基于公交优先的居民出行方式结构与社会效益最大化研究

为了得到城市居民出行方式最佳结构,以制定相应的公交优先政策措施.首先提出了居民出行社会效益的概念,根据居民出行时间价值、平均出行时间与出行费用,计算不同出行方式出行者平均每次出行的成本,在考虑各种出行方式比例的基础上,计算居民出行节约成本,从而计算居民出行社会效益.以居民出行社会效益最大化为目标函数,通过深入研究居民出行时间、出行费用与出行方式比例的函数关系,以及新增公交供给投入成本等参数变化规律,建立居民出行社会效益最大化的约束条件.最后以长春市居民出行调查结果为例进行了实证分析.结果表明:长春市居民出行社会效益最大时,小汽车、公共交通、步行及自行车的出行比例分别为11.27％、62.36％和26.37％,并针对计算结果给出了相应的出行方式结构合理化措施建议.     

家庭收入差异对出行方式选择的影响分析

出行者时间价值是影响出行决策的最重要因素之一,研究时间价值有利于准确构建方式划分离散选择模型.在分析居民出行调查数据的基础上,应用聚类树分析对家庭收入分组的合理性进行了探讨,分别对每种出行方式的出行者家庭收入统计分析,说明家庭收入对家庭成员出行方式选择的影响.在说明家庭收入与出行时间价值之间关系的前提下,提出家庭共享时间价值的概念,数据拟合的结果发现家庭共享时间价值服从对数正态分布.分别基于MNL模型和ML模型构建出行方式选择模型以家庭共享时间价值为主要变量的对比模型,研究了设置家庭收入变量与不设置家庭收入变量、设置家庭收入分段变量与设置家庭收入常量、设置服从对数正态分布的费用项随机系数与不设置费用项随机系数3类情况下模型的精度和准确程度.当设置家庭收入变量且费用项系数服从对数正态分布时,拟合效果最优,居民对交通出行的主观支付意愿期望值约为家庭共享小时收入的2倍.      

保定市居民出行特征调查报告

居民出行特征调查是编制城市综合交通规划及各类相关规划的基础,为政府的交通政策制定提供依据.该文以大量、翔实的实地调查数据为基础,详细分析了保定市2008年的居民出行基本特征、出行方式特征及出行时间特征,为编制综合交通规划、制定交通政策做准备.     

基于MNL模型的居民出行方式选择行为研究——以南方某小城市为例

为明确城市居民出行方式选择的关键影响因素，调节城市居民出行结构，提高城市交通系统效率，以南方某小城市居民出行数据为研究对象，基于随机效用最大化理论构建多项MNL(Multinomial Logit)模型，分析了个人属性、家庭属性和出行方案属性对出行方式选择产生的作用。通过分析MNL模型统计回归结果，得出以下结论：1)个人属性中，年龄、性别、个人月收入、受教育程度、驾照拥有情况对居民出行方式选择行为有显著影响；2)家庭属性中，住户小汽车拥有情况和摩托车拥有情况对居民出行方式选择行为有显著影响；3)出行属性中，出行时间和出行目的对居民出行方式选择行为有显著影响，而出行费用在出行选择过程中没有显著作用。     

莆田市城市居民出行特征分析及交通需求管理的对策建议

以莆田市2009年城市居民出行调查的有关数据为对象,通过出行强度、出行方式、出行的时间分布、出行的空间分布等一系列指标总结归纳出莆田市城市居民出行的规律、变化特征及其原因。针对莆田市居民出行总量、出行距离、出行耗时、私人交通方式、高峰小时峰值等特点,对莆田市城市交通需求管理提出相应的对策建议。     

杭州市居民出行特征分析及交通发展对策探讨

该文根据杭州市2005年居民出行调查的相关数据,结合杭州市城市交通现状,对杭州市居民出行次数、出行时段、出行方式等一系列的指标进行了分析研究,总结归纳杭州市居民出行特征的规律、变化特征及其原因,并在此基础上对杭州市的交通发展提出相关的建议。     

通勤者出行方式与出行链选择行为研究

通过分析2005年北京市居民出行调查数据,构造通勤者上班出行方式选择和出行链类型选择相互影响的NestedLogit模型,分别建立出行方式→出行链和出行链→出行方式两个方向模型结构,采用统计软件STATA9.0对模型进行标定,并利用包容系数对Nested Logit模型的结构关系进行辨识。结果表明,出行方式选择和出行链类型选择之间不是单方向影响关系,而是一种双向的相互作用关系;出行链→出行方式选择决策较为合理,反应通勤者倾向于首先考虑如何组织当天要参加的各种活动,然后在出行链安排的约束下考虑选择合适的出行方式。     

