トポロジカル表現に基づいた地理的道路網 に対するネットワーク中心性の分析
Aiming at enhancing geographical road network analysis from a network science perspective, we introduce a novel problem of analyzing the road network in a city, and consider providing a new network centrality metric that could be useful for that problem. The problem addresses vehicular evacuation in...
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| Published in | 人工知能学会論文誌 Vol. 40; no. 1; pp. A-O62_1 - 11 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 人工知能学会
01.01.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1346-0714 1346-8030 |
| DOI | 10.1527/tjsai.40-1_A-O62 |
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| Abstract | Aiming at enhancing geographical road network analysis from a network science perspective, we introduce a novel problem of analyzing the road network in a city, and consider providing a new network centrality metric that could be useful for that problem. The problem addresses vehicular evacuation in urban settings. During emergency and disaster scenarios of vehicular evacuation, the shortest distance routes might not always be the most optimal. Instead, routes that are easier to traverse can be more crucial, even if they involve detours. Also, destinations for evacuation do not necessarily have to be restricted to traditional facilities; broad and well-maintained streets might also serve as suitable alternatives. We focus on the streets as the basic units of the road network to be investigated, and consider a scenario in which people efficiently move from starting intersections around their current places to designated goal streets, following the routes of easiest traversal. For the road network, we employ its topological representation, where vertices and edges correspond to streets and intersections between them, respectively. We thus represent the road network as a vertex-weighted graph, where the weight of each vertex reflects its ease of traversal. By appropriately extending the recently developed edge-centrality metric, “salience”, to this vertex-weighted graph, we construct a new network centrality metric to detect critical streets for the newly introduced problem. Using a toy model of road network and real-world urban road networks obtained from OpenStreetMap, we experimentally reveal its distinctive characteristics by comparing it with several baselines. Moreover, we demonstrate that the proposed network centrality metric can successfully find critical streets for vehicular evacuation, which are difficult to detect using baseline methods. |
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| AbstractList | Aiming at enhancing geographical road network analysis from a network science perspective, we introduce a novel problem of analyzing the road network in a city, and consider providing a new network centrality metric that could be useful for that problem. The problem addresses vehicular evacuation in urban settings. During emergency and disaster scenarios of vehicular evacuation, the shortest distance routes might not always be the most optimal. Instead, routes that are easier to traverse can be more crucial, even if they involve detours. Also, destinations for evacuation do not necessarily have to be restricted to traditional facilities; broad and well-maintained streets might also serve as suitable alternatives. We focus on the streets as the basic units of the road network to be investigated, and consider a scenario in which people efficiently move from starting intersections around their current places to designated goal streets, following the routes of easiest traversal. For the road network, we employ its topological representation, where vertices and edges correspond to streets and intersections between them, respectively. We thus represent the road network as a vertex-weighted graph, where the weight of each vertex reflects its