Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data. Sustainable Cities and Society. In press.

Asparagus is a popular vegetable rich in healthy functional components. Asparagus spears are known to contain a large amount of rutin, which has been found to possess anti-inflammatory, antitumor, and antibacterial/viral properties, and protodioscin, which is an antitumor substance and present in the bottom parts (8 cm from the cut end). However, the process of its production leaves fern in the aboveground parts and roots in the underground parts as significant amounts of nonusable parts, and this issue should be solved. This study was conducted to examine the distributions of rutin and protodioscin, representative functional components in different parts of asparagus. The results suggested that large amounts of rutin were noted in the cladophylls and storage roots (brown and epidermis), and the protodioscin content was high in the buds, the soil-covered section of the spear, and the rhizome. A significant amount of rutin was detected in the aboveground parts, which is consistent with the results of previous studies, but it was also found in the storage roots. The largest amount of protodioscin was found in the buds, as well as in young fruits and seeds of the aboveground parts. Injury by continuously cropping asparagus may be associated with high rutin content in the storage roots of asparagus.

Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

No abstract

No abstract

No abstract

The tap-on smart-card data provides a valuable source to learn passengers' boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machinelearning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model's performance and better fit ridership's actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis.

No abstract