Comparative analysis of intelligent transportation systems using in traffic control, method BALANCE and method SCATS

2016 ◽  
Vol 31 ◽  
pp. 201-207
Author(s):  
M. Ziemska ◽  
Mateusz Srubka
Keyword(s):  
Comparative Analysis ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Control Method ◽  
Intelligent Transportation ◽  
Transportation Systems

scholarly journals Analytic architecture to overcome real time traffic control as an intelligent transportation system using big data

2018 ◽  
Vol 7 (2.18) ◽  
pp. 7 ◽  
Author(s):  
Venkata Ramana N ◽  
Seravana Kumar P. V. M ◽  
Puvvada Nagesh
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Control Logic ◽  
Systematic Analysis ◽  
Real Time Traffic

Big data is a term that describes the large volume of data – both structured and unstructuredthat includes a business on a day-to-day basis including Intelligent Transportation Systems (ITS). The emerging connected technologies created around ubiquitous digital devices have opened unique opportunities to enhance the performance of the ITS. However, magnitude and heterogeneity of the Big Data are beyond the capabilities of the existing approaches in ITS. Therefore, there is a crucial need to develop new tools and systems to keep pace with the Big Data proliferation. In this paper, we propose a comprehensive and flexible architecture based on distributed computing platform for real-time traffic control. The architecture is based on systematic analysis of the requirements of the existing traffic control systems. In it, the Big Data analytics engine informs the control logic. We have partly realized the architecture in a prototype platform that employs Kafka, a state-of-the-art Big Data tool for building data pipelines and stream processing. We demonstrate our approach on a case study of controlling the opening and closing of a freeway hard shoulder lane in microscopic traffic simulation. 

scholarly journals Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities

2017 ◽  
Vol 9 (7) ◽  
pp. 1120 ◽  
Author(s):  
Anandkumar Balasubramaniam ◽  
Anand Paul ◽  
Won-Hwa Hong ◽  
HyunCheol Seo ◽  
Jeong Kim
Keyword(s):  
Comparative Analysis ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Sustainable Environment

KNOWLEDGE BASED TRAFFIC SIGNAL CONTROL MODEL FOR SIGNALIZED INTERSECTION

Transport ◽  
2012 ◽  
Vol 27 (3) ◽  
pp. 263-267 ◽  
Author(s):  
Henrikas Pranevičius ◽  
Tadas Kraujalis
Keyword(s):  
Intelligent Transportation Systems ◽  
Control Method ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signalized Intersection ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Traffic Conditions

Intelligent transportation systems have received increasing attention in academy and industry. Being able to handle uncertainties and complexity, expert systems are applied in vast areas of real life including intelligent transportation systems. This paper presents a traffic signal control method based on expert knowledge for an isolated signalized intersection. The proposed method has the adaptive signal timing ability to adjust its signal timing in response to changing traffic conditions. Based on the traffic conditions, the system determines to extend or terminate the current green signal group. Using the information from its traffic detectors of isolated intersection, the proposed controller gives optimal signals to adapt the phase lengths to the traffic conditions. A comparative analysis between proposed control algorithm, fuzzy logic (FLC) and fixed-timed (pre-timed) controllers has been made in traffic flows control, with varying traffic volume levels, by using simulation software ‘Arena’. Simulation results show that the proposed traffic signal control method (EKC) has better performance over fuzzy logic and conventional pre-time controllers under light and heavy traffic conditions.

Research on Traffic Guidance and Control System Coordinated Agent Model

2011 ◽  
Vol 97-98 ◽  
pp. 872-876
Author(s):  
Zheng Hong Peng ◽  
Jin Jun Xu
Keyword(s):  
Artificial Intelligence ◽  
Control System ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Agent Model ◽  
Guidance And Control ◽  
Traffic Guidance ◽  
And Control

Traffic problem is one of serious problem in our society. Put forward the concept of intelligent transportation systems, is seeking to address traffic problems, and gradually become a research hotspot in traffic areas. As two subsystems, traffic control and traffic guidance systems tend to work independently, this paper aims to establish a coordination with the integration of the two systems, while use the agent technologies which emergy in artificial intelligence to establish an integrated model.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

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