ease of traversal. By appropriately extending the recently developed edge-centrality metric, “salience”, to this vertex-weighted graph, we construct a new network centrality metric to detect critical streets for the newly introduced problem. Using a toy model of road network and real-world urban road networks obtained from OpenStreetMap, we experimentally reveal its distinctive characteristics by comparing it with several baselines. Moreover, we demonstrate that the proposed network centrality metric can successfully find critical streets for vehicular evacuation, which are difficult to detect using baseline methods. |
| Author | 熊野, 雅仁 齊藤, 真希 木村, 昌弘 |
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| Copyright | JSAI (The Japanese Society for Artificial Intelligence) |
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| DOI | 10.1527/tjsai.40-1_A-O62 |
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R., Liu, H.-K., and Zhou, T.: Onset of cooperation betweenlayered networks, Physical Review E, Vol. 84, p. 026101 (2011) [Derrible 10a] Derrible, S. and Kennedy, C.: Characterizing metronetworks: state, form, and structure, Transportation, Vol. 37, No. 2,pp. 275–297 (2010) [Crucitti 06] Crucitti, P., Latora, V., and Porta, S.: Centrality in networksof urban streets, Chaos, Vol. 16, No. 015113, pp. 1–9 (2006) [Wasserman 94] Wasserman, S. and Faust, K.: Social Network Analysis:Methods and Applications, Vol. 8, Cambridge university press(1994) [Li 10] Li,G., Reis, S. D. S., Moreira, A. A., Havlin, S., Stanley,H. E., and J. S. Andrade,J.: Towards design principles foroptimal transport networks, Physical Review Letters, Vol. 104, pp.018701–1–018701–4 (2010) [Porta 06a] Porta, S., Crucitti, P., and Latora, V.: The network analysisof urban streets: a dual approach, Physica A: Statistical Mechanicsand its Applications, Vol. 369, No. 2, pp. 853–866 (2006) [Brandes 01] Brandes, U.: A Faster algorithm for betweenness centrality,Journal of Mathematical Sociology, Vol. 25, pp. 163–177(2001) [Ding 19] Ding, R., Ujang, N., Hamid, H., Abd. Manan, M. S.,Rong, l., Albadareen, S., Nochian, A., and Wu, J.: Application ofComplex Networks Theory in Urban Traffic Network Researches,Networks and Spatial Economics, Vol. 19, pp. 1281–1317 (2019) [Guti´errez-Jarpa 17] Guti´errez-Jarpa, G., Laporte, G., Marianov, V.,and Moccia, L.: Multi-objective rapid transit network design withmodal competition: The case of Concepci´on, Chile, Computers &Operations Research, Vol. 78, pp. 27–43 (2017) [Porta 06b] Porta, S., Crucitti, P., and Latora, V.: The network analysisof urban streets: a primal approach, Environment and PlanningB: Planning and design, Vol. 33, No. 5, pp. 705–725 (2006) [木村24] 木村昌弘, 熊野雅仁：アテンションダイナミクスに着目した地理的影響ネットワークの分析, 人工知能学会誌, Vol. 39,No. 4, pp. 496–504 (2024) [Porta 12] Porta, S., Latora, V., and Latora, L.: Street Centralityand the Location of Economic Activities in Barcelona, Urban Stud,Vol. 49, No. 7, pp. 1471–1488 (2012) [Buhl 06] Buhl, C., Gautrais, J., Reeves, N., Sol´e, R. V., Valverde, S.,Kuntz, P., and Theraulaz, G.: Topological patterns in street networksof self-organized urban settlements, Interdisciplinary Physics,Vol. 49, pp. 513–522 (2006) [Do 16] Do,M. andNoh,Y.: Comparative analysis of informationalevacuation guidance by lane-based routing, International Journal ofUrban Sciences, Vol. 20, No. sup1, pp. 60–76 (2016) [Ding 17] Ding, R., Ujang, N., Hamid, H. B., Manan, M. S. A., Li, R.,and Wu, J.: Heuristic urban transportation network design method, a multilayer coevolution approach, Physica A: Statistical Mechanicsand Its Applications, Vol. 479, No. 1, pp. 71–83 (2017) [Montis 05] Montis, A. D., Barthelemy, M., Chessa, A., and Vespignani,A.: The Structure of Interurban Traffic: A Weighted networkanalysis, Environment and Planning B: Planning and Design,Vol. 34, No. 5, pp. 905–924 (2005) [Saito 24] Saito, M., Kumano, M., and