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Khandhar, Aangi B. "A Review on Parking Occupancy Prediction and Pattern Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 23, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29597.
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Parking occupancy prediction and pattern analysis is a crucial component of modern urban management systems. Utilizing advanced data analysis techniques, this project aims to develop a predictive model for forecasting parking occupancy levels and analyzing patterns within parking data. By leveraging machine learning algorithms and statistical methods, the project seeks to provide insights into parking behavior and optimize resource allocation in urban areas. The implementation of parking occupancy prediction and pattern analysis contributes to efficient urban planning, improved traffic management, and enhanced user experience. Through the integration of predictive analytics, decision-makers can anticipate parking demand, optimize parking space utilization, and alleviate congestion in urban areas.This project explores the application of data- driven approaches to address challenges in parking management, including predicting peak parking times, identifying trends in parking occupancy, and optimizing parking infrastructure. By harnessing the power of data analysis, the project aims to enhance urban mobility, reduce environmental impact, and improve overall quality of life. Keywords: Parking occupancy prediction, Pattern analysis, Urban management systems, Data analysis techniques, Machine learning algorithms, Traffic management, Urban planning.
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Zhao, Ziyao, Yi Zhang, and Yi Zhang. "A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale." Journal of Advanced Transportation 2020 (February 14, 2020): 1–12. http://dx.doi.org/10.1155/2020/5624586.
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Parking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is critical to the coordination management of multiple parks (e.g., district-scale or city-scale), is relatively limited. This study aims to analyse the performance of different prediction methods with regard to parking occupancy, considering parking type and parking scale. Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. To compare the predictive performances of these models, real-world data of four parks in Shenzhen, Shanghai, and Dongguan were collected over 8 weeks to estimate the correlation between the parking lot attributes and forecast results. As per the case studies, among the four models considered, SVM offers stable and accurate prediction performance for almost all types and scales of parking lots. For commercial, mixed functional, and large-scale parking lots, FM1 with SVM made the best prediction. For office and medium-scale parking lots, FM2 with SVM made the best prediction.
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Ye, Wei, Haoxuan Kuang, Xinjun Lai, and Jun Li. "A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms." Mathematics 11, no. 21 (November 1, 2023): 4510. http://dx.doi.org/10.3390/math11214510.
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The near-future parking space availability is informative for the formulation of parking-related policy in urban areas. Plenty of studies have contributed to the spatial–temporal prediction for parking occupancy by considering the adjacency between parking lots. However, their similarities in properties remain unspecific. For example, parking lots with similar functions, though not adjacent, usually have similar patterns of occupancy changes, which can help with the prediction as well. To fill the gap, this paper proposes a multi-view and attention-based approach for spatial–temporal parking occupancy prediction, namely hybrid graph convolution network with long short-term memory and temporal pattern attention (HGLT). In addition to the local view of adjacency, we construct a similarity matrix using the Pearson correlation coefficient between parking lots as the global view. Then, we design an integrated neural network focusing on graph structure and temporal pattern to assign proper weights to the different spatial features in both views. Comprehensive evaluations on a real-world dataset show that HGLT reduces prediction error by about 30.14% on average compared to other state-of-the-art models. Moreover, it is demonstrated that the global view is effective in predicting parking occupancy.
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Jin, Bowen, Yu Zhao, and Jing Ni. "Sustainable Transport in a Smart City: Prediction of Short-Term Parking Space through Improvement of LSTM Algorithm." Applied Sciences 12, no. 21 (October 31, 2022): 11046. http://dx.doi.org/10.3390/app122111046.
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The carbon emission of fuel vehicles is a major consideration that affects the dual carbon goal in urban traffic. The problem of “difficult parking and disorderly parking” in static traffic can easily lead to traffic congestion, an increase in vehicle exhaust emissions, and air pollution. In particulate, when vehicles make an invalid detour and wait for parking with long hours, it often causes extra energy consumption and carbon emission. In this paper, adding a weather influence feature, a short-term parking occupancy rate prediction algorithm based on the long short-term model (LSTM) is proposed. The data used in this model is from Melbourne public data sets, and parking occupancy rates are predicted through historical parking data, weather information, and location information. At the same time, three commonly prediction models, i.e., simple LSTM model, multiple linear regression model (MLR), and support vector regression (SVR), are also used as comparison models. Taking MAE and RMSE as evaluation indexes, the parking occupancy rate during 10 min, 20 min, and 30 min are predicted. The experimental results show that the improved LSTM method achieves better accuracy and stability in the prediction of parking lots. The average MAE and RMSE of the proposed LSTM prediction during intervals of 10 min, 20 min, and 30 min with the weather influence feature algorithm is lower than that of simple LSTM, MLR and that of SVR.
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M. S, Vinayprasad, Shreenath K. V, and Dasangam Gnaneswar. "Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review." December 2023 5, no. 4 (December 2023): 369–84. http://dx.doi.org/10.36548/jismac.2023.4.006.
