本文提出一个新的整合城际高铁车站位置的两城市空间均衡模型,家庭可以在城市内通勤、城市间迁移和城市间通勤.该模型明确考虑车站选址对城市空间结构、家庭居住选择和住房市场的影响,并给出所有可能的两城市空间结构.理论分析指出车站选址会影响家庭的居住选择,管理部门忽略人口迁移带来的城际通勤会低估出行需求.数值结果表明,尽管改造既有站不是家庭效用水平最大的方案,但从节省拆迁成本的角度可能是福利最优方案;特定城市间的城际交通改善可能导致家庭向低收入城市迁移,起到平抑高收入城市人口和住房租金价格的作用.

高铁是区域快速综合交通网的主骨架,作为城市间高效快捷的出行方式,为提升通道客运能力和缓解既有铁路货运压力起到重要作用.本文从高铁对城市可达性、要素市场及空间结构的影响等3方面,综述了国内外高铁对城市经济空间结构影响研究的主要成果,归纳总结了共性结论和存在的分歧.通过分析高铁对城市可达性变化的程度和可达性变化在不同区域之间是否平衡,揭示高铁(网络)对城市间出行需求的直接影响.城市可达性的改善进一步重塑生产要素的区域空间分布,加速资本和人口流动,促进产业发展和就业改善,并对土地和住房市场产生深远影响.市场各种要素的相互作用给城市宏观经济发展,特别是高铁站区空间重构带来重大机遇.系统评估高铁对城市经济空间结构的影响,能够为管理部门高铁投资决策、规划建设及影响评估等提供参考.最后,结合理论研究与实践经验,并基于新时代背景展望了有待开展的若干重点研究方向:一是将城市群视作新的利益集合体,研究高铁投资的经济效益、收益分配和高铁开通后对城市群经济发展和要素空间分布的影响;二是探究高铁通勤行为对城市资本、人口和土地等要素市场的影响,剖析车站选址和票价变化等影响高铁通勤的关键性因素;三是考虑城市间高铁竞争与合作博弈行为和客货混跑模式对城市经济空间结构影响的新变化;四是运用大数据和机器学习等新兴技术分析高铁对城市经济微观层面的影响.

This paper aims to provide some new insights into the impacts of high-speed rail (HSR) on urban spatial structure. A two-city spatial equilibrium model is proposed to investigate the subtle effects of HSR station location on urban spatial structure, households' location choices of work and residence, and housing rents across the two cities (one metropolis with higher wages, another micropolis with lower wages). We systematically analyze and summarize eight spatial structures for micropolis, as a result of a great diversity of station locations. The conditions and analytic solutions of each structure are derived. It is shown that the HSR station of micropolis closer to metropolis may decrease the number of metropolis residents. The effects on intercity and intracity commuting depend on endogenous urban spatial structure. Numerical results from an example and a case study conducted for fifty city pairs in China, illustrate and verify our analyses and findings.

PurposeThis paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).Design/methodology/approachBy analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.FindingsAccording to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.Originality/valueThis study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

针对高速铁路日常客运量预测问题,提出消除节假日因素影响的数据替补修正法和融合变分模态分解(VMD)、遗传算法(GA)和BP神经网络的日常客运量VMD-GA-BP预测方法.数据替补修正法是根据日常客运量超常波动判定阈值识别节假日延续期,采用VMD-GA-BP预测方法得到预测值,用该预测值替换节假日延续期内的客运量.VMD-GA-BP预测方法首先采用VMD对被替换数据之前的数据序列进行分解,得到不同频率的模态分量;其次通过GA优化初始权值和阈值的BP神经网络对各模态分量分别预测;然后重构各模态分量的预测值,用预测值替换节假日延续期内的客运量,得到修正数据序列,据此预测得到高速铁路日常客运量.实例应用表明,VMD-GA-BP的预测误差远低于BP,EMD-GA-BP,SVR,EMD-BP等方法,且基于修正数据序列的预测误差明显低于基于原始数据序列.可见,VMD-GA-BP预测方法精度较高.