Review of multimodal transport research based on bibliometrics
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摘要: 为全面了解综合交通领域的研究进展，本文采用文献计量方法，系统检索了1990~2024年间在WOS核心数据库和中国知网(CNKI)数据库中收录的相关文献，共计32 111篇，涉及2 244位作者和1 422个关键词；通过科学知识图谱对文献分布、发文国家、研究机构及作者进行了系统分析和可视化呈现，并通过分析关键词共现图谱和聚类图谱揭示了该领域的研究热点；结合突现词分析，预测了未来研究方向。研究结果表明：综合交通研究经历了缓慢增长、平稳增长和快速增长3个阶段，研究热点集中在综合交通系统分析、规划与设计、运行及物流等方面，分别包括综合交通系统的供需关系、网络模型、出行行为与土地利用的关系，综合交通网络布局优化、区域连接性与互通性提升，多交通方式的无缝衔接和高效运作，物流网络、运输路径优化及货物流动效率提升等领域；目前，国内外研究重点高度重合，但仍存差异，国内学者更关注综合交通的战略发展、结构优化、综合规划及国家急需场景研究，而国际研究则侧重于多模式交通理论及新兴技术应用。未来，随着人工智能、大数据、区块链、数字孪生等技术的发展，综合交通研究将朝着更加综合化、立体化、数字化、网联化、共享化、低碳化的方向发展，以实现交通时空资源与能源、算力的高效利用，跨行业、跨领域、跨部门、跨层级的合作与协同将成为推动综合交通研究持续发展的关键，促进资源整合与优化，建设高效、智慧、安全和环保的综合交通体系。Abstract: To comprehensively understand the research progress in the field of multimodal transport, bibliometric methods was adopted to systematically retrieved relevant literature from the WOS core database and the China National Knowledge Infrastructure (CNKI) database from 1990 to 2024, totaling 32111 articles, involving 2 244 authors and 1 422 keywords. Through scientific knowledge mapping, the distribution of literatures, publishing countries, research institutions, and authors were systematically analyzed and visualized. Keyword co-occurrence maps and clustering maps were used to reveal the research hotspots in this field. Emergent term analysis was conducted to predict future research directions. Research results show that multimodal transport research has gone through three stages: slow growth, steady growth, and rapid growth. The hotspots are concentrated in the analysis, planning and design, operation, and logistics of multimodal transport systems. Specifically, these include: the supply-demand relationship of multimodal transport systems, network models, the relationship between travel behavior and land use, multimodal transport network layout optimization, regional connectivity and interoperability improvement, seamless connection and efficient operation of multiple transport modes, and logistics network, transportation path optimization, and freight flow efficiency improvement. Currently, the research focuses of domestic and international scholars are highly overlapping but still differ. Domestic scholars focus more on the strategic development, structural optimization, comprehensive planning, and national urgent scenario research of multimodal transport. In contrast, international research focuses more on multimodal transport theory and the application of emerging technologies. In the future, with the development of artificial intelligence, big data, blockchain, and digital twins, multimodal transport research will move towards more integrated, three-dimensional, digital, networked, shared, and low-carbon, to achieve efficient utilization of transport spatiotemporal resources, energy, and computing power. Cross-industry, cross-field, cross-department, and cross-level collaboration and coordination will become key to driving the continuous development of multimodal transport research. This will promote resource integration and optimization, helping to build an efficient, intelligent, safe, and environmentally friendly multimodal transport system.
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图 3 1990~2024年国际综合交通发文国家合作关系网络
Figure 3. International collaboration network of countries in multimodal transport publications from 1990 to 2024
图 5 综合交通领域研究机构知识图谱
Figure 5. Maps of knowledge of research institutions for multimodal transport field
图 6 综合交通领域发文作者知识图谱
Figure 6. Map of knowledge of authors for multimodal transport field
图 7 综合交通研究关键词共现图谱
Figure 7. Map of knowledge of keywords co-occurrence for multimodal transport research
图 8 国内外综合交通研究差异分析
Figure 8. Analysis of differences in multimodal transport research between China and abroad
图 9 综合交通研究关键词聚类图谱
Figure 9. Map of knowledge of keyword clustering of multimodal transport research
图 10 综合交通研究突现词图谱
Figure 10. Map of knowledge of emergent terms of multimodal transport research
表 1 1990~2024年国内外综合交通文献发文量前10期刊
Table 1. Top 10 journals of multimodal transport literature in China and abroad from 1990 to 2024
序号 国内 国际 期刊 篇数 期刊 篇数 1 《综合运输》 490 Transportation Research Record 1 245 2 《交通与运输》 229 IEEE Transactions on Intelligent Transportation Systems 719 3 《城市交通》 229 Transportation Research Part A: Policy and Practice 451 4 《交通运输系统工程与信息》 226 Transportation Research Part C: Emerging Technologies 350 5 《中国公路》 189 Transportation Research Part D: Transport and Environment 339 6 《交通企业管理》 139 Transportation Research Procedia 332 7 《城市道桥与防洪》 132 Journal of Advanced Transportation 255 8 《城市轨道交通研究》 114 Transportation Research Part E: Logistics and Transportation Review 235 9 《交通与港航》 105 Transport Policy 211 10 《交通建设与管理》 104 Journal of Transport Geography 206 
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表 2 1990~2024年国内综合交通被引频次前10的文献
Table 2. Top 10 cited Chinese literatures of multimodal transport from 1990 to 2024
