| 1. |
- Saleh, Roxan
(författare)
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Towards Smart Maintenance : Machine-Learning Based Prediction of Retroreflectivity and Color of Road Traffic Signs
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Proper maintenance of road traffic signs is vital for safety, as their low visibility can cause accidents and fatalities. Many countries, including Sweden, lack a systematic approach for replacing signs due to the risky, costly, and complex methods needed to measure their color and retroreflectivity.This thesis introduces a predictive maintenance method for road traffic signs to ensure their visibility day and night. The proposed data-driven models predict sign degradation, helping maintain optimal visibility, decreasing accidents, and enhancing safety, and environmental sustainability by reducing material consumption and waste reduction.This thesis suggests using machine learning methods to predict the values of retroreflectivity (coefficient of retroreflection) and color (daylight chromaticity), and to estimate the status (rejected/accepted) and longevity according to color and retroreflectivity. Datasets collected in Sweden, Denmark, and Croatia were used in this research.Regression and classification models, employing Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) utilized to predict the degradation of road traffic signs. ANN showed the highest performance, 94% R2 for retroreflectivity predictions and up to 94% accuracy for color and retroreflectivity status. SVM and RF also achieved acceptable accuracies.Statistical methods, including linear and logarithmic regression, were also applied to examine the impact of age on the retroreflectivity values and status, chromaticity, and color status of road traffic signs. Findings revealed age as a significant factor, with a generally linear relationship between chromaticity values and age, except for yellow signs which displayed non-linear patterns between 8 and 22 years. Logarithmic regression models achieved R2 values of 50% and 95%, which are more accurate than those from previous studies. These models reveal an annual decrease in retroreflectivity of 4-5% and a negative correlation with the sign's direction, indicating that signs facing south and west degrade faster due to more solar exposure.Logistic regression and Kaplan-Meier survival analyses were used to assess road traffic signs' longevity. The longevity based on retroreflectivity and color durability varies depending on color, retroreflective sheeting classes, direction, and location.In Sweden, the median lifespan of road traffic signs estimated based on retroreflectivity lasts up to 25 years for red, 20 for yellow, 20 for white, and 35 for blue sheeting. In Croatia, the lifespan is shorter, 12 years for red, 16 for yellow, and 17 for white, 20 for blue.Considering color degradation, the median lifespan of yellow road traffic signs is 45 years, 35 years for white, and blue signs, while red signs have a shorter lifespan. However, the red signs deteriorate in color before retroreflectivity with a median lifespan of 16 years, whereas other signs maintain their color longer. This emphasizes the effect of factors like pigment choice and environmental conditions on the durability of road traffic signs.
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| 2. |
- Paidi, Vijay, 1986-
(författare)
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Parking support model for open parking lots
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Parking is a common process performed by vehicle drivers when they arrive at their destination. It is considered to be the last mile transportation problem of personal vehicles. Some of the common problems observed by drivers are additional cruising, congestion, pollution, and driver frustration. This thesis is focused on open parking lots that provide free parking spaces. Since parking spaces are provided free, open parking lots are in high demand leading to additional cruising and pollution. One of the primary reasons for these problems is the lack of information on parking availability. Such information can be provided using a parking support model, or a smart parking system. As open parking lots do not provide any direct return on investments, no parking support models were available on the market. Therefore, this thesis aims to develop a parking support model suitable for open parking lots which can facilitate in providing real-time and short-term forecast of parking availability. This thesis also examines the magnitude of additional cruising and CO2 emissions observed in an open parking lot. A thermal camera was utilized for collecting data on open parking lots as it is not susceptible to varying illumination and environmental conditions. Since there were no pre-trained algorithms for enabling object detection using thermal camera images, a dataset was created with varying environmental and illumination conditions. This dataset was utilized by deep learning algorithms to facilitate multi-object, real-time detection. The developed parking support model facilitates in providing a real-time and short-term forecast of parking availability. Despite the use of low volume of data, the methods utilized in this thesis facilitated providing better detection and forecasting results. Algorithms, such as ResNet18 and Yolo, facilitated in providing real-time, multi-object detection with high accuracy. Similarly, a short-term forecast of parking availability was provided for the open parking lot using methods such as the Ensemble-based method, LSTM and SARIMAX. Ensemble-based method and LSTM provided better test prediction results with lower errors compared to SARIMAX. A new CO2 emissions model was proposed to estimate the magnitude of emissions observed at an open parking lot. The mean CO2 emissions of additional cruising is 2.7 times more than optimal cruising. Despite the individual CO2 emissions of vehicles being lower, aggregating CO2 emissions from multiple vehicles leads to higher pollution. This problem can be reduced by utilizing the parking support model.
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| 3. |
- Saleh, Roxan, et al.
