REVIEW OF MICROCSCOPIC TRAFFIC FLOW MODELS USING ARTIFICIAL INTELLIGENCE

1. BAYERO UNIVERSITY, KANO
CIVIL ENGINEERING DEPARTMENT
ADVANCED TRAFFIC ENGINEERING
CIV8331 ASSIGNMENT
REVIEW OF MICROCSCOPIC TRAFFIC FLOW MODELS USING ARTIFICIAL INTELLIGENCE
BY:
MUSA HURANTA MSHELIA
SPS/20/MCE/00025
EMAIL: musamshelia4@gmail.com
COURSE LECTURER:
PROF. HASHIM M. ALHASSAN

2. INTRODUCTION
• Artificial Intelligence (AI) research field has been developed with the aim of emulating human problem-solving behaviour in complex real-
world tasks. In recent years, the potentialities of expert systems and AI techniques in transport applications have received considerable and
increasing attention within the traffic engineering community (Bonsall and Kirby, 1986; Bielli, Ambrosino and Boero, 1994). Essentially AI
means knowledge processing. A knowledge base contains a set of facts and rules relevant to a specific domain, while the inference engine
provides the methods for using the knowledge base through heuristic, largely nonalgorithmic procedures.
• Traffic engineers have begun to recognize the significant progress made in the development and validation of Artificial Intelligence (AI) and
knowledge-based approaches to transportation problems, both for the high flexibility, extensibility and interactivity degrees shown by the
conventional methods in solving complex real-world tasks and for the computational burden that typical OR optimization procedures often
impose on the available hardware (Bielli, Ambrosino and Boero, 1994).
• AI methods can be limited into two broad categories: a) computational intelligence, which includes such methods as fuzzy systems (FS),
neural networks (NN), and evolutionary computing; (b) symbolic Artificial Intelligent, which focuses on the development of knowledge-based
systems (KBS)(Alahmadi, 2020). However, we are only concerned with those that have found application to microscopic traffic models.

3. APPLICATIONS OF AI IN TRANSPORTATION
• Artificial Intelligence can be used in signal control of traffic at road intersections
• Artificial Intelligence can be used in automatic accident detection
• It is used in image processing for traffic data collection & identifying cracks in pavements or bridge structures and transportation equipment
diagnosis
• Artificial Intelligence can be used in designing an optimal transit network
• Artificial Intelligence can be applied to traffic management & decision-making systems to enhance and streamline traffic management and
make our roads smarter
• Intelligent traffic management can be used to enforce traffic laws and instill road discipline.
• Artificial intelligence is vital for driverless vehicles due to their processing, control and optimization capabilities.

4. ADVANTAGES OF AI
• Artificial Intelligence is used to predict the paths of pedestrians and cyclists
• it will decrease traffic accidents and injuries
• Artificial Intelligence could soon be used to predict and prevent traffic jams
• Artificial Intelligence improves public safety
• Artificial Intelligence increases the ability to process and predict data and outcomes than humans, so, travel and transport operators will
schedule public and private transportation services in a significantly improved manner.
DISADVANTAGES OF AI
• AI techniques are not appropriate for dealing with every subtask comprised in a complex real-world problem
• Artificial intelligence will impact a significant number of blue-collar jobs in the transportation industry
• Costs is a major barrier to adoption

5. AIM
To review and understand microscopic traffic flow models using artificial intelligence
OBJECTIVES
• To identify areas of applications of artificial intelligence in microscopic traffic theory
• To gain deeper understanding on the relationship between artificial intelligence and traffic flow theory.

6. LITERATURE REVIEW
Neural Networks and Applications
The history of artificial neural networks (ANN) began with Warren MaCullochand Walters pits (1943) who
created a computational model for neural networks based on algorithms called treshold logic. This model paved
the way for research to split into two approaches. One approach focused on biological processes while the other
focused on the application of neural networks to artificial intelligence (Neural Network - Wikipedia)
Artificial Neural Networks (ANN) is another attempt to model associative reasoning and pattern matching
typical of human brain. At present, these networks only model the process that connects input data with output
data by exploiting computer ability to perform an iterative series of fast numerical computations (Hajek and
Hurdal, 1993).

7. LITERATURE REVIEW
• Neural Networks and Applications
The history of artificial neural networks (ANN) began with Warren McCulloch and Walter Pitts (1943) who created a computational model
for neural networks based on algorithm called threshold logic. This model paved the way for research to split into two approaches. One
approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence (Neural
Network - Wikipedia)
Artificial Neural Networks (ANN) is another attempt to model associative reasoning and pattern matching typical of human brain. At present,
these networks only model the process that connects input data with output data by exploiting computer ability to perform an iterative series
of fast numerical computations (Hajek and Hurdal, 1993).