序号 题目 第一作者 期刊 年份 被引频次 1 《中国交通工程学术研究综述·2016》 《中国公路学报》编辑部 《中国公路学报》 2016 609 2 《面向低碳的城市空间结构——城市交通与土地使用的新模式》 潘海啸 《城市发展研究》 2010 317 3 《城市绿色交通的实现途径》 陆化普 《城市交通》 2009 313 4 《国外典型综合交通枢纽布局设计实例剖析》 邱丽丽 《城市轨道交通研究》 2006 275 5 《开放条件下长江三角洲区域的综合交通可达性空间格局》 吴威 《地理研究》 2007 274 6 《中国智能交通系统发展战略研究》 王笑京 《交通运输系统工程与信息》 2006 238 7 《一种多式联运网络的最优分配模式研究》 张建勇 《铁道学报》 2002 220 8 《县域综合交通可达性测度及其与经济发展水平的关系——对湖北省79个县域的定量分析》 刘传明 《地理研究》 2011 219 9 《一种多式联运网络运输方式的组合优化模式》 王涛 《中国工程科学》 2005 213 10 《综合交通枢纽布局规划模型与方法研究》 袁虹 《公路交通科技》 2001 202
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表 3 1990~2024年国外综合交通被引频次前10的文献
Table 3. Top 10 cited foreign literatures of multimodal transport from 1990 to 2024
序号 题目 第一作者 期刊 年份 被引频次 1 A review of dynamic vehicle routing problems PILLAC V European Journal of Operational Research 2013 746 2 Multimodal freight transportation planning: a literature review STEADIESEIFI M European Journal of Operational Research 2014 394 3 Stochastic measures of network resilience: applications to waterway commodity flows BAROUD H Risk Analysis 2014 105 4 Congestion induced by the structure of multiplex networks SOLÉ-RIBALTA A Physical Review Letters 2016 103 5 On the distribution of urban road space for multimodal congested networks ZHENG N Transportation Research Part B: Methodological 2013 97 6 Location-routing problem in multimodal transportation network with time windows and fuzzy demands: presenting a two-part genetic algorithm FAZAYELI S Computers & Industrial Engineering 2018 90 7 Improving the sustainability of integrated transportation system with bike-sharing: a spatial agent-based approach LU M Sustainable Cities and Society 2018 82 8 Optimizing the spatio-temporal deployment of battery electric bus system WEI R Journal of Transport Geography 2018 71 9 Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California XIE F Bioresource Technology 2014 70 10 A framework to integrate mode choice in the design of mobility-on-demand systems LIU Y Transportation Research Part C: Emerging Technologies 2019 65
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表 4 1990至2024年国际综合交通发文前10国家
Table 4. Top 10 countries in international publications on multimodal transport from 1990 to 2024
序号 国家 篇数 频率前5位的关键词 1 USA 3 009 autonomous driving，electric vehicles，rail transportation，neural network，mathematical models 2 China 2 868 connected vehicles，machine learning，automated highways，air pollution，regression 3 Germany 718 sustainable transport，intermodal transport，road safety，energy consumption，logistics 4 England 702 equity，shared mobility，climate change，health，simulation 5 Canada 578 car ownership，fuel consumption，uber，artificial intelligence，emissions 6 Australia 523 urban form，GIS，PM2.5，life cycle assessment，greenhouse gas emissions 7 Italy 492 road safety，freight transport，genetic algorithm，hybrid systems，air pollution 8 Netherlands 435 social network，shared mobility，active noise control，route choice，integration 9 France 331 logistics，air traffic control，delivery problem，vehicle ownership，sustainable development 10 India 298 infrastructure，reliability，life cycle assessment，acceptance，deep learning
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表 5 国内外综合交通出现频率排名前10的研究机构
Table 5. Top 10 research institutions in frequency of multimodal transport in China and abroad
序号 国内 国际 研究机构 频次 研究机构 频次 1 北京交通大学 230 Beijing Jiaotong University 327 2 同济大学 195 University of California 251 3 西南交通大学 130 Delft University of Technology 215 4 上海市政工程设计研究院 82 Tongji University 209 5 中国交通部科学研究院 82 Florida State University 195 6 上海市城市综合交通规划研究所 78 Southeast University 185 7 长安大学 77 Hong Kong Polytechnic University 139 8 中铁第四勘察设计院集团有限公司 73 University of Texas System 134 9 东南大学 73 Tsinghua University 124 10 国家发展和改革委员会综合运输研究所 71 Chinese Academy of Sciences 124
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表 6 国内外综合交通出现频次和中心性排名前50的关键词
Table 6. Top 50 keywords in frequency and centrality of multimodal transport in China and abroad
序号 国内 国际 关键词 频次 中心性 首次出现年份 关键词 频次 中心性 首次出现年份 1 综合交通 424 0.42 1997 public transport 545 0.03 2004 2 交通规划 207 0.17 1999 optimization 467 0.01 2010 3 交通枢纽 191 0.16 2002 design 401 0.01 2000 4 交通运输 175 0.12 2004 transportation 384 0.01 2000 5 城市交通 126 0.09 1999 demand 379 0.02 2002 6 轨道交通 109 0.09 2004 systems 367 0.01 2008 7 一体化 75 0.04 2002 travel 336 0.01 2004 8 综合运输 61 0.05 1994 land use 303 0.04 2000 9 交通强国 60 0.03 2018 mobility 293 0.01 2016 10 城市群 53 0.02 2001 choice 282 0.02 1998 11 多式联运 51 0.02 2011 built environment 282 0.02 2012 12 大数据 49 0.02 2015 city 274 0.02 2002 13 铁路 49 0.03 2006 travel behavior 265 0.03 2017 14 公共交通 49 0.02 2003 network 252 0.01 2005 15 绿色交通 47 0.02 2006 policy 238 0.01 2004 16 交通组织 46 0.02 2004 intelligent transportation systems 232 0.02 1998 17 交通工程 43 0.03 2005 accessibility 225 0.01 2008 18 区域经济 38 0.01 2010 public transportation 206 0.02 2013 19 智能交通 37 0.03 2009 mode choice 193 0.01 2014 20 站城融合 35 0.02 2018 transit 179 0.01 2014 21 高速铁路 33 0.04 1994 electric vehicles 178 0.02 2013 22 地下空间 29 0.02 2011 emissions 165 0.00 2014 23 枢纽经济 28 0.01 2018 location 157 0.01 2008 24 发展策略 27 0.01 2011 physical activity 136 0.01 2014 25 智慧交通 26 0.00 2018 infrastructure 124 0.01 2013 26 换乘 25 0.01 2007 services 120 0.01 2014 27 区域交通 24 0.02 2001 public transit 116 0.01 2014 28 规划设计 22 0.00 2006 urban 113 0.01 1999 