利用网络游记分析不同类型游客的旅游流网络特征差异——以云南省为例

旅游流网络分析对理解游客的目的地选择以及目的地在旅游流网络中承担的角色有重要意义。以中国云南省为例,基于网络游记数据挖掘游客的多维度偏好,并以此对游客聚类,进而划分出不同类型的游客群体。针对各类游客游记中的旅游目的地序列建立旅游流网络,并从多个角度分析各类游客旅游流网络的结构特征和各目的地节点的角色特征。结果表明,不同类别游客的旅游流网络在整体结构上各有特点,反映出旅游目的地不同的空间交互模式和网络中心化程度。此外,部分旅游目的地在不同类别旅游流网络中承担截然相反的角色。上述分析有助于优化旅游流网络中各节点的协作机制,辅助旅游目的地制定差异化的旅游产品。     

A high-precision heuristic model to detect home and work locations from smart card data

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations. The model uses journey counts as an indicator of usage regularity, visit-frequency to identify activity locations for regular commuters, and stay-time for the classification of work and home locations and activities. London is taken as a case study, and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey. Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision. This study offers a new and cost-effective approach to travel behavior and demand research.     

Exploring the spatiotemporal relationships between search flows and travel flows

Numerous studies attempted to associate search engine data with travel behaviors. However, most existing studies focus on the destinations of search and travel, while ignoring the origins, which embed critical information of where the search requests were initiated and where the travelers came from. In this study, we explore the relationships between two types of intercity origin–destination flow data, namely travel flows and search flows, which, respectively, record the number of travelers and search requests from one city towards another. By comparing the two flows during holiday and non-holiday, we examine their complex spatiotemporal relationships from multiple perspectives, including time-lag effect, distance decay effect, spatial autocorrelation, network community, cities' rankings, and important factors of search and travel activities. The findings can deepen our understanding of search and travel behaviors, hence they can help decision makers to develop targeted strategies to enhance city's attractiveness, improve transportation infrastructure, and promote tourism.     

基于网页旅游攻略的旅行线路规划

为帮助旅行者规划旅行线路,以旅游攻略为数据源,针对旅行线路规划问题中可选景点的动态性问题,根据传统车辆路径问题求解方法中两阶段法的先分组再定路线的策略,重点研究并设计改进了一种基于攻略中景点出现频率的先分组再定路线的启发式旅行线路规划策略用于自动规划旅游行程。试验结果表明,使用本文所提出的方法生成的旅游线路,时间分配更加合理,游览的景点间路程更短,线路的游览时间与景点间距离的费效比更高,对于游客规划旅行线路有着很大的现实意义。     

Trip making, induced travel demand, and accessibility

Traditional transportation planning practice rests on the premise that the demand for transportation is derived. On the other hand, economic theory advances that enhancement to the transportation system leads to lower travel cost and hence to induced demand. Such an argument lends support to the view that the rate at which trips are generated is linked to the ease of making trips to potential destinations. Rather conflicting evidence has so far come out of aggregate trip generation modeling applications. This paper revisits this issue with proper characterization of integral accessibility. Poisson regression models of automobile trip generation by trip purpose are estimated on travel survey data in Minneapolis–St. Paul, MN. Alternative measures of accessibility are tested for statistical significance. Conclusions are drawn on the role of accessibility in trip making and on the comparison of integral accessibility measures.The first author gratefully acknowledges the financial support of NSF grant SBR- 9308394. We are particularly indebted to Rick Gelbmann, Robert Paddock, and Mark Philippi at the Metropolitan Council, Minneapolis–St. Paul, and William Barrett at the Office of Transportation Data Analysis, Minnesota Department of Transportation for their availability to answer all our data-related questions.     

基于出租车GPS数据的城市居民出行时空分布研究

随着城市的快速发展,道路拥堵、打车难等问题越来越突出,严重影响了居民的出行效率和生活质量。出租车GPS数据,在一定程度上包含了部分居民出行行为的丰富信息。考虑到出租车载客事件发生于一维道路网络空间,本文提出对出租车上下客事件所在路段进行分析,得到不同时段居民出行的热门路段和区域,分析居民出行时空分布特征,有助于了解交通现状和居民出行需求,提高城市对居民出行活动的服务水平。     

基于多智能体的城市居民通勤仿真研究

通勤是人们周期性往返于居住地与工作地之间的行为,研究城市居民的通勤过程有利于分析高峰时期的交通拥堵问题并为制定相应解决方案提供依据。为此,本文提出了一种基于多智能体的交通仿真框架,包括乘地铁和公交车两种主要的公共交通出行方式。论文重点讨论了利用职住数据模拟交通过程的方法,首先,根据居民的居住地与工作地构成的OD(起止点)矩阵为其分配路径,然后利用提出的框架进行交通仿真,最后基于仿真结果统计需要的交通特征。论文以深圳市为例进行试验,结果表明,提出的方法能较好地模拟居民通勤行为的过程和统计交通流大小等特征。     

基于Android和JavaEE旅游助手系统的实现

本系统通过Esri公司发布的ArcGIS for Android结合天地图发布的各种OGC(Open Geospatial Consortium)服务构建了Android客户端,服务端以Linux为操作系统,Apache Tomcat为Web服务器,Postgre SQL/PostGIS为数据库,使用Java EE技术进行开发,构建了旅游助手系统。该系统不仅在Android设备上实现了地图浏览、兴趣点查询等常规功能,还实现了旅游路线分享、进入景区地理围栏时接收景区多媒体信息推送、扫描景点二维码获取景点介绍等特色功能。通过旅游助手,游客便于分享自己的旅游路线和心得,获取其他游客分享的路线和心得;景区景点便于向游客推送相关的宣传信息和景点介绍,促进景区景点信息化的建设。     

An empirical test of the competing destinations model

 It has long been believed that properties of spatial structure have a strong effect on trip distribution, which thus leads to a bias in the estimated distance decay parameters of spatial interaction models. This paper is an attempt to identify to what extent the spatial structure effect affects the trip distribution and determine whether the incorporation of a term to account for the relative location of destinations into the conventional gravity models, results in a model that can more correctly represent the actual trip distribution. The main focus is on the comparison of the origin–specific estimates of the distance decay parameter, calibrated from the traditional production-constrained model and the production-constrained competing destinations model. The results show that the competing destinations model is superior to the conventional model in both reproducing the interaction flows and giving behavioral explanation to the distance decay parameters, but the essential aim of the competing destinations model to remove the map pattern from the distance decay parameters of the conventional model has not been identified. Received: 5 September 2001 / Accepted: 17 June 2002 We are grateful to Gloria. A. Swieczkowski for kindly providing the migration data. The authors also gratefully acknowledge the comments of the referees.     

基于居民出行活动特征的个体经济水平推断方法

提出了一种基于居民出行活动特征的个体经济水平推断方法。从出行轨迹的移动性指标、基于居住地的出行特征和出行活动链模式3个方面提取13维出行活动特征,以广州市居民出行日志调查数据为训练和测试数据,利用随机森林方法进行个体经济收入水平的推断与检验。结果表明,该方法能够获得最高80%的个体收入水平推断精度。基于家的出行特征（如工作时间（9:00-18:00）离家距离众数等、出行链模式）以及与出行范围有关的移动性指标（如最大距离、回旋半径）在推断个体经济水平上的重要性较高,而衡量出行地点空间异质性的指标（如空间多样性等）重要性相对较低。     

Estimation of transit trip production using Factorial Kriging with External Drift: an aggregated data case study

Studies in transportation planning routinely use data in which location attributes are an important source of information. Thus, using spatial attributes in urban travel forecasting models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.     

基于GIS的旅游住宿餐饮信息系统的研究与实现

在我国,被世界上誉为朝阳产业的旅游业在国家政策的支持下,正在健康飞速地发展。本文是以唐山市区的主要旅游景点为例,采用C#语言与Arc Engine组件式开发,研究并实现了基于GIS的旅游住宿餐饮信息系统,通过本系统的研究与实现不仅可以满足游客的不同需求,为游客的出行游玩带来便捷,还能促进唐山旅游业的发展,带动区域经济。     

Understanding the interplay between bus,metro, and cab ridership dynamics in Shenzhen,China

The most common mass transit modes in metropolitan cities include buses, subways, and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro, and taxicab ridership from Shenzhen, China to unveil the spatio‐temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio‐temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city's metro network and land‐use functionality. In general, different categorized urban spaces are associated with different accessibility levels (such as high‐, medium‐, and low‐ranked) and different urban functionalities (such as residential, commercial, leisure‐dominant, and home–work balanced). The results indicate that different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way to explore the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymakers and municipal managers in optimizing public transportation facility allocation and city‐wide daily commuting distribution.     

