景区智能导游系统中语音解说技术分析研究

本文探讨了应用于旅游景区的智能导游系统语音解说的技术,讨论了景点数据的存储、景点数据匹配和语言解说的可能性。在获取景点数据方面,提出了两种确定景点解说范围的方法,并通过软件编程解决了语音重复播放问题。     

从数据结构角度设计导游系统

从数据结构角度为旅游胜地设计导游系统。向游人提供景点的信息查询服务,根据指定的景点提供相关的景点信息。任意给定起点和终点,查询两点之间的最短路径。     

基于概率主题模型的景点知识挖掘及其可视化

针对旅游文本噪声多、景点多且展示不直观的问题,提出一种基于概率主题模型的景点-主题模型。模型假设同一篇文档涉及多个具有相关关系的景点,引入“全局景点”过滤噪声语义,并利用Gibbs采样算法估计最大似然函数的参数,获取目的地景点的主题分布。实验通过对景点主题特征进行聚类,评估聚类效果从而间接评价模型训练效果,并定性分析“全局景点”对模型的作用。实验结果表明,该模型对旅游文本的建模效果优于基准算法TF-IDF与隐含狄利克雷分布（LDA）,且“全局景点”的引入对建模效果有明显的改善作用。最后通过景点关联图的方式对实验结果进行可视化展示。     

基于物联网的旅游向导系统技术研究

近年来,国内自助旅游市场发展很快,因而基于物联网开发一款旅游向导系统很有必要。系统由手持设备模块和景点模块组成,以ARM9和MSP430F149为硬件核心,控制nRF24L01射频芯片实现短距离无线传输和景点的自动触发功能,在嵌入式操作系统上显示图形用户界面并进行相关操作。测试表明：系统可以给景点的自助游客带来很大便利。     

基于单片机的GPS智能导游机系统设计与实现

本文利用GPS定位技术,结合单片机及音频解码技术,实现户外景区智能导游系统的设计与制作,当游客到达景区某个景点时,该系统将根据GPS信号自动播放系统内预置的景区解说词,实现智能无人导游、解说、及广告宣传之功能。论文重点研究高集成度、低功耗的系统设计与器件选型,并研制出景点定位精度约6.0米,景点接收区域可调节的智能导游机系统,可满足户外景区智能导游系统的实际需求。     

从数据结构角度设计导游系统

从数据结构角度为旅游胜地设计导游系统。向游人提供景点的信息查询服务，根据指定的景点提供相关的景点信息。任意给定起点和终点，查询两点之间的最短路径。     

基于百度地图API的旅游景点查询系统开发

利用百度地图API提供的构建地图功能的相关接口实现了一个旅游景点查询系统.给出了系统的总体框架,描述了系统主要功能模块的作用.详细介绍了景点信息采集、景点信息查询、百度地图显示以及百度地图标注等主要功能模块的实现技术.     

基于RFID技术的景区流量导向系统

基于RFID技术的景区流量导向系统能通过技术应用,依靠阅读器收集到标签内的信息来判断景区的各个景点的人流情况,并通过网页查询、电子显示屏和广播等方式告知游客各景点的游客饱和度信息,对游客的流量趋向进行指引,保证游客在每个景点都可以畅通愉快的游玩。     

Floyd算法的景区最短路径查询系统的设计与实现

介绍一个基于改进的Floyd算法，并综合运用C语言文件操作技术和编程技术设计并实现了一个景区景点之间的最短路径查询生成系统，反映了路径上前后两个景点的先后关系，克服了经典Floyd算法只给出了路径上所经过的景点，而没有反映出景点之间的先后关系的不足.     

基于PDA与RFID技术的自助导览系统设计

主要介绍如何在嵌入式PDA上利用RFID技术、WiFi无线接入技术,设计一个移动终端的自助导览系统,通过IIS Server与RDA来进行数据库的操作,可实时下载更新所需的影音资料,为游客提供解说功能,实现对景点自助导览。同时可上传景点的读取次数,为上层系统提供统计数据服务。     

粒子群算法优化神经网络的旅游热门景点预测模型

旅游热门景点预测是当前旅游管理研究领域中的热点,针对传统旅游热门景点预测模型无法准确描述旅游热门景点的变化特点缺陷,为了提高旅游热门景点预测精度,提出基于粒子群算法优化神经网络的旅游热门景点预测模型。首先分析当前国内外对旅游热门景点预测问题研究方法,得到旅游热门景点具有较大非线性变化特点,这也是导致当前旅游热门景点预测错误大原因,然后引入非线性建模能力强的RBF神经网络描述旅游热门景点的非线性变化特点,并对RBF神经网络参数进行优化,建立最优的旅游热门景点预测模型,最后与传统旅游热门景点预测模型进行了对比测试,结果表明,粒子群算法优化神经网络可以更好的跟踪旅游热门景点变化规律,旅游热门景点预测精度要明显优于传统旅游热门景点预测模型,而且旅游热门景点预测效率也更高,能够满足旅游热门景点在线预测要求。     