29 交通政策 22 0.00 2001 travel time 98 0.01 2013 30 协调发展 22 0.01 2006 big data 100 0.01 2016 31 城市规划 21 0.01 2006 logistics 100 0.01 2014 32 可达性 20 0.00 2012 predictive models 96 0.01 2020 33 规划布局 20 0.00 2012 bicycle 95 0.01 2014 34 都市圈 20 0.01 2014 energy 92 0.01 2002 35 基础设施 18 0.01 2010 climate change 91 0.01 2010 36 上海 18 0.02 2005 preference 89 0.01 2017 37 信息化 18 0.00 2006 sustainable mobility 88 0.01 2016 38 一带一路 18 0.00 2015 cost 81 0.01 2008 39 机场 17 0.01 2009 network design 83 0.01 2014 40 综合开发 17 0.00 2013 vehicle routing problem 82 0.01 2014 41 交通网络 17 0.01 2004 freight transport 80 0.02 1997 42 碳排放 15 0.01 2012 operations 71 0.01 2014 43 京津冀 14 0.00 2007 supply chain 62 0.01 2010 44 功能定位 13 0.02 2010 city logistics 59 0.02 2009 45 空间格局 13 0.00 2011 rail transportation 57 0.00 2017 46 交通需求 13 0.00 2007 allocation 55 0.02 2016 47 公交优先 13 0.01 2007 travel demand 50 0.01 2010 48 城镇化 12 0.01 2013 multimodal transport 50 0.01 2014 49 铁路枢纽 11 0.00 2008 shared mobility 54 0.00 2020 50 出行方式 10 0.00 2018 route choice 32 0.01 2014
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表 7 综合交通领域关键词聚类分析结果
Table 7. Results of keyword clustering analysis in multimodal transport field
人工分类 子聚类编号 频率前5位的关键词 Ⅰ综合交通分析 国内-#0交通网络 网络规划，出行方式，空间分析，布局优化，评估 国内-#6城市交通 需求分析，交通供需，土地，大数据，出行效率 国际-#0 network design network, transportation networks, network design problem, location. resilience 国际-#1 travel behavior behaviors, choice model, attributes, commute, built environment Ⅱ综合交通规划与设计 国际-#2 predictive models traffic prediction, passenger flow, cloud computing, traffic flow prediction, big data 国内-#1交通枢纽 规划设计，四网融合，设施布局，衔接，交通组织 国内-#2规划设计 网规划理念，网络流分配，城市规划，综合开发，枢纽体系 国内-#4区域交通 规划方法，评价指标，多模式，融合，多源数据 国际-#3 planning and analysis planning and analysis, bicycle transportation, combined transport, performance evaluation, sustainability and resilience 国际-#5 public transport urban transportation, multimodal networks, comprehensive evaluation, rail transport, accessibility 国际-#7 comprehensive transportation hub comprehensive transportation hub, facility location, network, multimodal freight transport, intermodal transportation Ⅲ综合交通运行 国内-#3协同发展 集装箱，交通出行，多式联运，信息共享，一体化 国内-#7高速铁路 旅客运输，空铁联运，换乘中心，综合枢纽，地铁 国际-#4 electric vehicle commuters, urban freight, shared autonomous vehicles, willingness to pay, bike share, mobility as a service Ⅳ物流 国际-#6 vehicle routing problem city logistics, logistics, green logistics, shortest path problem, hub-and-spoke network 国内-#5交通运输 现代物流，城市，低碳，网络设计，最后一公里
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表 8 综合交通分析研究热点
Table 8. Research hotspots of multimodal transport analysis
研究分类 参考文献 研究内容 研究方法 供需分析 [18] 综合交通供应分析研究 嵌套Logit模型 [19] 大数据 [20] 多智能体仿真 [21] 综合交通需求分析研究 四阶段法 [22] 基于活动的方法 [23] 大数据分析方法 [24] 综合交通供需平衡分析研究 供需匹配方法及优化 [25] 供需平衡评价 网络分析 [26] 重要节点识别 网络结构 [27] 迭代优化 [28] 网络渗流 [29] 综合交通网络韧性研究 基于复杂网络理论分析 [30] 网络结构 出行行为分析 [31] 出行特征分析 多元Logistic回归模型 [32] 出行偏好分析 多元Logistic回归模型 [33] 出行模式选择 居民出行调查方法 [34] 换乘出行行为分析 基于智能卡数据方法 [35] 出行成本建模 马尔可夫决策方法 综合交通与土地利用 [36] 交通与土地利用影响因素 多目标决策方法 [37] 土地利用—交通互动关系研究 对比分析法 [38] 系统协调性评价 耦合协调模型
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表 11 物流研究热点
Table 11. Research hotspots of logistics
研究分类 参考文献 研究内容 研究方法 城市物流 [93] 物流网络结构 社会网络分析 [94] 轴辐式理论 [95] 车辆路径优化 多目标优化 [96] 基于模型和无模型方法 [97] 两阶段法 [98] 物流节点选址 层次分析法 [97] 绿色物流 两阶段法 [99] 最后一公里配送 混和整数规划方法 海运物流 [100] 港口和航运网络优化 嵌入蒙特卡洛模拟的集成优化方法 [101] 智能化与信息化管理 基于迁移学习和区块链方法 [102] 海运物流风险管理 灰色综合评价模型 逆向物流 [103] 逆向网络设计与优化 基于启发式方法 [104] 鲁棒优化方法 [105] 逆向物流回收模式 双层供应链
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[1] 王之中, 余荣杰, 皮大伟. 顶层设计目标导向交叉融通服务未来——国家自然科学基金委员会交通与运载工程学科建设回顾与展望[J]. 前瞻科技, 2023, 2(3): 5-20.WANG Zhi-zhong, YU Rong-jie, PI Da-wei. Top design, goal orientation, cross and integration, perspectiveness: review and prospect of the construction of transportation and vehicle engineering discipline by the National Natural Science Foundation of China[J]. Science and Technology Foresight, 2023, 2(3): 5-20. [2] ARCHETTI C, PEIRANO L, SPERANZA M G. Optimization in multimodal freight transportation problems: a survey[J]. European Journal of Operational Research, 2022, 299(1): 1-20. doi: 10.1016/j.ejor.2021.07.031 [3] TOLULOPE A. A review of multimodal transportation development in Lagos State[J]. Zamfara Journal of Politics and Development, 2023, 4(1): 335-343. [4] 毛保华, 孙全欣, 陈绍宽. 2008年中国综合交通结构分析[J]. 交通运输系统工程与信息, 2009, 9(1): 10-18. doi: 10.3969/j.issn.1009-6744.2009.01.003MAO Bao-hua, SUN Quan-xin, CHEN Shao-kuan. Structural analysis on 2008 intercity transport system of China[J]. Journal of Transportation Systems Engineering and Information Technology, 2009, 9(1): 10-18. doi: 10.3969/j.issn.1009-6744.2009.01.003 [5] 欧国立. 基于三维层面的综合交通运输认识论[J]. 综合运输, 2008(7): 4-8.OU Guo-li. Comprehensive transportation epistemology based on three-dimensional level[J]. China Transportation Review, 2008(7): 4-8. [6] 何洪文, 孙逢春, 李梦林. 我国综合交通工程科技现状及未来发展[J]. 中国工程科学, 2023, 25(6): 202-211.HE Hong-wen, SUN Feng-chun, LI Meng-lin. Current status and future development of integrated transportation technology in China[J]. Strategic Study of CAE, 2023, 25(6): 202-211. [7] YAO J, CHENG Z Y, CHEN A. Bibliometric analysis and systematic literature review of the traffic paradoxes (1968-2022) [J]. Transportation Research Part B: Methodological, 2023, 177: 102832. doi: 10.1016/j.trb.2023.102832 [8] MODAK N M, MERIGÓ J M, WEBER R, et al. Fifty years of transportation research journals: a bibliometric overview[J]. Transportation Research Part A: Policy and Practice, 2019, 120: 188-223. doi: 10.1016/j.tra.2018.11.015 [9] JIA G L, MA R G, HU Z H. Review of urban transportation network design problems based on CiteSpace[J]. Mathematical Problems in Engineering, 2019, 2019(1): 5735702. doi: 10.1155/2019/5735702 [10] LI Z C, HUANG H J, YANG H. Fifty years of the bottleneck model: a bibliometric review and future research directions[J]. Transportation Research Part B: Methodological, 2020, 139: 311-342. doi: 10.1016/j.trb.2020.06.009 [11] LI C, BAI L, YAO L N, et al. A bibliometric analysis and review on reinforcement learning for transportation applications[J]. Transportmetrica B: Transport Dynamics, 2023, 11(1): 2179461. doi: 10.1080/21680566.2023.2179461 [12] JIANG C M, BHAT C R, LAM W H K. A bibliometric overview of transportation research part B: methodological in the past forty years (1979—2019) [J]. Transportation Research Part B: Methodological, 2020, 138: 268-291. doi: 10.1016/j.trb.2020.05.016 [13] SUN L J, YIN Y F. Discovering themes and trends in transportation research using topic modeling[J]. Transportation Research Part C: Emerging Technologies, 2017, 77: 49-66. doi: 10.1016/j.trc.2017.01.013 [14] CHENG G Z, LIU X L, PEI Y L. A review of research on public transport priority based on CiteSpace[J]. Journal of Traffic and Transportation Engineering (English Edition), 2023, 10(6): 1118-1147. doi: 10.1016/j.jtte.2023.04.008 [15] ABENOZA R F, CATS O, SUSILO Y O. Travel satisfaction with public transport: determinants, user classes, regional disparities and their evolution[J]. Transportation Research Part A: Policy and Practice, 2017, 95: 64-84. doi: 10.1016/j.tra.2016.11.011 [16] RAHNAMA S, CORTEZ A, MONZON A. Navigating passenger satisfaction: a structural equation modeling-artificial neural network approach to intercity bus services[J]. Sustainability, 2024, 16(11): 4363. doi: 10.3390/su16114363 [17] GU Y, FU X, LIU Z Y, et al. Performance of transportation network under perturbations: reliability, vulnerability, and resilience[J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 133: 101809. doi: 10.1016/j.tre.2019.11.003 [18] JIANG X W, SHAN X N, DU M Q. Modeling network capacity for urban multimodal transportation applications[J]. Journal of Advanced Transportation, 2022, 2022(1): 6034369. [19] LEMONDE C, ARSENIO E, HENRIQUES R. Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon[J]. European Transport Research Review, 2021, 13(1): 64. doi: 10.1186/s12544-021-00520-3 [20] MEI Z Y, WEI D Q, DING W C, et al. Multi-agent simulation for multi-mode travel policy to improve park and ride efficiency[J]. Computers & Industrial Engineering, 2023, 185: 109660. [21] GARIKAPATI V M, YOU D, ZHANG W W, et al. Estimating household travel energy consumption in conjunction with a travel demand forecasting model[J]. Transportation Research Record, 2017, 2668(1): 1-10. doi: 10.3141/2668-01 [22] BAQUERI S F A, ADNAN M, KOCHAN B, et al. Activity-based model for medium-sized cities considering external activity-travel: enhancing FEATHERS framework[J]. Future Generation Computer Systems, 2019, 96: 51-63. doi: 10.1016/j.future.2019.01.055 [23] ZHAO Y M, ZHANG H M, AN L, et al. Improving the approaches of traffic demand forecasting in the big data era[J]. Cities, 2018, 82: 19-26. doi: 10.1016/j.cities.2018.04.015 [24] YUN L, CHEN L, TU D, et al. Research on supply and demand matching model of transportation modes in MaaS system of integrated passenger transport hub based on deep learning[J]. Soft Computing, 2023, 27(9): 5973-5983. doi: 10.1007/s00500-023-08065-4 [25] 汪雯文, 薛锋. 综合客运枢纽轨道交通系统运能匹配度研究[J]. 交通运输工程与信息学报, 2021, 19(3): 111-122.WANG Wen-wen, XUE Feng. Study on capacity matching and evacuation capacity of rail transit integrated passenger hubs[J]. Journal of Transportation Engineering and Information, 2021, 19(3): 111-122. [26] SHEN J W, ZONG H M. Identification of critical transportation cities in the multimodal transportation network of China[J]. Physica A: Statistical Mechanics and Its Applications, 2023, 628: 129174. doi: 10.1016/j.physa.2023.129174 [27] LI Z T, TANG J J, ZHAO C Y, et al. Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks[J]. Chaos, Solitons and Fractals, 2023, 167: 112998. doi: 10.1016/j.chaos.2022.112998 [28] KIMURA M, SAITO K, NAKANO R, et al. Extracting influential nodes on a social network for information diffusion[J]. Data Mining and Knowledge Discovery, 2010, 20(1): 70-97. doi: 10.1007/s10618-009-0150-5 [29] CHEN Y S, WANG X Y, SONG X W, et al. Resilience assessment of multimodal urban transport networks[J]. Journal of Circuits Systems and Computers, 2022, 31(18): 2250310. doi: 10.1142/S0218126622503108 [30] QI X Y, MEI G, PICCIALLI F. Resilience evaluation of urban bus-subway traffic networks for potential applications in IoT-based smart transportation[J]. IEEE Sensors Journal, 2021, 21(22): 25061-25074. doi: 10.1109/JSEN.2020.3046270 [31] 王天送, 邵昀泓, 周静. 