城市居民出行时耗特征分析研究

出行时耗是城市居民出行特征中的一项重要指标,通过典型城市居民出行调查资料,深入分析居民一次出行时耗与城市规模的关系,分析居民全日出行总时耗以及分方式的居民全日出行总时耗。相应的分析结果有助于城市交通发展政策的制订和交通系统的设计。     

基于出行时间的城市交通出行结构估算模型

本文以北京市相关工作过程为例,介绍了城市交通出行结构传统估算方法的应用,提出了基于熵理论以及出行时间的城市交通出行结构估算模型,给出了模型算法,并以实例演示了模型应用与结果分析过程,最后总结了模型的优势与尚待深入研究的问题。     

基于BP神经网络的居民出行方式选择模型

以城市居民出行方式选择行为作为研究对象,分析了影响出行方式选择行为的主要因素,利用BP神经网络可以自动获取研究对象的输入、输出间关系和较强的学习训练特性,建立了基于BP神经网络的居民出行方式选择模型,并通过2009年济南市居民出行调查数据对模型进行了实例分析。结果表明：BP神经网络模型能够较好地描述居民出行交通方式选择行为。     

Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model

Providing reliable travel time prediction is very much needed for commuters for their upcoming trips to reduce travel time and relieve traffic congestion. This article proposes an integrated model for path and multi-step-ahead travel time prediction on freeways using both historical and real-time heterogeneous traffic and weather data. The model's performance is investigated in a case study under various traffic scenarios. Results indicate that the proposed model provides satisfactory prediction results in various performance tests. For practical purposes, general guidelines for selecting the model's parameter sets as well as the efficient size of historical data are also presented.     

Bi-objective routing problem with asymmetrical travel time distributions

Recent studies have confirmed that travelers consider travel time reliability in addition to average travel time when making route choice decisions. In this study, we develop a bi-objective routing model that seeks to simultaneously optimize the average travel time and travel time reliability. The semi-standard deviation (SSD) is chosen as the reliability measure because it reflects travelers' concerns over longer travel time better than the commonly used standard deviation. The Pareto-optimal solutions to the bi-objective model are found by using an improved strength Pareto evolutionary algorithm. Tests on a real-world urban network with field measured travel time data have demonstrated good performance of the algorithm in the aspects, such as computational efficiency, quick convergence, and closeness to the global Pareto-optimal. Overall, the bi-objective routing model generates reasonable path recommendations. The SSD-based model is sensitive to the asymmetry of travel time distribution and tends to avoid paths with excessively long delays. This would be particularly helpful to those users placing high values on travel time reliability.     

基于L-偏度和L-峰度的行程时间可变性表征方法

为了定量化测度行程时间可变性右偏且长尾的实证特征，考虑实际观测中可能存在的数据样本量不足和离群值干扰问题，提出基于线性矩的L-偏度和L-峰度用于精确表征行程时间可变性。考虑到线性矩是顺序统计量期望的线性组合，给出了避免遍历所有子样本的线性矩估计方法。根据线性矩概念，探究了L-偏度和L-峰度的数学含义和其表征行程时间可变性的有效性，以及样本L-偏度和L-峰度的计算方法。理论研究发现，在表征范围和样本估计质量方面，相较于传统偏度和峰度，L-偏度和L-峰度对行程时间可变性具有更加优越的表征能力。采用深圳市车牌照识别系统的行程时间数据集进行案例分析，从无偏性、鲁棒性和有效性3个维度证明了L-偏度和L-峰度相较于传统偏度和峰度的优越性。分析结果如下：样本L-偏度和L-峰度在样本量不足时仍然是总体近似的无偏估计，而传统偏度和峰度的系统性误差较大；L-偏度和L-峰度对离群值具有鲁棒性，而传统偏度和峰度对离群值过于敏感；样本L-偏度和L-峰度对总体的估计波动小且精度高，具有良好的估计有效性；L-偏度和L-峰度分别与传统偏度和峰度有较高相关性，但又能够辨识出不同时空下行程时间可变性分布的差异。基于L-偏度和L-峰度所表征的行程时间可变性信息，出行者、规划者和管理者能够更加精准地认知不确定性的路网运行状态，从而做出更合理的行为选择和优化决策。     

