scholarly journals Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections

2021 ◽  
Vol 11 (15) ◽  
pp. 6816
Author(s):  
Fatih Gunes ◽  
Selim Bayrakli ◽  
Abdul Halim Zaim
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Signalized Intersections ◽  
Effective Control ◽  
Main Role ◽  
Smart Transportation ◽  
Traffic Lights ◽  
Service Rates

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing.

Fruitful Synergy Model of Artificial Intelligence and Internet of Thing for Smart Transportation System

2020 ◽  
Vol 4 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Vikram Puri ◽  
Chung Van Le ◽  
Raghvendra Kumar ◽  
Sandeep Singh Jagdev
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Urban Transportation ◽  
Fall Detection ◽  
Transportation System ◽  
Transportation Systems ◽  
Support Vector ◽  
Smart Transportation ◽  
Artificial Neural Network Ann

In urban transportation systems, bicycle sharing systems are majorly deployed in major cities of both developed and developing countries. The recent boom of bicycle sharing system along with its upgraded technology have opened new opportunities towards urban transportation system. With the enlargement of intelligent transportation systems (ITS's), smart bicycle sharing schemes are more popular to smart cities as a green transportation mode. In this article, the Internet of Things (IoT) and artificial intelligence-based monitoring devices have been proposed for the bicycles. This system contains a harmful exhaust gas sensor, wireless module, and a GPS receiver and camera that are capable to send data with time and date stamping. In addition, sensor also integrated on the bicycle for the fall detection. An artificial neural network (ANN) and support vector machine (SVM) applied to the data collected at central server is designed to analyze the root mean square error (RMSE), and coefficient of correlation (R2). Result shows that ANN performance is better when compared to SVM.

scholarly journals MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 17 ◽  
Author(s):  
Suriya Priya R. Asaithambi ◽  
Ramanathan Venkatraman ◽  
Sitalakshmi Venkatraman
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Consumer Satisfaction ◽  
Smart Cities ◽  
Transportation Systems ◽  
Transport Systems ◽  
Hybrid Architecture ◽  
Smart Transportation ◽  
Transport Services ◽  
Data Architecture

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

scholarly journals Regulation Models of Crossroad Based on Wireless Sensor Networks and Notions of the Fluid Mechanics

2020 ◽  
Vol 9 (3) ◽  
pp. 2536-2541
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Information Technologies ◽  
Smart Cities ◽  
Urban Traffic ◽  
Wireless Sensor ◽  
Effective Control ◽  
Control Mechanisms ◽  
Traffic Light ◽  
Traffic Lights

The permanent growth of the population in smart cities has increased the number of vehicles. Consequently the problem of traffic congestion has become one of the main problems to be solved by today's traffic control systems, especially at traffic intersections. In fact, the traditional method which avoids the congestion in a crossroads is the classic command (Timing) by means of traffic lights. However, the traffic light management modes are sometimes based on classic models which make them unsuitable for the treatment of different experienced situations in traffic (either dense or fluid traffic). Fortunately, thanks to the significant progress made, especially the use of New Information Technologies and Communications for example Wireless Sensor Network, for the regulation of traffic, are solutions become central in the field of urban traffic management. They have made it possible to propose more effective control mechanisms to reduce the effects of traffic congestion. In this article, we will present the continuation of our work [1], the objective is to offer to the users of the road a crossing time as long as possible, while preventing the car cap to propagate over a distance that is set between two wireless sensors, to do this, we can act on the setting of the traffic light to regulate traffic in intersections.

scholarly journals Towards Personal Virtual Traffic Lights

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 32 ◽  
Author(s):  
Vanessa Martins ◽  
João Rufino ◽  
Luis Silva ◽  
João Almeida ◽  
Bruno Miguel Fernandes Silva ◽  
...  
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Communication Systems ◽  
Field Tests ◽  
Transportation Systems ◽  
Smart Devices ◽  
Vehicular Communication ◽  
Traffic Light ◽  
Correct Operation ◽  
Traffic Lights

Traffic control management at intersections, a challenging and complex field of study, aims to strike a balance between safety and efficient traffic control. Nowadays, traffic control at intersections is typically done by traffic light systems which are not optimal and exhibit several drawbacks, such as poor efficiency and real-time adaptability. With the advent of Intelligent Transportation Systems (ITS), vehicles are being equipped with state-of-the-art technology, enabling cooperative decision-making which will certainly overwhelm the available traffic control systems. This solution strongly penalizes users without such capabilities, namely pedestrians, cyclists, and other legacy vehicles. Therefore, in this work, a prototype based on an alternative technology to the standard vehicular communications, Bluetooth Low Energy (BLE), is presented. The proposed framework aims to integrate legacy and modern vehicular communication systems into a cohesive management system. In this framework, the movements of users at intersections are managed by a centralized controller which, through the use of networked retransmitters deployed at intersections, broadcasts alerts and virtual light signalization orders. Users receive the aforementioned information on their own smart devices, discarding the need for dedicated light signalization infrastructures. Field tests, carried out with a real-world implementation, validate the correct operation of the proposed framework.

scholarly journals Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

2021 ◽  
Vol 13 (22) ◽  
pp. 12891
Author(s):  
Olasupo O. Ajayi ◽  
Antoine B. Bagula ◽  
Hloniphani C. Maluleke ◽  
Isaac A. Odun-Ayo
Keyword(s):  
Systematic Review ◽  
Information And Communication Technologies ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Western World ◽  
Smart Transportation ◽  
African Cities ◽  
The Impact

Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

Energy-Saving Methods for Urban Travel and Public Transportation in Smart Cities

2022 ◽  
Author(s):  
Shan Li ◽  
Ying Gao ◽  
Tao Ba ◽  
Wei Zhao
Keyword(s):  
Energy Saving ◽  
Public Transportation ◽  
Traffic Control ◽  
Smart Cities ◽  
Learning Technologies ◽  
General Strategy ◽  
Smart Transportation ◽  
Urban Travel ◽  
Emissions Mitigation ◽  
The Impact

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Comparing Machine Learning Models for the Predictions of Speed in Smart Transportation Systems

2022 ◽  
pp. 34-46
Author(s):  
Amtul Waheed ◽  
Jana Shafi ◽  
Saritha V.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Learning Algorithms ◽  
High Capacity ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Smart Transportation ◽  
Data Propagation ◽  
Machine Learning Models

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.

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