基于聚类分析算法的智能旅游规划

为了解决游客选取旅游景点以及最短旅行线路时的困惑,将智能导游引入到旅游规划中,在地图软件的基础上插入厦门的旅游景点数据进行二次开发。首先以层次聚类算法分解出游客喜好的景点集合类簇,再以改进的密度法在不同地区有针对性地以不同的搜索半径搜索附近满足要求的景点,为游客提供一个最优的旅游规划。     

A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet

Selecting tourist attractions to visit at a destination is a main stage in planning a trip. Although various online travel recommendation systems have been developed to support users in the task of travel planning during the last decade, few systems focus on recommending specific tourist attractions. In this paper, an intelligent system to provide personalized recommendations of tourist attractions in an unfamiliar city is presented. Through a tourism ontology, the system allows integration of heterogeneous online travel information. Based on Bayesian network technique and the analytic hierarchy process (AHP) method, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Spatial web services technology is embedded in the system to provide GIS functions. In addition, the system provides an interactive geographic interface for displaying the recommendation results as well as obtaining users’ feedback. The experiments show that the system can provide personalized recommendations on tourist attractions that satisfy the user.     

基于加权关联规则的智慧旅游推荐系统

针对智慧旅游中景点推荐方面的不足,给出一种智能推荐的实现方案,通过对用户交互操作行为数据的分析,获取用户对浏览景点的兴趣度和认同度,在此基础上,运用AWMS-FPgrowth(多最小支持度加权FP-growth)算法挖掘出潜在的模式,形成规则,作为景点推荐的依据,与传统推荐方式相比,系统在准确性和新颖性上有很大的提高。     

Urban Sensing Based on Mobile Phone Data: Approaches,Applications, and Challenges

Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in cellular data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, analyzing people’s activities, e.g., counting them at fixed locations and tracking them by generating origin-destination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring, and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.      

A neural multi-context modeling framework for personalized attraction recommendation

In attraction recommendation scenarios, how to model multifaceted tourism contexts so as to accurately learn tourist preferences and attraction tourism features is a keystone of generating personalized recommendations. However, most of existing works generally focused on modeling spatiotemporal contexts of historical travel trajectories to learn tourists’ preferences, while neglected rich heterogeneous tourism side information, i.e., personal tourism constraints of tourists and tourism attributes of attractions. To this end, we propose a Neural Multi-context Modeling Framework (NMMF) to learn tourism feature representations of tourists and attractions by modeling multiple tourism contexts. Initially, we leverage a travel knowledge graph and massive original travelogues to construct the tourism attribute context of attractions and the travel trajectory context of tourists. Then, we design two context embedding models, named TKG2vec and Traj2vec, to model two kinds of context respectively. Both models learn feature vectors of tourist and attraction in contexts by elaborating neural networks to project each tourist and attraction into a uniform latent feature space. Finally, our framework integrates feature vectors derived from two models to acquire complete feature representations of tourists and attractions, and recommends personalized attractions by calculating the similarity between tourist and candidate attractions in the latent space. Experimental results on a real-world tourism dataset demonstrate our framework outperforms state-of-the-art methods in two personalized attraction recommendation tasks.

     

基于情景上下文与信任关系的旅游景点推荐算法*

针对现有旅游景点推荐个性化的不足问题,本文提出了一种基于信任关系与于情景上下文的旅游景点推荐算法。首先在传统的协同过滤算法上以用户信任度代替相似度来解决数据稀疏性;其次引入用户情景上下文信息,更全面的反映出用户的个性化需求;最后基于用户的信任度和上下文信息优化,建立一个推荐结果准确度更高的旅游景点推荐模型。模拟实验结果表明,综合考虑信任度和情景上下文信息的推荐策略表现最优。     

政府绩效考核公众测评的信息化探索

科技进步使得信息化技术突飞猛进,全球信息化成为大势所趋.同时,在政府绩效考核中公众参与测评越来越被人们重视,为广泛全面的开展公众测评,需要引入信息化的技术.构建了政府绩效考核公众测评信息化系统,将信息化手段应用到政府绩效考核的公众参与测评过程,提高公众测评效率,保证考核的公开公正公平,是完政府善绩效考核体系的有益探索.     

Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications.Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics.