基于多层模型的城市建成环境对通勤行为的影响

为研究城市建成环境对居民通勤行为的影响,考虑个体社会经济属性和交通小区建成环境属性的跨层次结构,建立多层模型,分别分析了个体社会经济属性层面和交通小区建成环境层面对通勤时间及通勤距离的影响,并利用长春市居民出行调查数据标定模型参数. 结果表明：多层模型比传统的回归模型拟合效果更好;控制个体社会经济属性特征后,土地利用混合度、交叉口密度及公共交通站点密度对居民通勤出行时间和距离的影响均呈现显著的负效应,而居民居住地到城市中心商务区(CBD)的距离仅对通勤距离有显著的正向影响.研究结果可为通过城市规划优化通勤结构提供理论支持.     

Workplace travel plans: can they be evaluated effectively by experts?

Employers are regularly involved in transport planning and characteristic workplace-oriented tools include: (1) travel plans for building projects, (2) mandatory travel plans, (3) subsidies to employers with an advanced travel plan and (4) best travel plan awards. In all cases, experts judge the level of car use. We argue that decision-makers might benefit from a multiple regression-based benchmark modelling tool that estimates the expected share of the car. In this paper, we estimate the share of car users in the commuting modal split at workplaces. However, since the amount of information available to experts differs, we gradually add information to the model to measure the impact of data availability. Without historic data on modal split, the current share can only be predicted moderately well, i.e. within a 20% range. Besides adding the past, results improve by using homogenous and regional subsamples. Nevertheless, quantitative analyses do not make expert knowledge obsolete.     

城市轨道交通多方式组合出行与接驳设施优化

城市轨道交通出行是典型的多方式组合出行,其出行过程不连续、选择需求复杂、出行体验难以捕捉等特征,制约了轨道交通整体出行效率和满意度提升。以南京市为例,分析轨道交通通勤人群的"接入—轨道交通—接出"全过程出行模式特征。考虑接入与接出过程的关联性,构建轨道交通多方式组合出行行为模型,研究各类接驳设施服务对接入和接出方式选择的影响。从轨道交通的竞争方式——全程小汽车通勤出行角度,调查该人群对不同轨道交通接驳方式设施服务不便性指标的感知评价,提出轨道交通接驳设施优化措施。     

基于高速公路流水数据的通勤车辆特征研究

随着居民利用高速公路进行通勤出行车辆的增加，高速公路缓行和交通拥堵等问题时有发生，特别是在重大节假日期间.目前，解决上述交通问题的主要方法是交通需求管理措施，而实现有针对性的交通需求管理需要对高速公路收费流水数据进行精确的挖掘分析，掌握车辆在高速公路上的运行状态与时空分布特征.本文基于高速公路收费流水数据，借助 K-means++聚类方法识别使用高速公路日常通勤的车辆，进一步分析通勤车辆的出行时空分布特征.从通勤出行的角度，挖掘城市通勤快速出行廊道分布，研究高速公路网与城市道路网络的关系，对提高交通系统效率和缓解交通问题具有重要的意义.     

并列瓶颈路网下个人与家庭混合通勤行为研究

通勤者的出行行为对早高峰期间拥挤政策的制定有着至关重要的作用。本文基于瓶颈模型构建并列瓶颈路网下个人与家庭混合出行的均衡出行模型，分析两类通勤者的出行需求情况、 两个瓶颈处的通行能力以及学校-工作开始时间差这3个因素对用户均衡、用户出行成本和系统性能的影响。结果表明：在并列瓶颈路网下，混合出行的用户均衡状态并不唯一；无论两个瓶颈的通行能力大小如何变化，增加学校-工作开始时间差对个人通勤者都是有利的；受瓶颈通行能力的影响，增加学校-工作开始时间差对家庭通勤者的影响是否有利是不确定的；并不是在任何情况下调节学校-工作开始时间差对交通系统都是有效的，而是只有在特定情况下对其进行调节 才能提高系统运行效率，降低系统总出行成本。     

基于SP和RP融合数据的小汽车通勤出行频率选择模型

基于小汽车通勤出行特性和成本分析,采用意愿调查法对小汽车出行者的社会经济属性、通勤特性和不同燃油价格下的出行意向进行调查,定量分析停车位供应状况、停车费、燃油价格等因素对小汽车使用者通勤出行频率的影响,以获取高燃油价格下小汽车通勤行为随燃油价格(出行成本)变化的规律.基于多项选择模型,分别建立了RP模型、SP模型和基于...     

