Applications Of Artificial Intelligence In Transportation

http://www.iaeme.com/IJARET/index.asp 1074 editor@iaeme.com
International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 11, Issue 6, June 2020, pp. 1074-1083, Article ID: IJARET_11_06_097
Available online athttp://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=6
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
DOI: 10.34218/IJARET.11.6.2020.097
© IAEME Publication Scopus Indexed
APPLICATIONS OF ARTIFICIAL
INTELLIGENCE IN TRANSPORTATION
Dr. Raed Nayif Alahmadi
Civil Engineering Department,
Albaha University, Saudi Arabia
ABSTRACT
Artificial Intelligence (AI) has developed at a rapid pace and this provides a good
chance to enhance the execution of different fields like business, industries, and the
transportation sector. In the transportation field, the AI had applied to overcome the
challenges of pollution, environmental deterioration, increasing travel demand, and
safety concerns. The good understanding of the relationship between the AI and
input data on one hand and characteristics of the transportation system leads to a
perfect and successful application of AI. This paper provides an overview of the AI
methods with a concentration in two methods of AI, Knowledge Base System method
(KBS) and Artificial Neural network systems (ANNs). The paper tried to explain in
detail the two methods and their application in transportation with advice and points
of strength, weakness, and guidelines for application. The second part of the paper
deal with AI application areas in Transportation. The overview concludes by a
summary of the two methods with a brief of their important application in
transportation.
Key words: Artificial, Intelligence, Transportation, Neural Network, Knowledge-
based system.
Cite this Article: Dr. Raed Nayif Alahmadi, Applications of Artificial Intelligence in
Transportation, International Journal of Advanced Research in Engineering and
Technology, 11(6), 2020, pp. 1074-1083.
http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=6
1. INTRODUCTION
No doubt in contemporary time transportation fields faces many challenges of increasing
complexity. Required from the transport sector specialists to meet the requirements of
providing reliable transportation, safety, and to minimize the negative impact on the
environment and communities. To secure the requirements aforementioned has turned out to
be quite difficult due to the constant increase in travel demand charged by economic
development. The challenges that transportation specialists confront includes poor safety
record, CO2 emissions, wasted energy, capacity problems and unreliability. The complexity
of the transportation systems can be added as a fact to the challenges because the systems
involve a very large number of components with various parties, each having diverse and

Applications of Artificial Intelligence in Transportation
usually conflicting objectives. The interest of applying artificial intelligence (AI) models by
transportation researchers and practitioners to address some of the aforementioned problems
rises in recent years so to improve safety, efficiency, and environmental congeniality of
transportation systems. Modeling the transportation problems is a challenge and difficult to be
applied due to complexity in predicting travel patterns, which depend on system and user
behavior. Hence, the AI is suitable for the transportation system to subdue the problems and
challenges mentioned above which rises from an increasing number of population
accompanied by the steady growth of rural and urban traffic, especially in the developing
countries. In KSA, the cost of congestion is expected to increase as the population increase to
39 million by 2030. In Jeddah, KSA alone, more than 564 km of arterial roads are congested
during peak time, and the CO2 emission estimated by 266 million metric tons per year,
equivalent to 13.7 metric tons per person per year. In recent years, many researchers and
specialists in the transportation field tried to accomplish the best reliable transport system
with less effect on the environment and the people with a good degree of reliability using all
AI techniques.
2. AI DEFINITION AND METHODS
Artificial Intelligence (AI) is a young science whose history began, according to by and large,
in 1969 of the last century. Currently research and development in the field of artificial
intelligence are conducted in all developed countries. Artificial intelligence (AI) is a broad
area of computer science tried to simulate the action of the human brain using machines. The
AI is used to solve the issues that are difficult to clarify using traditional computational
methods.
The Knowledge-based system (KBS) and Artificial Neural network systems (ANNs)
explored by researchers as methods of AI, in1960 to 1970. The 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).
This paper will concentrate just on KBS and NN methods representing the two categories
aforementioned.
3. KNOWLEDGE-BASED SYSTEMS
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge
base to solve complex problems. The term is broad and refers to several different sorts of
systems. The one common theme that unites all knowledge-based systems is an effort to
represent knowledge explicitly and a reasoning system that permits it to derive new
knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge
domain and an inference engine. In practice, these systems have three components: a
knowledge base in the form of rules, frames or objects, for example; an inference engine in
the form of algorithms on how to control the processing of knowledge; and a database which
may be thought of to be the system’s window on the world Fig.(1). Many AI systems are
placed under the rubric of KBS, including expert systems, case based reasoning, agent-based,
FS, and lots of others. In recent years, emphasis has been less on developing independent
KBS and more on integrating them into other paradigms, such as geographic information
systems (GIS), object-oriented databases and even artificial NN.
The first part, the knowledge domain, represents facts about the planet, often in some sort
of subsumption ontology (rather than implicitly embedded in procedural code, within the way
a standard computer program does). Other common approaches in addition to a subsumption
ontology include frames, conceptual graphs, and logical assertions. The second part refers to