8. Unlike rule-based systems, ANN do not require detailed encoding of causal relationships and existing expertise. Neural
networks are parallel distributed information processing architectures suited to hardware implementation and real-time
operation. They consist of the following elements (Dougherty, Kirby and Boyle, 1994): - a set of nodes that receive a vector of
inputs and compute an analogue output according to a transfer function; - connection links of various strength - measured by
proper weights - joining the different nodes; - a transfer function and an associated threshold level for the weighted sum of
each node inputs, that possibly triggers its activation (that is, a switch between the two node states 'on/off'); - a set of layers,
that gives rise to a topological arrangement of nodes such that all nodes in adjacent layers are connected to each other. A
neural network thus has an input layer, an output layer and possibly one or more hidden (or internal) layers. Neural networks
represent a valuable methodological tool in the field of transportation research, particularly in the areas of traffic pattern
recognition, classification and prediction, congestion and incident detection, driver route choice modeling (Dougherty, Kirby
and Boyle, 1994. Artificial neurons (also called processing units or processing elements) mimic the functions of biological
neurons by adding the inputs presented to them and computing the total value as an output with a transfer function. Fig. 1
shows a simple example of an artificial neuron. The artificial neuron. also connects to other artificial neurons as the biological
neuron does. The strength of the connections is called weight (Faghri & Hua, n.d.).

9. The artificial neuron receives the input signals and generates the output signals. Every data from the surrounding or an output from other
neurons can be used as an input signal. The model for an artificial neuron is shown in Fig. 1
Figure 1. Artificial neuron Figure 2. Model of one layered artificial neural network

10. • Neural network: Neural network is composed of numerous mutually connected neurons grouped in layers. The complicity of the
network is determinate by the number of layers. Beside the input (first) and the output (last) layer, network can have one or few
hidden layers. The purpose of the input layer is to accept data from the surroundings. Those data are processed in the hidden layers
and sent into the output layer. The final results from the network are the outputs of the neurons from the last network layer and that
is actually the solution for the analysed problem
• Weight coefficients: Weight coefficients are the key elements of every neural network. They express the relative importance of each
neuron’s input and determine the input’s capability for stimulation of the neurons (Lazarevska et al., 2014). Every input neuron has
its own weight coefficient. By multiplying those weight coefficients with the input signals and by summing that, we calculate the
input signal from each neuron. One very important characteristic of neural networks is their ability for weight adjustment according
to the received history data, which is actually the learning process of the network (Lazarevska et al., 2014).

11. • Activation function: The main purpose of the activation (transformation) function is to determine whether the result from the summary
impulse X = W1X1 + W2X2 + .... + WnXm can generate an output. This function is associated with the neurons from the hidden layers and it
is mostly some nonlinear function. If there is no activation function and no transformation, the output value might be too large, especially for
complex networks that have few hidden layers.
• Structure of ANN: ANNs consist of various nodes that act like genetic neurons of the human brain (Stencl & Lendel, 2012). Interaction
between these neurons is assured by the links which are connected with these neurons. The input data is hence received by nodes and
executes the operation on the data which is passed on to other neurons. The final output at an individual node is termed as “node value”.
Every link is capable of learning as they are associated with the same weight. If output generated by using ANN is good, there is no need of
adjusting the weights, but if the overall output of the model generated is poor, then confidently weight should be altered to improve the
results.

12. Applications of ANN in Transportation Engineering
• Neural networks are used to detect long-lasting traffic jams
• They are used to analyze congestions of urban travel networks and seasonal fluctuations in the vehicle flow, to estimate origin-destination
matrices, and to evaluate travel network improvement
• Neural networks are also used for forecasting of traffic parameters.
• It is used for traffic control
• It is used in developing a vehicle control system for driverless movement and ensuring the safety of the driver.

13. Advantages of Artificial Neural Network (ANN)
• Parallel processing capability: Artificial neural networks have a numerical value that can perform more than one task simultaneously.
• Storing data on the entire network: Data that is used in traditional programming is stored on the whole network, not on a database. The
disappearance of a couple of pieces of data in one place doesn't prevent the network from working.
• Capability to work with incomplete knowledge: After ANN training, the information may produce output even with inadequate data. The
loss of performance here relies upon the significance of missing data.
• Having fault tolerance: Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature makes the
network fault-tolerant.

