A data-based framework for automatic road network generation of multi-modal transport micro-simulation

Qi Zhang; Yukai Wang; Ruyang Yin; Wenyu Cheng; Jian Wan; Lan Wu; Qi Zhang; Yukai Wang; Ruyang Yin; Wenyu Cheng; Jian Wan; Lan Wu

doi:10.3934/era.2023010

Electronic Research Archive

2023, Volume 31, Issue 1: 190-206. doi: 10.3934/era.2023010

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A data-based framework for automatic road network generation of multi-modal transport micro-simulation

1.
School of Transportation, Southeast University, China
2.
Institute of Transport Studies, Department of Civil Engineering, Monash University, Clayton, Australia
3.
Research and Development Center on ITS Technology and Equipment, China Design Group, China

Received: 09 August 2022 Revised: 04 September 2022 Accepted: 08 September 2022 Published: 27 October 2022

In microscopic traffic simulation, the fidelity of the road network model has a significant impact on the difference between the simulation and the actual urban traffic state. Accurately matching data on the simulated road network and the surroundings has become a central concern in traffic simulation research. This study provides a multi-source data-based framework for automatic road network generation (ARNG) to address the issue of manual procedures in the creation of the simulated road network and surroundings. First, the proposed method of fusion and matching of diverse road network data is used to acquire the basic road network information, and the combining of the features of different road network data can enhance the authenticity of the basic road network. Second, a multi-modal simulation road network is developed based on multi-modal traffic operation data to serve as the simulation operation's foundational environment. To address the requirements of the dynamic evolution of the simulated road network, an editor for the dynamic road network is built based on spatial closest neighbor matching. The case study illustrates the process of building the simulated road network and environment in the old city zone of Suzhou. Real-world examples demonstrate that the data-based ARNG approach provided in this study is highly automatic and scalable.
- data-based framework,
- road network modeling,
- micro traffic simulation,
- multi-modal traffic
Citation: Qi Zhang, Yukai Wang, Ruyang Yin, Wenyu Cheng, Jian Wan, Lan Wu. A data-based framework for automatic road network generation of multi-modal transport micro-simulation[J]. Electronic Research Archive, 2023, 31(1): 190-206. doi: 10.3934/era.2023010

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Abstract

In microscopic traffic simulation, the fidelity of the road network model has a significant impact on the difference between the simulation and the actual urban traffic state. Accurately matching data on the simulated road network and the surroundings has become a central concern in traffic simulation research. This study provides a multi-source data-based framework for automatic road network generation (ARNG) to address the issue of manual procedures in the creation of the simulated road network and surroundings. First, the proposed method of fusion and matching of diverse road network data is used to acquire the basic road network information, and the combining of the features of different road network data can enhance the authenticity of the basic road network. Second, a multi-modal simulation road network is developed based on multi-modal traffic operation data to serve as the simulation operation's foundational environment. To address the requirements of the dynamic evolution of the simulated road network, an editor for the dynamic road network is built based on spatial closest neighbor matching. The case study illustrates the process of building the simulated road network and environment in the old city zone of Suzhou. Real-world examples demonstrate that the data-based ARNG approach provided in this study is highly automatic and scalable.