the inference engine, which allows new knowledge to be inferred. A distinguishing feature of
KBS lies within the separation behind the knowledge, which may be represented during a
number of the way like rules, frames, or cases, and the inference engine or algorithm which
uses the knowledge domain to reach a conclusion. The knowledge component of KBS
consists of a group of independent knowledge elements within the sort 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.
Figure 1 Main Components of an Knowledge Based System
Rules of the form “if X, then Y” are the most common way of representing knowledge
because they are most often the way we express our heuristic knowledge. They are therefore
eminently understandable, fairly easy to extract from humans, and are very portable thus
allowing the system flexibility within the addition or change to its knowledge. Examples of
problems that are appropriate for KBS solution in transportation include, diagnosing
hazardous highway locations, planning construction activities, designing structural members
for and/or assessing the structural integrity of bridges, scheduling airline maintenance
activities, dispatch, and control of rail and transit, developing traffic management strategies
given a traffic disaster, and intelligent transportation systems (ITS). The sheer diversity of
disciplines involved and complexities that may be encountered in the Transportation
Engineering problem domain provides a rich environment for KBS development. Problems
most amenable to KBS solution, either suffer from lack of data in which heuristics may be
used to “fill in the holes”, or they are poorly defined or are too complex such standard
solutions using analytical or simulation tools might not be appropriate. For problems like
these last, heuristics are used as decision support, for instance , design of a sign plan for a
posh network of intersections and roads; or diagnosis of problems at a high crash signalized
intersection; crash data collection; recommending speed limits in speed zones, and providing
diagnostic safety reviews for intersection designs. These last three are all samples of systems
that have actually been implemented.
Knowledge Base
Domain Knowledge
Interface
Inference Engine
Working Memory
Problem
input
Solution
output

