scholarly journals Corridor-Wise Eco-Friendly Cooperative Ramp Management System for Connected and Automated Vehicles

2021 ◽  
Vol 13 (15) ◽  
pp. 8557
Author(s):  
Zhouqiao Zhao ◽  
Guoyuan Wu ◽  
Matthew Barth
Keyword(s):  
Energy Consumption ◽  
Environmental Sustainability ◽  
Intelligent Transportation Systems ◽  
Management System ◽  
Combustion Engine ◽  
Transportation Systems ◽  
Ramp Metering ◽  
Automated Vehicles ◽  
Traffic Condition ◽  
Real Time Traffic

Safety, mobility, and environmental sustainability are three fundamental issues that our transportation system has been confronting for decades. Intelligent transportation systems (ITS) aim to address these problems by leveraging disruptive technologies, such as connected and automated vehicles (CAVs). The cooperative potential of CAVs enable more efficient maneuvers and operation of a group of vehicles, or even the entire traffic system. In addition, CAVs may couple with other emerging technologies such as electrification to boost overall system performance and to further mitigate the aforementioned issues. In this study, we propose a hierarchical eco-friendly cooperative ramp management system, where macroscopically, a stratified ramp metering algorithm, is deployed to coordinate all of the ramp inflow rates along a corridor according to the real-time traffic condition; microscopically, a model predictive control (MPC)-based algorithm is designed for the detailed speed control of individual CAVs. Using the shared information from CAVs, the proposed ramp management system can smooth traffic flow, improve system mobility, and decrease the energy consumption of the network. Moreover, traffic simulation has been conducted using PTV VISSIM under various congestion levels for vehicles with different powertrain types, i.e., an internal combustion engine and an electric motor. Compared to conventional ramp metering, the proposed ramp management system may improve mobility by 48.6–56.7% and save energy by 24.0–35.1%. Compared to no control scenarios, savings in travel time and energy consumption are in the ranges of 79.4–89.1% and 0.8–2.5%, respectively.

scholarly journals The Way Forward for Indirect Structural Health Monitoring (iSHM) Using Connected and Automated Vehicles in Europe

2021 ◽  
Vol 6 (3) ◽  
pp. 43
Author(s):  
Konstantinos Gkoumas ◽  
Kyriaki Gkoktsi ◽  
Flavio Bono ◽  
Maria Cristina Galassi ◽  
Daniel Tirelli
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transport Infrastructure ◽  
Policy Framework ◽  
Strategic Decisions ◽  
Automated Vehicles ◽  
Civil Infrastructures ◽  
Vehicle Automation ◽  
Labor Requirements

Europe’s aging transportation infrastructure requires optimized maintenance programs. However, data and monitoring systems may not be readily available to support strategic decisions or they may require costly installations in terms of time and labor requirements. In recent years, the possibility of monitoring bridges by indirectly sensing relevant parameters from traveling vehicles has emerged—an approach that would allow for the elimination of the costly installation of sensors and monitoring campaigns. The advantages of cooperative, connected, and automated mobility (CCAM), which is expected to become a reality in Europe towards the end of this decade, should therefore be considered for the future development of iSHM strategies. A critical review of methods and strategies for CCAM, including Intelligent Transportation Systems, is a prerequisite for moving towards the goal of identifying the synergies between CCAM and civil infrastructures, in line with future developments in vehicle automation. This study presents the policy framework of CCAM in Europe and discusses the policy enablers and bottlenecks of using CCAM in the drive-by monitoring of transport infrastructure. It also highlights the current direction of research within the iSHM paradigm towards the identification of technologies and methods that could benefit from the use of connected and automated vehicles (CAVs).

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

scholarly journals IoT based Real Time Traffic Monitoring System Using M/G/1 Queuing Research

2019 ◽  
Vol 8 (6) ◽  
pp. 3818-3822
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Queuing Theory ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Theory Approach ◽  
The Road ◽  
Traffic Conditions ◽  
Performance Framework ◽  
Real Time Traffic

Vehicular Traffic crowding is paramount worry in urban cities. The use of technologies like Intelligent Transportation systems and Internet of Things can solve the problem of traffic congestion to some extent. The paper analyses the traffic conditions on a particular urban highway using queuing theory approach. It researches on performance framework such as time for waiting and queue length. The results can provide significant analysis to predict traffic congestion during peak hours. A congestion controlling action can be generated to utilize the road capacity fully during peak hours by using these results

Sustainable Transport Infrastructure

2010 ◽  
pp. 44-61 ◽  
Author(s):  
Fatih Dur ◽  
Tan Yigitcanlar ◽  
Jonathan Bunker
Keyword(s):  
Urban Development ◽  
Environmental Sustainability ◽  
Intelligent Transportation Systems ◽  
Urban Transportation ◽  
Transportation Systems ◽  
Transport Infrastructure ◽  
Sustainable Urban Development ◽  
Infrastructure Development ◽  
Technological Means ◽  
Sustainability Problems

Many economic, social and environmental sustainability problems associated with typical urban transportation systems have revealed the importance of three domains of action: vehicle, infrastructure and user. These domains need to be carefully reconsidered in search of a sustainable urban development path. Although intelligent transportation systems have contributed substantially to enhancing efficiency, safety and comfort of travel, questions related to users’ behaviors and preferences, which stimulate considerable environmental effects, still needed to be further examined. In this chapter, options for smart urban transportation infrastructure development and the technological means for achieving broader goals of sustainable communities and urban development are explored.

Detection and Tracking of Vehicles Based on Colour Probability Density

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaohua Shu ◽  
Yonghong Long ◽  
Xiyu Xiao ◽  
Pei Shu
Keyword(s):  
Probability Density ◽  
Intelligent Transportation Systems ◽  
Mean Shift ◽  
Intelligent Transportation ◽  
Parameter Extraction ◽  
Transportation Systems ◽  
Vehicle Tracking ◽  
Target Area ◽  
Traffic Condition ◽  
Tracking Method

Vehicle monitoring is a very important part in the intelligent transportation systems towards real-time monitoring of intersection traffic condition, the dynamic traffic incident detection and traffic parameter extraction. This paper proposes a vehicle tracking method based on mean shift. During the detection period, tracking objects of vehicles are constructed. The current vehicle position is predicted from the target area of former frame. In the candidate area of the target image, foreground area mask is adopted as a condition whether a pixel is selected; this makes the colour probability density to more accurately reflect the characteristics of the vehicle, and avoids the background region's influence on the mean shift iterations. Experiments show that this method can effectively detect the position of the vehicle, and provides an effective vehicle tracking method in the intelligent transportation system.

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 The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ding-Yuan Cheng ◽  
Chi-Hua Chen ◽  
Chia-Hung Hsiang ◽  
Chi-Chun Lo ◽  
Hui-Fei Lin ◽  
...  
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Sampling Period ◽  
Transportation Systems ◽  
Analytical Models ◽  
Traffic Information ◽  
Optimal Sampling ◽  
Vehicle Speed ◽  
Real Time Traffic ◽  
Positioning Algorithm

Using cellular floating vehicle data is a crucial technique for measuring and forecasting real-time traffic information based on anonymously sampling mobile phone positions for intelligent transportation systems (ITSs). However, a high sampling frequency generates a substantial load for ITS servers, and traffic information cannot be provided instantly when the sampling period is long. In this paper, two analytical models are proposed to analyze the optimal sampling period based on communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations from the same call and estimate vehicle speed. The experimental results show that the optimal sampling period is 41.589 seconds when the average call holding time was 60 s, and the average speed error rate was only 2.87%. ITSs can provide accurate and real-time speed information under lighter loads and within the optimal sampling period. Therefore, the optimal sampling period of a fingerprint positioning algorithm is suitable for estimating speed information immediately for ITSs.

Towards Knowledge-Oriented Smart Vehicle Adaptive Traffic Service

2013 ◽  
Vol 365-366 ◽  
pp. 459-462
Author(s):  
Jeong Sig Kim ◽  
Jin Hong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Condition ◽  
Traffic Pattern ◽  
Traffic System ◽  
Vehicle Navigation System ◽  
Long Time ◽  
Smart Vehicle ◽  
Vehicle Network

The most of very large traffic system by growing the variety of services, the relationships between the vehicle network and the infrastructure are more complex. Moreover, intelligent transportation systems are getting more and more to develop a better combination of travel safety and efficiency since long time ago. Vehicle is being evolved and traffic environment is especially also organized well-defined schedules priorities, which is real time based wireless network traffic condition, variable traffic condition, and traffic pattern from the vehicle navigation system. Accordingly, we propose to Knowledge-oriented Smart Vehicle Adaptive Traffic Service using genetic algorithm in this paper.

Intelligent transportation system for sustainable environment in smart cities

2021 ◽  
pp. 002072092098350
Author(s):  
Liang Zhao ◽  
Yuanhua Jia
Keyword(s):  
Environmental Sustainability ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Advanced Technology ◽  
Toxic Substances ◽  
Sustainable Environment

Advanced technology has ushered in the urge to enhance the travel experience. Besides the consistent desire to travel faster and more comfortably, the need to ensure transportation sustainability has remained constant. Smart cities employ top-grade technological applications to facilitate operations. Intelligent transportation systems involve the use of advanced transportation technologies. Through the integration of the Internet of Vehicles, cars in traffic can send and receive data between themselves and other vehicles and the environment. This data is processed to ensure efficient transportation by controlling traffic flows and preventing accidents. In this study, a literature review is conducted on how intelligent transportation systems contribute to environmental sustainability in smart cities. With technologies such as electricity-driven cars and autonomous vehicles, the systems minimize the emission of toxic substances to the environment while enhancing the interaction of the car with its surroundings to avoid accidents.

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