基于公交IC卡的地铁公交组合出行链识别与组合出行特征分析[J]. 综合运输, 2025, 47(3): 69-78, 104.WANG Tian-song, SHAO Yun-hong, ZHOU Jing. Using IC card data to identify trip chain and explore travel pattern for integrated traveling with bus and subway[J]. China Transportation Review, 2025, 47(3): 69-78, 104 [32] LI W, FENG W, YUAN H Z. Multimode traffic travel behavior characteristics analysis and congestion governance research[J]. Journal of Advanced Transportation, 2020, 2020(1): 6678158. [33] 牛凯, 罗瑞琪, 齐庆祥. 基于出行全过程的居民出行模式分析[J]. 交通与运输, 2021, 37(5): 18-23. doi: 10.3969/j.issn.1671-3400.2021.05.005NIU Kai, LUO Rui-qi, QI Qing-xiang. Residents' travel mode analysis based on the whole travel process[J]. Traffic and Transportation, 2021, 37(5): 18-23. doi: 10.3969/j.issn.1671-3400.2021.05.005 [34] CHEON S H, LEE C J, SHIN S. Data-driven stochastic transit assignment modeling using an automatic fare collection system[J]. Transportation Research Part C: Emerging Technologies, 2019, 98: 239-254. doi: 10.1016/j.trc.2018.09.011 [35] YUE M, MA S H, ZHOU W, et al. Estimation Markov decision process of multimodal trip chain between integrated transportation hubs in urban agglomeration based on generalized cost[J]. Journal of Advanced Transportation, 2022, 2022(1): 5027133. [36] WANG K, ZHANG J J, ZHANG D, et al. A priority in land supply for sustainable transportation of Chinese Cities: an empirical study from perception, discrimination, linkage to decision[J]. Land, 2022, 11(1): 78. doi: 10.3390/land11010078 [37] 谭琦川, 黄贤金. 城市土地利用与交通相互作用(LUTI)研究进展与展望[J]. 中国土地科学, 2018, 32(7): 81-89.TAN Qi-chuan, HUANG Xian-jin. Research progress and prospect on urban land use-transport interaction (LUTI)[J]. China Land Science, 2018, 32(7): 81-89. [38] LI C X, GAO X, HE B J, et al. Coupling coordination relationships between urban-industrial land use efficiency and accessibility of highway networks: evidence from Beijing- Tianjin-Hebei urban agglomeration, China[J]. Sustainability, 2019, 11(5): 1446. doi: 10.3390/su11051446 [39] HRUSHKA V V, HOROZHANKINA N A, BOYKO Z V, et al. Transport infrastructure of Spain as a factor in tourism development[J]. Journal of Geology, Geography and Geoecology, 2021, 30(3): 429-440. doi: 10.15421/112139 [40] SONG J N, CHEN F, WU Q Q, et al. Optimization of passenger transportation corridor mode supply structure in regional comprehensive transport considering economic equilibrium[J]. Sustainability, 2019, 11(4): 1172. doi: 10.3390/su11041172 [41] WANG Z J, CHEN Z X, SHI Y Y, et al. Extracting the relationship and evolutionary rule connecting residents' travel demand and traffic supply using multisource data[J]. Sensors, 2021, 21(6): 2179. doi: 10.3390/s21062179 [42] ZHANG L, LIU J M, YU B, et al. A dynamic traffic assignment method based on connected transportation system[J]. IEEE Access, 2019, 7: 65679-65692. doi: 10.1109/ACCESS.2019.2915993 [43] WANG L J, ZHENG S Y, WANG Y G, et al. Identification of critical nodes in multimodal transportation network[J]. Physica A: Statistical Mechanics and Its Applications, 2021, 580: 126170. doi: 10.1016/j.physa.2021.126170 [44] 熊文, 张大牛, 马小龙, 等. 城市水灾下交通路网基础设施(群)韧性研究综述[J]. 东南大学学报(自然科学版), 2024, 54(2): 329-345.XIONG Wen, ZHANG Da-niu, MA Xiao-long, et al. Review of resilience assessment in urban floods on transportation road network infrastructures[J]. Journal of Southeast University (Natural Science Edition), 2024, 54(2): 329-345. [45] HENRY E, FURNO A, EL FAOUZI N E. Approach to quantify the impact of disruptions on traffic conditions using dynamic weighted resilience metrics of transport networks[J]. Transportation Research Record, 2021, 2675(4): 61-78. doi: 10.1177/0361198121998663 [46] 张洁斐, 任刚, 马景峰, 等. 基于韧性评估的地铁网络修复时序决策方法[J]. 交通运输系统工程与信息, 2020, 20(4): 14-20.ZHANG Jie-fei, REN Gang, MA Jing-feng, et al. Decision-making method of repair sequence for metro network based on resilience evaluation[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 14-20. [47] ZHONG G, YIN T T, ZHANG J, et al. Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data[J]. Transportation, 2019, 46(5): 1713-1736. doi: 10.1007/s11116-018-9876-5 [48] 翁剑成, 马靖媛, 潘晓芳, 等. 考虑心理偏好的枢纽抵站旅客接续出行方式选择模型[J]. 北京交通大学学报, 2024, 48(4): 32-42.WENG Jian-cheng, MA Jing-yuan, PAN Xiao-fang, et al. A model of travel mode choice for hub arrival passengers considering psychological preferences[J]. Journal of Beijing Jiaotong university, 2024, 48(4): 32-42. [49] GÖRANSSON J, ANDERSSON H. Factors that make public transport systems attractive: a review of travel preferences and travel mode choices[J]. European Transport Research Review, 2023, 15(1): 32. doi: 10.1186/s12544-023-00609-x [50] JIN R, HUANG C Y, WANG P, et al. Identification of inefficient urban land for urban regeneration considering land use differentiation[J]. Land, 2023, 12(10): 1957. doi: 10.3390/land12101957 [51] TILLEMA F, VAN MAARSEVEEN M. Urban Transport X[M]. Southampton: WIT Press, 2004. [52] 苏明亮, 赵尚坤, 马聪. 城市群交通运输一体化规划理论方法研究[J]. 