Sistem Pemetaan Parkir Menggunakan Teknik Image Processing

The large number of people who use private vehicles has an impact on increasing people's need for parking areas, including in residential apartments. Limitations to getting information on the availability of parking slots result in difficulties for drivers finding available parking locations.  This study aims to develop an effective parking slot mapping system for apartment managers and users using the configuration of image processing and MySQL servers. System design works to process vehicle plates recognized on camera using segmentation and analysis image processing. Data processing uses a visual studio tool as an interface that is connected to MySQL as a database server and activates the visualization of slots and parking locations through the Blynk apps. The results show that the system can read detected vehicle plates and send notifications to Blynk apps to inform the slot status and available parking locations. System testing was carried out for any variations of distance and camera angle positions to determine the standard parameters for a good operating systemBanyaknya masyarakat yang menggunakan kendaraan pribadi berdampak pada kebutuhan masyarakat terhadap area parkir semakin meningkat, termasuk di kawasan hunian apartemen. Keterbatasan untuk memperoleh informasi ketersediaan slot parkir mengakibatkan pengemudi kesulitan menemukan lokasi parkir yang tersedia. Penelitian ini bertujuan mengembangkan sistem pemetaan slot parkir yang efektif bagi pengelola dan user apartemen dengan menggunakan konfigurasi segmentasi analisis image processing dan MySQL server. Desain sistem bekerja untuk memproses plat kendaraan yang terdeteksi oleh kamera menggunakan segmentasi analisis pengolahan citra. Pemrosesan data menggunakan perangkat visual studio sebagai interface yang terhubung dengan MySQL sebagai database server dan mengaktifkan visualisasi slot dan lokasi parkir melalui aplikasi Blynk. Hasil penelitian menunjukan sistem dapat membaca plat kendaraan yang terdeteksi dan mengirimkan notifikasi ke Blynk untuk menginformasikan status slot dan lokasi parkir yang tersedia. Pengujian sistem juga dilakukan untuk setiap variasi jarak dan posisi sudut kamera untuk menentukan standar parameter sistem operasi yang baik

Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques

Public road authorities and private mobility service providers need information derived from the current and predicted traffic states to act upon the daily urban system and its spatial and temporal dynamics. In this research, a real-time parking area state (occupancy, in- and outflux) prediction model (up to 60 minutes ahead) has been developed using publicly available historic and real time data sources. Based on a case study in a real-life scenario in the city of Arnhem, a Neural Network-based approach outperforms a Random Forest-based one on all assessed performance measures, although the differences are small. Both are outperforming a naive seasonal random walk model. Although the performance degrades with increasing prediction horizon, the model shows a performance gain of over 150% at a prediction horizon of 60 minutes compared with the naive model. Furthermore, it is shown that predicting the in- and outflux is a far more difficult task (i.e. performance gains of 30%) which needs more training data, not based exclusively on occupancy rate. However, the performance of predicting in- and outflux is less sensitive to the prediction horizon. In addition, it is shown that real-time information of current occupancy rate is the independent variable with the highest contribution to the performance, although time, traffic flow and weather variables also deliver a significant contribution. During real-time deployment, the model performs three times better than the naive model on average. As a result, it can provide valuable information for proactive traffic management as well as mobility service providers.Comment: Proc. of Transportation Research Board 2020 Annual Meeting, Washington D.C., USA, January 202

BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level

The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system, based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is the cost reduction of the waste collection service by means of the minimization in distance traveled by any truck to collect a container, hence the fuel consumption. At the same time the quality of service to the citizen is increased avoiding the annoying overflows of containers thanks to the accurate fill level predictions performed by BIN-CT. In this article we show the features of our software system, illustrating it operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary visits to containers, reduces the distance traveled to collect a container and therefore we obtain a reduction of total costs and harmful emissions thrown to the atmosphere.Comment: 11 pages, double column, 4 figures, 3 table

Bayesian Neural Architecture Search using A Training-Free Performance Metric

Recurrent neural networks (RNNs) are a powerful approach for time series prediction. However, their performance is strongly affected by their architecture and hyperparameter settings. The architecture optimization of RNNs is a time-consuming task, where the search space is typically a mixture of real, integer and categorical values. To allow for shrinking and expanding the size of the network, the representation of architectures often has a variable length. In this paper, we propose to tackle the architecture optimization problem with a variant of the Bayesian Optimization (BO) algorithm. To reduce the evaluation time of candidate architectures the Mean Absolute Error Random Sampling (MRS), a training-free method to estimate the network performance, is adopted as the objective function for BO. Also, we propose three fixed-length encoding schemes to cope with the variable-length architecture representation. The result is a new perspective on accurate and efficient design of RNNs, that we validate on three problems. Our findings show that 1) the BO algorithm can explore different network architectures using the proposed encoding schemes and successfully designs well-performing architectures, and 2) the optimization time is significantly reduced by using MRS, without compromising the performance as compared to the architectures obtained from the actual training procedure

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope