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Detecting and tracking the position of a mobile user has become one of the important subjects in many mobile applications. Such applications use location based services (LBS) for learning and training user movements in different places (cities, markets, airports, stations) along different modes of transport (bus, car, cycle, walk). To date, GPS is the key solution to all LBS but repeated GPS querying is not economical in terms of the battery life of the mobile phone. The purpose of this paper is to study how cheap and energy‐efficient air pressure sensors measuring the altitude could be used, as a complement to the dominant GPS system. The location detection and route tracking task is then accomplished by matching the collected altitude traces with the altitude curves of stored data to find the best matching routes.

The cornerstone of the authors' approach is that a huge amount of route data, collected with GPS devices, is available in various cloud services. In order to evaluate the mechanism of matching routes with altitude data, the authors build a prototype system of crowd‐sourced database containing only altitude data of different routes along different modes of transport. How accurately this stored altitude data could be matched with the collected altitude traces is the key question of this study.

Results show that, within a certain level of accuracy, older repeated routes can be detected from newly tracked altitude traces. Further, the level of accuracy varies depending on the length of path traversed, route curvature, speed of travel and sensor used for tracking.

The new contribution in this paper is to propose an alternative route detection mechanism which minimizes the use of GPS query. This concept will help in retrieving the GPS coordinates of already traversed routes stored in a large database by matching them with currently tracked altitude curves.

Increasingly, companies are looking for more efficient ways of distributing their goods and are turning to Computer‐assisted Vehicle Routeing Systems (CAVRS) to replace manual routeing systems. The aim of CAVRS is to reduce the cost of distribution without adversely affecting service. A framework of criteria developed to evaluate CAVRS packages is described, based on the proposed characteristics of an “ideal” CAVRS. The criteria represent the most important aspects that should be considered by potential users when selecting a package.

Increasingly, companies are looking for more efficient ways of distributing their goods and are turning to Computer‐assisted Vehicle‐routeing Systems (CAVRS) to replace manual routeing systems. A framework of criteria developed to evaluate CAVRS packages is described, based on the proposed characteristics of an “ideal” CAVRS.

This paper is more than just a guide to the points to look for when contemplating the purchase of a CAD system for electronics to run on personal computers. It offers many helpful hints and points out the main pitfalls to avoid falling into. The limitations of the simplest systems are explained, as are the advantages offered by the more complex ones. It is pointed out that some modern PC‐based softwares on the latest machines running on 80386 processors can out‐perform mini‐computer systems costing several times more. In particular, this paper explains many of the difficulties that may be encountered when using such softwares for SMT designs, including those using both conventional and SM devices. It is based on many years' experience in designing and selling such software, so that the potential user can judge better the philosophies behind the software concepts. It is therefore the opposite viewpoint to the more common article on the experience of a user having purchased a particular type of software.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This study aims to propose that providers should tailor recovery responses to consumers’ emotional states to improve evaluations and behavioral intentions.

A multi-study approach comprising field and lab data was used. The field study, conducted on the Boston public transport network during a weather crisis, sought to determine how a provider should deliver their recovery response to match the consumer’s affective state. In the lab studies, the importance of tailoring a recovery message to the consumer’s state is experimentally demonstrated while controlling for factors such as consumer brand involvement.

This study finds that an emotion-focused recovery emphasizing empathy should be given to those in an avoidance affective state (i.e. focused on the avoidance of negative outcomes) such as worry. A problem-focused recovery, in which the focus is on the process that led to the failure and the steps that will be taken to correct it, should be provided to those in an approach state (i.e. concerned with advancement and accomplishment) such as anger. This study also finds this effect is more salient under low involvement conditions.

Future research should examine how nonverbal behavior during recovery can be tailored to a consumer’s state.

Service providers are encouraged to tailor recovery messages to consumers’ affective states.

To the best of the authors’ knowledge, this study is the first to examine matching recovery messages to affective states, an important contribution as service failures can elicit a wide variety of affective states that influence how consumers react to recovery messages.

Presents a new model for resource discovery in grids and peer‐to‐peer networks designed to utilise efficiently small numbers of messages for query processing and building of the network. Outlines and evaluates the model through a theoretical comparison with other resource discovery systems and a mathematical analysis of the number of messages utilised in contrast with Chord, a distributed hash table. Shows that through careful setting of parameter values the model is able to provide responses to queries and node addition in fewer messages than Chord. The model is shown to have significant benefits over other peer‐to‐peer networks reviewed. Uses a case study to show the applicability of the model as a methodology for building resource discovery systems in peer‐to‐peer networks using different underlying structures. Shows a promising new method of creating a resource discovery system by building a timeline structure on demand, which will be of interest to both researchers and system implementers in the fields of grid computing, peer‐to‐peer networks and distributed resource discovery in general.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.