智能城市, 2020, 6(14): 120-121.SU Ming-liang, ZHAO Shang-kun, MA Cong. Research on theoretical methods for integrated planning of transportation in urban agglomerations[J]. Smart City, 2020, 6(14): 120-121. [53] WANG J, YANG X Y, ALI QALATI S, et al. Spatial spillover effect and spatial distribution characteristics of transportation infrastructure on economic growth: a case of the Yangtze River Delta[J]. Frontiers in Environmental Science, 2022, 10: 900209. [54] AYDIN N Y, DUZGUN H S, WENZEL F, et al. Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards[J]. Natural Hazards, 2018, 91(1): 37-68. [55] 丘健妮, 陈少沛. 多模式城市公共交通网络GIS数据模型研究——以广州市为例[J]. 测绘科学, 2010, 35(增1): 105-107, 145.QIU Jian-ni, CHEN Shao-pei. Multi-modal urban public transportation network GIS data modeling case study of Guangzhou city[J]. Science of Surveying and Mapping, 2010, 35(S1): 105-107, 145. [56] YUAN G, KONG D W, SUN L S, et al. Connectivity contribution to urban hub network based on super network theory-case study of Beijing[J]. Promet-Traffic and Transportation, 2021, 33(1): 35-47. [57] 马书红, 武亚俊, 陈西芳. 城市群多模式交通网络结构韧性分析——以关中平原城市群为例[J]. 清华大学学报(自然科学版), 2022, 62(7): 1228-1235.MA Shu-hong, WU Ya-jun, CHEN Xi-fang. Structural resilience of multimodal transportation networks in urban agglomerations: a case study of the Guanzhong Plain urban agglomeration network[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(7): 1228-1235. [58] YU W T, SUN H J, FENG T, et al. A data-based bi-objective approach to explore the accessibility of multimodal public transport networks[J]. ISPRS International Journal of Geo-Information, 2021, 10(11): 758. [59] ZHANG X F, LIU C L, PENG Y. Accessibility-based location of international multimodal logistics hubs: a case in China[J]. Transportation Research Record, 2022, 2676(12): 564-585. [60] ANJOS M F, VIEIRA M V C. Mathematical optimization approaches for facility layout problems: the state-of-the-art and future research directions[J]. European Journal of Operational Research, 2017, 261(1): 1-16. [61] 黄世玉, 杨敏, 王哲源, 等. 高铁枢纽衔接城市交通网络的动静态可达性分析[J]. 交通运输研究, 2023, 9(3): 57-72.HUANG Shi-yu, YANG Min, WANG Zhe-yuan, et al. Dynamic and static accessibility analysis of high-speed rail hub connecting urban transport network[J]. Transport Research, 2023, 9(3): 57-72. [62] LI L N, LOO B P Y. Towards people-centered integrated transport: a case study of Shanghai Hongqiao Comprehensive Transport Hub[J]. Cities, 2016, 58: 50-58. [63] 谢辉, 巴可伟. 长江中游城市群湖北区域综合交通布局规划研究[J]. 综合运输, 2020, 42(4): 19-24.XIE Hui, BA Ke-wei. On the comprehensive transportation planning of Hubei urban agglomeration in the middle of the Yangtze River[J]. China Transportation Review, 2020, 42(4): 19-24. [64] 严战友, 杨萌, 赵慧玲. 京津冀交通运输网络一体化与区域经济联动关系研究[J]. 石家庄铁道大学学报(社会科学版), 2015, 9(3): 1-6, 13.YAN Zhan-you, YANG Meng, ZHAO Hui-ling. On the com-movement relation between the integration of Jing-Jin-Ji transportation network and regional economy[J]. Journal of Shijiazhuang Tiedao University (Social Science), 2015, 9(3): 1-6, 13. [65] BOCEWICZ G. Robustness of multimodal transportation networks[J]. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2014, 16(2): 259-269. [66] 谭伟, 桂兴凯, 王钰文, 等. 大规模多模式交通网络构建方法对比分析[J]. 北京交通大学学报, 2023, 47(4): 45-55.TAN Wei, GUI Xing-kai, WANG Yu-wen, et al. Comparative analysis of construction methods for large-scale multimodal transportation networks[J]. Journal of Beijing Jiaotong University, 2023, 47(4): 45-55. [67] DONOVAN B, WORK D B. Empirically quantifying city-scale transportation system resilience to extreme events[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 333-346. [68] ZHU Y, XIE K, OZBAY K, et al. Data-driven spatial modeling for quantifying networkwide resilience in the aftermath of hurricanes irene and sandy[J]. Transportation Research Record, 2017, 2604(1): 9-18. [69] PRADHAN R P, BAGCHI T P. Effect of transportation infrastructure on economic growth in India: the VECM approach[J]. Research in Transportation Economics, 2013, 38(1): 139-148. [70] MEYER M D. Transportation Planning Handbook[M]. Hoboken: John Wiley & Sons, 2016. [71] 田洪. 基于交通一体化的铁路客站综合枢纽规划研究[J]. 铁道工程学报, 2023, 40(12): 78-82.TIAN Hong. Planning study of comprehensive hubs for railway passenger stations based on transport integration[J]. Journal of Railway Engineering Society, 2023, 40(12): 78-82. [72] 刚红润, 闫建朝. 联程运输中多种运输方式间票务信息共享研究[J]. 交通世界, 2022(1/2/3): 5-6, 16.GANG Hong-run, YAN Jian-chao. Research on ticket information sharing among multiple transportation modes in intermodal transport[J]. Traffic World, 2022(1/2/3): 5-6, 16. [73] 贾洪飞, 王楠, 吴文静. 城际旅客联程"行李直挂"服务最优定价[J]. 交通运输工程与信息学报, 2021, 19(3): 22-30.JIA Hong-fei, WANG Nan, WU Wen-jing. Optimal pricing of intermodal"baggage interline" service for intercity passengers[J]. Journal of Transportation Engineering and Information, 2021, 19(3): 22-30. [74] 杨敏, 李宏伟, 任怡凤, 等. 基于旅客异质性画像的公铁联程出行方案推荐方法[J]. 