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Major cities in India have a significant number of vehicles, and the rate of ownership is increasing every day. However, the lack of proper parking infrastructure in these cities causes problems such as difficulty in finding parking spaces. According to the Urban Mobility Survey 2023 by Times Network, nearly 74% of vehicle owners in metropolitan cities struggle to find a parking slot. Various measures have been implemented to address this issue. One of the most promising measures is a smart parking management system. This system can use technologies like Radio Frequency Identification (RFID) and Automatic License Plate Recognition (ALPR) to make check-in and check-out easier. It can also include Wireless Sensor Networks (WSN), wired sensors, or visual occupancy detection to provide real-time occupancy status. The smart parking management system can offer useful services through mobile or web applications such as parking occupancy monitoring, reservation, payment gateway, occupancy prediction, automated check-in and check-out, and parking record management. The purpose of this review paper is to summarize the works undertaken in the field of smart IoT parking systems and educate the technological community about the technologies, features, and procedures for implementing the smart parking management system. In the paper, we aim to summarise the works on occupancy monitoring in smart IoT parking systems addressing the advantages and issues of the present occupancy monitoring methods, also suggesting future inclusions for smart IoT parking systems.
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Channamallu, Sai Sneha, Sharareh Kermanshachi, Jay Michael Rosenberger, and Apurva Pamidimukkala. "Parking occupancy prediction and analysis - a comprehensive study." Transportation Research Procedia 73 (2023): 297–304. http://dx.doi.org/10.1016/j.trpro.2023.11.921.
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Channamallu, Sai Sneha, Vijay Kumar Padavala, Sharareh Kermanshachi, Jay Michael Rosenberger, and Apurva Pamidimukkala. "Examining parking occupancy prediction models: a comparative analysis." Transportation Research Procedia 73 (2023): 281–88. http://dx.doi.org/10.1016/j.trpro.2023.11.919.
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Subapriya Vijayakumar and Rajaprakash Singaravelu. "Time Aware Long Short-Term Memory and Kronecker Gated Intelligent Transportation for Smart Car Parking." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (April 26, 2024): 134–50. http://dx.doi.org/10.37934/araset.44.1.134150.
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Technology desires to improve quality of life and impart citizen’s health as well as happiness. The concept of Internet of Things (IoT) refers to smart world where prevailing objects are said to be embedded and hence interact with each other (i.e., between objects and human beings) to achieve an objective. In the period of IoT as well as smart city, there is requirement for Intelligent Transport System-based (ITS) ingenious smart parking or car parking space prediction (CPSP) for more feasible cities. With the increase in population and mushroom growth in vehicles are bringing about several distinct economic as well as environmental issues. One of pivotal ones is optimal parking space identification. To address on this problem, in this work, Time-aware Long Short-Term Memory and Kronecker product Gated Recurrent Unit (TLSTM-KGRU) for smart parking in internet of transportation things is proposed. The TLSTM-KGRU method is split into two sections. In the first section, smart parking occupancy is derived using Time-aware Long Short-Term Memory (Time-aware LSTM) for Kuala Lumpur Convention Centre car parking sensor dataset. Following which the resultant smart car occupancy results are subjected to Linear Interpolations and Kronecker product Gated Recurrent Unit for predicting smart parking. When compared against other predictive methods such as SGRU-LSTM and CPSP using DELM, our experimental outcomes denote that TLSTM-KGRU method has improved performance for smart parking occupancy forecast as it not only enhances sensitivity and specificity but also reduces prediction time with minimum delay.
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Qu, Haohao, Sheng Liu, Jun Li, Yuren Zhou, and Rui Liu. "Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction." Mathematics 10, no. 12 (June 12, 2022): 2039. http://dx.doi.org/10.3390/math10122039.
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Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102× faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.
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Xiao, Xiao, Zhiling Jin, Yilong Hui, Yueshen Xu, and Wei Shao. "Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction." Remote Sensing 13, no. 16 (August 23, 2021): 3338. http://dx.doi.org/10.3390/rs13163338.
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With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.
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Inam, Saba, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari, and Nazia Nawaz. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction." Sustainability 14, no. 12 (June 15, 2022): 7317. http://dx.doi.org/10.3390/su14127317.
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Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has not received due attention. In this work, we integrated parking occupancy, pedestrian, weather, and traffic data to analyze the impact of external factors on on-street parking prediction. A comparative analysis of well-known Machine (ML) Learning and Deep Learning (DL) techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GA), Adaptive Boosting (AB), and linear SVC for the prediction of OnStreet parking space availability has been conducted. The results show that RF outperformed other techniques evaluated with an average accuracy of 81% and an AUC of 0.18. The comparative analysis shows that less complex algorithms like RF, DT, and KNN outperform complex algorithms like MLP in terms of prediction accuracy. All four data sources have positively impacted the prediction, and the proposed solution can determine the best possible parking slot based on weather conditions, traffic flow, and pedestrian volume. The experiments on live prediction showed an ingest rate of 0.1 and throughput of 0.3 events per second, demonstrating a fast and reliable prediction approach for available slots within a 5–10 min time frame. The study is scalable for larger time frames and faster predictions that can be implemented for IoT-based big data-driven environments for on-street and off-street parking.
