science

Traffic estimation and prediction system

Traffic estimation and prediction system is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Traffic estimation and prediction system rather than just read about it. In short: Traffic estimation and prediction systems (TrEPS) have the potential to improve traffic conditions and reduce travel delays by facilitating better utilization of available capacity. These systems exploit currently available and emerging computer, communication, and control technologies to monitor, manage, and control the transportation system.

Key takeaways

Traffic estimation and prediction system belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Traffic estimation and prediction system to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Traffic estimation and prediction system from memory before moving on to harder problems.

Reference excerpt

Traffic estimation and prediction systems (TrEPS) have the potential to improve traffic conditions and reduce travel delays by facilitating better utilization of available capacity. These systems exploit currently available and emerging computer, communication, and control technologies to monitor, manage, and control the transportation system. They also provide various levels of traffic information and trip advisory to system users, including many ITS service providers, so that travelers can make timely and informed travel decisions.

Need for TrEPS The success of ITS technology deployments is heavily dependent on the availability of timely and accurate estimates of prevailing and emerging traffic conditions. As such, there is a strong need for a “traffic prediction system”. The needed system is to utilize advanced traffic models to analyze data, especially real-time traffic data, from different sources to estimate and predict traffic conditions so that proactive Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) strategies can be implemented to meet various traffic control, management, and operation objectives.

Research

United States In United States, the FHWA R&D initiated a Dynamic Traffic Assignment (DTA) research project in 1994 to meet the need for a traffic prediction system and to help address complex traffic control and management issues in the dynamic ITS environment. The main objective of this research is to develop a deployable real-time Traffic Estimation and Prediction System (TrEPS) to meet the information need in the ITS context. In October 1995, two parallel research contracts were awarded to Massachusetts Institute of Technology (MIT) and the University of Texas at Austin (UTX) with a follow-up development and support at the University of Maryland (UMD), respectively. Each team was required to develop a prototype of TrEPS demonstrating its potential for real time application capability. After three years of intensive R&D efforts, two prototype TrEPS were developed. The two prototype TrEPS developed by MIT and UTX/UMD are named DynaMIT-R and DYNASMART-X, respectively. Both systems are simulation-based DTA system.

France In France, the Centre national d’information routière (National Centre for Traffic Information/CNIR) directs, coordinates and monitors the work of seven regional traffic coordination and information centres (CRICRs). It publishes forecasts which are available on line at www.bison-fute.equipement.gouv.fr/en/ and are widely referred to in radio and television broadcasts. The advice of "Bison Futé" as the service is called is well known and has been in place for several decades.

China In China, Xi'an Jiaotong University (XJTU) initiated a similar simulation-based DTA research project in 2000 with a follow-up development and support at the Shandong Academy of Sciences after 2004. Dr. Yong Lin is the project leader and Houbing Song is the earliest project member. After six years of intense R&D efforts, one prototype TrEPS was developed in 2006. The overall prototype TrEPS developed by Dr. Lin and his team which has more than 20 members is named DynaCHINA (Dynamic Consistent Hybrid Information based on Network Assignment).

Singapore Singapore implemented the first practical application of congestion pricing in the world in 1975, the Singapore's Area Licensing Scheme. Thanks to technological advances in electronic toll collection, detection, and video surveillance, Singapore upgraded its system in 1998. (see Singapore's Electronic Road Pricing) In an effort to improve the pricing mechanism and to introduce real-time variable pricing, Singapore’s Land Transport Authority, together with IBM, ran a pilot from December 2006 to April 2007, with a traffic estimation and prediction tool, which uses historical traffic data and real-time feeds with flow conditions from several sources, in order to predict the levels of congestion up to an hour in advance. By accurate estimating prevailing and emerging traffic conditions, this technology is expected to allow variable pricing, together with improved overall traffic management, including the provision of information in advanced to alert drivers about conditions ahead, and the prices being charged at that moment. The pilot results show overall prediction results above 85 percent of accuracy. Furthermore, when more data was available, at peak hours, average accuracy raised near or above 90 percent.

References

External links Roads and Traffic Authority, NSW SCATS Sydney Coordinated Adaptive Traffic System

Worked examples

Example 1 — a first encounter with Traffic estimation and prediction system

Start with the simplest possible case. Write down what Traffic estimation and prediction system claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Traffic estimation and prediction system before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Traffic estimation and prediction system ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Traffic estimation and prediction system

In research: Traffic estimation and prediction system appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Traffic estimation and prediction system in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Traffic estimation and prediction system is common in secondary-school and first-year university syllabi. It links to neighbouring topics Intelligent transportation systems, so understanding it makes those chapters shorter.
In everyday life: Look for Traffic estimation and prediction system outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Traffic estimation and prediction system in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Traffic estimation and prediction system means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Traffic estimation and prediction system out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Traffic estimation and prediction system in simple terms?

Why does Traffic estimation and prediction system matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Traffic estimation and prediction system?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Traffic estimation and prediction system.