Institutional-Repository, University of Moratuwa

Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.



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Recent Submissions

item: Conference-Abstract
Proceedings of International Conference on Advances in Highway Engineering & Transportation Systems (ICAHETS) (Online)(Pre-text)
(Transport Engineering Division, Department of Civil Engineering, 2024) Pasindu, H.R.
item: Conference-Abstract
An Economical GPS-Based Driving Cycle for 3-Wheelers: Establishing a Framework for Emission Studies
(Transport Engineering Division Department of Civil Engineering, 2024) Jayawardhana, S.; Aponsu, K; Gamage, S.; Perera, L; Pasindu, H.R.
The research presents a detailed approach to developing a driving cycle tailored for 3-wheelers in Sri Lanka, addressing the need for accurate emission estimation in the context of increasing vehicle populations with a significant proportion of 3-wheelers, and environmental concerns. Previous studies have highlighted the critical role of driving cycles in estimating emissions, particularly in urban environments where traffic patterns are complex and variable. It identifies two primary approaches to emissions modelling: fuel-based and travel-based models. While fuel-based models rely on fuel consumption data, travel-based models, including driving cycles, utilize real-world driving behaviour data to provide a more accurate representation of emissions. The review identifies a notable gap in the development of driving cycles for 3-wheelers in Sri Lanka, despite their prevalence in the transport sector. Therefore it is clear that a dedicated driving cycle is required, leading to enhanced emission inventory estimations through localized emission factors, and informed policy decisions aimed at reducing air pollution. The study utilizes a micro-trip-based construction method to develop the driving cycle, which is particularly effective for capturing the stop-and-go nature of urban driving. The research involved a representative route selection, focusing on urban and suburban areas in Colombo and Matara, where 3-wheelers are commonly used. The selected routes were designed to reflect typical driving conditions, incorporating a mix of road types and traffic volumes. This careful selection process is crucial, as it ensures that the driving cycle accurately represents the diverse traffic behaviours in real-world scenarios. Data collection was conducted using an onboard GPS device, which recorded driving behaviour with high precision. This method was chosen over the chase car approach due to the unpredictable nature of driver behaviour in Sri Lanka, which can lead to incomplete data in congested traffic. The study gathered over a million data points from five drivers operating Bajaj 4-stroke petrol 3-wheelers, focusing on peak traffic hours to capture the most representative driving patterns. The data was then filtered and pre-processed using Python scripts to eliminate anomalies, ensuring the integrity of the dataset. The construction of the driving cycle involved categorizing driving data into micro-trips, which are segments of driving between stops. This approach allows for a detailed analysis of driving behaviour, capturing variations in speed, acceleration, and deceleration. The study utilized a systematic method to balance the representation of different driving conditions by binning micro-trips based on their average speeds. This technique ensured that the final driving cycle accurately reflected the average driving behaviour of 3-wheelers in the selected regions. The results of the study revealed an average speed of 15.12 km/h, with significant time spent in acceleration (40.58%), deceleration (33.84%), and cruising or idling (25.58%). These findings highlight the frequent stop-and-go conditions of urban traffic, validating the effectiveness of the micro-trip-based method for driving cycle development. The adaptability of the developed driving cycle methodology across South Asia is emphasized, providing a framework that can be tailored to local conditions in other countries where 3-wheelers are prevalent. In conclusion, this research not only fills a critical gap in the existing literature regarding driving cycles for 3-wheelers but also offers a methodology for emission estimation that can inform policy decisions aimed at improving air quality and public health in Sri Lanka and beyond. The study's findings advocate for the testing of the developed driving cycle on a chassis dynamometer to obtain precise emission data alongside emission factors, ultimately contributing to more sustainable urban transport solutions.
item: Conference-Abstract
Criteria for Assessing the Effectiveness of a Non Real Time Coordinated Cluster of Signalized Intersections
(Transport Engineering Division, Department of Civil Engineering, 2024) Kapuge, A. B. A.K.V.S.; Bandara, J. M. S. J.; Jayasooriya, N.; Pasindu, H.R.