Kimura, M.: Detecting criticalstreets in road networks based on topological representation, in Proceedingsof the 12th International Conference on Complex Networksand Their Applications (COMPLEX NETWORKS 2023), Vol. 4, pp.231–242 (2024) [Higuchi 19] Higuchi, M., Matsutani, K., Kumano, M., andKimura, M.: Discovering spatio-temporal latent influence in geographicalattention dynamics, in Berlingerio, M., Bonchi, F., Gartner,T., Hurley, N., and Ifrim, G. eds., Machine Learning and KnowledgeDiscovery in Databases, ECML PKDD 2018, LNCS 11052, pp.517–534, Springer (2019) [Jiang 08] Jiang, B., Zhao, S., and Yin, J.: Self-organized naturalroads for predicting traffic flow: a sensitivity study, Journal of StatisticalMechanics: Theory and Experiment, Vol. 2008, No. 07, pp.P07008:1–P07008:23 (2008) [Boeing 20] Boeing, G.: A multi-scale analysis of 27,000 urban streetnetworks:Every US City, Town, Urbanized Area, and Zillow Neighborhood,Environment & Planning B: Urban Analytics and City Science,Vol. 47, No. 4, pp. 590–608 (2020) [Wu 14] Wu, J., Xu, M., and Gao, Z.: Modeling the coevolution ofroad expansion and urban traffic growth, Advances in Complex Systems,Vol. 17, No. 01, p. 1450005 (2014) [Xu 16] Xu, Q., Mao, B., and Bai, Y.: Network structure of subwaypassenger flows, Journal of Statistical Mechanics: Theory and Experiment,Vol. 2016, No. 3, pp. 033404:1–16 (2016) [Xing 16] Xing, Y., Lu, J., and Chen, S.: Weighted complex networkanalysis of Shanghai Rail Transit System, Discrete Dynamics in Natureand Society, pp. 1290138:1–8 (2016) [Fujii 21] Fujii, T., Kumano, M., Gama, J., and Kimura, M.: Detectinggeographical competitive structure for POI visit dynamics, inBenito, R., Cherifi, C., Cherifi, H., Moro, E., Rocha, L., and Sales-Pardo, M. eds., Complex Networks & Their Applications IX, SCI 944,pp. 27–38, Springer (2021) [Ding 15] Ding, R., Ujang, N., Hamid, H. B., and Wu, J.: Complexnetwork theory applied to the growth of Kuala Lumpur’s Public UrbanRail Transit Network, PLoS One, Vol. 10, No. 10, p. e0139961(2015) [Ma 18] Ma,D., Omer, I., Osaragi, T., Sandberg, M., and Jiang, B.:Why topology matters in predicting human activities, Environmentand Planning B: Urban Analytics and City Science, Vol. 46, No. 7,pp. 1297–1313 (2018) [Derrible 10b] Derrible, S. and Kennedy, C.: Evaluating, comparing,and improving metro networks: Application to plans for Toronto,Canada, Journal of the Transportation Research Board, Vol. 2146,No. 1, pp. 43–51 (2010) [Zhong 14] Zhong, C., Arisona, S. M., Huang, X., Batty, M., andSchmitt, G.: Detecting the dynamics of urban structure through spatialnetwork analysis [Wang 18] Wang, S., Yu, D., Lin, C., Shang, Q., and Lin, Y.: How toconnect with each other between roads? An empirical study of urbanroad connection properties, Physica A: Statistical Mechanics and ItsApplications, Vol. 512, No. 3, pp. 775–787 (2018) [Xu 07] Xu,X., Hu, J., Liu, F., and Liu, L.: Scaling and correlationsin three bus-transport networks of China, Physica A: Statistical Mechanicsand its Applications, Vol. 374, No. 1, pp. 441–448 (2007) [Buldyrev 10] Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E.,and Havlin, S.: Catastrophic cascade of failures in interdependentnetworks, Nature, Vol. 464, pp. 1025–1028 (2010) [Jiang 04] Jiang, B. and Claramunt, C.: Topological analysis of UrbanStreet Networks, Environment and Planning B: Planning andDesign, Vol. 31, pp. 151–162 (2004) |
