scholarly journals BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL

2020 ◽  
Vol 19 (2) ◽  
pp. 087
Author(s):  
Natalija Stojanović ◽  
Dragan Stojanović
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Smart Cities ◽  
Heavy Traffic ◽  
Traffic Monitoring ◽  
Monitoring And Control ◽  
Dynamic Configuration ◽  
Large Cities ◽  
Traffic Lights ◽  
Mobility Data

With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform.

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

2018 ◽  
Vol 8 (2) ◽  
pp. 10
Author(s):  
Mustapha Kabrane ◽  
Salah-ddine Krit ◽  
Lahoucine El Maimouni
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Road Network ◽  
Smart Cities ◽  
Optimization Techniques ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
The Road ◽  
Road Signs

In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the  system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

scholarly journals A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3623 ◽  
Author(s):  
Shivam Gupta ◽  
Albert Hamzin ◽  
Auriol Degbelo
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Smart Cities ◽  
Low Cost ◽  
Heavy Traffic ◽  
Traffic Monitoring ◽  
Traffic Data ◽  
Open Hardware ◽  
Loop Detectors ◽  
Hardware System

Road traffic and its impacts affect various aspects of wellbeing with safety, congestion and pollution being of significant concern in cities. Although there have been a large number of works done in the field of traffic data collection, there are several barriers which restrict the collection of traffic data at higher resolution in the cities. Installation and maintenance costs can act as a disincentive to use existing methods (e.g., loop detectors, video analysis) at a large scale and hence limit their deployment to only a few roads of the city. This paper presents an approach for vehicle counting using a low cost, simple and easily installable system. In the proposed system, vehicles (i.e., bicycles, cars, trucks) are counted by means of variations in the WiFi signals. Experiments with the developed hardware in two different scenarios—low traffic (i.e., 400 objects) and heavy traffic roads (i.e., 1000 objects)—demonstrate its ability to detect cars and trucks. The system can be used to provide estimates of vehicle numbers for streets not covered by official traffic monitoring techniques in future smart cities.

scholarly journals Dynamic Traffic Light Control

2020 ◽  
Vol 8 (6) ◽  
pp. 3228-3231
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Monitoring ◽  
Intelligent Transport System ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Light Control ◽  
Traffic Management System

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

scholarly journals Travel Time Prediction on Un-Monitored Roads: A Spatial Factorization Machine Based Approach (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13855-13856 ◽  
Author(s):  
Lile Li ◽  
Wei Liu
Keyword(s):  
Traffic Congestion ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Time Of Arrival ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Large Cities ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Factorization Machine

Real-time traffic monitoring is one of the most important factors for route planning and estimated time of arrival (ETA). Many major roads in large cities are installed with live traffic monitoring systems, inferring the current traffic congestion status and ETAs to other locations. However, there are also many other roads, especially small roads and paths, that are not monitored. Yet, live traffic status on such un-monitored small roads can play a non-negligible role in personalized route planning and re-routing when road incident happens. How to estimate the traffic status on such un-monitored roads is thus a valuable problem to be addressed. In this paper, we propose a model called Spatial Factorization Machines (SFM) to address this problem. A major advantage of the SFM model is that it incorporates physical distances and structures of road networks into the estimation of traffic status on un-monitored roads. Our experiments on real world traffic data demonstrate that the SFM model significantly outperforms other existing models on ETA of un-monitored roads.

Modeling and Simulation of Urban Arterial Traffic Signal Coordinated Control

2014 ◽  
Vol 543-547 ◽  
pp. 1417-1422
Author(s):  
Wei Li ◽  
Xin Bi ◽  
Yun Xia Cao ◽  
Jin Song Du
Keyword(s):  
Traffic Congestion ◽  
Control Method ◽  
Coordinated Control ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Monitoring And Control ◽  
Total Delay ◽  
Arterial Traffic ◽  
Urban Arterial

Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In order to reduce traffic delay, a novel urban arterial traffic signal coordinated control method is presented. The total delay of downstream and upstream vehicles is considered and the function describing the relationship between vehicles delay and signal offset among intersections is established. Finally, comparing the performance of traffic signal under method proposed in this paper with the traditional isolated traffic signal control method, the microscopic simulation results show that the method proposed in this paper has better performance in the aspect of reducing the vehicles delay. The offset model is tested in a simulation environment consisting of a core area of three intersections. It can be concluded that the proposed method is much more effective in relieving oversaturation in a network than the isolated intersection control strategy.

scholarly journals Automated Pollution Detection System using IoT and AWS Cloud

2019 ◽  
Vol 8 (11) ◽  
pp. 3955-3960
Keyword(s):  
Mobile Application ◽  
Web Application ◽  
Detection System ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Traffic Lights ◽  
Pollution Detection ◽  
Smart Cars ◽  
Smart Traffic ◽  
The Web

Smart cities are one of the upcoming trends in the world. These smart cities include smart traffic light system, smart cars, smart homes, smart traffic monitoring system. As environmental pollution has become the major cause of various problems like climatic changes, improper irrigation methods, depletion of the ozone layer etc. “Automated Pollution Detection System using IoT and AWS Cloud” provides an architecture for integrating IoT and Cloud Computing and an application which is used to detect air pollution by fitting in arduino devices at public places like traffic lights, industrial areas, construction areas etc., and transferring the data using GSM modem to a cloud database server AWS RDS. The cloud server is linked with the EC2 instance (Ubuntu server) in order to publish the web application using EC2. Web Application which is created using Word press and a Mobile application using Android Studio. The Web application shows the value of pollutant at a particular place along with the map facility by using GPS in the Arduino. This is also linked to a mobile application which sends a push notification service (SNS) to our mobile application

scholarly journals DEMO - A Simulation Framework for Multi-modal Commuting and Parking Optimization.

10.29007/94j5 ◽  
2019 ◽  
Author(s):  
Lara Codeca ◽  
Jérôme Härri ◽  
Jakob Erdmann
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
State Of The Art ◽  
Smart Cities ◽  
Ease Of Use ◽  
Sustainable Growth ◽  
Simulation Framework ◽  
Smart Mobility

In the last decade, many efforts to solve traffic congestion and sustainable growth issues are going in the direction of research and investments in smart cities and consequently smart mobility.We use the proposed simulation framework is compatible with SUMO 1.1.0. We use it to study multi-modal commuting and parking optimization issues in a state-of-the-art large-scale mobility scenario, and we intend to demonstrate the ease of use and its capabilities.

scholarly journals Metaheuristic Techniques in Optimizing Traffic Control Lights: A Systematic Review

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Systematic Review ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Monitoring ◽  
Optimization Techniques ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Traffic Signal Timing ◽  
Metaheuristic Techniques

Traffic congestion is a serious problem on every roadway and streets in many cities around the world. This systematic review is devoted to analyze research papers that deal with the optimization of traffic signal timing. The main objective of such optimization is maximizing the number of the vehicles leaving the network in a given period of time. This will lead to enhancing the performance of the road system. In this work, we researched the most recent metaheuristic optimized traffic light control techniques. It was shown that integrating optimization techniques in the field of traffic lights control had a great impact on the performance of traffic monitoring. During our research, we found that the most used method was the Genetic Algorithm (GA).

scholarly journals Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

2021 ◽  
Vol 11 (4) ◽  
pp. 3176
Author(s):  
Aditi Agrawal ◽  
Rajeev Paulus
Keyword(s):  
Control System ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Heavy Traffic ◽  
Green Light ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
Traffic Condition ◽  
Traffic Lights

Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.

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.

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