Given the limited resources available for installing advanced signal controllers, many researchers and professionals believe that well-designed coordinated fixed-time signal control, combined with well-defined corridor coordination, is a cost-effective option. In this study, two closely spaced intersections were considered for analysis. It was identified that a subsystem consists of spatial elements (the link road that connects two signalized intersections) and temporal elements (the relative offset between the signals of the two intersections). Throughput, travel time, delay, queue length, and flow efficiency were considered basic performance measurement parameters. The traffic flow analysis was conducted using the Vissim model, with calibration parameters adjusted to suit Sri Lankan conditions based on previous research. Twenty demand models were evaluated, with volume-to-capacity (v/c) ratios ranging from 0.2 to 0.9 across the two intersections. The traffic flow was analyzed under three scenarios: Scenario 1 consisted of 60% through traffic, 20% left-turn traffic, and 20% right-turn traffic. In Scenario 2, the traffic distribution was 60% through traffic, 30% left-turn traffic, and 10% right-turn traffic. Scenario 3 featured 60% through traffic, 10% left-turn traffic, and 30% right-turn traffic. The Vissim model's link road geometry was kept at 500 meters, and both junctions were identical. The east-west direction was treated as the coordinated direction with three lanes, while the north-south cross streets had two lanes. Base saturation flows and offset optimization were calculated using Akcelik’s (1981) method, and cycle timing was determined based on the Webster formula. The study extended its analysis to compare unsynchronized (Case 1) and synchronized (Case 2) models. Initially, travel time was plotted against throughput. Based on the Elbow method, three clusters were identified as the optimal cluster number and then K-means clustering was performed. Generally, Cluster 1 had low flow and moderate traffic, Cluster 2 had moderate to high flow but moderate travel time, and Cluster 3 had moderate flow and high travel time. When the mean values for each cluster were compared in the unsynchronized and synchronized models, signal synchronization improved all key metrics in each cluster, except for queue length on the link road in Cluster 3, and throughput and flow efficiency in Cluster 2. Based on the Independent-Samples T-Test p-value, statistically significant improvements were found in travel time, with an 8.74% improvement in Cluster 2 and a 14.34% improvement in Cluster 3. Delay improvements were 29% and 30% in Clusters 2 and 3, respectively. However, there was a 35.17% increase in queue length on the link road, suggesting that while flow efficiency and delays improved, congestion remained an issue. As a further performance check, the Min-Max Normalization and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods were used. Both methods ranked Cluster 3 as the highest performer, despite the increase in queue length. Finally, considering the Level of Service (LOS) criteria from the Highway Capacity Manual, Cluster 3 showed an improvement from LOS E and D to LOS C, whereas Cluster 2 showed an improvement from LOS C and B to LOS B and Cluster 1 remained at LOS B and A. The most notable result from this research is the identification of Cluster 3 as the best-performing cluster across multiple scenarios, while Cluster 2 provided moderate improvement and Cluster 1 showed no significant improvement. Therefore, this study suggests that to gain the benefits of synchronization, the intersections considered should initially fall within the range of Cluster 3 or Cluster 2. Further research is required to explore how intersection geometry and coordination direction affect synchronization.