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Ali, Ghulam, Tariq Ali, Muhammad Irfan, Umar Draz, Muhammad Sohail, Adam Glowacz, Maciej Sulowicz, Ryszard Mielnik, Zaid Bin Faheem, and Claudia Martis. "IoT Based Smart Parking System Using Deep Long Short Memory Network." Electronics 9, no. 10 (October 15, 2020): 1696. http://dx.doi.org/10.3390/electronics9101696.
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Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.
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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (August 1, 2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.
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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (August 1, 2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.
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Bouhamed, Omar, Manar Amayri, and Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning." Sensors 22, no. 9 (April 21, 2022): 3186. http://dx.doi.org/10.3390/s22093186.
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Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along with various features, were fed into several algorithms to predict future occupancy. This paper mainly proposes a weakly supervised occupancy prediction framework based on office sensor readings and occupancy estimations derived from an interactive learning approach. Two studies are the main emphasis of this paper. The first is the prediction of three occupancy states, referred to as discrete states: absence, presence of one occupant, and presence of more than one occupant. The purpose of the second study is to anticipate the future number of occupants, i.e., continuous states. Extensive simulations were run to demonstrate the merits of the proposed prediction framework’s performance and to validate the interactive learning-based approach’s ability to contribute to the achievement of effective occupancy prediction. The results reveal that LightGBM, a machine learning model, is a better fit for short-term predictions than known recursive neural networks when dealing with a limited dataset. For a 24 h window forecast, LightGBM improved accuracy from 38% to 50%, which is an excellent result for non-aggregated data (single office).
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Kytölä, Ulla, and Anssi Laaksonen. "Prediction of Restraint Moments in Precast, Prestressed Structures Made Continuous." Nordic Concrete Research 59, no. 1 (December 1, 2018): 73–93. http://dx.doi.org/10.2478/ncr-2018-0016.
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Abstract This paper studies restraint moments developing in simple-span precast, prestressed beams made continuous. Methods of evaluating restraint moments produced by creep and differential shrinkage are presented. Shrinkage and creep properties of composite structures, beam and deck parts were tested and compared to values defined according to Eurocode models. Finally, the restraint moments were calculated with both material models for the two-span parking deck structure. The study confirmed the findings of previous studies: that the methods that are used overestimate the negative restraint moment produced by differential shrinkage.
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Elomiya, Akram, Jiří Křupka, Stefan Jovčić, and Vladimir Simic. "Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models." Engineering Applications of Artificial Intelligence 129 (March 2024): 107670. http://dx.doi.org/10.1016/j.engappai.2023.107670.
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Pešić, Saša, Milenko Tošić, Ognjen Iković, Miloš Radovanović, Mirjana Ivanović, and Dragan Bošković. "BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction." International Journal on Artificial Intelligence Tools 28, no. 06 (September 2019): 1960005. http://dx.doi.org/10.1142/s0218213019600054.
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Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting solution. The presented solution comprises three main building blocks: (1) A fog computing indoor positioning system (BLEMAT — Bluetooth Low Energy Microlocation Asset Tracking) which, combined with wireless access network monitoring processes, produces indoor location information in a semi-unsupervised manner; (2) Data analysis and pattern searching pipelines responsible for fusing data coming from different smart building and networking systems and deriving information on temporal and spatial occupancy patterns; (3) Long short-term memory (LSTM) neural networks trained to predict occupancy patterns in different areas of a smart building. Data analysis and neural network training are conducted on real-world smart building dataset which authors provide in public online repository. Experimental validation confirms that the proposed solution can provide actionable occupancy detection and prediction information, required by smart building management systems.
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Yang, Shuguan, Wei Ma, Xidong Pi, and Sean Qian. "A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources." Transportation Research Part C: Emerging Technologies 107 (October 2019): 248–65. http://dx.doi.org/10.1016/j.trc.2019.08.010.
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Niu, Zhipeng, Xiaowei Hu, Mahmudur Fatmi, Shouming Qi, Siqing Wang, Haihua Yang, and Shi An. "Parking occupancy prediction under COVID-19 anti-pandemic policies: A model based on a policy-aware temporal convolutional network." Transportation Research Part A: Policy and Practice 176 (October 2023): 103832. http://dx.doi.org/10.1016/j.tra.2023.103832.
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Kasper-Eulaers, Margrit, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland, and Per Egil Kummervold. "Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5." Algorithms 14, no. 4 (March 31, 2021): 114. http://dx.doi.org/10.3390/a14040114.
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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.
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Jabbar, Saba Qasim, and Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming." Journal of Engineering 27, no. 1 (January 1, 2021): 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.
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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.
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Jabbar, Saba Qasim, and Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming." Journal of Engineering 27, no. 1 (January 1, 2021): 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.
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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.
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Sprodowski, Tobias, and Jürgen Pannek. "Analytical Aspects of Distributed MPC Based on an Occupancy Grid for Mobile Robots." Applied Sciences 10, no. 3 (February 4, 2020): 1007. http://dx.doi.org/10.3390/app10031007.
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In this paper, we evaluate theoretical aspects of a distributed system of noncooperative robots controlled by a distributed model predictive control scheme, which operates in a shared space. Here, for collision avoidance, the future predicted state trajectories are projected on a grid and exchanged via discrete cell indexes to reduce the communication burden. The predicted trajectories are obtained locally by each robot and carried out in the continuous space. Therefore, the quantisation does not impose the quality of the solution. We derive sufficient conditions to show convergence and practical stability for the distributed control system by using an idea of a temporary roundabout derived from crossing patterns of street traffic rules, which is established in a fixed and flexible circle size. Furthermore, a condition for the sufficient prediction horizon length to recognise necessary detours is presented, which is adapted for the occupancy grid. The theoretical results match with the trajectory patterns from former numerical simulations, showing that this pattern is naturally chosen as an overall solution.
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Yu, Shanshan, and Hao Wang. "Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (May 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/5508623.
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This paper analyzes and studies the structure and parameters of the VGGNet network model and selects the most commonly used and efficient VGG-16 as the prototype of the improved model. A multiscale sampling layer is added at the end of the VGG-16 convolution part so that the model can input images of any size for training and testing while reducing the number of neurons in the fully connected layer. This improves the training speed of the model under the premise of ensuring the accuracy. This paper uses multisource street spatial data combined with geographic information spatial analysis technology to measure and evaluate the spatial quality of streets in the main urban area. From the three dimensions of vitality, safety, and greenness of urban street space quality, a systematic structure for evaluation and analysis of street space quality is constructed. Street vitality includes eight index factors: entrance and exit density, street furniture density, street sketch density, street characteristic landscape density, POI density, POI diversity, commercial POI ratio, and street population density. There are five index factors: degree, roadside parking occupancy ratio, traffic signal system density, sidewalk width proportion, and street facility density. We use ArcGIS to build an index factor information database for statistical analysis and visualization. According to the natural discontinuous point classification method, the safety level of urban street public space is divided into five grades. The sample size of the first four grades has a small fluctuation range. The sample sizes are 153, 172, 153, and 158, respectively, accounting for 21%, 23%, 21%, and 21% of the total street samples, of which the first two grades occupy a total of 44%, so 44% of the streets in the main urban area have a low-quality level of street space. Level 5 has a sample of 102 streets, accounting for 14%, with an average street space quality value of 0.43.
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Zhou, Junjie, Siyue Shuai, Lingyun Wang, Kaifeng Yu, Xiangjie Kong, Zuhua Xu, and Zhijiang Shao. "Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs." Applied Sciences 12, no. 11 (May 25, 2022): 5340. http://dx.doi.org/10.3390/app12115340.
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With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.
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Coleman, Sylvia, Marianne Touchie, John Robinson, and Terri Peters. "Rethinking Performance Gaps: A Regenerative Sustainability Approach to Built Environment Performance Assessment." Sustainability 10, no. 12 (December 18, 2018): 4829. http://dx.doi.org/10.3390/su10124829.
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Globally, there are significant challenges to meeting built environment performance targets. The gaps found between the predicted performance of new or retrofit buildings and their actual performance impede an understanding of how to achieve these targets. This paper points to the importance of reliable and informative building performance assessments. We argue that if we are to make progress in achieving our climate goals, we need to reframe built environment performance with a shift to net positive goals, while recognising the equal importance of human and environmental outcomes. This paper presents a simple conceptual framework for built environment performance assessment and identifies three performance gaps: (i) Prediction Gap (e.g., modelled and measured energy, water consumption); (ii) Expectations Gap (e.g., occupant expectations in pre- and post-occupancy evaluations); and, (iii) Outcomes Gap (e.g., thermal comfort measurements and survey results). We question which of measured or experienced performance is the ‘true’ performance of the built environment. We further identify a “Prediction Paradox”, indicating that it may not be possible to achieve more accurate predictions of building performance at the early design stage. Instead, we propose that Performance Gaps be seen as creative resources, used to improve the resilience of design strategies through continuous monitoring.
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Jacoby, Margarite, Sin Yong Tan, Mohamad Katanbaf, Ali Saffari, Homagni Saha, Zerina Kapetanovic, Jasmine Garland, et al. "WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction." Journal of Sensor and Actuator Networks 10, no. 4 (December 6, 2021): 71. http://dx.doi.org/10.3390/jsan10040071.
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Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.
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Khan, Arshad Mahmood, Qingting Li, Zafeer Saqib, Nasrullah Khan, Tariq Habib, Nadia Khalid, Muhammad Majeed, and Aqil Tariq. "MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia." Forests 13, no. 5 (May 2, 2022): 715. http://dx.doi.org/10.3390/f13050715.
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Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using 37 different environmental variables. The maximum entropy (MaxEnt) algorithm in MaxEnt software was used to forecast the potential habitat suitability under current and future (i.e., 2050s and 2070s) climate change scenarios (i.e., Shared Socio-economic Pathways (SSPs): 245 and 585). A total of 238 species occurrence records were collected from Afghanistan, Pakistan and India, and employed to build the predictive distribution model. The results showed that normalized difference vegetation index, mean temperature of coldest quarter, isothermality, precipitation of driest month and volumetric fraction of the coarse soil fragments (>2 mm) were the leading predictors of species presence prediction. High accuracy values (>0.9) of predicted distribution models were recorded, and remarkable shrinkage of potentially suitable habitat of Chilgoza pine was followed by Afghanistan, India and China. The estimated extent of occurrence (EOO) of the species was about 84,938 km2, and the area of occupancy (AOO) was about 888 km2, with 54 major sub-populations. This study concluded that, as the total predicted suitable habitat under current climate scenario (138,782 km2) is reasonably higher than the existing EOO, this might represent a case of continuous range contraction. Hence, the outcomes of this research can be used to build the future conservation and management plans accordingly for this economically valuable species in the region.
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Kitali, Angela E., Priyanka Alluri, Thobias Sando, and Wensong Wu. "Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 24, 2019): 901–14. http://dx.doi.org/10.1177/0361198119849053.
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Secondary crashes (SCs) have increasingly been recognized as a major problem leading to reduced capacity and additional traffic delays. However, the limited knowledge on the nature and characteristics of SCs has largely impeded their mitigation strategies. There are two main issues with analyzing SCs. First, relevant variables are unknown, but, at the same time, most of the variables considered in the models are highly correlated. Second, only a small proportion of incidents results in SCs, making it an imbalanced classification problem. This study developed a reliable SC risk prediction model using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized logistic regression model with Synthetic Minority Oversampling TEchnique-Nominal Continuous (SMOTE-NC). The proposed model is considered to improve the predictive accuracy of the SC risk model because it accounts for the asymmetric nature of SCs, performs variable selection, and removes highly correlated variables. The study data were collected on a 35-mi I-95 section for 3 years in Jacksonville, Florida. SCs were identified based on real-time speed data. The results indicated that real-time traffic variables and primary incident characteristics significantly affect the likelihood of SCs. The most influential variables included mean of detector occupancy, coefficient of variation of equivalent hourly volume, mean of speed, primary incident type, percentage of lanes closed, incident occurrence time, shoulder blocked, number of responding agencies, incident impact duration, incident clearance duration, and roadway alignment. The study results can be used by agencies to develop SC mitigation strategies, and therefore improve the operational and safety performance of freeways.
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Tosin Michael Olatunde, Azubuike Chukwudi Okwandu, Dorcas Oluwajuwonlo Akande, and Zamathula Queen Sikhakhane. "REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION." Engineering Science & Technology Journal 5, no. 4 (April 10, 2024): 1243–56. http://dx.doi.org/10.51594/estj.v5i4.1015.
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Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction of energy demand. One of the key advantages of AI in energy efficiency optimization is its ability to adapt and learn from data, leading to continuous improvement in energy-saving strategies. AI algorithms can optimize energy consumption based on factors such as weather conditions, occupancy patterns, and energy prices, resulting in significant cost savings and environmental benefits. Furthermore, AI enables the integration of renewable energy sources into existing energy systems by predicting renewable energy generation and optimizing its use. This integration helps reduce reliance on fossil fuels and mitigates greenhouse gas emissions, contributing to a more sustainable energy future. However, the implementation of AI in energy efficiency optimization is not without challenges. These include data privacy concerns, the need for specialized skills to develop and deploy AI solutions, and the complexity of integrating AI systems into existing energy infrastructure. Addressing these challenges will be crucial for realizing the full potential of AI in energy efficiency optimization. In conclusion, AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems. By leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future.
Keywords: Role, AI, Energy, Efficiency, Optimization.
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Schank, Cody J., Michael V. Cove, Marcella J. Kelly, Clayton K. Nielsen, Georgina O’Farrill, Ninon Meyer, Christopher A. Jordan, et al. "A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir." Environmental Conservation 46, no. 03 (July 19, 2019): 184–92. http://dx.doi.org/10.1017/s0376892919000055.
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SummarySpecies distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.
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Rajeeve, Sridevi, Matt Wilkes, Nicole Zahradka, Kseniya Serebyrakova, Katerina Kappes, Hayley Jackson, Nicole Buchenholz, et al. "Early detection of CRS after CAR-T therapy using wearable monitoring devices: Preliminary results in relapsed/refractory multiple myeloma (RRMM)." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e13626-e13626. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13626.
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e13626 Background: Chimeric Antigen Receptor T-cell (CART) therapy is almost universally given inpatient due to risks of cytokine release syndrome (CRS). This burdens patients, increases bed occupancy, infection risk and costs. In an investigator initiated clinical trial (IIT), we evaluated the feasibility of using wearable devices for detecting CRS following autologous CART therapy in RRMM, in addition to standard of care (SoC). Reliable early CRS monitoring may help transition to outpatient CART. Methods: The remote wearable device (Current Health Inc.) was worn by patients (pts) as part of an IRB approved IIT from CART infusion to discharge and compared to SoC nursing vital signs. The wearable collected continuous measures of temperature, pulse, respiratory rate and O2 saturation. Clinical CRS timepoints were compared to Current Health (CH) data by 1) 2 clinician independent marking of points where CRS began in CH data plots (31 events) 2) Timestamp when a threshold of 2 standard deviations above each pt’s mean baseline temperatures was breached (12 initial events). Outcomes were time to detection of CRS v/s SoC. Wearable adherence was the duration pts wore the device over the total monitoring period. Results: To date, 16 pts were screened and 14 enrolled (87.5% uptake). 12 of 14 pts experienced CRS with concurrent CH and nursing data. Max CRS grades were 1 (10 pts), 2 (1 pt) and 3 (1 pt). Median length of monitoring was 13 (9-14) days. Wearable adherence during overall monitoring period was 71 (50-81) %, and 88 (69-89) % during high-risk period. By visual inspection method, the wearable detected temperature changes consistent with CRS at a median of 205 (range 50-479) mins earlier than SoC for all events. There was excellent inter-observer correlation (95% CI) = 0.952 (0.901-0.977). Inspection revealed associated variability in pulse, respiratory rate and O2 saturation across all CRS grades, with higher tachycardia and drop in SpO2 noted in Grade 2 and 3 CRS. By threshold temperature method, the wearable detected CRS at a median of 195 (range 16-924) mins earlier than SoC; false positive rate 16%, false negative 0%. Conclusions: Initial results suggest wearables can reliably monitor for CRS in the immediate post-CAR-T infusion period and may facilitate earlier detection. High uptake and adherence reflect pts’ acceptance of the technology. Clearly identifiable deviations in all vital signs on visual inspection, with quantifiable trajectory changes relative to baseline offer a strong foundation for a future machine learning approach to CRS alerting, leveraging network physiology. Augmenting CRS prediction by data from cytokine variations and expanding monitoring to bispecific therapy are planned. Reliable CRS detection by wearables may support the transition from inpatient to outpatient administration of cellular therapies.
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Chowdhury, Soumya, Parth Brahmaxatri, and J. Selvin Paul Peter. "Car parking occupancy prediction." International journal of health sciences, May 5, 2022, 6323–30. http://dx.doi.org/10.53730/ijhs.v6ns1.6954.
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Nowadays in modern cities, with the continuous growth of cars, parking slot availability is becoming a more and more difficult task. So, an efficient car parking occupancy detection system is becoming a necessity in order to reduce traffic congestion in parking lots. This paper proposes a system based on computer vision algorithms and basic image processing techniques that is capable of determining if a parking space is occupied or not, using aerial images captured through a camera. It aims to solve the issue of detecting a parking space that minimizes the time spent in searching parking lots which in turn reduces the carbon emissions that lead to a better quality of life. Current solutions available to this problem use hardware devices, IoT, sensors, CNN, and various deep learning algorithms. But the proposed system eliminates all the above complexities, thus resulting in greatly decreased expenses.
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Ye, Wei, Haoxuan Kuang, Jun Li, Xinjun Lai, and Haohao Qu. "A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism." IET Intelligent Transport Systems, October 10, 2023. http://dx.doi.org/10.1049/itr2.12433.
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AbstractParking occupancy prediction is an important reference for travel decisions and parking management. However, due to various related factors, such as commuting or traffic accidents, parking occupancy has complex change features that are difficult to model accurately, thus making it difficult for parking occupancy to be accurately predicted. Moreover, how to give appropriate weights to these changing features in prediction becomes a new challenge in the era of machine learning. To tackle these challenges, a parking occupancy prediction method called time series decomposition–long and short‐term memory neural network (LSTM)–temporal pattern attention mechanism, which consists of three modules, namely 1) time series decomposition: modelling parking occupancy changes by extracting features such as trend, period, and effect; 2) encoder: extracting temporal correlations of feature sequences with LSTM; 3) temporal pattern attention mechanism: assigning attention to different features, are proposed. The evaluation results of 30 parking lots in Guangzhou city show that the proposed model 1) improves accuracy over the baseline model LSTM by 9.14% on average; 2) performs outstanding in four prediction time intervals and six types of parking lots, proving its validity and generality; 3) demonstrates its rationality and interpretability through ablation experiments and Shapley additive explanation.
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SEBATLI SAĞLAM, Aslı, and Fatih ÇAVDUR. "PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS." Endüstri Mühendisliği, April 8, 2023. http://dx.doi.org/10.46465/endustrimuhendisligi.1241453.
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It may be critical for drivers to have information about the occupancy rates of the parking spaces around their destination in order to reduce the traffic density, a non-negligible part of which caused by the trips to find an available parking space. In this study, we predict parking occupancy rates (and thus, space availability) using three different techniques: (i) auto-regressive integrated moving average model, (ii) seasonal auto-regressive integrated moving average model and (iii) neural networks. In the implementation phase, we use the data set of the on-street parking spaces of the well-known “SFpark” project carried out in San Francisco. We take into account not only the past occupancy rates of parking spaces, but also exogenous variables that affect the corresponding occupancy rates as day type and time period of the day. We make predictions with different model structures of each of the considered methods for each parking space with different parking occupancy patterns in the data set and then compare the results to find the best model design for each parking space. We also, evaluate the results in terms of the superiority of the methods over each other and note that the performance of neural networks is better than those of the other approaches in terms of the mean squared errors.
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Gutmann, Sebastian, Christoph Maget, Matthias Spangler, and Klaus Bogenberger. "Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion." Frontiers in Future Transportation 2 (July 2, 2021). http://dx.doi.org/10.3389/ffutr.2021.693708.
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For haul truck drivers it is becoming increasingly difficult to find appropriate parking at the end of a shift. Proper, legal, and safe overnight parking spots are crucial for truck drivers in order for them to be able to comply with Hours of Service regulation, reduce fatigue, and improve road safety. The lack of parking spaces affects the backbone of the economy because 70% of all United States domestic freight shipments (in terms of value) are transported by trucks. Many research projects provide real-time truck parking occupancy information at a given stop. However, truck drivers ultimately need to know whether parking spots will be available at a downstream stop at their expected arrival time. We propose a machine-learning-based model that is capable of accurately predicting occupancy 30, 60, 90, and 120 min ahead. The model is based on the fusion of Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) with the help of a feed-forward neural network. Our results show that prediction of truck parking occupancy can be achieved with small errors. Root mean square error metrics are 2.1, 2.9, 3.5, and 4.1 trucks for the four different horizons, respectively. The unique feature of our proposed model is that it requires only historic occupancy data. Thus, any truck occupancy detection system could also provide forecasts by implementing our model.
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Kasera, Rohit Kumar, and Tapodhir Acharjee. "Parking slot occupancy prediction using LSTM." Innovations in Systems and Software Engineering, September 10, 2022. http://dx.doi.org/10.1007/s11334-022-00481-3.
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ANAR, Yusuf Can, Ercan AVŞAR, and Abdurrahman Özgür POLAT. "Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods." MANAS Journal of Engineering, November 17, 2021. http://dx.doi.org/10.51354/mjen.986631.
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In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spots of Adana, Turkey for predicting the parking lot occupancy rates of future values. The experiments were performed for making predictions with different prediction horizons that are 1 minute, 5 minutes, and 15 minutes. The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. The experiments were performed on data from five different days. According to the results, when the prediction horizon is set to 1 minute, LSTM achieved RMSE and MAE values of 0.98 and 0.72, respectively. For the same prediction horizon, ARIMA achieved RMSE and MAE values of 0.62 and 0.35, respectively. On the other hand, LSTM achieved smaller error values for larger prediction horizons. In conclusion, it was shown that LSTM is more suitable for larger prediction horizons, however, ARIMA is better at predicting near-future values.
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Shao, Wei, Yu Zhang, Pengfei Xiao, Kyle Kai Qin, Mohammad Saiedur Rahaman, Jeffrey Chan, Bin Guo, Andy Song, and Flora D. Salim. "Transferrable contextual feature clusters for parking occupancy prediction." Pervasive and Mobile Computing, August 2023, 101831. http://dx.doi.org/10.1016/j.pmcj.2023.101831.
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Martín Calvo, Pablo, Bas Schotten, and Elenna R. Dugundji. "Assessing the Predictive Value of Traffic Count Data in the Imputation of On-Street Parking Occupancy in Amsterdam." Transportation Research Record: Journal of the Transportation Research Board, August 30, 2021, 036119812110296. http://dx.doi.org/10.1177/03611981211029644.
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On-street parking policies have a huge impact on the social welfare of citizens. Accurate parking occupancy data across time and space is required to properly set such policies. Different imputation and forecasting models are required to obtain this data in cities that use probe vehicle measurements, such as Amsterdam. In this paper, the usage of traffic data as an explanatory variable is assessed as a potential improvement to existing parking occupancy prediction models. Traffic counts were obtained from 164 traffic cameras throughout the city. Existing models for predicting parking occupancy were reproduced in experiments with and without traffic data, and their performance was compared. Results indicated that (i) traffic data are indeed a useful predictor and improves performance of existing models; (ii) performance does not improve linearly with an increase in the number of counting points; and (iii) placement of the cameras does not have a significant impact on performance.
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Li, Jun, Haohao Qu, and Linlin You. "An Integrated Approach for the Near Real-Time Parking Occupancy Prediction." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–10. http://dx.doi.org/10.1109/tits.2022.3230199.
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Zeng, Chao, Changxi Ma, Ke Wang, and Zihao Cui. "Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3171330.
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Leobin Joseph, Ajay Krishna, Maschio Berty, Pramod P, and Velusamy A. "Advanced Parking Slot Management System Using Machine Learning." International Journal of Advanced Research in Science, Communication and Technology, April 26, 2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.
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The continuous development of economy, personal vehicles have become an indispensable part of our daily lives. The commodity has become affordable to most working class providing comfortable way of life; however on the other hand multiple problems strike back which need to be solved. One problem is of parking spaces. A variety of sophisticated car parking systems are in use nowadays; however they all require a considerable design time, installation and maintenance cost. In many parking areas the management uses the counter at the checkpoint in order to track the number of vehicle that enter and exit the parking area. More sophisticated systems detect the exact location of the empty spaces and guide the incoming drivers accordingly. Some advanced vehicles have their own parking systems installed but still hard for the system itself to confirm whether a vacant parking area truly exists or not. Despite of all these systems, there are still places where parking facilities need to be set up on temporary or urgent bases; this application provides a cost effective, space based solution for such scenarios. It just need to mount cameras on the location to take video at regular intervals. This project focuses on developing a parking management system based on video processing to detect vacant parking slot in an area where automated systems are not installed. Camera images of the parking area are subjected to image processing algorithm which marks virtual slots in the area and extracts occupancy information to guide the incoming drivers about availability and position of vacant spaces.
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Leobin Joseph, Ajay Krishna, Maschio Berty, Pramod P, and Velusamy A. "Advanced Parking Slot Management System Using Machine Learning." International Journal of Advanced Research in Science, Communication and Technology, April 26, 2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.
Full textAbstract:
The continuous development of economy, personal vehicles have become an indispensable part of our daily lives. The commodity has become affordable to most working class providing comfortable way of life; however on the other hand multiple problems strike back which need to be solved. One problem is of parking spaces. A variety of sophisticated car parking systems are in use nowadays; however they all require a considerable design time, installation and maintenance cost. In many parking areas the management uses the counter at the checkpoint in order to track the number of vehicle that enter and exit the parking area. More sophisticated systems detect the exact location of the empty spaces and guide the incoming drivers accordingly. Some advanced vehicles have their own parking systems installed but still hard for the system itself to confirm whether a vacant parking area truly exists or not. Despite of all these systems, there are still places where parking facilities need to be set up on temporary or urgent bases; this application provides a cost effective, space based solution for such scenarios. It just need to mount cameras on the location to take video at regular intervals. This project focuses on developing a parking management system based on video processing to detect vacant parking slot in an area where automated systems are not installed. Camera images of the parking area are subjected to image processing algorithm which marks virtual slots in the area and extracts occupancy information to guide the incoming drivers about availability and position of vacant spaces.
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Guerrero, Sebastian E., Shashank Pulikanti, Bridget Wieghart, Joseph G. Bryan, and Tim Strow. "Modeling Truck Parking Demand at Commercial and Industrial Establishments." Transportation Research Record: Journal of the Transportation Research Board, August 23, 2022, 036119812211035. http://dx.doi.org/10.1177/03611981221103597.
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In urban areas, there exists a mismatch between where trucks need to park and the availability of spaces, forcing many truck drivers to park in undesignated locations that are unsafe, disrupt traffic, and cause a nuisance to the community. This mismatch also decreases the productivity of the sector, as drivers spend more time searching for spaces, take longer detours, and end their workday early. Most commercial and industrial establishments currently do not allow trucks to park at their facilities beyond the loading or unloading of cargo, which generates parking demand in the surrounding areas. This paper quantifies the truck parking requirements of different commercial and industrial establishments in Phoenix, AZ, to inform land use and development decision-making. GPS data were combined with land use and employment data to identify where, when, and for how long trucks are parking. Parking rates were calculated at 2-digit and 3-digit NAICS level, for three parking metrics: stops per weekday, cumulative stop duration per weekday, and peak occupancy. Linear and negative binomial models were estimated, along with other types of models, to improve the prediction of parking demand. Results show that parking demand is the highest for establishments that store trucks overnight, such as truck terminals, and establishments with large in-house fleets. Parking demand is also high for establishments moving bulky commodities or having significant transportation needs, like in warehousing and the wholesale trade. Manufacturing establishments have a wide range of parking demand rates, based on the bulkiness of products and use of in-house fleets.
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Lyu, Mengqi, Yanjie Ji, Chenchen Kuai, and Shuichao Zhang. "Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning." Journal of Traffic and Transportation Engineering (English Edition), January 2024. http://dx.doi.org/10.1016/j.jtte.2022.05.004.
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Errousso, Hanae, El Arbi Abdellaoui Alaoui, Siham Benhadou, and Hicham Medromi. "Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values." Progress in Artificial Intelligence, September 25, 2022. http://dx.doi.org/10.1007/s13748-022-00291-5.
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Balmer, Michael, Robert Weibel, and Haosheng Huang. "Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study." Geo-spatial Information Science, July 15, 2021, 1–20. http://dx.doi.org/10.1080/10095020.2021.1937337.
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Canlı, H., and S. Toklu. "Design and Implementation of a Prediction Approach Using Big Data and Deep Learning Techniques for Parking Occupancy." Arabian Journal for Science and Engineering, September 4, 2021. http://dx.doi.org/10.1007/s13369-021-06125-1.
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