scholarly journals Smart Transportation System using IOT

2021 ◽  
Vol 10 (5) ◽  
pp. 434-438
Author(s):  
Deeplaxmi V. Niture ◽  
Vivekanand Dhakane ◽  
Piyush Jawalkar ◽  
Ankit Bamnote
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
Low Cost ◽  
Transportation System ◽  
Pollution Level ◽  
Smart Transportation ◽  
Data Centre ◽  
Toll Collection ◽  
Smart Vehicle ◽  
Traffic Rule

In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.

scholarly journals Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

2021 ◽  
Vol 3 (3) ◽  
pp. 164-179
Author(s):  
Haoxiang Wang
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Transportation System ◽  
Traffic Condition ◽  
Smart Transportation ◽  
Centralized Management ◽  
The City

In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.

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.

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 Traffic Big Data in the Development of Intelligent Transportation System

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Qiang Shi ◽  
Lei Wang ◽  
Taojie Wang
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Paper Briefly ◽  
System A ◽  
Basic Characteristics

With the continuous development and advancement of computer technology, big data guarantees the establishment of an urban intelligent transportation system, a solid environmental basis to reform its application, and the construction of a deeply integrated data mechanism for big data-driven traffic management. This review paper briefly elaborates on the basic characteristics and sources of traffic big data as well as expound on the problems and application mechanisms of big data in intelligent transportation systems.

scholarly journals Intelligent Transportation System

2020 ◽  
Vol 9 (2) ◽  
pp. 590-593
Keyword(s):  
Gas Sensor ◽  
Intelligent Transportation System ◽  
Low Cost ◽  
Pressure Sensors ◽  
Web Server ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Raspberry Pi ◽  
Alcohol Sensor ◽  
Vehicle Security

This paper proposes a low cost, portable and flexible vehicle security system. It bestows the use of an embedded micro – web server in Raspberry pi -3B microcontroller, with IP connectivity for remotely controlling the devices from another location. The proposed system does not require a dedicated server PC with respect to similar systems and offers an offbeat channel to record and implicate the vehicle environment with more than just the switching functionality. The system for it’s the feasibility and effectiveness will be integrated with external devices such as alcohol sensor, gas sensor, ultrasonic sensor and pressure sensors. All of the above features will predict the system to form an intelligent transportation system for a smarter and more secure way of travelling.

Intellıgent Transportatıon System Applıcatıons: A Traffıc Management Perspectıve

2021 ◽  
pp. 419-433
Author(s):  
Varsha Bhatia ◽  
Vivek Jaglan ◽  
Sunita Kumawat ◽  
Vikas Siwach ◽  
Harkesh Sehrawat
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Management Perspective

scholarly journals Dynamic Toll Charge using openCV and Spark

2019 ◽  
pp. 33-37
Author(s):  
Merlin Mathew ◽  
Aishwarya Balakrishnan ◽  
Bikky Kumar Goit ◽  
Mounica. B
Keyword(s):  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Metropolitan Cities ◽  
Toll Collection

There are many implementations of intelligent transportation system which is mandatorily required to curb the rising traffic in metropolitan cities. One such implementation is dynamic toll generation which reduces the time taken to pay toll at the toll gates compared to the relatively old method of manual toll collection, although it is still being implemented. One crucial factor to curb traffic and reduce pollution in the cities would be to charge toll according to the seat occupancy in the four wheeler i.e. commuters who use the vehicle appropriately will be charged less and others who use the vehicle luxuriously will be charged more. The system thus implemented produces the toll based on the seat occupancy and the data stored in the databases when the RFID is flashed is used for further analysis.

scholarly journals Towards AI-Based Traffic Counting System with Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Duc-Liem Dinh ◽  
Hong-Nam Nguyen ◽  
Huy-Tan Thai ◽  
Kim-Hung Le
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Control Strategies ◽  
Low Cost ◽  
Counting System ◽  
Traffic Conditions ◽  
Flow Information ◽  
Edge Based

The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management.

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