scholarly journals A Review of Research on Intersection Control Based on Connected Vehicles and Data-Driven Intelligent Approaches

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 885 ◽  
Author(s):  
Kai Gao ◽  
Shuo Huang ◽  
Jin Xie ◽  
Neal N. Xiong ◽  
Ronghua Du
Keyword(s):  
Control Theory ◽  
Real Time ◽  
Traffic Control ◽  
Performance Optimization ◽  
New Technologies ◽  
Transportation Systems ◽  
Data Driven ◽  
Connected Vehicles ◽  
Precision Data ◽  
Intersection Control

Benefiting from the application of vehicle communication networks and new technologies, such as connected vehicles, video monitoring, automated vehicles and vehicle–road collaboration, traffic network data can be observed in real-time. Applied in the field of traffic control, these technologies can provide high-quality input data and make a more comprehensive evaluation of the effectiveness of traffic control. However, most of the control theories and strategies adopted by adaptive control systems cannot effectively use these real-time, high-precision data. In order to adapt to the development of the times, intersection control theory needs to be further developed. This paper reviews the intersection control strategies from many perspectives, including intelligent data-driven control, conventional timing control, induction control and model-based traffic control. There are three main directions for intersection control based on the connected vehicle environment: (1) data-driven reinforcement learning control; (2) adaptive performance optimization control; (3) research on traffic control based on the environment of connected vehicles (CV); and (4) multiple intersection control based on the CV environment. The review gives a clear view of the data-driven intelligent control theory and its application for intelligent transportation systems.

Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

scholarly journals Transport modeling: An artificial immune system approach

2006 ◽  
Vol 16 (1) ◽  
pp. 3-30
Author(s):  
Dusan Teodorovic ◽  
Jovan Popovic ◽  
Panta Lucic
Keyword(s):  
Immune System ◽  
Real Time ◽  
Traffic Control ◽  
System Approach ◽  
Artificial Immune System ◽  
Control Strategies ◽  
Transportation Systems ◽  
Artificial Immune ◽  
Real Time Traffic ◽  
Wide Range

This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

scholarly journals Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8477
Author(s):  
Roozbeh Mohammadi ◽  
Claudio Roncoli
Keyword(s):  
Traffic Control ◽  
Partial Information ◽  
Control Strategies ◽  
Synthetic Data ◽  
Real Data ◽  
Significant Loss ◽  
Data Driven ◽  
Estimation Methods ◽  
Connected Vehicles ◽  
Vehicle Data

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

Data Infrastructure for Connected Vehicle Applications

2020 ◽  
Vol 2674 (5) ◽  
pp. 85-96
Author(s):  
Xingmin Wang ◽  
Shengyin Shen ◽  
Debra Bezzina ◽  
James R. Sayer ◽  
Henry X. Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Transportation Systems ◽  
Connected Vehicle ◽  
Performance Measurements ◽  
Trajectory Data ◽  
Test Environment ◽  
Data Infrastructure ◽  
Time Space ◽  
Wide Range

Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.

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 A Single Intersection Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 409 ◽  
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Traffic Safety ◽  
New Technologies ◽  
Traffic Signal ◽  
Pollutant Emissions ◽  
Connected Vehicles ◽  
Floating Car Data ◽  
Real Time Traffic ◽  
Signal Systems

New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate traffic signal systems, in real time, has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate traffic signal systems in real-time in a single intersection and in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of a single intersection signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

scholarly journals Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data

2018 ◽  
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Traffic Safety ◽  
New Technologies ◽  
Traffic Signal ◽  
Pollutant Emissions ◽  
Connected Vehicles ◽  
Floating Car Data ◽  
Real Time Traffic ◽  
Signal Systems

New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate, in real-time, traffic signal systems has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate in real-time traffic signal systems in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

scholarly journals Data ratcheting and data-driven organisational change in transport

2019 ◽  
Vol 6 (2) ◽  
pp. 205395171986735 ◽  
Author(s):  
Liam Heaphy
Keyword(s):  
Real Time ◽  
Public Transport ◽  
Traffic Control ◽  
Organisational Change ◽  
Digital Data ◽  
Data Driven ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Control Rooms ◽  
Rational Order

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration.

Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization

2021 ◽  
Vol 73 (10) ◽  
pp. 63-64
Author(s):  
Chris Carpenter
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Performance Optimization ◽  
Production Optimization ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Well Performance ◽  
Learning Techniques ◽  
Expected Performance ◽  
Gas Lift

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201696, “Robust Data-Driven Well-Performance Optimization Assisted by Machine-Learning Techniques for Natural-Flowing and Gas-Lift Wells in Abu Dhabi,” by Iman Al Selaiti, Carlos Mata, SPE, and Luigi Saputelli, SPE, ADNOC, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Despite being proven to be a cost-effective surveillance initiative, remote monitoring is still not adopted in more than 60% of oil and gas fields around the world. Understanding the value of data through machine-learning (ML) techniques is the basis for establishing a robust surveillance strategy. In the complete paper, the authors develop a data-driven approach, enabled by artificial-intelligence methodologies including ML, to find an optimal operating envelope for gas-lift wells. Real-Time Well-Performance Optimization Wellsite Measurement and Control. - Flow Tests. - Past tests include sporadic measurement of multiphase rates and the associated flowing pressure and temperature, collected at various points of the production system, from bottomhole to separator conditions. Flow tests are also known as well tests; however, the authors use the term “flow test” in this paper to avoid confusion with well testing as used in pressure transient tests, including temporary shut-in pressure buildups (for producers) and pressure falloff tests (for injectors). Normally, a well would have limited data points from the past well tests (i.e., less than 50 valid flow tests in a period of 5–10 years). This data is the basis of creating ML models. Continuous Monitoring. - Every well should have adequate instrumentation, and its supporting infrastructure should include reliable power supply, minimum latency telemetry, and desktop access to production parameters. Alignment among real-time data and relational databases is required. Remote Control and Automated Actuation. - In addition to controllable valves, every well should be enabled with actuators to control the process variables. Remote control allows the operator to make changes to the current well-site configuration. Regulatory and Supervisory Control. - The value of automated closed-loop regulatory and supervisory control is to sustain optimal production while providing high well availability. Real-Time Production Optimization. - Continuous production optimization means that expected performance is challenged frequently by updating an optimal forecast with upper-level targets and current asset status. This is achieved by applying actions that close the gap between actual and expected performance. Faster surveillance loops compare actual vs. expected performance to determine minute, hourly, and daily gaps. A slower surveillance loop updates the asset’s expected performance. Well-Management Guidelines. - These are established, known limits to address and honor restrictions such as concession-contract obligations and legal limits, optimal reservoir management, flow assurance, economics, safety, and integrity.

Managing Incidents on Urban Arterial Roadways

1997 ◽  
Vol 1603 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Richard A. Raub ◽  
Joseph L. Schofer
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
New Technologies ◽  
Transportation Systems ◽  
Public Works ◽  
Incident Management ◽  
Systems Planning ◽  
Long Duration ◽  
Advanced Planning ◽  
The Impact

Limiting the impact on traffic of nonrecurring events such as crashes, traffic stops, or disabled vehicles through effective incident management should be one objective for emergency response professionals. Moreover, such management is an integral part of Intelligent Transportation Systems planning. The Arterial Incident Management Study, sponsored by the Illinois Department of Transportation and conducted by Northwestern University, examined the impact and management of arterial street incidents to determine what steps could be taken to improve the handling of such events. Several approaches were used to accomplish this, including (a) analysis of incident data from police and fire agencies, (b) debriefings of responders about specific incidents, (c) observation and videotaping of incidents, and (d) an incident management simulation workshop involving police, fire, emergency medicine, tow operator, public works, insurance, and media professionals who have responsibilities for incident management. A series of incidents were simulated in a workshop and consensus was sought on specific and general management tactics. This paper describes study methods and summarizes the important issues and recommendations to improve incident management. Among the key needs identified are (a) response in a manner and with adequate resources to minimize the time an incident affects a scene, (b) rapid removal of vehicles and debris, (c) effective traffic control at and around the incident, (d) communication with motorists who may be affected by long-duration incidents, and (e) advanced, intra- and interagency planning for incident management. Recommended changes include education of drivers and professionals, legislation, communications, use of new technologies for communications and data collection, and advanced planning and coordination of on-site procedures, responsibilities, and priorities.

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