Smart Traffic Control: Identifying Driving-Violations using Fog Devices with Vehicular Cameras in Smart Cities

2021 ◽  
pp. 102986
Author(s):  
M. Mazhar Rathore ◽  
Anand Paul ◽  
Seungmin Rho ◽  
Murad Khan ◽  
S. Vimal ◽  
...  
Keyword(s):  
Traffic Control ◽  
Smart Cities ◽  
Smart Traffic

scholarly journals AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 783-802
Author(s):  
Cristofer Englund ◽  
Eren Erdal Aksoy ◽  
Fernando Alonso-Fernandez ◽  
Martin Daniel Cooney ◽  
Sepideh Pashami ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Traffic Control ◽  
Smart Cities ◽  
New Paradigm ◽  
Driver Assistance Systems ◽  
Road Vehicle ◽  
Holistic View ◽  
Data Annotation ◽  
Vehicle Automation ◽  
Smart Traffic

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

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>

Internet of Thing Based Smart Traffic Control Signal Using Solar Energy

2020 ◽  
Vol 17 (12) ◽  
pp. 5334-5338
Author(s):  
V. Kalpana ◽  
S. Shanthi ◽  
A. Sagai Francis Britto ◽  
N. B. Prakash
Keyword(s):  
Solar Energy ◽  
Information And Communication Technologies ◽  
Traffic Control ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Control Signal ◽  
Raspberry Pi ◽  
Emergency Vehicle ◽  
Smart Traffic ◽  
Internet Of Thing

Nowadays traffic congestion is major problem in all over the cities. The cities are renovated to “smart cities” by using Information and Communication Technologies (ICT). The IoT are playing a vital role in smart cities. This work proposes Internet of Thing (IoT) based smart traffic control signal using solar energy for smart cities. This signal is always coordinated with the emergency vehicle like ambulance to discover the signal and select the route where road traffic is dynamically controlled and the traffic violation vehicles are identified by traffic monitoring officers through internet. The traffic control signal lights are automatically controlled by Raspberry Pi controller with the help of IR sensor and RF signal. If any emergency vehicle will come nearby traffic control signal then the green signal shows for emergency vehicle and the remaining paths are shows as red signal.

scholarly journals Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3658
Author(s):  
Qingfeng Zhu ◽  
Sai Ji ◽  
Jian Shen ◽  
Yongjun Ren
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Location Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Security Analysis ◽  
Geographical Location ◽  
Experimental Simulation ◽  
Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

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%.

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