Research on spatiotemporal characteristics of urban crowd gathering based on Baidu heatmap

• Autorzy: Meng Yunwei , Li Shibao , Chen Kang , Li Binbin , Zang Ji'en , Qing Guangyang

Identyfikatory

DOI

10.61089/aot2023.1g53c194

• Języki publikacji: EN

Abstrakty

EN

With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic dynamics congestion. In order to solve the problem that it is difficult to determine the dynamics of traffic congestion areas caused by uneven distribution of vitality in different regions of mountainous cities, a crowded mega mountainous city is selected as research object and it proposes a model to calculate the change characteristics of regional crowd gathering. Baidu Heatmap is used as it could distinguish crowd gathering in certain urban core area. The heat map pictures in dozens of consecutive days is extracted and researchers conducted pixel statistical classification on thermal map images. Based on the pixel data of different levels of the pictures, the calculation model is established and an algorithm based on particle swarm optimization is proposed. The calibration of the relative active population equivalent density is conducted, and the distribution characteristics of crowd gathering in time and space are analyzed. The results show that there are obvious spatiotemporal characteristics for this selected city. In time, holidays have an important impact on crowd gathering. The peak time of crowd gathering on weekdays is different from that on rest days. The research in this paper has a direct practical value for the identification of traffic congestion areas and the corresponding governance measures. The dynamic identification of population gathering areas in mountainous mega cities, demand prediction for various transportation regions, and future population OD(Origin-Destination) planning are of great significance.

Słowa kluczowe

EN

urban transportation; problem of urban traffic; traffic congestion; uneven distribution; population density

PL

transport miejski; problem ruchu miejskiego; natężenie ruchu; gęstość zaludnienia

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2023
• Tom: Vol. 68, iss. 4
• Strony: 41--54
• Opis fizyczny: Bibliogr. 31 poz., il., tab., wykr.

Twórcy

autor

Meng Yunwei

• School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, China

autor

Li Shibao

• School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, China

• https://orcid.org/0009-0008-3078-9205

autor

Chen Kang

• School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China

autor

Li Binbin

• School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, China

autor

Zang Ji'en

• School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, China

autor

Qing Guangyang

• China Merchants Roadway Information Technology (Chongqing) Co., Ltd, Chongqing, China

