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The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

This paper reports results of analyses made at an all-female Gulf Arab university measuring the nature and extent of biases in students' evaluation of faculty. Comparisons are made with research reporting the nature of similar relationships in North America. Two issues are investigated: 1) What variables (if any) bias faculty evaluation results at an all-female Arab university? 2) Are biasing variables different in nature or magnitude to those reported at North America universities? Using the population of 13,300 faculty evaluation records collected over two school years at Zayed University, correlations of faculty evaluation results to nine potentially biasing factors are made. Results show biases to faculty evaluation results do exist. However, biases are small, and strikingly similar in nature to those reported at North American universities.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution.

The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments.

Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes.

The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept.

The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

The purpose of this paper is to investigate the food and beverage automatic retail environment by analysing the impact of planograms, conceived as a visual merchandising practice and shopping time – the time spent making a purchase – as part of food consumer purchasing behaviour to further enrich the debate on the ability of companies to absorb customer knowledge.

A real-world experiment was conducted using a sample of 27,230 valid observations of consumer purchasing decision-making processes at automatic vending machines (AVMs). Data were collected by a shopper behaviour analytics system that allows for a better understanding of the AVM users' behaviour. Two sets of regressions were run to test the two hypotheses.

The experimental results demonstrated that planograms – the planned, systematic organisation of products in an AVM – positively impact food purchases. A planogram acts as a mediator in the relationship between shopping time and purchase, resulting in shorter shopping times and more purchases.

This work adds to the customer knowledge literature by focussing on customer behaviour in the food and beverage automated shopping environment. The shopper analytics technology adopted to collect real-time data leads to a better understanding of the purchasing behaviour of AVMs' users and provides new marketing and retail insights into AVMs' performance that retailers can use to improve their marketing strategies.

This paper aims to provide a driving behavior scoring model to decide the personalized automobile premium for each driver.

Driving behavior scoring model.

The driving behavior scoring model could effectively reflect the risk level of driver’s safe driving.

A driving behavior scoring model for UBI.

This study aims to explore how haute cuisine excellence is and can be self-represented on the websites of three-star restaurants and juxtaposed onto the websites of external authoritative food guides.

In total, 26 French Michelin three-star restaurant websites and their reviews in the prominent Michelin and Gault and Millau dining guides were examined. This data was then processed using lexicometric software.

Five semantic universes emerged, showing that restaurants and dining guides do not emphasize the same elements of culinary excellence. While restaurant websites emphasize the charismatic leadership role of the chef through family history, professional recognition and vicarious learning, the two iconic guides are far from rating the criteria they claim to: For the Michelin Guide, criteria other than cuisine appear central. Conversely, Gault and Millau, far from its nouvelle cuisine principles advocating democratization at lower cost, insists on fine products.

It remains essential for restaurants to use a repertoire of cultural components and symbols, capitalize on the charismatic and architectural roles of their chef and showcase fine products that are representative of classical cuisine. Storytelling and dynamic narrative add-ons, regularly updated on large-audience social media, appear central to increasing restaurants’ perceived value, communicating innovation and attesting to their singularity and uniqueness.

To the best of the authors’ knowledge, this is the first empirical study to overlap the lexical perspectives of three-star restaurants and iconic guides’ websites.

This article explores, identifies and conceptualises everyday audiobook reading practices amongst young adults.

Semi-structured interviews were conducted with ten Swedish audiobook users aged 18–19. The material was analysed using qualitative content analysis and focused on their audiobook use during an average weekday, as this was the time that they listened the most. The theoretical framework consists of theories on practice, time and everyday routine.

Five timespaces emerged when audiobook practices were most prevalent: morning routines, commuting routines, school routines, after school routines and bedtime routines. Within these timespaces, several practices could be identified and conceptualised. Three mobile practices were commute listening, exercise listening and chore listening while more stationary practices were homework listening, schoolwork listening and leisure listening. An unexpected finding was how audiobooks routinely were used to aid respondents’ wellbeing. This wellbeing listening was used to alleviate stress, loneliness and help listeners relax or fall asleep. Furthermore, respondents switch between Music, Audiobooks and Podcasts, which is conceptualised as MAP-switching.

There is a scarcity of research on audiobook use, and this paper contributes with new knowledge on audiobook reading practices, how audiobooks fit into everyday routine and provides concepts to aid further research on audiobook practices.

The purpose of this paper is to elaborate the picture of situational relevance by examining how modes of music information are viewed as situationally relevant at different stages of information-seeking processes among music students.

Empirical data of the present longitudinal study were collected in two phases by utilizing questionnaire and interview methods. Informants comprised of 14 university-level music students representing the fields of music performance, music education and music theory and composition. Modes of music information were approached through the information typology presented by Rousi, Savolainen and Vakkari.

The findings indicate that not only the modes of music information were seen as situationally relevant for different reasons by the three participating music student groups when at the beginning of their tasks, but also that the perceived situational relevance of the information modes underwent changes as their tasks progressed to focus formulation and post-focus stages.

Due to the small number of participants, further research is needed to verify the results concerning the differences in information-seeking processes between diverse music student groups.

The paper showcases that approaching music information through frameworks that classify information sources at diverse levels of abstraction enables an accurate description of information-seeking processes and illuminates context-sensitive development of situational relevance of music information of diverse modes.