(författare)
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Assessing the color status and daylight chromaticity of road signs through machine learning approaches
- 2023
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Ingår i: IATSS Research. - 0386-1112. ; 47:3, s. 305-317
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Tidskriftsartikel (refereegranskat)abstract
- The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. © 2023 International Association of Traffic and Safety Sciences
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| 4. |
- Al-Hammadi, Mustafa, 1995-, et al.
(författare)
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Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis : A Systematic Literature Review
- 2024
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Ingår i: Journal of Alzheimer's Disease. - 1387-2877 .- 1875-8908. ; 100:1, s. 1-27
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.OBJECTIVE: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.METHODS: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.RESULTS: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.CONCLUSIONS: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.
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| 5. |
- Paidi, Vijay, 1986-, et al.
(författare)
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CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot
- 2022
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Ingår i: Sustainability. - : MDPI AG. - 2071-1050. ; 14:7
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Tidskriftsartikel (refereegranskat)abstract
- Parking lots are places of high air pollution as numerous vehicles cruise to find vacant parking places. Open parking lots receive high vehicle traffic, and when limited empty spaces are available it leads to problems, such as congestion, pollution, and driver frustration. Due to lack of return on investment, open parking lots are little studied, and there is a research gap in understanding the magnitude of CO2 emissions and cruising observed at open parking lots. Thus, this paper aims to estimate CO2 emissions and cruising distances observed at an open parking lot. A thermal camera was utilized to collect videos during peak and non-peak hours. The resulting videos were utilized to collect cruising trajectories of drivers searching for empty parking spaces. These trajectories were analyzed to identify optimal and non-optimal cruising, time to park, and walking distances of drivers. A new CO2 model was proposed to estimate emissions in smaller geographical regions, such as open parking lots. The majority of drivers tend to choose parking spaces near a shopping center, and they prefer to cruise non-optimal distances to find an empty parking space near the shopping center. The observed mean non-optimal cruising distance is 2.7 times higher than the mean optimal cruising distance. Excess CO2 emissions and non-optimal cruising were mainly observed during visitor peak hours when there were limited or no empty parking spaces. During visitor peak hours, several vehicles could not find an empty parking space in the region of interest, which also leads to excess cruising.
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| 6. |
- Paidi, Vijay, 1986-
(författare)
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Developing decision support systems for last mile transportation problems
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Last mile transportation is the most problematic phase of transportation needing additional research and effort. Longer waits or search times, lack of navigational directions and real-time information are some of the common problems associated with last mile transportation. Inefficient last mile transportation has an impact on the environment, fuel consumption, user satisfaction and business opportunities. Last mile problems exist in several transportation domains, such as: the landing of airplanes, docking of ships, parking of vehicles, attended home deliveries, etc. While there are dedicated inter-connected decision support systems available for ships and aircraft, similar systems are not widely utilized in parking or attended handover domains. Therefore, the scope of this thesis covers last mile transportation problems in parking and attended handover domains. One problem area for parking and attended handovers is due to lack of real-time information to the driver or consumer. The second problem area is dynamic scheduling where the handover vehicle must traverse additional distance to multiple handover locations due to lack of optimized routes. Similarly, during parking, lack of navigational directions to an empty parking space can lead to increased fuel consumption and CO2 emissions. Therefore, aim of this thesis is to design and develop decision support systems for last mile transportation problems by holistically addressing real time customer communication and dynamic scheduling problem areas. The problem areas discussed in this thesis consists of persistent issues even though they were widely discussed in the literature. In order to investigate the problem areas, microdata analysis approach was implemented in the thesis. The phases involved in Microdata analysis are: data collection, data processing, data storage, data analysis and decision-making. Other similar research domains, such as: computer science or statistics also involve phases such as data collection, processing, storage and analysis. These research domains also work in the fields of decision support systems or knowledge creation. However, knowledge creation or decision support systems is not a mandatory phase in these research domains, unlike Microdata analysis. Three papers are presented in this thesis, with two papers focusing on parking domains, while the third paper focuses on attended handover domains.The first paper identifies available smart parking tools, applications and discusses their uses and drawbacks in relation to open parking lots. The usage of cameras in identifying parking occupancy was recognized as one of the suitable tools in this paper. The second paper uses a thermal camera to collect the parking lot data, while deep learning methodologies were used to identify parking occupancy detection. Multiple deep learning networks were evaluated for identifying parking spaces and one method was considered suitable for acquiring real time parking occupancy. The acquired parking occupancy information can be communicated to the user to address real-time customer communication problems. However, the decision support system (DSS) to communicate parking occupancy information still needs to be developed. The third paper focuses on the attended handovers domain where a decision support system was reported which addresses real-time customer communication and dynamic scheduling problems holistically. Based on a survey, customers accepted the use of mobile devices for enabling a real-time information flow for improving customer satisfaction. A pilot test on vehicle routing was performed where the decision support system reduced the vehicle routing distance compared to the route taken by the driver. The three papers work in developing decision support systems for addressing major last mile transportation problems in parking and attended handover domains, thus improving customer satisfaction, and business opportunities, and reducing fuel costs, and pollution.
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| 7. |
- Paidi, Vijay, 1986-, et al.
(författare)
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Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter
- 2021
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Ingår i: Journal of Advanced Transportation. - : Hindawi Publishing Corporation. - 0197-6729 .- 2042-3195.
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Tidskriftsartikel (refereegranskat)abstract
- Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.
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| 8. |
- Saleh, Roxan, et al.
(författare)
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An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs
- 2022
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 9. |
- Saleh, Roxan
(författare)
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Analysis of Retroreflection and other Properties of Road Signs
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Road traffic signs provide regulatory, warning, guidance, and other important information to road users to prevent hazards and road accidents. Therefore, the traffic signs must be detectable, legible, and visible both in day and nighttime to fulfill their purpose. The nighttime visibility is critical to safe driving on the roads at night. The state of the art gives clear evidence that the retroreflectivity improves the nighttime visibility (detectability and legibility) of the road traffic signs and that the nighttime visibility can be improved by using an adequate level of retroreflectivity. Furthermore, nighttime visibility can be affected by human, sign, vehicle, environmental, and design factors. The retroreflectivity and colors of the road signs deteriorate over time and thus the visibility worsens, therefore, maintaining the road signs is one of the important issues to improve the safety on the roads. Thus, it is important to judge whether the retroreflectivity and colors of the road sign are within the accepted levels for visibility and the status of the signs are accepted or not and need to be replaced. This thesis aims to use machine learning algorithms to predict the status of road signs in Sweden. To achieve this aim, three classifiers were invoked: Artificial Neural Network (ANN), Support Vector Machines (SVM), and Random Forest (RF). The data which was collected in Sweden by The Road and Transport Research Institute (VTI) was used to build the prediction models. High accuracy was achieved using the three algorithms (ANN, SVM, and RF) of 0.84.3, 0.93, and 0.98, respectively. Scaling the data was found to improve the accuracy of the prediction for all three models and better accuracy is achieved when the data was scaled using standardization compared with normalization. Additionally using principal component analysis (PCA) has a different impact on the accuracy of the prediction for each algorithm.Another aim was to build prediction models to predict the retroreflectivity performance of the in-use road signs without the need to use instruments to measure the retroreflectivity or color. Experiments using linear and logarithmic regression models were conducted in this thesis to predict the retroreflectivity performance. Two datasets were used, VTI data and another data which was collected in Denmark by voluntary Nordic research cooperation (NMF group). The age of the road traffic sign, the chromaticity coordinate X for colors, and the class of retroreflectivity were found significant to the retroreflectivity in both datasets. The logarithmic regression models were able to predict the retroreflectivity with higher accuracy than linear models. Two suggested logarithmic regression models provided high accuracy for predicting the retroreflectivity (R2 of 0.50 on VTI data and 0.95 on NMF data) by using color, age, class, GPS position, and direction as predictors. Nearly the same accuracy (R2 of 0.57 on VTI data and 0.95 on NMF data) was achieved by using all parameters in the data as predictors (including chromaticity coordinates X, Y for colors). As a conclusion, omitting chromaticity coordinates X, Y for colors from the logarithmic regression models does not affect the accuracy of the prediction.
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| 10. |
- Saleh, Roxan, et al.
(författare)
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Factors Affecting Night-Time Visibility of Retroreflective Road Traffic Signs : A Review
- 2021
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Ingår i: International Journal for Traffic and Transport Engineering. - Serbia : City Net Scientific Research Center Ltd., Belgrade. - 2217-5652 .- 2217-544X. ; 11:1, s. 115-128
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Tidskriftsartikel (refereegranskat)abstract
- Road traffic signs define a visual language that can be interpreted by drivers.They represent the current traffic situation on the road, show danger and difficulties aroundthe drivers, give them warnings, and help them with their navigation by providing usefulinformation that makes driving safe and convenient. The main part of the road traffic sign isthe retroreflective material which reflects the light from the vehicle headlights to the driver.Driving during night-time is a challenge, and the rertoreflective material on the sign boardhelps the drivers to perceive and interpret the information on the road traffic sign properly.The aim of this paper is to study the factors affecting the performance of driving during nighttimeand the role the retroreflective material that plays in this regard. The vehicle headlights,ambient conditions, and the type of retroreflection material affect the light reflected from theroad traffic signs. It is also found that the retroreflectivity depends on vehicle factors such asheadlights colour and angle of illumination. Other factors such as environmental factors andsign factors can also affect the retroreflectivity.
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