Bibliografia

• [1] Bao, C., Zhang, S. (2018). Route Optimization of cold chain logistics in joint distribution with consideration of carbon emission. Industrial Engineering and Management, 23(05): 95- 100+107. https://doi.org/10.19495/j.cnki.1007- 5429.2018.05.013.
• [2] Bao, X., Song, A. (2023). Improved NSGA-II algorithm for solving multi-objective logistics vehicle routing optimization problem. Computer Applications and Software, 40(02): 274-280. https://doi.org/10.3969/j.issn.1000-386x.2023.02.043.
• [3] Büşra, O., Çağrı, K., Fulya, A. (2021). A Hyper Heuristic for the Green vehicle routing problem with simultaneous pickup and Delivery. Computers & Industrial Engineering, 153. https://doi.org/10.1016/j.cie.2020.107010.
• [4] Elhedhli, S., Merrick, R. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D, 17(5): 370-379. https://doi.org/10.1016/j.trd.2012.02.002.
• [5] Gao, Y. (2006). Non-dominated sorting genetic algorithm and its applications. Zhejiang University.
• [6] Guo, X., Zhang, W., Liu, B. (2022). Low-carbon routing for cold-chain logistics considering the time-dependent effects of traffic congestion. Transportation Research Part D, 113. https://doi.org/10.1016/j.trd.2022.103502.
• [7] He, D., Li, Y. (2018). Optimization model of green multi-type vehicles routing problem. Journal of Computer Applications, 38(12): 3618-3624+3637. https://doi.org/10.11772/j.issn.1001-9081.2018051085.
• [8] Ji, Y., Du, J., Wu, X. (2021). Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand. Environment, Development and Sustainability, 23, 13731-13754. https://doi.org/10.1007/s10668-021-01236-z.
• [9] Leng, L. (2020). Research on low-carbon logistics location-routing problem and hyper-heuristic algorithm. Zhejiang University of Technology. https://doi.org/10.27463/d.cnki.gzgyu.2020.00 0009.
• [10] Li, B., Zhao, G. (2017). Model of low-carbon remanufacturing logistics network based on robust optimization. Journal of Shandong University (Science Edition), 52(01): 43-55. https://doi.org/10.6040/j.issn.1671-9352.0.2016.174.
• [11] Li, C., Li, S., Liu, S. (2020). Research on urban logistics distribution network from the perspective of low-carbon. Ecological Economics, 36(01): 106-110.
• [12] Li, J. (2015). Credibility-based Multi-objective fuzzy programming problem for low-carbon logistics network design. Systems Engineering Theory & Practice, 35(06): 1482-1492.
• [13] Li, Q. (2022). Research on multi-objective agricultural cold chain logistics vehicle path planning. Tianjin Normal University. https://doi.org/10.27363/d.cnki.gtsfu.2022.000 365.
• [14] Li, W., Zhang, C., Ma, C. (2020). An optimization model and solution algorithm of Multi-objective vehicle path under low carbon conditions. Traffic Information and Safety, 38(01): 118-126+144. https://doi.org/10.3963/j.jssn.1674-4861.2020.01.015.
• [15] Liu, F. (2022). Research on logistics distribution fleet configuration and route optimization under low carbon. Shandong University. https://doi.org/10.27272/d.cnki.gshdu.2022.00 5427.
• [16] Liu, S., Zhang, C. (2023). Multiobjective optimization of railway cold-chain transportation route based on dynamic train information. Journal of Rail Transport Planning & Management, 26. https://doi.org/10.1016/j.jrtpm.2023.100381.
• [17] Lv, P. (2013). Study of logistics network optimization model considering carbon emission. Application Research of Computer, 30(10): 2977-2980. https://doi.org/10.3969/j.issn.1001-3695.2013.10.024.
• [18] Martinez-Puras, A., Pacheco, J. (2016). MOAMP-Tabu search and NSGA-II for a real bi objective scheduling-routing problem. Knowledge-Based Systems, (112): 92-104. https://doi.org/10.1016/j.knosys.2016.09.001.
• [19] Masoud, H., Mohammad Ali, F., Ali, H. (2023). A two-echelon location routing problem considering sustainability and hybrid open and closed routes under uncertainty. Heliyon, 9(3). https://doi.org/10.1016/j.heliyon.2023.e14258.
• [20] Peng, Y., Mo, Z., Liu, S. (2021). Passenger's routes planning in stochastic common-lines' multi-modal transportation network through integrating Genetic Algorithm and Monte Carlo simulation. Archives of Transport, 59(3), 73-92. https://doi.org/10.5604/01.3001.0015.0123.
• [21] Ren, T., Luo, T., Gu, Z. (2022). Optimization of urban logistics co-distribution path consider ing simultaneous pick-up and delivery. Computer Integrated Manufacturing System, 28(11): 3523-3534. https://doi.org/10.13196/j.cims.2022.11.016.
• [22] Song, M., Li, J., Han, Y. (2020). Metaheuristics for solving the vehicle routing problem with the time windows and energy consumption in cold chain logistics. Applied Soft Computing Journal, 95: 106561. https://doi.org/10.1016/j.asoc.2020.106561.
• [23] Tong, H., (2022). Research on the site selection and path layout of the logistics distribution center of marine ships based on a mathematical model. Archives of Transport, 63(3), 23-34. https://doi.org/10.5604/01.3001.0015.9925.
• [24] Vincent, F.Y., Pham, T., Roberto, B. (2023). A robust optimization approach for the vehicle routing problem with cross-docking under demand uncertainty. Transportation Research Part E, 173. https://doi.org/10.1016/j.tre.2023.103106.
• [25] Wang, Y., Shi, Q., Song, W. (2020). Dynamic multi-objective optimization model and algorithm for logistics networks. Computer Integrated Manufacturing Systems, 26(04): 1142- 1150. https://doi.org/10.13196/j.cims.2020.04.027.
• [26] Yang, J., Guo, J., Ma, S. (2016). Low-carbon city logistics distribution network design with resource deployment. Journal of Cleaner Production,2016, 119: 223-228. https://doi.org/10.1016/j.jclepro.2013.11.011.
• [27] Yuan, Y., Diego, C., Maxime, O., (2021). A column generation based heuristic for the generalized vehicle routing problem with time windows. Transportation Research Part E, 152. https://doi.org/10.1016/j.tre.2021.102391.
• [28] Zhang, D., Qiao, X., Xiao, B., (2021). Multi objective vehicle routing optimization based on low carbon perspective and random demand. Journal of Railway Science and Engineering, 18(08): 2165-2174. https://doi.org/10.19713/j.cnki.43-1423/u.T20200883.
• [29] Zhang, L., Tseng, M., Wang, C., (2019) Low carbon cold chain logistics using ribonucleic acid-ant colony optimization algorithm. Journal of Cleaner Production, 233: 169-180. https://doi.org/10.1016/j.jclepro.2019.05.306.
• [30] Zhang, S., Chen, N., Li, Y. (2022). Research on optimization decision of urban cold chain logistics distribution system from perspective of low carbon. Industrial Engineering and Management, 27(01): 56-64. https://doi.org/10.19495/j.cnki.1007-5429.2022.01.007.
• [31] Zhu, A., Wen, Y. (2021). Green logistics location-routing optimization solution based on improved GA Algorithm considering low-carbon and environmental protection. Journal of Mathematics. https://doi.org/10.1155/2021/6101194.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)