Research Article
Tackling Urban Traffic Congestion with Smart Adaptive Transport Pods (SATPods)
Ali Mansoor Pasha*
Issue:
Volume 10, Issue 3, June 2025
Pages:
62-68
Received:
11 June 2025
Accepted:
26 June 2025
Published:
15 July 2025
Abstract: An innovative article proposing a solution to a daily life problem i.e. Traffic congestion, that persists despite scientific and technological advancements. A solution is proposed using Smart Adaptive Transport Pods (SATPods). To address the escalating urban traffic congestion crisis, this expansion explores additional dimensions of the SATPods solution, emphasizing scalability, user experience, and global applicability. By integrating advanced sensor networks and machine learning, SATPods can dynamically adapt to diverse urban environments, ensuring equitable access and fostering smart city ecosystems. This approach not only mitigates congestion but also enhances urban livability by prioritizing user-centric design and environmental sustainability. The system’s potential to integrate with emerging technologies like 5G and blockchain for secure, real-time data management further strengthens its viability as a transformative urban mobility solution. Furthermore, SATPods leverage magnetic levitation technology to achieve frictionless transport, significantly reducing energy consumption and maintenance costs. The incorporation of renewable energy sources, such as solar and kinetic energy harvesting, ensures a minimal carbon footprint, aligning with global net-zero objectives. Economically, SATPods promise substantial savings by reducing time lost in traffic and fostering job creation in manufacturing and AI sectors. The system’s modular design supports cargo transport and emergency response, addressing urban freight demands and disaster resilience. By integrating with multi-modal transport networks, SATPods promote equitable mobility, reducing reliance on private vehicles. This solution is adaptable to both developed and developing urban contexts, offering a scalable model for global cities to combat congestion while enhancing public health and economic efficiency.
Abstract: An innovative article proposing a solution to a daily life problem i.e. Traffic congestion, that persists despite scientific and technological advancements. A solution is proposed using Smart Adaptive Transport Pods (SATPods). To address the escalating urban traffic congestion crisis, this expansion explores additional dimensions of the SATPods sol...
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Research Article
Comparison of Srtm and Aster Derived Elevation Models Along Ali Ethiopia - Deghamedo and Abala - Irepti Road Corridors
Gadisa Shiferaw*
Issue:
Volume 10, Issue 3, June 2025
Pages:
69-79
Received:
9 June 2025
Accepted:
30 June 2025
Published:
22 July 2025
DOI:
10.11648/j.ajtte.20251003.12
Downloads:
Views:
Abstract: The earthwork estimation has a huge impact on the construction cost of the road. Therefore, the initial road design relies on precise terrain modeling. In this context, a low cost data offered by Digital Elevation Models (DEMs) are introduced. This study assessed the performances of three freely available DEMs; ALOS, SRTM and ASTER in flat, rolling and mountainous terrain for road design. Digital Elevation Models (DEMs) are helpful for estimating cut-and-fill volumes in a road construction project. Not being accurate can cost too much. This study aims to examine the accuracy of DEMs by comparing them with field survey data and their suitability for earthwork estimation in various terrain conditions. The study used statistical methods for error Analysis like Root mean square error calculations. Elevation profile and percentage error for comparison between DEM data versus reference ground survey data. The cut-and-fill volumes were estimated using TIN surfaces created from each DEM and the actual surveyed estimates were compared. The study found that the most accurate DEM was from ALOS with RMSE value for flat terrain, rolling terrain and mountainous terrain being 4.89 m, 5.14 m and 27.96 m respectively. The heights recorded by SRTM were relatively better with RMSE of 5.02 m, 5.47 m and 29.75 m whereas those from ASTER were the worst with RMSE of 9.15 m, 8.51 m and 30.51 m. The percentage errors in earthwork estimations for cut volumes recorded using ALOS were the lowest 21.28% (flat), 15.64% (rolling) and 47.93% (mountainous) while those of ASTER were 42.45%, 47.33% and 55.20% respectively and STRM were 31.28%, 36.26% and 47.43%. The study found that ALOS produces the best elevation and earthwork estimates, compared to SRTM and ASTER. ALOS is a very accurate sensor with very high accuracy on flat and rolling terrains and reduced accuracy on all models in mountainous terrain. ALOS is so effective that using them could help improve the cost estimation significantly and lessen the uncertainty of money in the road construction. In the future, scientists should work on dealing ALOS data with other sets of geospatial data. Also, a cost-benefit analysis of ALOS-based models in large-scale infrastructure projects can help us understand its application better.
Abstract: The earthwork estimation has a huge impact on the construction cost of the road. Therefore, the initial road design relies on precise terrain modeling. In this context, a low cost data offered by Digital Elevation Models (DEMs) are introduced. This study assessed the performances of three freely available DEMs; ALOS, SRTM and ASTER in flat, rolling...
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