Institutional-Repository, University of Moratuwa
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Recent Submissions
item: Conference-Abstract
Transport Research Forum 2025
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025)
item: Conference-Abstract
Study on road user safety issues in road construction work zones in Sri Lanka
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Madushanka, KHGS; Jayantha, N; Pasindu, HR
Critical road user safety deficiencies within road work zones across Sri Lanka are addressed in this research, where the construction and maintenance of transportation infrastructure significantly influence risk exposure. A comprehensive nationwide analysis revealed that widespread non-compliance with the national guidelines on traffic control for road work areas primarily contributed to elevated hazards. Core safety challenges identified include inadequate traffic control measures, insufficient illumination, absence or inadequacy of warning signage, inconsistent enforcement of safety protocols, and adverse geographic or environmental conditions. Crucially, the study diagnosed a fundamental methodological gap: inconsistent risk assessment by consulting engineers due to varying levels of expertise, subjective judgment, and the lack of a standardized, quantitative framework. This inconsistency impedes accurate identification of risk levels (high, medium, low) and hinders effective mitigation planning. To overcome these limitations, this study introduces a novel, intelligent risk assessment platform leveraging machine learning (ML) for the automated, objective, and consistently classified work zone safety levels. The core innovation lies in integrating mathematical rigor with real-world engineering validation. The methodology employed a mixed-method strategy across 40 representative work zones. Data encompassed detailed field surveys capturing physical conditions, traffic parameters, and compliance metrics, alongside expert insights. Risk was quantified along three key dimensions: severity, exposure, and likelihood. These indices were derived using a weighted Likert-scale system applied to observed risk factors. The calculated risk index level was identified through a clustering algorithm (K-means), which was then applied to these indices to empirically define high, medium, and low-risk categories, providing a data-driven baseline. Subsequently, multiple supervised ML algorithms were trained and optimized using the field data and the cluster-derived risk labels as ground truth. The primary task was classification into the predefined risk categories based on the input risk factor assessments. Crucially, model validation incorporated rigorous comparison against independent safety evaluations conducted by experienced engineering professionals. This ensured the ML platform's outputs aligned with practical, on-ground engineering judgment, bridging the gap between algorithmic prediction and real-world applicability. Results indicated a concerning prevalence of medium to high-risk classifications across the surveyed zones, confirming significant non-compliance with national standards, particularly regarding signage, lighting, and protective barriers. Among the ML models evaluated, the random forest algorithm demonstrated superior performance, achieving the highest classification accuracy while maintaining strong interpretability through feature importance analysis, making it optimal for field deployment. This research delivers a validated, intelligent ML-based platform that provides scalable, consistent, and objective safety assessments for road work zones in Sri Lanka. It directly addresses the identified problem of inconsistent engineer evaluations by offering a standardized, data-driven methodology. The platform calculates a composite risk index for work zones by integrating predictions from a random work zone model with an audited work zone database. This index, derived from input risk factors, classifies work zone risk into three levels: low (0 ≤ index < 0.204), medium (0.204 ≤ index < 0.298), and high (0.306 ≤ index ≤ 1). Using the established classification levels and developed platform, a comprehensive risk identification system was formulated based on variations in the risk index and its relationship with key factor combinations. This approach enables a dual-path risk identification method that enhances accuracy and reliability. The platform functions as a powerful decision-support tool for engineers, policymakers, and contractors, facilitating targeted and data-driven safety interventions. By integrating advanced analytics with domain expertise validation, the study delivers a significant technological advancement in road safety management. This robust solution holds particular relevance for Sri Lanka's extensive ongoing and planned highway infrastructure projects, supporting more informed and effective safety strategies.
item: Conference-Abstract
Real-time multi-lane speed measurement edge module with license plate recognition
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Pasqual, A; Lokugeegana, D; Thiriloganathan, M; Rathnayake, N; Perera, S; Samarasinghe, K
Overspeeding is a leading cause of road accidents. In 2024, Sri Lanka police reports indicated that over 90% of serious crashes were due to reckless driving. Automated speed enforcement systems have been adopted in many countries to address this issue, typically using radar or LiDAR to estimate vehicle speed and capturing images of vehicles that violate speed limits. Currently, in Sri Lanka, only handheld speed guns are used to detect speed violations. Installing fixed speed cameras in addition to speed guns can improve speed enforcement through fully automated operation and detecting speed violations of multiple vehicles simultaneously. Studies on fixed speed camera systems in other countries show that they lead to a notable reduction in severe and fatal crashes. However, implementing such systems in Sri Lanka presents several challenges. Most commercial solutions are designed for roadways where vehicles usually adhere to their lanes. On normal roads in Sri Lanka, this lane discipline is not strictly followed, and there might be multiple vehicles parallel on the same lane. Furthermore, smaller vehicles such as motorbikes are common in the country and are harder to detect. Existing systems are not customizable for these scenarios and are highly costly for a developing country. Hence, there is a need for a robust and low-cost static speed measurement device that can be easily deployed on Sri Lankan roads. This project presents a stationary edge module for multi-lane speed measurement and license plate recognition, customized for the above-mentioned challenges of Sri Lankan roads. The dual approach of capturing speed and license plate information allows automatic collection of speed violation data, enabling better enforcement of speed regulations and traffic management without increased manpower. Furthermore, the module performs real-time processing within 100 ms such that the speed and license plate number of passing vehicles can be instantly shown on an overhead display, serving as a visual deterrent for overspeeding. The device utilizes 77 GHz frequency-modulated continuous wave (FMCW) radar for speed and position estimation of multiple target vehicles. License plate recognition is done by passing camera-captured images into custom deep learning models for license plate detection followed by character recognition. A data fusion algorithm that involves time synchronization, spatial calibration, and point matching is used to accurately combine radar and image data. The hardware platform is built around the Zynq Ultrascale+ system-on-chip (SoC) with an integrated FPGA fabric, and it uses the Texas Instruments IWR1843 radar module. The FPGA is used for hardware acceleration of the deep learning models and data fusion unit, enabling real-time processing while keeping overall system power and cost low. To address the limitations of existing datasets when training the license plate recognition models, we created a dataset by capturing images from normal roads and expressways in Sri Lanka. The dataset contains 2970 multi-lane road images and 3425 unique license plate images. When trained on this dataset, the license plate detection model achieved 93.2% mAP and the character recognition model achieved 91.2% plate-level accuracy on test data. The models showed accurate performance in complex road scenarios and on motorbike license plates. Speed estimation was validated against vehicle speedometers, with a maximum error of 3 kmph. The system is theoretically capable of detecting speeds up to 250 kmph based on the radar specifications. Furthermore, in the field tests conducted in normal road, busy road, and expressway scenarios, the data fusion process matched the speed and license plate data with accuracies of 90.8%, 71.6%, and 94.5% respectively. Accuracy in busy road scenarios can be further improved through precise calibration. Using a camera operating at 10 FPS, the complete system achieved real-time speed estimation and license plate recognition with a maximum processing time of 70 ms per frame. These results demonstrate the effectiveness of the developed module as a reliable and affordable speed deterrent and enforcement system for Sri Lankan Road environments. Future work will focus on thorough verification of the speed estimation accuracy, integration with a cloud database for data storage and analysis and using IR-capable cameras to enable accurate nighttime license plate recognition.
item: Conference-Abstract
Integrated analysis of skid resistance, texture, and geometric factors for enhanced road safety on the Southern Expressway
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Jayasinghe, W; Jayantha, N; Mampearachchi, W
Road safety remains a critical concern worldwide, especially on high-speed roads such as expressways, where traffic flow is dense and road geometries are complex. The Southern Expressway in Sri Lanka, the country’s first expressway, has contributed significantly to reducing travel time and promoting economic development. However, it has also been associated with a considerable number of road accidents, which raise serious concerns regarding road safety. This research aims to explore the factors contributing to the severity of accidents on the Southern Expressway, focusing on the impacts of road geometry, surface conditions, environmental factors, and lighting. This study investigates a decade of data from 2012 to 2022, collected from the Expressway Operations Maintenance and Management Division (EOM & M). The data encompasses various accident characteristics, including accident time, location, weather conditions, and lighting conditions, as well as road geometry parameters such as horizontal curvature, vertical alignment (K-value), and surface texture. The research applies a combination of statistical techniques and machine learning models to identify key factors influencing accident severity and predict accident outcomes. The study found that road geometry, especially curves, gradients, and poor lighting conditions, significantly influences the severity of accidents. Wet weather conditions were also identified as a key factor, as they reduced the effectiveness of skid resistance, leading to increased accident risk. The analysis highlighted the role of road surface conditions such as skid resistance, which is essential in preventing accidents under adverse weather conditions. Principal Component Analysis (PCA) and K means clustering were employed to reduce the dataset’s dimensionality and identify the most significant contributing factors. Additionally, machine learning models, including Random Forest, Logistic Regression, and Support Vector Machines (SVM), were used to predict accident severity. The Random Forest model achieved an accuracy rate of 91.5%, demonstrating its potential to accurately predict accident severity and identify high-risk zones. The study emphasises the importance of improving road design, particularly in high-risk areas. Enhancing road surface conditions, such as improving skid resistance, and upgrading lighting in poorly lit zones, could significantly reduce accident severity. Furthermore, integrating real-time traffic and weather data into predictive models would enable timely interventions and better safety management. The research also advocates for the use of advanced technologies such as GIS-based hotspot analysis and digital road signs to increase driver awareness and promote safer driving behaviors. In conclusion, this research underscores the need for a comprehensive approach to road safety management on expressways, combining better road design, predictive modelling, and real-time data integration. By implementing these recommendations, the Southern Expressway’s safety performance can be significantly improved, ultimately reducing accident severity and contributing to safer transportation. The findings provide valuable insights for road safety management, particularly for expressways in emerging economies like Sri Lanka, and can serve as a model for other regions facing similar challenges.
item: Conference-Abstract
Enhancing road accident information management through the development of a web GIS-based road accident information system
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Rodrigo, P; Jayasinghe, P
Road accidents have become a significant issue worldwide, and they are a leading cause of death and injury. According to the World Health Organization (2024), approximately 1.19 million lives are lost annually due to road accidents. Globally, 20 million to 50 million people are injured, causing many to suffer permanent disabilities. In Sri Lanka, it is reported that 19.5 males out of 100,000 lose their lives annually due to road accidents. However, little attention is paid to enhancing road safety through GIS based tools in Sri Lanka. Road accident details are collected through the 297–B form by the Sri Lankan Police, and that data is then entered into the Modular Accident Analysis Program (MAAP) software. Even though the data collection systematically takes place, the limited use of data is observed, and its potential in the decision-making process is also neglected by employing data only for statistical purposes. The only method employed by individual police stations to identify high-risk locations and peak accident periods involves manually plotting accidents on physical maps and time charts. Additionally, the Modular Accident Analysis Program (MAAP) software previously used by the Sri Lanka Police suffers from significant limitations, including reliance on an outdated offline base map from 1999, which is incompatible with the current road network. Due to these technical inefficiencies and data reliability issues, the police discontinued the collection of geolocation data (East and North coordinates) for accidents after 2019. Accordingly, this study proposes the development of a Web GIS-based road accident information system, with the Mirihana Police Area serving as the case study location. The Web GIS-based system was developed using the ArcGIS Online platform, leveraging Survey123 for structured, field-based data collection and Web App Builder for creating interactive web applications without the need for coding. Survey123 was configured to capture detailed road accident information, including attributes such as date, time, location, type of collision, severity, and contributing factors. The survey form was optimized for mobile device usage, enabling real-time data entry by field officers. The collected data was automatically integrated into the ArcGIS Online environment, where Web AppBuilder was utilized to design a customized web application featuring dynamic maps, dashboards, and query tools. These tools allow users to visualize accident hotspots, filter data by time periods, and analyze spatial patterns interactively. The system architecture ensures centralized data storage, ease of access for multiple stakeholders, and supports decision-making through real-time spatial analysis and visualization. This system made the data insertion process significantly efficient, given the capabilities of Survey123, such as precisely locating accident locations through a mobile device or the web in real time. Efficient data filtering options greatly increased data retrieval, and the existing data downloading option made this system useful for research purposes. Furthermore, the availability of spatial statistical analysis tools such as Hot Spot analysis, Point Density analysis, Outlier analysis, and point cluster analysis makes decision making processes prompt, which enables the identification of concentrations of severe accident locations, enabling authorities to organize mitigation strategies in a speedy manner compared to the use of manual methods. Ultimately, this Web GIS-based system not only improves the local-level accuracy and efficiency of accident data management but also serves as a decision-support tool for policymakers. Being modular in nature and easy to deploy, it is scalable to deploy in other police divisions of Sri Lanka, facilitating a more data-driven and proactive nationwide response to road safety.