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Experience sampling methods (ESM) have enabled researchers to capture intensive longitudinal data and how worker well-being changes over time. The conceptual advances in understanding the variability of well-being are discussed. These emerging forms in the literature include affective inertia, affective variability, affective reactivity, and density distributions. While most ESM research has relied on the active provision of data by participants (i.e., self-reports), technological advances have enabled different forms of passive sensing that are useful for assessing and tracking well-being and its contextual factors. These include accelerometer data, location data, and physiological data. The strengths and weaknesses of passively sensed data and future ways forward are discussed, where the use of both active and passive forms of ESM data in the assessment and promotion of worker well-being is expected.

This paper aims to test whether passive Wi-Fi visitor analytics is a useful and effective method to measure consumer engagement towards food trucks located within an outdoor activation area at an Australian metropolitan university.

Using passive Wi-Fi visitor analytics to ping and track smart devices, data was collected over 90 weekdays capturing data from 522,548 unique smart devices.

The data collected in this feasibility study was able to identify the most and least popular food trucks by displaying the differences in both bounce and engagement rates, suggesting that passive Wi-Fi visitor analytics are feasible and useful in this context. Furthermore, the results also demonstrate that food truck vendors and marketers should not engage in random rotation, but instead remain static to try and increase familiarity.

Current visitor tracking technology (i.e. ticketed sales, sales data and survey) is limited as it may not provide an accurate measurement of foot traffic, identify engaged patrons who passed by but did not complete a purchase and be available due to commercial sensitivity and confidentiality. Thus, the current research is the first to examine customer engagement (i.e. unengaged walk-by vs engaged but bounced vs engaged sales) with food trucks within an activation area by using passive Wi-Fi visitor analytics.

当前的论文旨在研究被动 Wi-Fi 访客分析是否是衡量消费者对位于澳大利亚城市大学户外活动区域内的流动餐车的参与度的有用且有效的方法。

使用被动 Wi-Fi 访客分析来跟踪智能设备, 从 522,548 个独特的智能设备收集了超过 90 个工作日的数据。

该可行性研究中收集的数据能够通过显示跳出率和参与率的差异来识别最受欢迎和最不受欢迎的流动餐车, 这表明被动 Wi-Fi 访客分析在这种情况下是可行和有用的。 此外, 我们的结果还表明, 流动餐车供应商和营销人员不应随意轮换, 而应保持静止从而增加顾客熟悉度。

当前的访客跟踪技术（即售票销售、销售数据和调查）是有限的, 因为它可能无法：（1）提供客流量的准确测量; (2) 识别路过但未完成购买的参与顾客; (3) 由于商业敏感性和保密性而可用。 因此, 目前的研究是第一个通过使用被动 Wi-Fi 访客分析来检查激活区域内流动餐车的客户参与度（即, 未参与路过, 相比于参与但跳出, 相比于参与售出额）。

Purpose — In order to analyse applicability, comparability and limitations of GPS technology in travel surveys, different mobility survey techniques were tested in an Austrian pilot study.

Methodology/approach — Four groups of voluntary respondents recorded their travel behaviour over a time period of three consecutive days. The groups were assigned to three different and combined methods of data collection: Paper–pencil trip diaries, passive GPS tracking, active GPS tracking and prompted recall interviews.

Findings — The resulting mobility parameters show that self-reported paper– pencil surveys yield accurate sociodemographic information on the respondents as well as trip purposes and modes of transportation, although too few trips are reported. Passive GPS-based methods minimize the strain for respondents. Methods that combine GPS-based data collection and questionnaire provide the most reliable mobility data at the moment.

Research limitations/implications — Due to funding restrictions the sample sizes had to be relatively small (235 participants). Further development in research methodology will increase the effectiveness of automated data analysis, for example more accurate detection of activities and transport modes. The usefulness of GPS-based data collection in a large-scale surveys is planned to be tested in the next Austrian national travel survey.

Originality/value of paper — The pilot study allows a detailed comparison of traditional and GPS-based travel survey methods for the first time, due to data collection combined with prompted recalls.

Tourism reconfigures the metropolitan dynamics and the patterns of use of the urban systems. The seasonal nature of tourism produces an impact on the urban hierarchies, since it affects the labor, residential, and recreational markets. As a result, people move to and in the destination and it challenges the supply of sustainable modes of transport such as public transport. This research is set within the context of three demanding challenges that tourist destinations need to face-up: to increase environmental sustainability, to enhance destination competitiveness, and finally to assure quality and comfort of public transport services for the local resident population. Camp de Tarragona region, where Costa Daurada (one of the most important Spanish tourist brands) is located, is analyzed to illustrate how different data sources can aid to confront the aforementioned challenges. Given that seasonality is a dynamic phenomenon, suitable data should be flexible in terms of its time framework. To this end data from smart travel cards provided by the consortium that manages the public transport system in the region has been analyzed. Data unveiled the impact of seasonality on the evolution of demand throughout the year, the type of transport tickets used, or changes occurred in the geographical distribution of the mobility Alternative data sources such as surveys and passive mobile positioning data have also been examined, and their pros and cons have been addressed.

Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems.

The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme.

The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance.

