Artificial Intelligent

Ali Emad Jehad P71084KKKA6424

urban traffic management system

Artificial Intelligent

Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.

What is Artificial Intelligent ?

Neural Networks are biologically inspired systems consisting of a massively connected network of computational “neurons,” organized in layers. By adjusting the weights of the network, NNs can be “trained” to approximate virtually any nonlinear function to a required degree of accuracy. NNs typically are provided with a set of input and output exemplars. A learning algorithm (such as back propagation) would then be used to adjust the weights in the network so that the network would give the desired output, in a type of learning commonly called supervised learning.

Neural Networks (NNs)

The most commonly used neural network feature is the ability of prediction of various road traffic parameters, such as: throughput, intensity and length of vehicle queues, basing on historical data. 1st generation traffic control systems, e.g. Neural network acts as a multi variable function approximator. A schematic of neural network used to determine the queue length

NEURAL NETWORKS APPLICATION IN TRAFFIC CONTROL AND MANAGEMENT SYSTEMS

lq(t+j) – length of queue at juction input D(t+j) – intensity at junction output A(t+j) – intensity at junction inputA(t-1+j) , ..., A(t-1+j) – intensity at junction input in preceding time period t, t – initial value of time j – length of time lq(t+j+1) is a predicted length of the queue

Genetic algorithms are stochastic algorithms whose search methods are based on the principle of survival of the fittest. GAs have been applied to a wide range of difficult optimization problems for which classical mathematical programming solution approaches were not appropriate.

Genetic Algorithms(GAs)

a GA is a suitable choice for problems that are difficult to formulate and solve using derivative-based and other traditional optimization techniques. Problems that are characterized by complex objective functions including multiobjective problems, problems with no-closed form objective function, and ones with large number of variable and mixed solution space are particularly suited for optimization with GAs.

TYPES OF PROBLEMS TO WHICH GAs ARE MOST SUITED

- Traffic Signal Timing and Control - GA for Transit Network Design- Other Transportation Applications

EXAMPLES OF TRANSPORTATION PROBLEMS

A KBS can be defined as a computer system capable of giving advice in a particular domain, utilizing knowledge provided by a human expert. A distinguishing feature of KBS lies in the separation behind the knowledge, which can be represented in a number of ways such as rules, frames, or cases, and the inference engine or algorithm which uses the knowledge base to arrive at a conclusion.

The knowledge component of KBS consists of a set of independent knowledge elements in the form of rules, frames, or objects. The choice of which form to use depends largely upon the problem to be solved and the tools that are available for use in coding the system.

KNOWLEDGE-BASED EXPERT SYSTEMS

Knowledge based expert systems and dynamic programming are used for the development of a comprehensive pavement management system tool to help engineers and planners to make objective, consistent, and cost effective decisions regarding pavement maintenance, rehabilitation, and reconstruction.

KNOWLEDGE BASED EXPERT SYSTEM IN PAVEMENT MANAGEMENT OPTIMIZATION

Fuzzy set theory was proposed by Zadeh (1965) as a way to deal with the ambiguity associated with almost all real-world problems. Fuzzy set membership functions provide a way to show that an object can partially belong to a group. Classic set theory defines sharp boundaries between sets, which mean that an object can only be a member or a nonmember of a given set.

FUZZY SYSTEMS

In this example, traffic volumes between 800 and 1,000 vehicles per hour (vph) fully belong to the medium traffic level set. Traffic volumes less than 400 vph or more than 1,400 vph would not be regarded as medium at all (membership function value = 0). However, values between 400 and 800 vph, or between 10,00 and 1,400 vph would have partial membership in the medium traffic level set.

FUZZY MEMBERSHIP FUNCTION FOR MEDIUM TRAFFIC VOLUME

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