scholarly journals Markov modeling of traffic flow in Smart Cities

2021 ◽  
Vol 53 ◽  
pp. 21-44
Author(s):  
Norbert Bátfai ◽  
Renátó Besenczi ◽  
Péter Jeszenszky ◽  
Máté Szabó ◽  
Márton Ispány
Keyword(s):  
Traffic Flow ◽  
Smart Cities ◽  
Markov Modeling

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

scholarly journals An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7209
Author(s):  
Janetta Culita ◽  
Simona Iuliana Caramihai ◽  
Ioan Dumitrache ◽  
Mihnea Alexandru Moisescu ◽  
Ioan Stefan Sacala
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Smart City ◽  
Smart Cities ◽  
Hybrid Approach ◽  
Well Being ◽  
Problem Definition ◽  
Urban Traffic ◽  
Integrative Approach ◽  
City Management

Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented.

scholarly journals Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Noor Afiza Mat Razali ◽  
Nuraini Shamsaimon ◽  
Khairul Khalil Ishak ◽  
Suzaimah Ramli ◽  
Mohd Fahmi Mohamad Amran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Short Term Memory ◽  
Smart Cities ◽  
Experimental Studies ◽  
Machine Learning Techniques ◽  
Traffic Flow Prediction ◽  
Flow Prediction

AbstractThe development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

scholarly journals Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Volker Lücken ◽  
Nils Voss ◽  
Julien Schreier ◽  
Thomas Baag ◽  
Michael Gehring ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Integrated Approach ◽  
Traffic Information ◽  
Limiting Factor ◽  
Personal Mobility ◽  
Real Time Traffic ◽  
Ultrasonic Sensing

Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations.

scholarly journals ST-AFN: a spatial-temporal attention based fusion network for lane-level traffic flow prediction

2021 ◽  
Vol 7 ◽  
pp. e470
Author(s):  
Guojiang Shen ◽  
Kaifeng Yu ◽  
Meiyu Zhang ◽  
Xiangjie Kong
Keyword(s):  
Traffic Flow ◽  
Smart Cities ◽  
Information Matrix ◽  
Traffic Data ◽  
Temporal Attention ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model ◽  
Precise Prediction ◽  
First Time

Traffic flow prediction is the foundation of many applications in smart cities, and the granular precision of traffic flow prediction has to be enhanced with refined applications. However, most of the existing researches cannot meet these requirements. In this paper, we propose a spatial-temporal attention based fusion network (ST-AFN), for lane-level precise prediction. This seq2seq model consists of three parts, namely speed process network, spatial encoder, and temporal decoder. In order to exploit the dynamic dependencies among lanes, attention mechanism blocks are embedded in those networks. The application of deep spatial-temporal information matrix results in progresses in term of reliability. Furthermore, a specific ground lane selection method is also proposed to ST-AFN. To evaluate the proposed model, four months of real-world traffic data are collected in Xiaoshan District, Hangzhou, China. Experimental results demonstrate that ST-AFN can achieve more accurate and stable results than the benchmark models. To the best of our knowledge, this is the first time that a deep learning method has been applied to forecast traffic flow at the lane level on urban ground roads instead of expressways or elevated roads.

Traffic Flow Prediction Using Long-Short Term Memory Technique for Connected Vehicles in Smart Cities

2021 ◽  
pp. 411-422
Author(s):  
Nuraini Shamsaimon ◽  
Noor Afiza Mat Razali ◽  
Khairani Abd Majid ◽  
Suzaimah Ramli ◽  
Mohd Fahmi Mohamad Amran ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Smart Cities ◽  
Connected Vehicles ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart Cities and the Internet of Things

2021 ◽  
pp. 218-228
Author(s):  
Kieron O’Hara
Keyword(s):  
Internet Of Things ◽  
Urban Environment ◽  
Traffic Flow ◽  
Smart Cities ◽  
Great Power ◽  
The Internet ◽  
The Status ◽  
Iot Devices ◽  
Internet Connections ◽  
The Internet Of Things

The Internet of Things is created by giving Internet connections to objects embedded in the environment, including wearable items. When IoT devices are connected and coordinated in an urban environment, smart cities are created, which can allow control of the environment, for example to improve carbon emissions or traffic flow. Instrumentation of the environment creates problems of consent, privacy, security, safety, and trust. The status of the IoT with respect to Internet ideology is discussed. The Silicon Valley Open Internet supports citizen-centric development, but may lack coordination at scale. The DC Commercial Internet creates great power for platforms. The Brussels Bourgeois Internet values rights and privacy, which may suppress innovation. In China, India, and elsewhere, smart cities are seen as key to developing a paternal social vision under digital modernity. Given its key role in the IoT, this is where America’s battle against Huawei may be most consequential.

scholarly journals The Simulation and Prototyping of a Density-Based Smart Traffic Control System for Learning Purposes

2020 ◽  
Vol 16 (12) ◽  
pp. 70
Author(s):  
Mashael Khayyat ◽  
Omar Aboulola ◽  
Nahla Aljojo ◽  
Basma Alharbi ◽  
Nada Almalki ◽  
...  
Keyword(s):  
Control System ◽  
Traffic Flow ◽  
Waiting Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Technological Progress ◽  
Smart Cities ◽  
Traffic Control System ◽  
Smart Traffic

<span> With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient.</span>

scholarly journals Radio-Based Traffic Flow Detection and Vehicle Classification for Future Smart Cities

2017 ◽  
Author(s):  
Marcus Haferkamp ◽  
Manar Al-Askary ◽  
Dennis Dorn ◽  
Benjamin Sliwa ◽  
Lars Habel ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Smart Cities ◽  
Vehicle Classification ◽  
Flow Detection
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