| References_xml | – reference: [Derrible 10a] Derrible, S. and Kennedy, C.: Characterizing metronetworks: state, form, and structure, Transportation, Vol. 37, No. 2,pp. 275–297 (2010) – reference: [Boeing 20] Boeing, G.: A multi-scale analysis of 27,000 urban streetnetworks:Every US City, Town, Urbanized Area, and Zillow Neighborhood,Environment & Planning B: Urban Analytics and City Science,Vol. 47, No. 4, pp. 590–608 (2020) – reference: [Derrible 10b] Derrible, S. and Kennedy, C.: Evaluating, comparing,and improving metro networks: Application to plans for Toronto,Canada, Journal of the Transportation Research Board, Vol. 2146,No. 1, pp. 43–51 (2010) – reference: [Wang 18] Wang, S., Yu, D., Lin, C., Shang, Q., and Lin, Y.: How toconnect with each other between roads? An empirical study of urbanroad connection properties, Physica A: Statistical Mechanics and ItsApplications, Vol. 512, No. 3, pp. 775–787 (2018) – reference: [Crucitti 06] Crucitti, P., Latora, V., and Porta, S.: Centrality in networksof urban streets, Chaos, Vol. 16, No. 015113, pp. 1–9 (2006) – reference: [Julianto 19] Julianto, Mawengkang, H., and Zarlis, M.: A Car FlowNetwork Model For Lane-Based Evacuation Routing Problem, Journalof Physics: Conference Series, Vol. 1255, No. 1, p. 012040(2019) – reference: [Porta 06a] Porta, S., Crucitti, P., and Latora, V.: The network analysisof urban streets: a dual approach, Physica A: Statistical Mechanicsand its Applications, Vol. 369, No. 2, pp. 853–866 (2006) – reference: [Buhl 06] Buhl, C., Gautrais, J., Reeves, N., Sol´e, R. V., Valverde, S.,Kuntz, P., and Theraulaz, G.: Topological patterns in street networksof self-organized urban settlements, Interdisciplinary Physics,Vol. 49, pp. 513–522 (2006) – reference: [Do 16] Do,M. andNoh,Y.: Comparative analysis of informationalevacuation guidance by lane-based routing, International Journal ofUrban Sciences, Vol. 20, No. sup1, pp. 60–76 (2016) – reference: [Higuchi 19] Higuchi, M., Matsutani, K., Kumano, M., andKimura, M.: Discovering spatio-temporal latent influence in geographicalattention dynamics, in Berlingerio, M., Bonchi, F., Gartner,T., Hurley, N., and Ifrim, G. eds., Machine Learning and KnowledgeDiscovery in Databases, ECML PKDD 2018, LNCS 11052, pp.517–534, Springer (2019) – reference: [Ma 18] Ma,D., Omer, I., Osaragi, T., Sandberg, M., and Jiang, B.:Why topology matters in predicting human activities, Environmentand Planning B: Urban Analytics and City Science, Vol. 46, No. 7,pp. 1297–1313 (2018) – reference: [Brandes 01] Brandes, U.: A Faster algorithm for betweenness centrality,Journal of Mathematical Sociology, Vol. 25, pp. 163–177(2001) – reference: [Ducruet 14] Ducruet, C. and Beauguitte, L.: Spatial science and networkscience: Review and outcomes of a complex relationship, Networksand Spatial Economics, Vol. 14, No. 3-4, pp. 297–316 (2014) – reference: [Fujii 21] Fujii, T., Kumano, M., Gama, J., and Kimura, M.: Detectinggeographical competitive structure for POI visit dynamics, inBenito, R., Cherifi, C., Cherifi, H., Moro, E., Rocha, L., and Sales-Pardo, M. eds., Complex Networks & Their Applications IX, SCI 944,pp. 27–38, Springer (2021) – reference: [Xu 16] Xu, Q., Mao, B., and Bai, Y.: Network structure of subwaypassenger flows, Journal of Statistical Mechanics: Theory and Experiment,Vol. 2016, No. 3, pp. 033404:1–16 (2016) – reference: [Xing 16] Xing, Y., Lu, J., and Chen, S.: Weighted complex networkanalysis of Shanghai Rail Transit System, Discrete Dynamics in Natureand Society, pp. 1290138:1–8 (2016) – reference: [Ding 15] Ding, R., Ujang, N., Hamid, H. B., and Wu, J.: Complexnetwork theory applied to the growth of Kuala Lumpur’s Public UrbanRail Transit Network, PLoS One, Vol. 10, No. 10, p. e0139961(2015) – reference: [Saito 24] Saito, M., Kumano, M., and Kimura, M.: Detecting criticalstreets in road networks based on topological representation, in Proceedingsof the 12th International Conference on Complex Networksand Their Applications (COMPLEX NETWORKS 2023), Vol. 4, pp.231–242 (2024) – reference: [Li 10] Li,G., Reis, S. D. S., Moreira, A. A., Havlin, S., Stanley,H. E., and J. S. Andrade,J.: Towards design principles foroptimal transport networks, Physical Review Letters, Vol. 104, pp.018701–1–018701–4 (2010) – reference: [Porta 12] Porta, S., Latora, V., and Latora, L.: Street Centralityand the Location of Economic