Toward using social media to support ridesharing services: challenges and opportunities

Ridesharing has been attracting increasing attention from both academia and industry. One of the challenges posed by the study of ridesharing is to identify the most valuable information for improving the ridesharing decisions taken by participants. Another challenge is to use harvesting techniques to extract specific types of travel-related information. Many methods have been developed by the community in order to solve these issues. However, due to a lack of information sharing between different transit authorities and the difficulty of identifying subjective perceptions of the experience of ridesharing, understanding and evaluating how social media data might support or obstruct goals for mobility, safety and environmental sustainability in ridesharing is a difficult task. In this survey, we first analyze the literature on ridesharing with a focus on the properties and model of service, and introduce a framework to describe the major components required for a ridesharing service. Then, we illustrate the potential value of information extracted from social media and present the rationale for harvesting travel-related data. Finally, we detail some possible directions and different approaches for using social media data, and highlight their assets and drawbacks.     

中国音乐网站现况分析

21世纪网络的发展给社会生活带来了许多新现象和新问题,也为音乐传播带来了新的生机和活力。本文从音乐网站在社会传播中的现状出发,指出网络在音乐传播中存在的问题,并进一步从受众、传播媒介、把关人和法律建设等角度分析问题背后的原因。     

新媒体环境下全地形车营销策略分析

互联网+和大数据技术的不断发展和进步让信息经济时代真正地到来了。伴随着电子商务从起步到发展,很多的电商运营平台也逐渐凸显出自己的价值和地位。与此同时,电子商务的营销模式也从传统的B2B、B2C模式向着O2O、C2B的方向发展。全地形车,是一种十分独特的车型,适用的范围和人群也具有特定性。为此,在新媒体的环境之下,如何进行全地形车的营销,如何能够为全地形车吸引到更多的受众群体,这正是本文所要探究的关键所在。为此,文章在对全地形车的基本内涵、主要分类进行阐述和分析以后,针对全地形车这一个特殊车型的营销模式的创新和进步,提出了一些见解和主张。     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.     

基于物理规律的气垫船气液介质仿真系统研究

针对全垫升气垫船气液介质复杂场景实时再现仿真需求,基于粒子系统原理和OSG渲染平台,考虑气垫船近场波浪高程模型、气垫船垫升系统压力及流量仿真模型以及气垫船气流场特点,提出了一种基于多模型的气垫船航行时气液介质特效仿真框架和实现方法。搭建了该技术的体系结构和实现流程,解决了不同开发环境下建立的异构模型仿真问题,并通过仿真模拟计算验证了该技术的可行性和正确性。仿真结果符合气垫船航行时的气液介质外形特征、作用范围以及气液介质产生的随机性和规律性特征,能快速逼真地实现气垫船航行时气液介质视景特效,对增强气垫船模拟训练效果具有重要的意义。     

双层开孔斜挡板透空式防波堤消浪性能研究

针对一种新型双层开孔斜挡板透空式防波堤消浪性能问题,研究挡板开孔率和挡板倾斜角度对防波堤消浪性能的影响。采用物理模型试验方法,得出其反射和透射系数、波能分布和堤后波幅频谱特征结果。结果表明,斜挡板开孔率越大,防波堤透射系数也越大,但在试验范围内,透射系数均小于0. 5;该类型防波堤对波浪有较好的消能性,对较长周期波以反射为主,较短周期波以消浪室消能为主,整体透射系数较短周期波略大;防波堤设计斜挡板比平挡板透射系数小,具有更好的消浪性能,最小透浪仅为后者的39%。     

基于 SIP 协议航管内部通信系统的设计

论文介绍了航管内部通信系统的特点,会话初始协议 SIP（Session Initialization Protocol）协议的功能特性,给出了基于 SIP 协议航管中心内部通信系统的设备组成,陈述了整个内部通信系统的系统架构和主机的软件架构,内部通信系统工程实现的关键技术,最后对航管内部通信进行了小结。