北京市道路交通资源占有率分析

为了从宏观上了解城市交通基础设施的利用和使用情况,引入了交通资源占有率的概念表征车辆在全天、全路网上的运行状况。以北京市为研究对象,通过历年的统计数据和交通调查数据,分别从客运交通结构、行车里程、行车时间、交通调查中核查线车型比例等角度对北京市2000年和2005年的公交车、小汽车、出租车等不同交通方式的道路资源占有率进行计算。     

Bus travel time modelling using GPS probe and smart card data: A probabilistic approach considering link travel time and station dwell time

Abstract

Path travel time estimation for buses is critical to public transit operation and passenger information system. State-of-the-art methods for estimating path travel time are usually focused on single vehicle with a limited number of road segments, thereby neglecting the interaction among multiple buses, boarding behavior, and traffic flow. This study models path travel time for buses considering link travel time and station dwell time. First, we fit link travel time to shifted lognormal distributions as in previous studies. Then, we propose a probabilistic model to capture interactions among buses in the bus bay as a first-in-first-out queue, with every bus sharing the same set of behaviors: queuing to enter the bus bay, loading/unloading passengers, and merging into traffic flow on the main road. Finally, path travel time distribution is estimated by statistically summarizing link travel time distributions and station dwell time distributions. The path travel time of a bus line in Hangzhou is analyzed to validate the effectiveness of the proposed model. Results show that the model-based estimated path travel time distribution resembles the observed distribution well. Based on the calculation of path travel time, link travel time reliability is identified as the main factor affecting path travel time reliability.     

Analyzing travel time reliability and its influential factors of emergency vehicles with generalized extreme value theory

Travel time reliability is very critical for emergency vehicle (EV) service and operation. The travel time characteristics of EVs are quite different from those of ordinary vehicles (OVs). Although EVs own highest road privilege, they may still experience unexpected delay that results in massive loss to the society. In this study, we employ the generalized extreme value (GEV) theory to measure extremely prolonged travel time and analyze the potential influential factors. First, among three GEV distributions, Weibull distributions are found to be the best distribution model according to several goodness-of-fit tests; a new reliability index is derived to measure travel time reliability. Numerical examples demonstrate the advantages of GEV-based reliability index over variance and percentile value in the applications of EV. This index will be of great practicability in the EV operation performance and reliable route choices. Second, we further investigate the potentially influential factors of EV travel time reliability. Results show that link length and left-turn traffic volume may have an adverse impact on the link reliability while more left-turn lanes may increase the travel time reliability. The influential factor study will help us understand the causes of the EV travel time delay and the differences of travel time reliability between OVs and EVs.     

出行时间成本的测算方法及其影响因素分析

介绍了出行时间成本理论的假定,基于机会成本原理对出行时间成本进行了测算,针对其基本特性对其影响因素进行了深入分析,并据此在工资法的基础上提出了出行时间成本的计算模型。     

Urban travel time reliability at different traffic conditions

The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is.     

A route guidance system considering travel time unreliability

Under a stochastic roadway, drivers need a route guidance system incorporating travel time variability. To recommend a customized path depending on the trip purpose and the driver’s risk-taking behavior, various path ranking methods have been developed. Unlike those methods, our proposed disutility method can easily incorporate a target arrival time in the ranking process by measuring how late the travel is and by penalizing it depending on the severity of lateness. In addition, the disutility-based route guidance system can properly address travel time unreliability that causes unacceptable disruptions to the driver’s schedule (i.e., unexpected long delay). We compare the disutility-based path ranking method with other ranking methods, the percentile travel time, the mean excess travel time, and the on-time arrival probability. We show that the disutility has stronger discriminating power and requires less solution space to find an optimal path. The most important advantage is that it can estimate a driver’s risk-taking behavior for each trip purpose by using the discrete choice analysis. We construct a simulation framework to acquire the travel time data on a hypothetical roadway. We analyze the data and show how various ranking methods recommend a customized path. Using the data, we show the advantage of the disutiltiy method over the other methods, which is generating a customized path with respect to the target arrival time by properly penalizing the travel time lateness.     

支持向量机在路段行程时间预测中的应用研究

主要探讨支持向量机理论在路段行程时间预测中的应用。具体的方法是,首先将研究路段根据路段交通状态和车辆检测器设置情况进行分段,然后以前几个时段的各个小路段的交通流量、平均速度和车道占有率和整个路段的行程时间为输入,以下一时段的整个路段的行程时间为输出,选取高斯径向基函数作为核函数,建立了基于支持向量机的路段行程时间预测模型,从而探讨支持向量机在路段行程时间预测中的应用效果。最后,利用交通仿真软件的模拟数据进行验证,并与BP神经网络计算结果比较,计算结果的对比表明本文提出的方法预测效果更好。