The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

This book provides an international perspective on improving information to support transportation decision making. It comprises a selection of papers plus workshop syntheses from the 9th International Conference on Transport Survey Methods in Chile in November 2011. The conference was organized into 14 workshops with both paper presentations and discussions in the workshops forming the majority of the conference activity. The papers reported primarily on research pertaining to continuous improvement in transport survey methods — the backbone of the transportation data pipeline in most countries. But some papers also addressed the new ways in which innovation — notably technological innovation — is being applied to the capture and analysis of data to produce necessary information faster, better, and less expensively. The conference program built on a rich legacy of intellectual pursuits spanning the past two decades, and it is anticipated that the conference will continue into the future. Thus, the contents of this book represent a 5–10 year view through a moving window on the international state of the practice and concerns in transport survey methods.

This paper aims to describe how building information model (BIM) and big data can be combined in the same interface for providing new value to stakeholders, such as the property owner and user, as well as property service and workplace service companies. The research presents a new concept, which shows how the BIM can be exploited efficiently during maintenance.

Initially, existing facility management (FM) processes were investigated to find out how to digitize them and identify bottlenecks. Second, BIM’s data content was explored to identify the information that could be used to streamline FM processes. Third, the potential of the active data measured in the building was evaluated. Finally, research was undertaken to find out how constantly fluctuating information can be combined with BIM objects and what kind of added value that combination could offer. The literature review was used to support the primary contribution. In addition, the research problems were described and the basics of the research were obtained by interviews. The author has interviewed 27 professionals from several stakeholders.

The first finding is that the BIM can serve as a platform for building use, various services and management when it has been adequately generated during the planning and construction phases and enriched before being commissioned. The other essential finding is the theory of conditions data model (CDM), which is a technical environment that combines active data with BIM. The most important advantages of BIM in FM are as follows: • Building owner attains better user satisfaction, acquires better quality and smarter services, saves energy, ensures better indoor conditions and improves building profitability. • Service providers can develop and offer new services, speed up operations, save resources and generate more profits. • The occupant gets a better user experience, faster and higher quality services and better indoor conditions.

The CDM enables to generate for the real estate and construction (RE&C) sector a novel BIM-based ecosystem with standard rules, instead of every individual operator developing his/her own unique solution for BIM use in FM. This will have an impact on the entire RE&C sector’s operating methods and will have significant financial implications in the near future. Application of this research is limited to office buildings where indoor condition measuring is undertaken continuously and where the knowledge of the use cases of spaces is available. In addition, the proper BIM in the Industry Foundation Classes format must exist. The evaluation of the validity of big data is not discussed in this article. Visualization of data and content of user interfaces will be the topic of another article by the author. This article does not deal with intricate technical details, but crucial issues are defined.

The article presents a unique method for BIM use in FM. The theory of CDM (how to combine active data with BIM) is completely new and a similar solution has not been presented earlier. The theory of the presented method will be the crucial key for BIM use and will lead worldwide commissioning. Currently, the theory is under test in the practical pilot project. The results of the project will be published in the next article.

Understanding the essence of tourism flows is one of the fundamental undertakings of tourism geography research and a key issue behind effective destination management and development. However, thus far, few studies have analysed tourist movement on a national scale. This is due to the deficiency of spatially accurate data that can be used for recording tourists’ intra-national movements. This paper aims to illustrate the impact of major gateways on national tourism flows by using tracking data; and demonstrate and compare the use and applicability of tracking data on a national scale.

In this study, the authors analyse foreign tourists’ movements using two spatially and temporally precise tracking data sets – call detail records from passive mobile positioning data and GPS data from smartphones – in two countries, Estonia and Israel. The movements of international tourists entering the countries via main gateways are studied, with a focus on the impact of gateways on intra-national tourism flows.

The results clearly show the impact of gateways on the concentration of tourists. In the two respective countries, the critical mass of time was spent in close proximity to the gateway and, due to distance decay, a dramatic decrease was seen in visitation to areas that were distanced from both countries’ core areas.

To the best of authors’ knowledge, this is the first time when tourism flows attained from tracking data are compared on a national scale for two countries.

To provide a new generic model equipped with a “process visualizing” feature by capturing RFID logistics data to represent different logistics processes' status, and sharing them through web‐based technology to supply chain parties.

In order to provide logistics service companies an effective way to manage their logistics processes and help both the up and down stream supply chain parties maximize their activities linkage within the value chain, the proposed system integrates radio frequency identification (RFID) and IT applications to optimize information flow in a supply chain. In doing so, the objective of visualizing logistics process is achieved.

It is found that the proposed model enhanced the performance of third party logistics providers and their supply chain partners in three main areas. They include a significant reduction in inventory level, a substantial drop in the chance of out‐of‐stock and, a significant improvement in the efficiency of delivery. They are among the most critical problems that often exist in a supply chain.

The proposed model requires supply chain participants to share their process data on the web environment so as to achieve the target of collaboration. This demands the need of tight security control over the public internet.

This paper provides useful information and practical guidance for supply chain participants to formulate a “process visualizing featured” information system.

This paper provides a solution to solve common supply chain management problems and offers practical helps to supply chain participants effectively.