Transportation, 35(3), pp. 347-362, 2008. DOI: https://doi.org/10.1007/s11116-007-9156-2. DOI: https://doi.org/10.1007/s11116-007-9156-2

Sacha, B., Kaveh, K., Mozhgan, P., Rumpler, R., and Hadachi, A., Near-real-time dynamic noise mapping and exposure assessment using calibrated microscopic traffic simulations, Transportation Research Part D, 124, art. 103922, 2023. DOI: https://doi.org/10.1016/j.trd.2023.103922. DOI: https://doi.org/10.1016/j.trd.2023.103922

Cassidy, M.J., Jang, K., and Daganzo, C.F., Macroscopic fundamental diagrams for freeway networks: theory and observation. Transportation Research Record, 2260(1), pp. 8-15, 2011. DOI: https://doi.org/10.3141/2260-02. DOI: https://doi.org/10.3141/2260-02

Di, S., Gao, J., Yang, D., Zuo, F., and Ozbay, K., Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos, Accident Analysis and Prevention, 179, art. 106878, 2022. DOI: https://doi.org/10.1016/j.aap.2022.106878. DOI: https://doi.org/10.1016/j.aap.2022.106878

Zang, Z., Xu, X., Qu, K., Chen, R., and Chen, A., Travel time reliability in transportation networks: A review of methodological developments, Transportation Research Part C, 143, art. 103866, 2022. DOI: https://doi.org/10.1016/j.trc.2022.103866. DOI: https://doi.org/10.1016/j.trc.2022.103866

Dalla, C.G., Krutein, K.F., Ranjbari, A., and Goodchild, A., Providing curb availability information to delivery drivers reduces cruising for parking, Scientific Reports, 12(1), art. 19355, 2022. DOI: https://doi.org/10.1038/s41598-022-23987-z. DOI: https://doi.org/10.1038/s41598-022-23987-z

Dai, S., Liu G., Zhu J., Gong, J., and Qu, X., On-street parking management strategies and practice, Urban Transport of China, 12, art. 6-11, 2014. DOI: https://doi.org/10.13813/j.cn11-5141/u.2014.01.007.

Cao, Y., Yang, Z.Z., and Zuo, Z.Y., The effect of curb parking on road capacity and traffic safety, European Transport Research Review, 9(1), art. 4, 2017. DOI: https://doi.org/10.1007/s12544-016-0219-3. DOI: https://doi.org/10.1007/s12544-016-0219-3

Mei, Z., and Chen, J., Modified motor vehicles travel speed models on the basis of curb parking setting under mixed traffic flow, Mathematical Problems in Engineering, 2, pp. 139-139, 2012. DOI: https://doi.org/10.1155/2012/351901. DOI: https://doi.org/10.1155/2012/351901

Cheng, Q., Liu, Z., Lin, Y., and Zhou, X., An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship, Transportation Research Part B: Methodological, 153, pp. 246-271, 2021. DOI: https://doi.org/j.trb.2021.09.004. DOI: https://doi.org/10.1016/j.trb.2021.09.004

Saric, A., and Lovric, I., Improved volume-delay function for two-lane rural highways with impact of road geometry and traffic-flow heterogeneity, Journal of Transportation Engineering, Part A. Systems, 10, art. 147, 2021. DOI: https://doi.org/10.1061/JTEPBS.0000575. DOI: https://doi.org/10.1061/JTEPBS.0000575

Bally, M.M., Khairy, A.A., and Vien, L.L., Compatibility between delay functions and highway capacity manual on Iraqi highways, Open Engineering, 12(1), pp. 359-372, 2022. DOI: https://doi.org/10.1515/eng-2022-0022. DOI: https://doi.org/10.1515/eng-2022-0022

Ma, D., Wang, D., Bie, Y., Sun, F., and Jin, S., A method for queue length estimation in an urban street network based on roll time occupancy data, Mathematical Problems in Engineering, 9, pp. 285-292, 2012. DOI: https://doi.org/10.1155/2012/892575. DOI: https://doi.org/10.1155/2012/892575

Aksoy, G., and Oeguet, K.S., Direct usage of occupancy data for multiregime speed-flow rate models, Journal of Transportation Engineering, Part A. Systems, 149(1), art. 04022112, 2023. DOI: https://doi.org/10.1061/JTEPBS.0000773. DOI: https://doi.org/10.1061/JTEPBS.0000773

Olstam, J., and Tapani, A., A review of guidelines for applying traffic simulation to level-of-service analysis. Procedia Social and Behavioral Sciences, 16(1), pp. 771-780, 2011. DOI: https://doi.org/10.1016/j.sbspro.2011.04.496. DOI: https://doi.org/10.1016/j.sbspro.2011.04.496

Otkovic, I.I., Tollazzi, T., and Sraml, M., Calibration of microsimulation traffic model using neural network approach, Expert Systems with Applications, 40(15), pp. 5965-5974, 2013. DOI: https://doi.org/10.1016/j.eswa.2013.05.003. DOI: https://doi.org/10.1016/j.eswa.2013.05.003

Huang, F., Liu, P., Yu, H., and Wang, W., Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections, Accident Analysis and Prevention, 50, pp. 1014-1024, 2013. DOI: http://dx.doi.org/10.1016/j.aap.2012.08.018. DOI: https://doi.org/10.1016/j.aap.2012.08.018

Song, G., Yu, L., and Zhang, Y., Applicability of traffic microsimulation models in vehicle emissions estimates, Transportation Research Record: Journal of the Transportation Research Board, 2270, pp. 132-141, 2012. DOI: https://doi.org/10.3141/2270-16. DOI: https://doi.org/10.3141/2270-16

Cómo citar

IEEE

[1]

H. Liu, H. Deng, J. Li, Y. Zhao, y S. Yang, «Simulation analysis of school road traffic characteristics», DYNA, vol. 91, n.º 231, pp. 37–46, ene. 2024.

ACM

[1]

Liu, H., Deng, H., Li, J., Zhao, Y. y Yang, S. 2024. Simulation analysis of school road traffic characteristics. DYNA. 91, 231 (ene. 2024), 37–46. DOI:https://doi.org/10.15446/dyna.v91n231.109853.

ACS

(1)

Liu, H.; Deng, H.; Li, J.; Zhao, Y.; Yang, S. Simulation analysis of school road traffic characteristics. DYNA 2024, 91, 37-46.

APA

Liu, H., Deng, H., Li, J., Zhao, Y. y Yang, S. (2024). Simulation analysis of school road traffic characteristics. DYNA, 91(231), 37–46. https://doi.org/10.15446/dyna.v91n231.109853

ABNT

LIU, H.; DENG, H.; LI, J.; ZHAO, Y.; YANG, S. Simulation analysis of school road traffic characteristics. DYNA, [S. l.], v. 91, n. 231, p. 37–46, 2024. DOI: 10.15446/dyna.v91n231.109853. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/109853. Acesso em: 6 mar. 2025.

Chicago

Liu, Huasheng, Haoran Deng, Jin Li, Yuqi Zhao, y Sha Yang. 2024. «Simulation analysis of school road traffic characteristics». DYNA 91 (231):37-46. https://doi.org/10.15446/dyna.v91n231.109853.

Harvard

Liu, H., Deng, H., Li, J., Zhao, Y. y Yang, S. (2024) «Simulation analysis of school road traffic characteristics», DYNA, 91(231), pp. 37–46. doi: 10.15446/dyna.v91n231.109853.

MLA

Liu, H., H. Deng, J. Li, Y. Zhao, y S. Yang. «Simulation analysis of school road traffic characteristics». DYNA, vol. 91, n.º 231, enero de 2024, pp. 37-46, doi:10.15446/dyna.v91n231.109853.

Turabian

Liu, Huasheng, Haoran Deng, Jin Li, Yuqi Zhao, y Sha Yang. «Simulation analysis of school road traffic characteristics». DYNA 91, no. 231 (enero 24, 2024): 37–46. Accedido marzo 6, 2025. https://revistas.unal.edu.co/index.php/dyna/article/view/109853.

Vancouver

1.

Liu H, Deng H, Li J, Zhao Y, Yang S. Simulation analysis of school road traffic characteristics. DYNA [Internet]. 24 de enero de 2024 [citado 6 de marzo de 2025];91(231):37-46. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/109853

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1. Xiaojian Hu, Haoran Deng, Huasheng Liu, Jiayi Zhou, Hongyu Liang, Long Chen, Li Zhang. (2025). Assessment of the collision risk on the road around schools during morning peak period. Accident Analysis & Prevention, 210, p.107854. https://doi.org/10.1016/j.aap.2024.107854.

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