14. Disadvantages of Artificial Neural Network
• Assurance of proper network structure: There is no particular guideline for determining the structure of artificial neural networks. The
appropriate network structure is accomplished through experience, trial, and error.
• Unrecognized behavior of the network: It is the most significant issue of ANN. When ANN produces a testing solution, it does not provide
insight concerning why and how. It decreases trust in the network.
• Hardware dependence: Artificial neural networks need processors with parallel processing power, as per their structure. Therefore, the
realization of the equipment is dependent.
• Difficulty of showing the issue to the network: It relies on the user's ability to convert problems into numerical values
• The duration of the network is unknown: The network is reduced to a specific value of the error, and this value does not give us optimum
results.

15. GENETIC ALGORITHM
Genetic Algorithms are numerical optimization algorithms inspired by both natural selection and natural genetics. They were invented by John
Holland in the 1960s and were developed by Holland and his student and colleagues at the University of Michigan in the 1960s and the 1970s
(David, 1999). John Holland invented the method to understand the “adaptation” phenomenon as it occurs in nature and to develop ways in
which the mechanisms of natural adaptation might be imported into computer systems. Genetic algorithms are capable of solving many large
complex problems (David, 1999). Genetic algorithms, as powerful techniques in the field of global optimization (Davis, 1989), have been
successfully applied to real-world problems. GA has showed better search efficiency compared with the traditional optimization search
techniques. Genetic Algorithms have been proven to be very effective to highway alignment optimization problems (Kim, 2001). Genetic
algorithms start with an initial set of random solutions (represented by chromosomes) called population. Each chromosome 28 represents a
potential solution to the problem and it is evaluated through successive iterations called generations to give same measure of its fitness. Then,
based on their fitness, solutions are selected to form new offspring. The fitter the solutions the more chances these solutions will have to be
selected (Gen & Cheng, 2000).

16. Genetic Algorithm Flow Chat

17. • Genetic Encoding: Encoding is a process of representing the solution of a problem into chromosomes. The process can be performed using
bits, numbers, arrays or any other objects.
• Population: Traditionally, the population is generated randomly, allowing the entire range. Initially many individual solutions are (usually)
randomly generated to form an initial of possible solutions (the search space). The population sizes depend on the nature of the problem. The
larger the population is, the easier it is to explore the search space.
• Fitness: The fitness function is defined over the genetic representation and measure the quality of the represented solution. The fitness of an
individual is the value of an objective function. Individuals are selected according to their fitness for the production of offspring. The fitness
value is not only an indicator for showing good solution, but also corresponds to how close the chromosome is to the optimal.
• Selection: Selection is the process of choosing two parents from the population for crossing. During each successive generation, individuals
are selected for breeding based upon their fitness value. Fitter solutions are more likely to be selected to breed for the next generation.

18. • Genetic Operators: A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given
problem. Genetic operators are used to create and maintain genetic diversity (mutation operator), combine existing solutions (also known as
chromosomes) into new solutions (crossover).
• Crossover: It is the process in which two chromosomes combined their genetic material to produce a new offspring to possess the
characteristics of both. Crossover makes clones of good strings but does not create new ones.

19. • Mutation: This process is done by replacing the gen at random position with a new value by a low mutation probability. If crossover is
supposed to exploit current solution to find better ones, mutation is supposed to help for the exploration of the whole search space and find
the global optimal solution. Normally, offspring are mutated after being created by crossover.
Figure 5. Example of mutation
• Convergence: Convergence criterion finally brings the search to a halt. This act happens when the specified number of generation's have
evolved, or if there is no change to the population's best fitness for specified number of generations

20. Applications of Genetic Algorithm:
Genetic algorithms have a variety of applications, and one of the basic applications of genetic algorithms can be
the optimization of problems and solutions. We use optimization for finding the best solution to any problem.
Optimization using genetic algorithms can be considered genetic optimization. For example, In most logistics
companies, one main task is vehicle routing. Genetic optimization is used to figure out how to route and
transport goods to different clients using a fleet of vehicles. Consequently, genetic algorithms are used to derive
cost-effective routes to transport these goods to the rightful client at the right time (baeldung, 2022).

21. The advantages of GA
• Solution space is wider
• Easy to discover global optimum
• Easily modified for different problems.
• Handles noisy functions well.
• Handles large, poorly understood search spaces easily
• Good for multi-modal problems Returns a suite of solutions.
• Very robust to difficulties in the evaluation of the objective function.