References

[1]	D. Huang, J. Xing, Z. Liu, Q. An, A multi-stage stochastic optimization approach to the stop-skipping and bus lane reservation schemes, Transportmetrica A Transp. Sci., 17 (2021), 1272–1304. https://doi.org/10.1080/23249935.2020.1858206 doi: 10.1080/23249935.2020.1858206
[2]	M. Pei, P. Lin, J. Du, X. Li, Z. Chen, Vehicle dispatching in modular transit networks: a mixed-integer nonlinear programming model, Transp. Res. Part E Logist. Transp. Rev., 147 (2021), 102240. https://doi.org/10.1016/j.tre.2021.102240 doi: 10.1016/j.tre.2021.102240
[3]	Q. Cheng, Z. Liu, J. Guo, X. Wu, R. Pendyala, B. Belezamo, et al., Estimating key traffic state parameters through parsimonious spatial queue models, Transp. Res. Part C Emerging Technol., 137 (2022), 103596. https://doi.org/10.1016/j.trc.2022.103596 doi: 10.1016/j.trc.2022.103596
[4]	J. Huo, X. Wu, C. Lyu, W. Zhang, Z. Liu, Quantify the road link performance and capacity using deep learning models, IEEE Trans. Intell. Transp. Syst., 2022 (2022), 1–11. https://doi.org/10.1109/TITS.2022.3153397 doi: 10.1109/TITS.2022.3153397
[5]	Q. Yang, H. N. Koutsopoulos, A microscopic traffic simulator for evaluation of dynamic traffic management systems, Transp. Res. Part C Emerging Technol., 4 (1996), 113–129. https://doi.org/10.1016/S0968-090X(96)00006-X doi: 10.1016/S0968-090X(96)00006-X
[6]	J. Sun, X. Yu, G. Baciu, M. Green, Template-based generation of road networks for virtual city modeling, in Proceedings of the ACM Symposium on Virtual Reality Software and Technology, ACM, Hong Kong, China, (2002), 33–40. https: //doi.org/10.1145/585740.585747
[7]	G. Chen, G. Esch, P. Wonka, P. Müller, E. Zhang, Interactive procedural street modeling, in ACM SIGGRAPH 2008 Papers, ACM, Los Angeles, USA, (2008), 1–10. https://doi.org/10.1145/1399504.1360702
[8]	D. Wilkie, J. Sewall, M. C. Lin, Transforming GIS data into functional road models for large-scale traffic simulation, IEEE Trans. Visual Comput. Graphics, 18 (2011), 890–901. https://doi.org/10.1109/TVCG.2011.116 doi: 10.1109/TVCG.2011.116
[9]	J. Wang, G. Lawson, Y. Shen, Automatic high-fidelity 3D road network modeling based on 2D GIS data, Adv. Eng. Software, 76 (2014), 86–98. https://doi.org/10.1016/j.advengsoft.2014.06.005 doi: 10.1016/j.advengsoft.2014.06.005
[10]	G. Nishida, I. Garcia-Dorado, D. G. Aliaga, Example-driven procedural urban roads, in Computer Graphics Forum, 35 (2016), 5–17. https://doi.org/10.1111/cgf.12728
[11]	T. Mao, H. Wang, Z. Deng, Z. Wang, An efficient lane model for complex traffic simulation, Comput. Anim. Virtual Worlds, 26 (2015), 397–403. https://doi.org/10.1002/cav.1642 doi: 10.1002/cav.1642
[12]	S. Ross, G. Gordon, D. Bagnell, A reduction of imitation learning and structured prediction to no-regret online learning, in Proceedings of the fourteenth international conference on artificial intelligence and statistics, PMLR, 15 (2011), 627–635.
[13]	P. Newson, J. Krumm, Hidden Markov map matching through noise and sparseness, in Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, Seattle, USA, (2009), 336–343. https://doi.org/10.1145/1653771.1653818
[14]	L. Zhu, J. R. Holden, J. D. Gonder, Trajectory segmentation map-matching approach for large-scale, high-resolution GPS data, Transp. Res. Rec., 2645 (2017), 67–75. https://doi.org/10.3141/2645-08 doi: 10.3141/2645-08
[15]	H. Yin, O. Wolfson, A weight-based map matching method in moving objects databases, in Proceedings 16th International Conference on Scientific and Statistical Database Management, IEEE, Santorini, Greece, (2004), 437–438. https://doi.org/10.1109/SSDM.2004.1311248
[16]	W. Bian, G. Cui, X. Wang, A trajectory collaboration based map matching approach for low-sampling-rate GPS trajectories, Sensors, 20 (2020), 2057. https://doi.org/10.3390/s20072057 doi: 10.3390/s20072057
[17]	X. Fu, J. Zhang, Y. Zhang, An online map matching algorithm based on second-order hidden markov model, J. Adv. Transp., 2021 (2021), 9993860. https://doi.org/10.1155/2021/9993860 doi: 10.1155/2021/9993860
[18]	S. Taguchi, S. Koide, T. Yoshimura, Online map matching with route prediction, IEEE Trans. Intell. Transp. Syst., 20 (2018), 338–347. https://doi.org/10.1109/TITS.2018.2812147 doi: 10.1109/TITS.2018.2812147
[19]	D. Huang, Y. Wang, S. Jia, Z. Liu, S. Wang, A Lagrangian relaxation approach for the electric bus charging scheduling optimisation problem, Transportmetrica A Transp. Sci., 2022 (2022), 1–24. https://doi.org/10.1080/23249935.2021.2023690 doi: 10.1080/23249935.2021.2023690
[20]	Y. Zheng, B. Ran, X. Qu, J. Zhang, Y. Lin, Cooperative lane changing strategies to improve traffic operation and safety nearby freeway off-ramps in a connected and automated vehicles environment, IEEE Trans. Intell. Transp. Syst., 21 (2019), 4605–4614. https://doi.org/10.1109/TITS.2019.2942050 doi: 10.1109/TITS.2019.2942050
[21]	Q. Cheng, Z. Liu, Y. Lin, X. S. Zhou, An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship, Transp. Res. Part B Methodol., 153 (2021), 246–271. https://doi.org/10.1016/j.trb.2021.09.004 doi: 10.1016/j.trb.2021.09.004
[22]	Y. Liu, C. Lyu, Y. Zhang, Z. Liu, W. Yu, X. Qu, DeepTSP: Deep traffic state prediction model based on large-scale empirical data, Commun. Transp. Res., 1 (2021), 100012. https://doi.org/10.1016/j.commtr.2021.100012 doi: 10.1016/j.commtr.2021.100012
[23]	Q. Cheng, Y. Chen, Z. Liu, A bi-level programming model for the optimal lane reservation problem, Expert Syst. Appl., 189 (2022), 116147. https://doi.org/10.1016/j.eswa.2021.116147 doi: 10.1016/j.eswa.2021.116147
[24]	J. Huo, X. Fu, Z. Liu, Q. Zhang, Short-term estimation and prediction of pedestrian density in urban hot spots based on mobile phone data, IEEE Trans. Intell. Transp. Syst., 23 (2021), 10827–10838. https://doi.org/10.1109/TITS.2021.3096274 doi: 10.1109/TITS.2021.3096274
[25]	P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y. P. Flötteröd, R. Hilbrich, et al., Microscopic traffic simulation using sumo, in 2018 21st international conference on intelligent transportation systems (ITSC), IEEE, Maui, USA, (2018), 2575–2582. https://doi.org/10.1109/ITSC.2018.8569938

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