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All Journal Civil Engineering Journal Unnes Journal of Public Health
Arhin, Stephen
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Civil Engineering, Building, Construction & Architecture Public Health

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Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks Arhin, Stephen; Manandhar, Babin; Baba-Adam, Hamdiat
Civil Engineering Journal Vol 6, No 11 (2020): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/cej-2020-03091615

Abstract

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF
Assesment of Occupational Risks and Health Hazards among Healthcare Workers in A Ghanaian Hospital Prah, James; Aggrey, Ebenezer; Andreas, Kudom; Abdulai, Mohammed; Banson, Cecil; Addo-Yeboa, Benedict; Arhin, Stephen
Unnes Journal of Public Health Vol. 13 No. 2 (2024)
Publisher : Universitas Negeri Semarang (UNNES) in cooperation with the Association of Indonesian Public Health Experts (Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI))

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujph.v13i2.168

Abstract

 Information on measuring risks prevalent among healthcare workers in Ghana andglobally is limited. With anecdotal evidence suggesting a high level of occupational injuries among health workers working in a Ghanaian University Hospital, this studywas conducted to identify the common hazards faced by the health workers and use thedecision matrix risk assessment technique to determine the risks associated with somehazards identified. The study also determined these workers’ knowledge, attitude, awareness, and practices toward occupational health and safety. A triangulation of methods was used. The study used a survey, a review of incident registers, and an expertevaluation. There were a total of 133 participants made up of various health professional groups, with nurses and midwives being the majority (31.6%). Knowledge, attitude, awareness, and practices towards occupational health and safety were high.Knowledge scores were significantly associated with age groups (X2-18.996, p-0.001)and cadre of staff (X2-14.690, p-0.005). Attitude was significantly associated withage groups (X2-10.467, p-0.033), years of working (X2-11.112, p-0.011), and cadreof staff (X2-15.467, p-0.004). Awareness was significantly associated with years ofworking (X2-8.57, p-0.035). There was a high prevalence of self-reported needle stickinjuries. A review of incident registers revealed a high underreporting rate of occupational injuries. Staff were found to be at high risk of musculoskeletal injuries and stress. 
Co-Authors Abdulai, Mohammed Addo-Yeboa, Benedict Aggrey, Ebenezer Andreas, Kudom Baba-Adam, Hamdiat Banson, Cecil Manandhar, Babin Prah, James