22. MEMETIC ALGORITHM
A memetic algorithm is an extension of the traditional genetic algorithm. Inspired by both Darwinian principles of natural
evolution and Dawkins' notion of a meme, the term memetic algorithm (MA) was introduced by Pablo Moscato in his
technical report in 1989 where he viewed MA as being close to a form of population-based hybrid genetic algorithm (GA)
coupled with an individual learning procedure capable of performing local refinements.
The genetic algorithm is not well suited for fine-tuning structures which are close to optimal solution. The memetic algorithms
can be viewed as a marriage between a population-based global technique and a local search made by each of the individuals.
They are a special kind of genetic algorithms with a local hill climbing. Like genetic algorithms, memetic algorithms are also
population-based metaheuristic search algorithm. They have shown that they are orders of magnitude faster than traditional
genetic Algorithms for some problem domains. Metaheuristics are general heuristic approach that guides the search process
through the solution space, the evolutionary algorithm is starting by manipulating the initial solution built by some heuristic,
metaheuristics improve the solution quality iteratively until a some stopping criterion is met. The stopping criterion can be a
number of iterations, elapsed time, and etc. (Kumar & Memoria, 2020).

23. • Local Search approach: Enhancement in the Memetic algorithm is incorporated by the application of local search
techniques. The most critical and unique feature of MA is the inclusion of local search. Local search is an iterative process;
it makes local modification on the configuration. Normally Local search is work by application of mutation operator on
the current solution. This technique improves the solution at the local level.
Each individual makes local search to improve its fitness. To form a new population for the next generation, higher quality
individuals are selected. The selection phase is identical inform to that used in the classical genetic algorithm selection
phase. Once two parents have been selected, their chromosomes are combined and the classical operators of crossover are
applied to generate new individuals. The latter are enhanced using a local search technique. The role of local search in
memetic algorithms is to locate the local optimum more efficiently then the genetic algorithm (Garg, 2009).

24. Memetic Algorithm Flow Chat

25. CURRENT STATE OF RESEARCH
• Researchers are now seeking adaptive memetic methodologies that satisfy partially all three principles of a ‘truly’ evolving system, i.e.,
heredity, variation and selection as defined by the theory of Universal Darwinism (Dawkins, 1976). This includes multi-meme MA
(Krasnogor, 2002) hyper-heuristic (Burke, 2003) and meta-Lamarckian MA (Nguyen et al, 2008; Ong at al., 2004), co-evolution and self-
generation MAs (Krasnogor et al., 2002), multi-agent memetic computing (Ullah et al., 2007).
• The main drawback of GAs is premature convergence. The chaotic systems are incorporated into GAs to alleviate this problem. The diversity
of chaos genetic algorithm removes premature convergence. Crossover and mutation operators can be replaced with chaotic maps.
• Tiong et al. (2012) integrated the chaotic maps into GA for further improvement in accuracy. They used six different chaotic maps. The
performance of Logistic, Henon and Ikeda chaotic GA performed better than the classical GA. However, these techniques suffer from high
computational complexity.

26. • Ebrahimzadeh and Jampour (2013) used Lorenz chaotic for genetic operators of GA to eliminate the local optima problem. However, the
proposed approach was unable to find relationship between entropy and chaotic map. Javidi and Hosseinpourfard (2015) utilized two chaotic
maps namely logistic map and tent map for generating chaotic values instead of random selection of initial population. The proposed chaotic
GA performs better than the GA. However, this method suffers from high computational complexity.
• Fuertes et al. (2019) integrated the entropy into chaotic GA. The control parameters are modified through chaotic maps. They investigated the
relationship between entropy and performance optimization. Chaotic systems have also used in multi-objective and hybrid GAs.
• Abo-Elnaga and Nasr (2020) integrated chaotic system into modified GA for solving Bi-level programming problems. Chaotic helps the
proposed algorithm to alleviate local optima and enhance the convergence.

27. FUTURE RESEARCH
• There should be some way to choose the appropriate degree of crossover and mutation operators. For example, Self-Organizing GA adapt the
crossover and mutation operators according to the given problem. It can save computation time that make it faster.
• Future work can also be considered for reducing premature convergence problem. Some researchers are working in this direction. However,
it is suggested that new methods of crossover and mutation techniques are required to tackle the premature convergence problem.
• Genetic algorithms mimic the natural evolution process. There can be a possible scope for simulating the natural evolution process such as
the responses of human immune system and the mutations in viruses.
• In real-life problems, the mapping from genotype to phenotype is complex. In this situation, the problem has no obvious building blocks or
building blocks are not adjacent groups of genes. Hence, there is a possibility to develop novel encoding schemes to different problems that
does not exhibit same degree of difficulty.

28. CONCLUSION
• The development of Artificial Intelligence in Microscopic traffic flow models has improved the efficiency of the models to
capture human behavior and therefore, produce a more sophisticated and more reliable models that enhances safe and efficient
transportation. However, there is a need for further research to gain deeper understanding of how artificial intelligence can be
used for best optimization of transportation related problems.