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

Pluvial flash flood (PFF) can cause serious traffic disruption in big cities. We conducted interdisciplinary research by integrating flood modeling and traffic analysis to reveal the spatiotemporal pattern of the interplay between these two processes. A simplified simulation tool, which is capable of building a road network model, assigning trip paths with the effect of road closures, and evaluating travel delay and vehicle volume redistribution in a given PFF scenario, was developed to capture the traffic disruption in the face of PFF events. Modeling outputs from a case study in the city center of Shanghai showed that the delay of vehicles diverted to dry links or trapped in flooded links may reach 0.5 to 8 times the travel time in no‐flood scenarios. Overall, approximately 1–7% of vehicle volumes on flooded links would be redistributed onto dry links (more likely major arterial roads). However, the vehicle volume variation during each time interval demonstrated evident disparity with the spatiotemporal change of flood inundations. Simulating and mapping the congestion can largely facilitate the identification of vulnerable links. Future research will test the method in other intra‐urban areas and try to bridge the gap between modeling outputs and smart city planning and management.     

大数据背景下的虚拟地理认知实验方法

如何构建新一代的虚拟地理认知实验研究范式,从海量人类活动和地理数据中挖掘人地交互过程中的新模式、新知识成为新一代虚拟地理认知实验需要解决的问题。本研究首先提出了在数据密集型科学研究范式下的虚拟地理认知实验框架,阐述了从环境心理学的视角,结合多源人类活动大数据、城市环境大数据构建实验平台的方法,进而支持地理知识工程的构建。其次,本文介绍了基于海量街景数据和对应的个体情感评分数据,利用深度学习的相关方法和统计模型来进行认知知识挖掘的案例。实验发现了可视域中与个体对场景的情感维度-压抑感具有较高相关性的一系列视觉要素,如植被、建筑、车辆等,并通过回归分析予以量化。     

中国古代测绘历史在博物馆中展陈的内容研究

针对目前对测绘历史的研究没有与博物馆学科相结合,难以在博物馆中展陈的问题,提出了一种新的中国古代测绘历史框架。基于对测绘历史的研究,按照博物馆展陈的规律,将测绘历史按时间顺序分成萌芽、奠基、发展与繁荣、衰弱与转折4个阶段,每个阶段归纳为若干问题,并结合这些问题梳理测绘历史。这种方法较好地平衡了历史的严谨性和展览的趣味性之间的矛盾。基于浙江测绘与地理信息科技博物馆布展与装修工程的实践表明：所提的中国古代测绘历史框架,不仅能够保证展陈历史的科学性和完整性,还有利于让社会大众认识测绘、了解测绘,宣传测绘历史和文化。实现了在博物馆展陈的背景下对测绘历史的挖掘,从全新的视角诠释了中国古代测绘历史。     

长三角空间范围认知地图调查与分析

认知地图是心理学与地理学跨学科结合的产物,广泛应用于城市空间规划、出行行为优化等方面。本文从认知地图的角度,分析和讨论了人们对于长三角地区的认知情况及认知构建原因。运用认知地图法中的圈域图示法,对华东师范大学学生进行问卷抽样调查,共发放问卷350份。将获取的问卷数据运用ArcGIS软件数字化,Corel DRAW软件和Excel进行统计数据分析,研究华东师范大学学生长三角认知地图与官方所给出的长三角范围的异同及其原因。分析结果显示,长三角认知地图与客观实际存在明显差异,体现了社会大众认知的滞后性。文科、理工科学生因为思维模式的差异而导致认知地图的差异性。华东师范大学学生绝大多数是从自然环境和社会经济的角度来构建对长三角的模糊认知。     

出租车轨迹数据的南京人群出行模式挖掘

针对现有出租车轨迹数据挖掘中时间序列邻近度量方法存在的问题,提出一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,进而研究城市人群出行行为的时空差异。以南京市为例,结合电子地图对出行模式的空间分布特征进行分析,证明了本文所提出的方法的有效性。实验结果表明:在空间分布上,工作日出租车出行模式按照平均出行频次由高到低排序,从城市中心向四周扩散,呈中心环状分布,出行模式区域界限较为明显,同类出行模式分布区域对应相似的功能。提出了一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,有效地分析城市人群出行行为的时空差异。