Internet of Things based adaptive traffic management system as a part of Intelligent Transportation System (ITS)

2017 ◽  
Author(s):  
Ankit Dubey ◽  
Mayuri Lakhani ◽  
Shivansh Dave ◽  
Jignesh J. Patoliya
Keyword(s):  
Internet Of Things ◽  
Traffic Management ◽  
Management System ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Traffic Management System

scholarly journals Сontrol of Intelligent Transport System in Minsk

2018 ◽  
Vol 17 (5) ◽  
pp. 401-412
Author(s):  
D. V. Kapskiy ◽  
D. V. Navoy ◽  
P. A. Pegin
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Management System ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Design System ◽  
Intelligent Transport System ◽  
Traffic Management System

The paper considers algorithms for searching a maximum traffic volume of road vehicles in a traffic light cycle with a distributed intensity pulse and optimization of shifts under coordinated traffic flow control. Modeling of traffic flows have been made while using a computer program developed by the authors and it has made it possible to improve efficiency of traffic management by taking into account the distributed pulse of transport intensity. The paper proposes a model for minimizing total losses in road traffic during the integration of an incident control sub-system and route guidance for and an automatic road traffic management system as part of Minsk intelligent transportation system which has been studied as a tool for modeling a computer-aided design system "Backbone management". The model that minimizes vehicle delays, uses an algorithm implementing traffic flow intensity parameters depending on the time of day, days of the week. As a result of the simulation it has been revealed that the most effective parameter is an indicator of vehicle delays which does not always satisfy drivers trying to choose routes of their traffic which are based on a minimum transportation speed. However, from the point of view of managing an intelligent transportation system, it is necessary to choose parameters based on the requirements for minimizing delays on the road traffic network of the largest city in our country. All the proposed algorithms and models are used in the automatic traffic management system of Minsk city and will be used while creating an integrated intellectual transportation system of the city.

scholarly journals IOT Based Traffic Management System

2021 ◽  
pp. 184-187
Author(s):  
Shashank S ◽  
Kiran P ◽  
Nischay D ◽  
Vinay Kumar M ◽  
B R Vatsala ◽  
...  
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Transportation System ◽  
Raspberry Pi ◽  
Urban Residents ◽  
System Of Cities ◽  
Traffic Management System ◽  
Global Population

In 2014, 54% of the total global population was urban residents. The prediction was a growth of nearly 2% each year until 2020 leading to more pressure on the transportation system of cities. Cities should be making their streets run smarter instead of just making them bigger or building more roads. This leads to the proposed system which will use a Raspberry pi and Camera for tracking the number of vehicles leading to time-based monitoring of the system.

Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

2000 ◽  
Vol 1719 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Byron J. Gajewski ◽  
Shawn M. Turner ◽  
William L. Eisele ◽  
Clifford H. Spiegelman
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation System ◽  
Monitoring Data ◽  
Statistical Techniques ◽  
Parameter Distribution ◽  
Data Archiving ◽  
Wide Range

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.

Implementation Vision for Distributed Control of Traffic Signal Subsystems

1996 ◽  
Vol 1554 (1) ◽  
pp. 43-47
Author(s):  
Darcy Bullock
Keyword(s):  
Distributed Control ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Adaptive Algorithms ◽  
Intelligent Transportation ◽  
Traffic Signal ◽  
Emergency Vehicle ◽  
Systematic Method ◽  
Traffic Management System ◽  
Advanced Traffic Management System

The developments that have led to the construction of the 2070 controller are reviewed. The intelligent transportation system community has proposed many features and user services that will likely use this new controller. In general, many of the functions proposed for this controller, such as emergency vehicle preemption, transit priority, weather monitoring, dynamic lane assignment, enhanced malfunction diagnostics, and adaptive algorithms, are all technically feasible. To achieve widespread deployment of systems that integrate several advanced traffic management system features, however, a systematic method for integrating a variety of distributed computing subsystems must be thoughtfully defined. The fundamental benefits of adopting a distributed control model for traffic signal subsystems are described and summarized.

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