Abstract
When an emergency occurs in a city, it causes a large accumulation of vehicles on the roads. It is particularly important to provide effective emergency route planning decisions for vehicles. To make vehicle evacuation more effective and mitigating traffic congestion, a Grid Map Emergency Route Planning (GMERP) methodology is designed. In particular, we first divide the road network into multiple grids and use the connectivity between the grids and the commuting capacity of the road as the weight of the grid. Then, a Grid Map Evacuation Area Recommendation (GM-EAR) method is introduced by considering the road speed, which calculates the commuting capacity of the raster through a sorting algorithm and recommends the raster with stronger computing capacity as the evacuation area. To allow more vehicles to reach the evacuation area in a short time, when planning a path, an Emergency Route Planning Analytic Hierarchy Process (ERP-AHP) method is introduced. The ERP-AHP calculates the road weight by taking into account the factors that affect the road traffic comprehensively so that the planned route has better evacuation ability. The experimental results show that the GMERP model can effectively evacuate vehicles around the congested area.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
He, Z., Qi, G., Lu, L.: Network-wide identification of turn-level intersection congestion using only low-frequency probe vehicle data. Transp. Res. Part C Emerg. Technol. 108, 320–339 (2019)
Yang, Z.H.: Analysis of the impacts of open residential communities on road traffic based on AHP and fuzzy theory. Ingénierie des Systèmes dInformation 25(2), 183–190 (2020)
Sharma, S.: Simulation and modeling of group behavior during emergency evacuation. In: IEEE Symposium on Intelligent Agents. IEEE (2009)
Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Netw. Spatial Econ. 1, 267–292 (2001)
Lu, Q., George, B., Shekhar, S.: Capacity constrained routing algorithms for evacuation planning: a summary of results. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 291–307. Springer, Heidelberg (2005). https://doi.org/10.1007/11535331_17
Hu, X.B., Zhang, M.K., Zhang, Q.: Co-evolutionary path optimization by ripple-spreading algorithm. Transp. Res. Part B Methodol. 106, 4535–4542 (2017)
Chun-Hui D, Science S O, University N M.: Urban traffic emergency evacuation route optimization simulation. Comput. Simul. (2017)
Giovanna, C., Giuseppe, M., Antonio, P.: Transport models and intelligent transportation system to support urban evacuation planning process. IET Intell. Transp. Syst. 10, 279–286 (2016)
Konstantinidou, M.A., Kepaptsoglou, K.L., Karlaftis, M.G.: Joint evacuation and emergency traffic management model with consideration of emergency response needs. Transp. Res. Rec. J. Transp. Res. Board 2532, 107–117 (2015)
Lv, Y., Zhang, X., Kang, W.: Managing emergency traffic evacuation with a partially random destination allocation strategy: a computational-experiment-based optimization approach. IEEE Trans. Intell. Transp. Syst. 16, 2182–2191 (2015)
Yamabe, S., Hasegawa, F., Suzuki, T.: Driver behavior response to information presentation based on the emergency evacuation procedure of the great east japan earthquake. Int. J. Intell. Transp. Syst. Res. 17, 223–231 (2019)
Milenkovic, M., Kekic, D.: Using GIS in emergency management. In: International Scientific Conference on ICT and E-Business Related Research (2016)
Lochhead, I., Hedley, N.: Mixed reality emergency management: bringing virtual evacuation simulations into real-world built environments. Int. J. Digit. Earth 12, 1–19 (2018)
Sheffi, Y.: A transportation network evacuation model. Transp. Res. Part A Gen. 16, 209–218 (1982)
Yamada, T.: A network flow approach to a city emergency evacuation planning. Int. J. Syst. Sci. 27, 931–936 (1996)
Brunilde, S., Soumis, F.: Communication and transportation network reliability using routing models. IEEE Trans. Reliab. 40, 29–38 (1991)
Jia S, Wen-Mei G.: Selection of optimal emergency logistics path under a time-varying condition. China Saf. Sci. J. (2015)
Yuan, Z., Xue-Qiang, W., Guo-Ming, C.: Evacuating route optimization based on the minimum toxic dose in toxic gas-leaking accidents. J. Saf. Environ. 13, 266–270 (2013)
Yang, B., Ding, Z., Yuan, L.: A novel urban emergency path planning method based on vector grid map. IEEE Access 9, 338–353 (2020)
L. Page, B. Sergey, M. Rajeev, and W. Terry.: The PageRank Citation Ranking: Bringing Order to the Web, Stanford InfoLab, pp. 1–14 (1999)
Acknowledgment
This work is supported by National Key R&D Program of China (No. 2017YFC0803300), the Beijing Natural Science Foundation (No. 4192004), the National Natural Science of Foundation of China (No. 61703013, 91646201), the Project of Beijing Municipal Education Commission (No. KM201810005023, KM201810005024).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, L., Yang, B., Chi, Y., Liu, Z., Guo, L. (2021). Vehicle Emergency Route Planning Based on Grid Map. In: Meng, X., Xie, X., Yue, Y., Ding, Z. (eds) Spatial Data and Intelligence. SpatialDI 2020. Lecture Notes in Computer Science(), vol 12567. Springer, Cham. https://doi.org/10.1007/978-3-030-69873-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-69873-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69872-0
Online ISBN: 978-3-030-69873-7
eBook Packages: Computer ScienceComputer Science (R0)