城市系统交通需求模拟预测技术框架构建及应用  

Construction and Application of An Urban System-Based Travel Demand Forecasting Technology Framework

作　　者：赵鹏军[1,2] 陈霄依 王祎勍 侯勇企 郑昱 ZHAO Pengjun;CHEN Xiaoyi;WANG Yiqing;HOU Yongqi;ZHENG Yu(College of Urban and Environmental Sciences,Peking University,Beijing 100871,China;School of Urban Planning and Design,Peking University Shenzhen Graduate School,Shenzhen 518055,China)

机构地区：[1]北京大学城市与环境学院,北京100871 [2]北京大学深圳研究生院城市规划与设计学院,深圳518055

出　　处：《地球信息科学学报》2025年第3期539-552,共14页Journal of Geo-information Science

基　　金：国家自然科学基金项目(41925003);深圳市科技计划资助项目(JCYJ20220818100810024、KQTD20221101093604016)。

摘　　要：【目的】城市交通需求的规模、分布、方式结构与交通流是人类社会经济及其在不同区位上的空间相互作用的结果,社会经济运行的复杂系统性决定了交通需求预测必须从城市系统出发,才能破解当前交通需求预测“就交通论交通”的技术难题。【方法】本文解析了城市交通的系统性特征,提出了土地-人口-住房-交通一体化模拟技术框架,总结了基于城市系统的交通需求模拟预测技术。技术涵盖交通需求分布、交通方式分担与路径分配、土地利用模拟、人口与就业分布、房地产价格、碳排放等子模块以反映完整的城市系统;设置广义出行成本、区位可达性、房地产价格、职住关系系数、用地混合度等一系列子模块关联变量,反映子系统间的相互影响与时滞效应;设计子模块核心算法实现城市系统模拟预测。【结果】本文以北京为例,展示了该技术在交通需求模拟预测上的技术效果与应用:将2020年北京市的真值与模拟值进行比较,发现对交通需求、拥堵情况、土地利用、人口分布等模拟结果的准确度可达85%以上;进一步应用该技术平台,对北京市2030年的交通需求、交通流量、拥堵指数等进行了预测。【结论】本文提出的基于城市系统的交通需求模拟预测技术完善了城市交通理论,为城市交通规划提供了的新方法与新技术支撑。[Objectives]The scale,distribution,travel mode structure,and traffic flow of passenger travel demand are the results of spatial interactions within the human social economy across different locations.The complexity of the social and economic operation systems dictates that travel demand prediction must start from the urban system to address the technical challenges of current travel demand forecasting.This paper analyzes the systematic nature of urban transportation and proposes an integrated simulation technology framework that incorporates land,population,housing,and transportation.It also summarizes traffic demand simulation and prediction technology based on urban systems and develops China's first urban system travel demand forecasting technology platform.[Methods]This technology covers sub-modules such as transportation demand distribution,transportation mode share and path allocation,land use simulation,population and employment distribution,real estate price,and carbon emissions to reflect the complete urban system.It includes a series of sub-module variables,including generalized travel cost,location accessibility,real estate price,job-housing relationship coefficients,and land use mixing degrees,to reflect the interactions among subsystems and the time lag effect.Additionally,core algorithms of sub-modules are designed to achieve urban system simulation and prediction.Using Beijing as a case study,the application of this technology platform is demonstrated.A comparison between the actual and simulated values for 2020 shows that the accuracy of simulated results for travel demand,traffic congestion situation,land use,and population distribution is above 85%.[Results]Applying this platform to Beijing,the travel demand,traffic flow,congestion index,population distribution,and land use projections for 2030 were predicted.According to the forecast results,from 2020 to 2030,the total number of traffic trips in Beijing will show a generally stable and slowly declining trend,with strong centripetal characterist

关 键 词：交通需求 客运流 交通预测 城市系统 城市模型 交通规划 交通拥堵 

分 类 号：U12[交通运输工程] TU984.191[建筑科学—城市规划与设计]