基于排序二叉树的摘挂列车编组钩计划自动编制方法

根据摘挂列车编组调车作业原理,将摘挂列车下落问题抽象为排序问题,提出一种基于排序二叉树的编组钩计划自动编制方法.根据待编列车序列构造排序二叉树;利用排序二叉树的有序性快速搜索出有序车组序列,将其作为下落方案的可选集.考虑邻组、暂合列内收编固定组组别和空闲组别、端组等因素,从可选集中筛选出较优的下落方案.通过定义收编固定组简化列车收编过程,实现列车收编过程的计算机自动编制.通过实例验证,采用该方法降低了选择下落方案的复杂性,减少了列车编组钩计划的调车钩数,而且可根据实际调车线数灵活调整方案.     

现代化都市圈轨道交通规划思考与广州探索

建设现代化都市圈是实现城市群高质量发展、实践新型城镇化的重要途径。都市圈轨道交通已成为实现都市圈时空高效组织和交通一体化的必要条件。目前,中国以超大城市为中心、1 h通勤圈为基本范围的都市圈已初具雏形,尤其以广州为中心的都市圈已发展相对成熟。基于都市圈内涵辨析,总结国外都市圈及轨道交通发展的相关经验,分析目前国内都市圈跨城通勤出行及轨道交通特征,回顾广州在都市圈轨道交通规划方面3个阶段的探索历程,总结经验并重点提出需要注意的4个方面,为建设现代化都市圈和有条件地区编制都市圈轨道交通规划提供经验参考。     

Neighborhood services,trip purpose,and tour-based travel

Communities are increasingly looking to land use planning strategies to reduce drive-alone travel. Many planning efforts aim to develop neighborhoods with higher levels of accessibility that will allow residents to shop closer to home and drive fewer miles. To better understand how accessible land use patterns relate to household travel behavior, this paper is divided into three sections. The first section describes the typical range of services available in areas with high neighborhood accessibility. It explains how trip-based travel analysis is limited because it does not consider the linked (chained) nature of most travel. The second section describes a framework that provides a more behavioral understanding of household travel. This framework highlights travel tours, the sequence of trips that begin and end at home, as the basic unit of analysis. The paper offers a typology of travel tours to account for different travel purposes; by doing so, this typology helps understand tours relative to the range of services typically offered in accessible neighborhoods. The final section empirically analyzes relationships between tour type and neighborhood access using detailed travel data from the Central Puget Sound region (Seattle, Washington). Households living in areas with higher levels of neighborhood access are found to complete more tours and make fewer stops per tour. They make more simple tours (out and back) for work and maintenance (personal, appointment, and shopping) trip purposes but there is no difference in the frequency of other types of tours. While they travel shorter distances for maintenance-type errands, a large portion of their maintenance travel is still pursued outside the neighborhood. These findings suggest that while higher levels of neighborhood access influences travel tours, it does not spur households to complete the bulk of their errands close to home.     

Time windows in workers' activity patterns: Empirical evidence from the Netherlands

This paper assumes that activities at the home and work location are important determinants of individuals' paths through time and space. Fixed activities at these locations determine to a large extent the duration and timing of time windows – blocks of time available for participation in travel and out-of-home non-work activities. Taking the time spent at home and at the workplace as a starting point, this paper classifies activity patterns on workdays into six groups with distinct home- and work-stay patterns. For this, data are used from the 1998 Netherlands National Travel Survey. The six clusters vary in terms of the duration and timing of time windows and some of the differences can be explained by commute characteristics, types of non-work activities performed, workers' sociodemographic attributes, and their spatiotemporal environment. However, the impact of sociodemographic and spatiotemporal variables on cluster membership is shown to be weak.     

Daily activity and travel sequences of students,faculty and staff at a large Canadian university

This paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.     

基于客流变化的行车组织分析

阐明客流变化时的行车调整策略,即在地铁运营过程中产生突发客流时,行车人员运用抽线、加开列车以及在信号系统中加入设备干预等措施进行相应的行车调整,并针对性地提出数据分析、客流曲线生成软件的开发以及时刻表编制分析等方面的问题和建议。     

个体对通勤时间的态度和偏好视角下的职住空间失调

研究职住空间失调能够帮助我们更好地理解个体对职住分离的态度和偏好,并有助于在制定政策时考虑通勤者更真实的需求和期望.本次研究采用理想通勤时间和实际通勤时间的匹配程度来度量职住空间失调,并以昆明市为案例进行了实证研究.描述性统计结果表明,大多数通勤者认为他们的职住空间是失调的,有41.04%的通勤者遭受重度空间失调.进一步,基于序次Logistic回归模型分析了职住空间失调的影响因素.结果表明,出行方式、居住区位、年龄、受教育程度和家庭有无小孩对职住空间失调有显著影响.同时,由于个体对通勤时间的态度和偏好的异质性,更长的通勤时间并不总是意味着职住空间更加失调.     

公路城际出行链模式划分及其形成机制

为划分公路城际出行链模式并分析其形成机制,本文考虑城市内部换乘过程,解析了公路旅客城际出行过程.利用昆明市1 757份公路旅客出行调查问卷数据,提取了公路城际出行链结构特征.通过测算出行链结构值,提出了公路城际出行链模式的划分方法,将公路客运城际出行链模式划分为Ⅰ、Ⅱ、Ⅲ类并分别获取了其形成机制.结果表明:昆明市公路城际通道的平均运距为237.95 km,公共交通是公路城际出行过程中城市内部换乘的主要交通方式;昆明市公路城际出行链的3类模式占比为13.9∶6.4∶4.7,整体结构较合理;其中,Ⅱ类出行链模式最能体现均衡、稳定的形成机制.对公路城际出行链的形成机制分析发现,收入因素是促使出行链结构合理化的重要动力;造成出行链结构不合理的重要因素是市内出行延误,而非城际通道出行过程.     

交通大数据视角看广佛同城

广佛同城发展由来已久,是中国城市群中同城化程度最高的区域。简要分析广佛同城的交通基础设施发展演变历程,并基于模糊大数据(手机信令数据)和准确大数据(运行监测数据)对广佛通勤交通特征进行分析。结合手机信令数据对广州南站的客流组成及空间分布进行研究,对广州南站选址偏远问题进行解析。结果表明:广佛同城具有双向对等性的联系;地铁在同城化推进过程中起到重要的促进作用,拓展同城化活动范围;广州市机动车交通需求管理政策不够系统,非广佛车牌在通勤小汽车中比例超过40%,需要引起足够的关注;广州南站服务的客流中广州客流与佛山客流比为7:3,与对应的常住人口规模比例相当,初步实现了交通战略规划提出的共享理念。     

基于区内出行比例的城市交通小区半径计算方法

针对国内城市交通小区划分多靠定性分析的情况,分析了国内外交通小区划分方法,提出了基于区内出行比例的交通小区半径定量计算方法。基于交通小区半径和出行距离的三种不同组合,推导出了相应的区内出行比例计算模型,利用Excel软件的VBA编程功能,进行了三者之间的数量关系计算,通过对区内出行比例的控制,得出交通小区半径的合理范围。实例计算结果表明:当区内出行比例范围为5%～10%时,西安中心市区交通小区的合理半径在0.7～1.1km之间,而实际调查区内出行比例为9.28%,交通小区半径为0.9km,调查值与计算值接近,小区半径定量计算方法可行。