出行距离与特大城市外围大型社区居民出行——以上海市为例

近十年我国特大型城市位于城市外环以外的大型居住社区引发了很多交通问题。本文以2012年上海市外围五处大型居住社区居民出行距离为研究对象,详细对比分析了社区区位、周边交通及公共服务设施、居民出行方式等因素与居民出行距离的关系,并分通勤交通和非通勤交通两种方式进行了详细对比。研究发现,通勤交通出行距离与其距离市中心距离成正比,且选择公共交通出行的比例较高。非通勤交通出行距离主要与社区周围公共服务设施水平成反比,公共服务设施服务水平越高则出行距离越短。同时,居民出行距离越长,选择公共交通出行的比例越高;若社区周边设有大型购物娱乐设施可有效减少以购物和休闲娱乐为目的的出行距离。     

产城融合导向下开发区职住分离评估及改善对策——以武汉经开区为例

针对开发区在实现产城融合过程中的职住分离问题，以通勤OD大数据为基础，从通勤结构和通勤尺度两个维度初步构建了开发区职住分离评估指标体系。以武汉经开区为例，基于开发区案例间指标的横向对比，发现开发区对外依存度偏高，中高端群体职住更易失衡，内部通勤距离偏长和跨区通勤两级分化等特征问题。阐明应当正视其业已形成的空间布局基础和通勤特征，从强化高等级设施配置、构建“有限延展空间”引导职住平衡、营造骑行友好环境等角度提出改善职住分离的规划对策。     

深圳不同收入家庭居住地选择特征及影响因素

居住地选择分析是职住空间关系研究的重要内容，有助于深刻理解职住演化的机理。基于深圳2020年居民出行调查数据，采用Moran’s I检验方法对街道尺度不同收入家庭占比的空间相关性进行评估，采用多项Logit模型检验了交通小区尺度城市建成环境对高收入、中低收入家庭居住地选择的影响。结果表明：高收入、中低收入家庭居住地选择在空间上整体呈圈层分布，中收入家庭在全市分布较均衡；商品住房用地与教育用地的高密度建设有利于吸引更多高收入家庭入住，城中村、工业建筑密度较高地区，中低收入家庭选择居住的概率更高；主干路及以上等级道路网密度的增加吸引更多高收入家庭居住；土地利用混合程度提高降低高收入家庭选择概率。     

大伦敦职住空间关系及通勤交通研究

超大城市的职住空间关系和庞大的通勤交通需求是构建高效综合交通系统面临的严峻挑战。以大伦敦地区为例,面对中心区人口增速回归和就业密度攀升,职住空间分布圈层化、走廊化的发展趋势,大伦敦构建了多模式、多层次的综合交通体系来应对日益增长的通勤压力,可为我国超大城市提供经验借鉴。     

MaaS出行选择倾向性研究

为探索MaaS出行下,用户对不同交通出行方式组合的选择倾向性及其影响因素,针对目标用户设计了网络问卷,并回收233份有效问卷,筛选得出1个因变量和14个自变量,借助SPSS软件清洗数据并建立多项Logit模型,数据分析结果表明月生活开销和距离因素是促成用户选择MaaS不同出行方式组合的关键因素。     

大城市城中村居民出行特征研究

本文在对深圳市原特区内外城中村片区进行入户调查的基础上,分析城中村居民的出行强度、出行目的、出行结构三个方面的日常出行基本特征;并以深圳典型的城中村上下沙为例,分析了城中村居民通勤范围、通勤距离、通勤方式三个方面的通勤出行特征。最后得出城中村居民有着较高的出行需求,公共交通是主要出行方式,城中村承接了大量就业疏散人口等主要结论。     

上海市居民共享自动驾驶汽车使用意愿研究

作为极具潜力的新型出行方式，共享自动驾驶汽车的出现必将引起居民出行行为的转变。为了研究上海市居民共享自动驾驶汽车的使用意愿及影响因素，本文利用网络问卷调查的RP与SP数据，结合百度地图API数据，构建基于解析逼近估计方法的多项Probit模型。结果显示：共享自动驾驶汽车通勤和非通勤出行中时间价值分别为64.34元/小时和81.55元/小时，弹性分析结果显示居民对于等候时间的敏感性显著高于费用；男性、已婚、高收入、高学历、自由职业者、合乘通勤者、年龄小于18岁的人群，以及家庭有未成年儿童、拥有沪C牌照小汽车的居民使用意愿较强。居民对于共享自动驾驶汽车越了解，使用意愿越强烈，而体验过自动驾驶服务的居民使用意愿更为强烈。     