居住新城交通规划策略——以广州市白云湖为例

构建合理的通勤交通体系对特大城市郊区居住新城的开发至关重要.以广州市白云湖居住新城为例,首先将白云湖地区与香港新市镇进行对比,分析了近郊居住新城的交通特点及面临的问题.然后,提出了白云湖居住新城的交通规划指标体系,并对客流需求进行预测.最后,提出白云湖地区交通规划策略及具体措施,包括以公交为导向优化调整用地布局,完善对...     

Transition to a cyclable city: Latent variables affecting bicycle commuting

An understanding of the key factors influencing bicycle commuting is essential for developing effective policies towards a cyclable city. This paper contributes to this line of research by proposing a methodology for including cycling-related indicators in mobility surveys based on the Theory of Planned Behaviour (TPB), and applying an exploratory factor analysis (EFA) to evaluate the structure of latent variables associated with bicycle commuting. The EFA identified six cycling latent variables: Lifestyle, Safety and comfort, Awareness, Direct disadvantages, Subjective norm, and Individual capabilities. These were complemented with a latent variable related to habit: Non-commuting cycling habit. Statistical differences and regression analysis were applied with the cycling latent variables. The study also includes the relationship between objective factors and bicycle commuting, which reveals minor associations. This methodology was applied to the “starter cycling city” of Vitoria-Gasteiz (Spain). The results confirm that in this context – in transition to a cyclable city – safety and comfort issues are not the main barriers for all commuters, although more progress needs to be made to normalise cycling. A set of customised policy initiatives is recommended in the light of the research findings, including marketing campaigns to encourage non-commuting cycling trips, bicycle measures to target social groups as opposed to individuals, bicycle-specific programs such as “Bike-to-work Days”, and cycling courses.     

Extracting patterns from Twitter to promote biking

Emphasis on non-motorized travel modes (for example, biking) reduces motorized trips and provides positive effects on the environment and the quality of human life. Understanding factors that influence people to biking or bike commuting can help decision makers, transportation planners, and bike commuting networks. Historically, conventional methods like surveys and crash data analyses were conducted to understand relevant factors. Survey and crash data analysis are difficult to perform in broad scale due to data availability and efforts. An innovative approach to determining these factors is to conduct social media mining to understand sentiments or motivations of bike commuters. People use terms (with hashtag at the beginning of the term) in Twitter, a popular social media network, to express their thoughts, activities or information. This study developed a framework for using Twitter data in understating the sentiments of the bikers with minimal effort. In this study, Twitter data associated with bike commuting hashtags were obtained for eight years (2009–2016). This study provided a framework of data collection and application of various natural language processing (NLP) tools (for example, text mining, sentiment analysis) to extract knowledge from the unstructured text data. Findings show that biking is associated with weather and seasonal patterns. The general sentiment towards biking is positive. However, negative sentiments are associated with bad weather, crime, and other challenges. The polarity scores indicate somewhat positiveness in the recent few years. The developed framework and the findings of this study will help planners and decision makers to promote biking on a broader scale.     

班车通勤次数选择行为研究

通过对私家车通勤出行者RP和SP调查,分析了性别、年龄、驾龄、燃油价格等因素对班车通勤次数选择行为的影响,建立了基于Ordinal Logit（OL）模型的私家车转向班车通勤出行的频率选择模型。模型系数表明,驾龄小于3a的私家车出行者,在燃油价格上涨时,更倾向于班车通勤出行;驾龄大于3a的私家车通勤出行者,随着驾龄的增加,受燃油价格的影响越小,转向班车出行的频率越低。