清华大学学报(自然科学版), 2022, 62(7): 1220-1227.YANG Min, LI Hong-wei, REN Yi-feng, et al. Road-rail intermodal travel recommendations based on a passenger heterogeneity profile[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(7): 1220-1227. [75] DANIELA A M, JUAN CARLOS G P, JAVIER G. On the path to mobility as a service: a MaaS-checklist for assessing existing MaaS-like schemes[J]. Transportation Letters, 2023, 15(2): 142-151. [76] SCHIKOFSKY J, DANNEWALD T, KOWALD M. Exploring motivational mechanisms behind the intention to adopt mobility as a service (MaaS): Insights from Germany[J]. Transportation Research Part A: Policy and Practice, 2020, 131: 296-312. [77] AHANI P, ARANTES A, MELO S. An optimization model for structuring a car-sharing fleet considering traffic congestion intensity[J]. Journal of Advanced Transportation, 2023, 2023(1): 9283130. [78] YU Liang, FENG Tao, LI Tie, et al. Demand prediction and optimal allocation of shared bikes around urban rail transit stations[J]. Urban Rail Transit, 2023, 9(1): 57-71. [79] 王清斌, 韩增霞, 计明军, 等. 基于节点作业随机特征的集装箱多式联运路径优化[J]. 交通运输系统工程与信息, 2011, 11(6): 137-144.WANG Qing-bin, HAN Zeng-xia, JI Ming-jun, et al. Path optimization of container multimodal transportation based on node operation randomness[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(6): 137-144. [80] 贺竹磬, 孙林岩, 李晓宏. 时效性物流联运方式选择模型及其算法[J]. 管理科学, 2007(1): 8-12.HE Zhu-qing, SUN Lin-yan, LI Xiao-hong. Mode choice model and algorithm in intermodal for time definite logistics[J]. Journal of Management Science, 2007(1): 8-12. [81] DERSE O, GÖÇMEN E. Transportation mode choice using fault tree analysis and mathematical modeling approach[J]. Journal of Transportation Safety & Security, 2021, 13(6): 642-660. [82] FIGUEROA-LORENZO S, GOYA J, AÑORGA J, et al. Alarm collector in smart train based on ethereum blockchain events-log[J]. IEEE Internet of Things Journal, 2021, 8(17): 13306-13315. [83] 朱颖婷, 杨立鹏, 单杏花. 基于区块链技术的旅客联程运输数据共享和售票方案研究[J]. 铁道运输与经济, 2022, 44(4): 16-21.ZHU Ying-ting, YANG Li-peng, SHAN Xing-hua. Data sharing and ticket selling scheme for intermodal passenger transport based on blockchain technology[J]. Railway Transport and Economy, 2022, 44(4): 16-21. [84] 陈琳, 姚恩建, 杨扬, 等. 联程中转旅客的城市枢纽间换乘行为建模[J]. 北京交通大学学报, 2021, 45(4): 79-88.CHEN Lin, YAO En-jian, YANG Yang, et al. Modeling of transfer behavior between urban hubs of intermodal passengers[J]. Journal of Beijing Jiaotong University, 2021, 45(4): 79-88. [85] MUKHTAR-LANDGREN D, SMITH G. Perceived action spaces for public actors in the development of Mobility as a Service[J]. European Transport Research Review, 2019, 11(1): 32. [86] HENSHER D A, MULLEY C. Mobility bundling and cultural tribalism-Might passenger mobility plans through MaaS remain niche or are they truly scalable? [J]. Transport Policy, 2021, 100: 172-175. [87] 茅佳庆, 吴志周. 面向长三角一体化的MaaS平台构设[J]. 交通科技, 2021(3): 119-123.MAO Jia-qing, WU Zhi-zhou. MaaS platform for Yangtze River Delta integration[J]. Transportation Science & Technology, 2021(3): 119-123. [88] 刘向龙, 刘好德, 李香静, 等. 中国出行即服务(MaaS)体系框架与发展路径研究[J]. 交通运输研究, 2019, 5(3): 1-9.LIU Xiang-long, LIU Hao-de, LI Xiang-jing, et al. Framework and developing roadmap of MaaS (Mobility as a Service) in China[J]. Transport Research, 2019, 5(3): 1-9. [89] 王天实, 陆化普. MaaS出行选择偏好研究综述[J]. 交通工程, 2019, 19(3): 16-21.WANG Tian-shi, LU Hua-pu. Literature Review of MaaS travel choice preference research[J]. Journal of Transportation Engineering, 2019, 19(3): 16-21. [90] MEI Y Y, WANG S M, GONG M J, et al. Urban traffic dominance: a dynamic assessment using multi-source data in Shanghai[J]. Sustainability, 2024, 16(12): 4956. doi: 10.3390/su16124956 [91] 张荣花, 关宏志, 赵磊, 等. 考虑汽车共享服务的出行方式选择影响因素研究[J]. 交通运输研究, 2020, 6(3): 48-54.ZHANG Rong-hua, GUAN Hong-zhi, ZHAO Lei, et al. Influence factors of travel mode choice considering car sharing[J]. Transport Research, 2020, 6(3): 48-54. [92] XUE X J, WANG Z X, LIU X J, et al. A choice behavior model of bike-sharing based on user perception, psychological expectations, and loyalty[J]. Journal of Advanced Transportation, 2022, 2022(1): 6695977. [93] 马欣颖, 王红春. 京津冀区域物流网络结构研究—基于社会网络分析视角[J]. 北京建筑大学学报, 2020, 36(3): 95-102.MA Xin-ying, WANG Hong-chun. Research on the structure of Beijing-Tianjin-Hebei regional logistics network[J]. Journal of Beijing University of Civil Engineering and Architecture, 2020, 36(3): 95-102. [94] 仇延昭, 徐伟. 基于轴辐理论的山东省区域物流网络的构建研究[J]. 物流工程与管理, 2019, 41(11): 9-12, 3. doi: 10.3969/j.issn.1674-4993.2019.11.004QIU Yan-zhao, XU Wei. Research on the construction of regional logistics network in Shandong Province based on hub-and-spoke theory[J]. Logistics Engineering and Management, 2019, 41(11): 9-12, 3. doi: 10.3969/j.issn.1674-4993.2019.11.004 [95] GHOSEIRI K, GHANNADPOUR S F. Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm[J]. Applied Soft Computing, 2010, 10(4): 1096-1107. doi: 10.1016/j.asoc.2010.04.001 [96] MARDEŠIĆ N, ERDELIĆ T, CARIĆ T, et al. Review of stochastic dynamic vehicle routing in the evolving urban logistics environment[J]. Mathematics, 2024, 12(1): 28. [97] YU J J Q. Two-stage request scheduling for autonomous vehicle logistic system[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(5): 1917-1929. doi: 10.1109/TITS.2018.2849091 [98] 张树, 朱莲美. 基于层次分析法的煤炭物流节点选址方法研究[J]. 南京理工大学学报, 2015, 39(3): 301-305.ZHANG Shu, ZHU Lian-mei. Coal logistics node location method based on analytic hierarchy process[J]. Journal of Nanjing University of Science and Technology, 2015, 39(3): 301-305. [99] BALDI M M, MANERBA D, PERBOLI G, et al. A generalized bin packing problem for parcel delivery in last-mile logistics[J]. European Journal of Operational Research, 2019, 274(3): 990-999. [100] 吴迪, 韩欣丽, 石帅杰, 等. 需求不确定下边远群岛海运物流网络选址-库存-路径优化[J]. 交通运输系统工程与信息, 2024, 24(5): 268-282.WU Di, HAN Xin-li, SHI Shuai-jie, et al. Location-inventory-routing optimization of maritime logistics network in remote islands under demand uncertainty[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(5): 268-282. [101] PRIYA J C, RUDZKI K, NGUYEN X H, et al. Blockchain-enabled transfer learning for vulnerability detection and mitigation in maritime logistics[J]. Polish Maritime Research, 2024, 31(1): 135-145. [102] YANG L G, LI C L, LU L, et al. Evaluation of port emergency logistics systems based on grey analytic hierarchy process[J]. Journal of Intelligent and Fuzzy Systems, 2020, 39(3): 4749-4761. [103] TEMUCIN T, TUZKAYA G. A multi-objective reverse logistics network design model for after-sale services and a tabu search based methodology[J]. Journal of Intelligent & Fuzzy Systems, 2020, 38(4): 4139-4157. [104] SHAHPARVARI S, SOLEIMANI H, GOVINDAN K, et al. Closing the loop: redesigning sustainable reverse logistics network in uncertain supply chains[J]. Computers & Industrial Engineering, 2021, 157: 107093. [105] SAVASKAN R C, BHATTACHARYA S, VAN WASSENHOVE L N. Closed-loop supply chain models with product remanufacturing[J]. Management Science, 2004, 50(2): 239-252. [106] CUI H X, CHEN X Y, GUO M, et al. A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability[J]. Physica A: Statistical Mechanics and Its Applications, 2023, 610: 128392. [107] RINCON-GARCIA N, WATERSON B, CHERRETT T J, et al. A metaheuristic for the time-dependent vehicle routing problem considering driving hours regulations-an application in city logistics[J]. Transportation Research Part A: Policy and Practice, 2020, 137: 429-446. [108] WANG X Q, LI H Z, YANG J, et al. Optimal path selection for logistics transportation based on an improved ant colony algorithm[J]. International Journal of Embedded Systems, 2020, 13(2): 200-208. [109] YU J J Q, YU W, GU J T. Online vehicle routing with neural combinatorial optimization and deep reinforcement learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3806-3817. http://www.onacademic.com/detail/journal_1000042300150899_c962.html [110] CHEN C, DEMIR E, HUANG Y, et al. The adoption of self-driving delivery robots in last mile logistics[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 146: 102214. [111] REN J J, SALLEH S S. Green urban logistics path planning design based on physical network system in the context of artificial intelligence[J]. The Journal of Supercomputing, 2024, 80(7): 9140-9161. [112] KARAK A, ABDELGHANY K. The hybrid vehicle-drone routing problem for pick-up and delivery services[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 427-449. [113] RAN W X, LIU R L, LIU S W. Area differences in regional logistics efficiency and the law governing its temporal and spatial evolution[J]. Journal of Advanced Transportation, 2022, 2022: 3596524. [114] XU B, SUN J, ZHANG Z M, et al. Research on cold chain logistics transportation scheme under complex conditional constraints[J]. Sustainability, 2023, 15(10): 8431. [115] ALVAREZ-GALLO S M, ECHAVARRIA-CUERVO J H, MAHEUT J. Analysis and strategies for urban freight logistics in a low emission zone[J]. Journal of Industrial Engineering and Management, 2024, 17(2): 403-423. [116] RIQUELME-RODRÍGUEZ J P, GAMACHE M, LANGEVIN A. Location arc routing problem with inventory constraints[J]. Computers & Operations Research, 2016, 76: 84-94. [117] MAYANTI B, HELO P. Circular economy through waste reverse logistics under extended producer responsibility in Finland[J]. Waste Management & Research, 2024, 42(1): 59-73. [118] QUINTANA L, CORONADO M A, AYALA J R, et al. Waste vegetable oil collection: a comprehensive logistics system for sustainable and efficient resource recovery[J]. Journal of Material Cycles and Waste Management, 2024, 26(1): 642-653. [119] ZHANG X Q, AHMED R R. A queuing system for inert construction waste management on a reverse logistics network[J]. Automation in Construction, 2022, 137: 104221. [120] 张玲, 潘晓弘, 王正肖, 等. 废旧汽车逆向物流回收模式的研究[J]. 汽车工程, 2011, 33(9): 823-828.ZHANG Ling, PAN Xiao-hong, WANG Zheng-xiao, et al. A study on different take-back modes for end-of-life vehicle reverse logistics[J]. Automotive Engineering, 2011, 33(9): 823-828. [121] FENG Ding-zhong, YU Xing-hui, MAO Yong-bo, et al. Pricing decision for reverse logistics system under cross-competitive take-back mode based on game theory[J]. Sustainability, 2019, 11(24): 6984. [122] DZIUGIEL B, MAZUR A M, LIBERACKI A, et al. Multimodal 3D transport system implementation barriers in populated municipalities[J]. Aircraft Engineering and Aerospace Technology, 2023, 95(9): 1453-1458. [123] DIRKS N, SCHIFFER M, WALTHER G. On the integration of battery electric buses into urban bus networks[J]. Transportation Research Part C: Emerging Technologies, 2022, 139: 103628. -
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