市际出行交通方式选择行为研究

以荷兰奈梅亨市居民市际交通出行调查数据为基础,分析居民出行特征,建立binary logit模型定量分析影响居民出行方式选择的主要因素,并借助BIOGEME、SPSS软件对出行方式选择模型参数进行标定。分析结果表明:车外时间、车内时间、出行目的是影响出行交通方式选择的主要因素,并提出相关城市交通规划管理对策。     

“春运”交通选择行为调查研究

旅客交通选择是交通供给和需求相互作用的均衡结果。春运交通是我国交通运输的周期性现象,具有距离远、强度高和时间集中的特点。本文以北京的一家研究所为例,选择春运交通的距离、时间和费用指标,对春运选择行为进行了调查研究,主要发现如下:一是普速铁路仍然是我国春运中最具适应性、最经济的春运方式;二是自驾车作为春运方式越来越普遍;三是民航仍然是超长距离以上春运的最主要运输方式;四是500~1000公里距离上高速铁路日益成为民航的竞争对手。从长期看,随着收入水平逐渐提高,飞机或高速铁路将越来越成为春运旅客中长途旅行首选,自驾车的比重也会有所提升。     

基于时间指标的P＆R换乘需求预测研究

城市外围停车换乘需求预测是停车换乘系统规划设计的重要组成部分,也是规划设计的基础和依据。以距离为指标划定P＆R设施吸引范围在山区城市有一定的局限性,文章基于山区地形、居民分布、路网等因素考虑,提出了基于时间指标划分吸引范围的方法,并且通过依据交通方式选择影响因素的相关研究,建立了换乘轻轨、公共汽车、继续使用小汽车的多项logit选择模型。     

Adaptive choice behaviour of motorists in congested shopping centre parking lots

Currently existing models of parking choice behaviour typically focus on the choice of types of parking spaces. Implicitly these models assume that motorists have a free choice in that spaces are available. The adaptive behaviour which they reveal when faced with congested parking spaces is not explicitly modelled. The aim of this paper is to contribute to the growing literature on parking choice modelling by developing and testing a stated choice model of adaptive behaviour of motorists who are faced with fully occupied parking lots. The findings of the analyses indicate that the model performs satisfactory as indicated by its goodness-of-fit and the fact that all significant parameters were in anticipated directions.     

Investigating the joint choice behavior of intercity transport mode and high‐speed rail cabin with a strategy map

This paper investigates the joint choice behavior of intercity transport modes and high‐speed rail cabin class within a two‐dimensional choice structure. Although numerous studies have been conducted on the mode choice behavior, little is known about the influence of cabin class on their intercity traveling choice. Hence, this study is conducted with a revealed preference survey to investigate the intercity traveling behavior for the western corridor of Taiwan. The results of nested logit model reveal that a cabin strategy has a more significant influence on cabin choice than on mode choice. Furthermore, this study proposes a new strategy map concept to assist transport operators in defining and implementing their pricing strategies. The results suggest that to capture a higher market share, high‐speed rail operators should choose an active price reduction strategy, while bus and rail operators are advised to implement a passive price increase strategy to raise unit revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall.     

The influence of route guidance advice on route choice in urban networks

The paper begins by reviewing what is known about route choice processes and notes the mismatch between this knowledge and the route choice assumptions embedded in the most widely used assignment models. Empirical evidence on the influence of route guidance advice on route choice is reviewed and, despite its limited nature, is seen to suggest that users are reluctant to follow advice unless they find it convincing and that, the more familiar they are with the network, the less likely they are to accept advice. Typically only a small minority of journeys are made in total compliance with advice.Results from an interactive route choice simulator (IGOR) are summarised and are seen to reveal that compliance depends on the extent to which the advice is corroborated by other factors, on the drivers' familiarity with the network and on the quality of advice previously received. It is noted that the IGOR results are in a form which would enable response models to be calibrated.Recent approaches to the modelling of route choice in the context of guidance are discussed. Some are seen to make simplifying assumptions which must limit the relevance of their results; most make no allowance for the fact that drivers are unlikely to comply with all advice and several are not able to represent the benefits which guidance might bring in the context of sporadic congestion or incidents.As an alternative, a two phase model comprising a medium term strategic equilibrium and a day-specific simulation with explicit representation of driver response is proposed.Updated and extended from an earlier version published in theProceedings of the Japan Society of Civil Engineers (JSCE No 425/IV-4, 1991-1).     

