地块交通影响下的交通需求预测模型及应用

经典的“四阶段”交通需求预测模型适合开发建设成熟的地区,对于新建地区并不适用。本文以土地利用为交通需求产生根源作为切入点,旨在研究各路段高峰小时交通量与各规划地块的量化关系。通过地块可达性、高峰小时交通生成率和地块规模预测地块高峰小时交通生成,引入地块交通影响因子,由路段通行能力、空间吸引力水平及路段与地块网络最小路径综合确定。基于地块交通影响因子,本文结合河南郑州楚河汉界文化产业园综合交通规划项目,完成规划路网的交通需求预测,对综合交通规划方案起到有效的量化支撑作用。

用于模拟无车日的交通需求预测模型

无车日活动是在有限的时间内,在城市规定区域里限制非公交机动车辆的通行,以减少拥堵、污染和噪音,并鼓励人们使用有利于环保的交通方式。介绍了一个专用于模拟无车日活动的交通需求预测模型,以对无车日期间的交通网络状况作量化分析。模型运行结果表明,在整个城市范围内,无车日措施使公交和非机动出行比例大幅增长,特别是自行车。同时,城市道路的拥堵情况和机动车废气排放量明显减少。缺点是除步行外,各类交通方式的平均出行时间有所增加。

基于活动的交通出行模型应用和发展趋势

作为交通需求模型的创新发展方向,基于微观个体的宏中观模型建模在世界范围内的各大都市区均得以广泛应用,总结基于微观个体活动的交通出行模型的模型基本结构、应用和发展趋势,可以帮助政策研究制定者选择合适定量建模工具,从而有针对性地提升大都市区的交通治理水平,改善居民出行体验。研究通过系统梳理相关模型开发技术报告,访谈国内外模型研发专家和相关工作人员,厘清这一类模型的主要架构、应用软件、通过对其国内外的发展脉络与应用效果进行系统综述经验总结,可以更好地将此类模型应用到具体的建设项目效益评价和城市交通政策制定过程中。

An empirical study on travel demand management modeling based on discrete choice method

In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.