item: Thesis-Abstract
Real-time human detection analytics in constrained image inputs
(2022) Fernando, Heshan; Perera, I; De Silva, C
Real-time video surveillance is a growing trend today. Our surrounding is being monitored daily by an increasing number of surveillance camera systems. Analyzing human movement can be used for the wellbeing of humans. There are a set of analytical tools and algorithms which can be used to detect, track, and analyze humans in images. Human movement analytics has various subdomains including human detection, human recognition, human tracking, human localization, human reidentification, human behavior analysis, and abnormal activity detection. Human detection is the most crucial step among them, and which helps to derive other sub domains. Human detection analytics in constrained lighting conditions would be a challenging task to apply due to the low contrast of the image context. Currently available systems focused on the daytime. The background light is an essential factor in the camera images, which rigorously affects the quality of the image. We can identify considerable differences if we compare two images at the rich light condition and constrained light condition. Fewer features of the objects can be extracted in constrained light conditions than rich light conditions. Illumination of the background context is an important factor if we focus on such applications. Currently, most researchers have used human detection analytics in visible light. RGB image shows a clear view when there is sufficient light existing, and it is highly sensitive to visible light conditions compared to infrared. In this research, we considered infrared images as constrained image inputs. Our proposed methodology contains a novel human detection approach based on machine learning and a motion dynamic model. Here we have addressed the problem using a combination of Deep Convolutional Neural Networks (DCNN) for human detection and Kernelized Correlation Filters (KCF) for human tracking. MobileNet pre-trained model is used for frame-wise human detection as the first step. Then the KCF object tracking algorithm is used to increase the human detection accuracy while tracking the human in the context. Furthermore, we applied some preprocessing techniques to reduce the noise effects. Currently, the progress made by this research-based project is sufficient to initiate the development of a complete human detection analysis solution based on live CCTV camera footage. This solution provides the core functionality of human detection analytics and it can be easily adapted to different domain solutions such as customer behavior analytics in a supermarket or worker movement analytics in an industrial premise.
item: Conference-Abstract
Noise Investigation Around Bandaranaike International Airport
(Transport Engineering Division Department of Civil Engineering, 2024) Peiris, N; Umaluxman, K; Perera, L; Pasindu, H.R.
Katunayake Bandaranaike International Airport is a crucial transportation hub in Sri Lanka, facilitating both passenger travel and cargo movement. However, the rapid growth of air traffic and urbanization around the airport has led to increasing concerns over noise pollution, which poses significant environmental and public health challenges. While road and railway noise are continuous and often predictable, airport noise is intermittent yet more intense, especially during take-off and landing. Although airport noise affects fewer people compared to road and railway noise, it has a more intense impact due to the high decibel levels. This persistent exposure can disrupt daily life, leading to sleep disturbances, stress, and long-term health problems such as cardiovascular disease and cognitive impairment. This study explores the extent and impact of noise pollution in areas surrounding the airport, aiming to measure noise levels, identify the primary sources, and evaluate the socio-environmental consequences for local communities. Noise levels were measured using sound level meters at 62 strategically selected locations, encompassing residential, commercial, and industrial zones within a three-kilometre radius of the runway and outside the airport boundary. Over the four days of data collection, a total of 161 readings were taken. The measurements were conducted during flight operations and compared with permissible noise thresholds defined by local and international standards. The data were analysed using ArcGIS to produce noise contour maps, enabling the identification of high-impact zones and patterns of noise exposure. Results indicate that noise levels in many residential areas and commercial zones exceed both local regulatory limits of 70 dB and international thresholds of 65 dB for residential areas. Aircraft operations, particularly during take-off and landing, were found to be the most significant contributors to elevated noise levels. Higher noise level zones were observed on both sides of the runway compared to other areas. When comparing noise levels between large and medium aircraft, the difference in values was minimal and not prominently distinguishable in graphical representations. Predictive modelling of future noise levels suggested that potential airport expansions, including the addition of runways, would exacerbate noise pollution, increasing the burden on surrounding communities. The study also highlights the impact on sensitive infrastructure such as schools and hospitals, further emphasizing the urgent need for mitigation measures. Proposed solutions include constructing noise barriers around the airport and between residential areas and the runway, promoting the use of quieter aircraft models, and implementing stricter noise abatement procedures. In addition, proper land-use planning to minimize the impact of noise on residential and sensitive areas is essential. Collaborative efforts among airport authorities, policymakers, and local stakeholders are critical to developing sustainable noise management strategies. Public awareness programs focusing on the health implications of airport noise and community engagement in mitigation efforts can foster a collective approach to addressing this issue. This research underscores the importance of balancing the growth of aviation infrastructure with environmental sustainability and public health. The findings provide valuable insights for policymakers, airport planners, and regulatory authorities, serving as a foundation for effective noise management strategies. Concluding, the study highlights that immediate interventions are necessary to mitigate noise pollution, protect public health, and ensure the sustainable development of Bandaranaike International Airport and its surrounding regions.