Estimating Passenger Demand Using Machine Learning Models: A Systematic Review

This article investigated machine learning models used to estimate passenger demand. These models have the potential to provide valuable insights into passenger trip behaviour and other inferences. The estimate of passenger demand using machine learning model research and the methodologies used are fragmented. To synchronise these studies, this paper conducts a systematic review of machine learning models to estimate passenger demand. The review investigates how passenger demand is estimated using machine learning models. A comprehensive search strategy is conducted across the three main online publishing databases to locate 911 unique records. Relevant record titles, abstracts, and publication information are extracted, leaving 102 articles. Furthermore, articles are evaluated according to eligibility requirements. This procedure yields 21 full-text papers for data extraction. 3 research thematic questions covering passenger data collection techniques, passenger demand interventions, and intervention performance are reviewed in detail. The results of this study suggest that mobility records, LSTM-based models, and performance metrics play a critical role in conducting passenger demand prediction studies. The model evaluation was mostly restricted to 3 performance metrics which needs improved metric for evaluation. Furthermore, the review determined an overreliance on the longand short-term memory model to estimate passenger demand. Therefore, minimising the limitation of the LSTM model will generally improve the estimation models. Furthermore, having an acceptable trainset to avoid overfitting is crucial. In addition, it is advisable to consider multiple metrics to have a more comprehensive evaluation

A proposed Ghanaian intercity public transport departure scheduling model based on the dynamic rate leaky bucket algorithm

In this paper, the random departure of intercity public transport buses in Ghana which is influenced by the arrival patterns of passengers is shaped into a deterministic one with regular departure intervals based on the dynamic rate leaky bucket algorithm. This is essential because a regulated bus departure system is capable of providing transit information to travellers thereby reducing passenger waiting times. The arrival patterns of passengers were simulated and grouped into three blocks based on the frequency of arrival of passengers. The three blocks represented peak seasons, moderate seasons and off-peak seasons. Departure intervals were then calculated to suit each block. The recommended fleet size required to maintain the departure consistency was also determined based on the fixed departure interval and the round-trip time. The second phase involved developing a bus schedule based on the determined fixed departure intervals and recommended fleet size. The results as depicted with graphs showed that the fixed departure schedule reduced passenger waiting times. The minimum fleet size could also give transit operators an idea of how to manage their fleet to efficiently use resources. The KNUST Campus shuttle was used as a test case for the model. A fixed departure schedule was developed based on the proposed model. Simulation results showed a significant reduction in passenger waiting times for various passenger arrival patterns and also proposed a recommended fleet size and bus capacity

Extremely randomised trees machine learning model for electricity theft detection

Electricity ranks among the world’s most plundered commodities. The fraudulent act of acquiring electrical power without paying for it is termed electricity theft. Electricity theft is captured in power distribution systems as non-technical losses (NTL), representing a major loss in revenue for power utility companies. Electricity theft has far-reaching financial consequences owing to unrealised revenue, and this has a knock-on effect on both developed and developing countries because electricity represents a major part of a country’s GDP and facilitates other industries. AMI-based smart energy meters (SM) gather large amounts of electricity consumption (EC) data that power utilities can utilise to monitor and detect fraudulent customers. This EC data is fed to a machine learning (ML) based electricity theft detection model to learn the behaviour of fraudulent customers. However, existing ML-based electricity theft detection (ETD) models do not produce the best outcomes because of; consecutive missing values in EC datasets, data class imbalance problems, inappropriate hyperparameter tuning of ML models, etc. This research introduces an ETD model using an extremely randomised trees classifier to detect electricity theft in smart grids efficiently. SMOTE Tomek sampling is used to deal with the data class imbalance, and the grid search optimisation technique is employed to optimise the hyperparameters of the proposed model. The proposed model shows its capacity to detect electricity theft by obtaining 98%, 95.06%, 98%, 97%, 98%, and 99.65% accuracy, Matthew’s correlation coefficient, detection rate, Precision, F1-score, and area under the curve receiver operating characteristic, respectively

Design and Theoretical Analysis of Highly Negative Dispersion-Compensating Photonic Crystal Fibers with Multiple Zero-Dispersion Wavelengths

This paper presents a highly negative dispersion-compensating photonic crystal fiber (DC-PCF) with multiple zero dispersion wavelengths (ZDWs) within the telecommunication bands. The multiple ZDWs of the PCF may lead to high spectral densities than those of other PCFs with few ZDWs. The full-vectorial finite element method with a perfectly matched layer (PML) is used to investigate the optical properties of the PCFs. The numerical analysis shows that the proposed PCF, i.e., PCF (b), exhibits multiple ZDWS and also achieves a high negative chromatic dispersion of −15089.0 ps/nm·km at 1.55 μm wavelength, with the multiple ZDWs occurring within the range from 0.8 to 2.0 μm range. Other optical properties such as the confinement loss of 0.059 dB/km, the birefringence of 4.11×10−1, the nonlinearity of 18.92 W−1km−1, and a normalized frequency of 2.633 was also achieved at 1.55 μm wavelength. These characteristics make the PCF suitable for high-speed, long-distance optical communication systems, optical sensing, soliton pulse transmission, and polarization-maintaining applications

Communication Medium Used by Clients and Health Professionals in Accessing and Providing Healthcare in Low Resource Setting: A Descriptive Cross-Sectional Study

Background. There exist continuing challenges with communication medium used during health service provision. These challenges relate to clients and health institution, intra- and interhealth institution communications. This study reviewed the existing healthcare communication medium from the perspectives of clients and health professionals at a tertiary hospital in Ghana. Methods. Cross-sectional design was employed with a multilevel sampling method to select a total of 650 participants consisting of 303 clients, 303 health workers, and 44 hospital directorate managers for the study. A structured survey questionnaire was used to collect data from respondents. Results. Close to ninety percent (89.8%) of staff resort to direct means (face-to-face medium) to communicate among each other. Majority (64.4%) of them also communicated with management through meetings sections. Nearly all healthcare providers (97.4%) communicated with clients through direct means (face-to-face medium). Almost all forms of communication between the hospital management members and the general public were done through letters and official memos. Conclusions. The study revealed blended forms of communication media used by health providers and health service consumers. These differences in medium of communication could amount to possible difficulties such as lack of information and truncation of information flow. Developing a systematic way of information flow using a common information platform will improve access to health services