An assignment model with modified Logit,which obviates enumeration and overlapping problems

One of the main components of stochastic assignment models is the route choice model solved with implicit or explicit path enumeration algorithms. Such models are used both for congested networks within equilibrium or dynamic models and for non-congested networks within static or pseudo-dynamic network loading models. This paper proposes a C-Logit model specification within a Dial algorithm structure for the implicit assignment of network flows. The model and its solution algorithm, called D-C-Logit, combine several positive features found in the literature for choice set generation and choices from a given choice set: generation of a set of alternatives with a selective approach; calculation of the path choice probability in a closed form; simulation of the overlapping effect among alternative paths; computation of just one tree for each origin avoiding explicit path enumeration.This paper has two main objectives: the proposition of a Dial-like algorithm to solve a C-Logit assignment model and application of the algorithm to different networks in order to demonstrate certain properties.     

Microscopic decision model for pedestrian route choice at signalized crosswalks

In this paper, two‐tier mathematical models were developed to simulate the microscopic pedestrian decision‐making process of route choice at signalized crosswalks. In the first tier, a discrete choice model was proposed to predict the choices of walking direction. In the second tier, an exponential model was calibrated to determine the step size in the chosen direction. First, a utility function was defined in the first‐tier model to describe the change of utility in response to deviation from a pedestrian's target direction and the conflicting effects of neighboring pedestrians. A mixed logit model was adopted to estimate the effects of the explanatory variables on the pedestrians' decisions. Compared with the standard multinomial logit model, it was shown that the mixed logit model could accommodate the heterogeneity. The repeated observations for each pedestrian were grouped as panel data to ensure that the parameters remained constant for individual pedestrians but varied among the pedestrians. The mixed logit model with panel data was found to effectively address inter‐pedestrian heterogeneity and resulted in a better fit than the standard multinomial logit model. Second, an exponential model in the second tier was proposed to further determine the step size of individual pedestrians in the chosen direction; it indicates the change in walking speed in response to the presence of other pedestrians. Finally, validation was conducted on an independent set of observation data in Hong Kong. The pedestrians' routes and destinations were predicted with the two‐tier models. Compared with the tracked trajectories, the average error between the predicted destinations and the observed destinations was within an acceptable margin. Copyright © 2016 John Wiley & Sons, Ltd.     

Implicit choice set generation in discrete choice models: Application to household auto ownership decisions

Latent choice set models that account for probabilistic consideration of choice alternatives during decision making have long existed. The Manski model that assumes a two-stage representation of decision making has served as the standard workhorse model for discrete choice modeling with latent choice sets. However, estimation of the Manski model is not always feasible because evaluation of the likelihood function in the Manski model requires enumeration of all possible choice sets leading to explosion for moderate and large choice sets. In this study, we propose a new group of implicit choice set generation models that can approximate the Manski model while retaining linear complexity with respect to the choice set size. We examined the performance of the models proposed in this study using synthetic data. The simulation results indicate that the approximations proposed in this study perform considerably well in terms of replicating the Manski model parameters. We subsequently used these implicit choice set models to understand latent choice set considerations in household auto ownership decisions of resident population in the Southern California region. The empirical results confirm our hypothesis that certain segments of households may only consider a subset of auto ownership levels while making decisions regarding the number of cars to own. The results not only underscore the importance of using latent choice models for modeling household auto ownership decisions but also demonstrate the applicability of the approximations proposed in this study to estimate these latent choice set models.     

A discounted recursive logit model for dynamic gridlock network analysis

Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks.     

Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art

ABSTRACT

The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.     

Methodological challenges in modelling the choice of mode for a new travel alternative using binary stated choice data – The case of high speed rail in Norway

Binary stated choices between traveller’s current travel mode and a not-yet-existing mode might be used to build a forecasting model with all (current and future) travel alternatives. One challenge with this approach is the identification of the most appropriate inter-alternative error structure of the forecasting model.By critically assessing the practise of translating estimated group scale parameters into nest parameters, we illustrate the inherent limitations of such binary choice data. To overcome some of the problems, we use information from both stated and revealed choice data and propose a model with a cross-nested logit specification, which is estimated on the pooled data set.