Activities in Barcelona, Urban Stud,Vol. 49, No. 7, pp. 1471–1488 (2012) – reference: [Porta 09] Porta, S., Strano, E., Iacoviello, V., Messora, R., Latora, V.,Cardillo, A., Wang, F., and Scellato, S.: Street Centrality and Densitiesof Retail and Services in Bologna, Italy, Environment and PlanningB, Vol. 36, No. 3, pp. 450–465 (2009) – reference: [Buldyrev 10] Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E.,and Havlin, S.: Catastrophic cascade of failures in interdependentnetworks, Nature, Vol. 464, pp. 1025–1028 (2010) – reference: [Jiang 08] Jiang, B., Zhao, S., and Yin, J.: Self-organized naturalroads for predicting traffic flow: a sensitivity study, Journal of StatisticalMechanics: Theory and Experiment, Vol. 2008, No. 07, pp.P07008:1–P07008:23 (2008) – reference: [Porta 06b] Porta, S., Crucitti, P., and Latora, V.: The network analysisof urban streets: a primal approach, Environment and PlanningB: Planning and design, Vol. 33, No. 5, pp. 705–725 (2006) – reference: [Zhong 14] Zhong, C., Arisona, S. M., Huang, X., Batty, M., andSchmitt, G.: Detecting the dynamics of urban structure through spatialnetwork analysis, – reference: [Sun 15] Sun, L., Lu, Y., and Lee, D.-H.: Understanding the structureof urban bus networks: The C-space representation approach,in Proceedings of the 15th COTA International Conference of TransportationProfessionals ’15, pp. 1557–1567 (2015) – reference: [Xu 07] Xu,X., Hu, J., Liu, F., and Liu, L.: Scaling and correlationsin three bus-transport networks of China, Physica A: Statistical Mechanicsand its Applications, Vol. 374, No. 1, pp. 441–448 (2007) – reference: [木村24] 木村昌弘, 熊野雅仁：アテンションダイナミクスに着目した地理的影響ネットワークの分析, 人工知能学会誌, Vol. 39,No. 4, pp. 496–504 (2024) – reference: [Ding 17] Ding, R., Ujang, N., Hamid, H. B., Manan, M. S. A., Li, R.,and Wu, J.: Heuristic urban transportation network design method, a multilayer coevolution approach, Physica A: Statistical Mechanicsand Its Applications, Vol. 479, No. 1, pp. 71–83 (2017) – reference: [Gu 11] Gu,C.-G., Zou, S.-R., Xu, X.-L., Qu, Y.-Q., Jiang, Y.-M.,He, D. R., Liu, H.-K., and Zhou, T.: Onset of cooperation betweenlayered networks, Physical Review E, Vol. 84, p. 026101 (2011) – reference: [Ding 19] Ding, R., Ujang, N., Hamid, H., Abd. Manan, M. S.,Rong, l., Albadareen, S., Nochian, A., and Wu, J.: Application ofComplex Networks Theory in Urban Traffic Network Researches,Networks and Spatial Economics, Vol. 19, pp. 1281–1317 (2019) – reference: [Grady 12] Grady, D., Thiemann, C., and Brockmann, D.: Robustclassification of salient links in complex networks, Nature Communications,Vol. 3, pp. 864+ (2012) – reference: [Jiang 04] Jiang, B. and Claramunt, C.: Topological analysis of UrbanStreet Networks, Environment and Planning B: Planning andDesign, Vol. 31, pp. 151–162 (2004) – reference: [Guti´errez-Jarpa 17] Guti´errez-Jarpa, G., Laporte, G., Marianov, V.,and Moccia, L.: Multi-objective rapid transit network design withmodal competition: The case of Concepci´on, Chile, Computers &Operations Research, Vol. 78, pp. 27–43 (2017) – reference: [Montis 05] Montis, A. D., Barthelemy, M., Chessa, A., and Vespignani,A.: The Structure of Interurban Traffic: A Weighted networkanalysis, Environment and Planning B: Planning and Design,Vol. 34, No. 5, pp. 905–924 (2005) – reference: [Wasserman 94] Wasserman, S. and Faust, K.: Social Network Analysis:Methods and Applications, Vol. 8, Cambridge university press(1994) – reference: [Wu 14] Wu, J., Xu, M., and Gao, Z.: Modeling the coevolution ofroad expansion and urban traffic growth, Advances in Complex Systems,Vol. 17, No. 01, p. 1450005 (2014) |
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| Title | トポロジカル表現に基づいた地理的道路網 に対するネットワーク中心性の分析 |
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| ispartofPNX | 人工知能学会論文誌, 2025/01/01, Vol.40(1), pp.A-O62_1-11 |
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