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基于广州居民出行调查成果,本文从模型架构、效用函数基本形式、参数标定及校核等几个方面对交通方式选择模型的构建进行研究. 提出了方式划分的基本原则,并针对无车家庭、有小汽车家庭和摩托车家庭构建层次划分. 以小汽车家庭为例,阐述方式划分的基本形式. 进而,提出了方式划分参数标定的方法和基本步骤以及系数校核的关键点. 最后将预测结果和实际观测数据进行比较,验证模型的有效性. 本文提出不同层次交通方式划分模型,提高了模型的精度,并在效用函数中引入收入、车辆拥有和吸引量等因素,增强了模型的适用性,为其他类似城市交通方式选择模型的构建提供一定的参考.     

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城市公共交通乘客出行成本的量化研究,对于科学合理地评价公共交通系统的社会效益意义重大,而出行时间价值是进行乘客出行成本估计的一个重要环节. 本文以北京市市区居民SP调查数据为依托,通过分析公共交通出行时间价值的影响因素,选取Logit模型为基础模型,引入“出行者收入”作为模型新增变量,并针对工作商务、社会娱乐两种出行目的在不同换乘情况下出行时间价值模型进行参数标定,建立了基于SP调查数据的改进型时间价值求解模型,并计算得到北京市公共交通出行者在不同出行目的和有无换乘情况下的换乘时间价值、候车时间价值以及运行时间价值. 研究发现,工作商务出行目的出行时间价值普遍高于社会娱乐的出行时间价值,出行者在不同出行目的和有无换乘情况下的候车时间价值,大于换乘时间价值以及运行时间价值,并且有换乘情况下的候车时间价值大于无换乘情况下的候车时间价值.     

隧道掘进机（TBM）选型探讨

我国山岭隧道的长度不断增加,数十公里的隧道不断投入到施工阶段,然而由于埋深大、施工支洞布置困难、环境保护要求高等因素影响,越来越多的硬岩掘进机（TBM）得以应用。TBM对地质条件的适应性不及钻爆法灵活,但适宜的地质条件下可以取得很高的施工进度,它可分为开敞式、单护盾、双护盾等不同类型。不同的TBM有不同的特点,不同的工...     

城市土地利用特征对居民出行方式的影响——以深圳市为例

为缓解日益严重的城市交通问题、优化居民出行方式,有必要研究城市土地利用与交通系统的协调问题.以深圳市为例,应用多元Logit模型(MNL模型),纳入城市土地利用变量与出行者的家庭、社会、经济等变量,分析其对居民工作与非工作出行方式的影响.研究结果表明,出行起点的城市区位是影响居民出行方式的重要因素之一.相对于组团中心和...     

多源交通信息条件下的出行行为选择研究

在考虑多源交通信息可能导致信息过剩,从而影响决策者出行选择的条件下,引入决策论的思想,通过对多源交通信息方式发布的特点分析,应用模糊物元理论,建立个体出行的行为选择模型,在熵值法确定客观权重的条件下,根据贴近度的大小对决策方案进行选择,克服出行者的主观经验判断。通过实例计算,证明了不确定型决策在多源交通信息影响下出行者路径选择方面的可用性,客观上能为其提供最优的出行路径。     

交通信息对驾驶员逐日路径选择行为影响研究

在无信息、发布历史信息和发布预测信息3种信息条件下,分别建立了驾驶员的路径理解行程时间期望值的更新模型.通过建立一个含有2条平行路径的简单路网,对3种交通信息条件下驾驶员逐日路径选择进行了仿真,结果表明:交通信息对驾驶员的作用与驾驶员路径选择的随机程度和对信息依赖程度有关;在3种交通信息条件下,路网均不能达到用户均衡平衡状态;交通信息的预测方法不同,预测信息对驾驶员路径选择的影响有差异.     

道路交通统计生命价值测算模型及其验证

基于参数服从SB分布的混合Logit 模型进行道路交通统计生命价值的测算研究. 首先，结合意愿选择法和正交实验法设计出行路径选择调查问卷；然后，基于死亡风险系数服从对数正态分布和SB分布的混合Logit 模型，构建统计生命价值测算模型；接着以大连市私家车出行者为调查对象获得调查数据，并利用Monte Carlo 仿真方法进行模型参数标定；最后，对模型进行比较分析，并获得统计生命价值的测算值. 研究结果表明：死亡风险参数服从限制域为(0.0, 0.5)SB分布的混合Logit 模型，精确性更高且更合理；道路交通统计生命价值测算值为 105.76万元，这一结果可以作为道路交通安全项目经济评价的参考数据.     

Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets

This paper presents an investigation of the temporal evolution of commuting mode choice preference structures. It contributes to two specific modelling issues: latent modal captivity and working with multiple repeated crossectional datasets. In this paper latent modal captivity refers to captive reliance on a specific mode rather than all feasible modes. Three household travel survey datasets collected in the Greater Toronto and Hamilton Area (GTHA) over a ten-year time period are used for empirical modelling. Datasets collected in different years are pooled and separate year-specific scale parameters and coefficients of key variables are estimated for different years. The empirical model clearly explains that there have been significant changes in latent modal captivity and the mode choice preference structures for commuting in the GTHA. Changes have occurred in the unexplained component of latent captivities, in transportation cost perceptions, and in the scales of commuting mode choice preferences. The empirical model also demonstrates that pooling multiple repeated cross-sectional datasets is an efficient way of capturing behavioural changes over time. Application of the proposed mode choice model for practical policy analysis and forecasting will ensure accurate forecasting and an enhanced understanding of policy impacts.     

Development and validation of a direct mode choice model

A direct discrete mode choice model is introduced using relative attributes of competing modes as well as socioeconomic characteristics of travelers. The model is calibrated and validated for two available historic databases in the Dallas–Fort Worth region. The validation is conducted against the outputs of a current nested logit model used by the regional planning organization as well as the observed values based on transit ridership surveys for a newly inaugurated commuter rail service. The calibrated model is applied after the introduction of this new transit mode. The results show that the estimated mode shares by the proposed model have a statistically better consistency with the observed values than the estimates of the conventional nested logit model. Unlike the logit model, the structure of the direct model based on relative attributes also has the advantage of not needing recalibration each time a new travel mode is introduced. The model is found to be easier to calibrate and produces more accurate results than the nested logit model, commonly used by many metropolitan planning organizations.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.