EAI Endorsed Transactions on Smart Cities

The Development and Evaluation of E-Learning for Professional Bus driver in Tanzania

Marwa Chacha — 2025-05-05

INTRODUCTION: Commercial buses serve as the predominant mode of public transportation in Tanzania, with 90% of travellers opting for them, especially for inter-regional and urban-rural journeys. Despite their widespread use, the affordability of this mode has led to a rise in road safety issues, resulting in an alarming surge in accidents, injuries, and fatalities. This paper explores the potential of eLearning as an alternative approach to enhance road safety in Tanzania's commercial bus sector.

OBJECTIVES: The primary aim of this study is to assess bus drivers' perceptions of eLearning deployment and develop a tailored course to improve road safety practices. The overarching goal is to contribute to existing knowledge by creating a training course addressing identified gaps in the context of changing driver behaviours in Tanzania.

METHODS: Employing a quantitative approach, data for this study were collected through an online survey on Qualtrics and semi-structured interviews over three weeks. Participants included individuals from the National Institute of Transport (NIT), Vocational Education Training Authority (VETA), road safety NGOs, and bus drivers in Dar es Salaam, in collaboration with the Land Transport Regulatory Authority (LATRA). The study involved the development of an eLearning package tailored for professional bus drivers, utilizing social network analysis techniques.

RESULTS: The survey, comprising 153 participants, provided insights into bus drivers' preferences for a 35-hour theory and practical training program. Findings indicated a high willingness among drivers to adopt eLearning, with smartphones being the preferred device. The study also proposed a comprehensive eLearning package, incorporating six modules derived from research findings, to enhance road safety awareness among professional bus drivers.

CONCLUSION: This research advocates for developing and implementing eLearning as a viable strategy to enhance road safety awareness and skills among commercial bus drivers in Tanzania. The proposed eLearning modules and the learning management system (LMS) aim to address the limitations of traditional in-person training, providing a flexible and accessible alternative. Future efforts should involve stakeholders, policy discussions, and integration with GPS tracking for targeted feedback and continuous improvement in driving behaviours. Overall, the introduction of eLearning has the potential to impact safety cultures within companies positively and contribute to reducing road traffic accidents.

Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

Sefa Dursun — 2024-11-13

INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods.
OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings.
METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments.
RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text.
CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

Balancing Efficiency and Equity: Ethical Considerations for Automation in Urban Planning

Abhishek Raisinghani — 2025-02-21

The integration of automation into urban planning introduces a complex dynamic where efficiency often clashes with equity, especially for marginalized communities. This necessitates a delicate balance between these two aspects. This article investigates the ethical principles and equity considerations in urban planning decisions, revealing a historical and contemporary bias towards efficiency, marginalizing certain groups. Automation, while beneficial in sectors like transportation, land use, and infrastructure, can perpetuate existing inequities and pose ethical challenges such as algorithmic bias and data privacy concerns. The article explores the impacts of automation on plan execution and monitoring, highlighting the need for current best practices to address these challenges. It provides an overview of automation in urban planning and calls for continuous research, collaboration, and improvement to ensure efficiency and equity are mutually reinforced.

Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR

Do Thanh Huong — 2024-11-20

Effective crop production and harvesting decisions rely on proper farm monitoring and management. Each region has distinct needs for farm oversight, but the primary focus remains on collecting and evaluating environmental data such as temperature, soil moisture, air humidity, all of which are vital to plant growth. Gathering this data on a large scale requires significant effort and is often based on intuition or simple measurement tools. This paper proposes a novel solution for farming data collection using an IoT platform integrated Long-Range Wide Area Networks (LoRaWAN) network application with Augmented Reality (AR) technology and Machine Learning (ML) algorithms to predict key environmental daily indexes. In a pilot study in Quang Tho, Vietnam, the system accurately predicted environmental conditions, reduced the risk of crop failure, and improved farm management efficiency. This approach enhances real-time data interaction and offers predictive analytics, supporting sustainable agriculture.