Key questions that must be answered in helping to decide upon which type of tool to use
include:
a) Is there an analytical or simulation tool that could be used to solve the problem at hand?
b) Would the problem best be solved using these more traditional techniques?
For example, the determination of queue length at a signalized intersection or even the
Level of Service (LOS) of that intersection would be more amenable to analytical models than
to KBS. Determination of the operational parameters of a complex network of intersections
and roads would probably best be done using simulation models. The design of that complex
network or diagnosis of its problems or its real-time control on the other hand may best be
conducted using a KBS since these types of problems are characterized by missing data,
complexity, and time-criticality. In short, the type of problem to be addressed drives the
decision as to the type of tool to be used (for example, matching and optimization problems
are not amenable to KBS solution whereas the others described above do enjoy the appliance
of knowledge). KBS offers many significant advantages over its traditional counterpart tools.
They allow engineers to work with uncertain problems. Most problems of any complexity
involve some level of uncertainty either from data quality or another source. Many are such
that we are willing to live with that uncertainty but for some, we are not. KBS allows us to
express concepts in ways in which we are more comfortable (the concepts of fairly good,
somewhat old, and so on) and to avoid problems with crisp boundaries such as using delay
levels to assess the LOS of the highway intersections. It is possible to consider problems
requiring judgment and that are not amenable to a procedural approach. Design and
evaluation problems are excellent samples of this sort of problem.
The KBS is designed to improve with experience. By their nature, with knowledge break
away control, these systems are easily updated based upon experience. The KBS also
promises as best educational tools, where even simple knowledge bases can have practical
value for education. Work in genetic psychology indicates that actual “learning” must happen
by “doing”. Of course, such a system is not necessarily a good teacher of the material but
nevertheless would expose students, in an interactive and nonthreatening way, to expert
reasoning processes as well as to his or her domain knowledge. Another important advantage
of using KBS as teaching aides is that the capability of pooling heuristic knowledge into a
standard repository. This type of knowledge is not normally published, and the only way it is
shared is between teacher-student or master-apprentice. Unfortunately, many, especially in the
early years of AI applications in transportation, have been carried away with all of this
wonderful potential and have become enamored with the hype. Consequently, very often KBS
has been used for all types of problems under all conditions. The fact is that these systems are
indeed powerful problem solvers and they hold great promise for the solution of a plethora of
problems. However, they are not a panacea and they have some major drawbacks in their
application mainly, that they often only have surface knowledge about the problem at hand.
The best of those systems have an excellent deal of surface knowledge a few much-focused
subsets of drag and really little about anything. Therefore developing the KBS method is a
good issue, there are many steps in developing a successful KBS. The following three are a
distillation of those that are critical to success:
a) Determine if your problem is appropriate for a KBS tool versus a conventional tool. Do
conventional tools do what you need to do? Would an analytic or simulation model be better
applied to the matter for example? In the case of modeling applications where viable
methodologies exist both in the mathematical and soft computing domains there are clearly
trade-offs to be evaluated in model selection. For example, there could also be a trade-off
between the potential for brand spanking new insight versus simple implementation or
between the motivation to tell the modeling with accurate prior knowledge versus the aversion

to biasing the results through misconceptions and faulty assumptions. Explicit presentation of
the evaluation of those sorts of trade-offs is usually missing from papers on transportation
modeling applications.
b) Establish an evaluation plan for the system at the outset. At a minimum, the plan should
include system goals, specifications and constraints, and measures of effectiveness. This helps
to assure that the system is meant to facilitate its own validation and verification.
c) Assure that you have the resource commitment for full development, implementation, and
maintenance. This will include staff requirements, developer salaries, the time commitment of
people intimate the domain of interest, software (and possibly hardware) costs, and so on.
4. NEURAL NETWORKS
A neural network could even be a circuit or network of neurons, composed of artificial
neurons or nodes. Thus, a neural network is either a biological neural network, made from
real biological neurons or a man-made neural network, for solving AI (AI) problems. The
model of the biological neuron connections to be defined as weights. Negative values mean
restrained connections, while a positive weight reflects an excitatory connection. All inputs
are modified by weight and summed. This activity is mentioned as a linear combination. An
activation function controls the amplitude of the output, this as a final process. For example, a
suitable range of output is typically between 0 and 1, or it might be −1 and 1. 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 backpropagation) would then be used to adjust the
weights in the network so that the network would give the desired output, which is a type of
learning commonly called supervised learning. These artificial networks could also be used
for predictive modeling, adaptive control, and applications where they will be trained via a
dataset. Self-learning resulting from experience can occur within networks, which may derive
conclusions from a posh and seemingly unrelated set of data. Generally, Artificial Neural
Networks (ANNs) are nonlinear models that are non-parametric and are utilized to determine
the approximate functioning of a system for a real-life application. Commonly, the core action
of Artificial Neural Networks (ANNs) tends to fall within the method and control categories.
Furthermore, the synthetic Neural Networks (ANNs) also represent the entire interconnection
of the systems alongside numeric weights which will be tuned supported experience, making
neural nets adaptive to inputs, and capable of learning. ANN is additionally a strong data-
driven, and versatile computational tool having the potential of capturing nonlinear and
sophisticated underlying characteristics of any physical process with a high degree of
accuracy. Furthermore, ANN is suitable for inverse modeling when the numerical relations
between input and output variables are unknown, and cannot be established in Fig 2. The
main advantages of using Artificial Neural Networks (ANN) include the ability to
 Operate a large number of data sets.
 Implicitly detect complex nonlinear relationships between dependent and independent
variables.
 Detect all possible interactions between predictor variables.
Moreover, the important advantage of ANNs is the ability to unravel complex system
problems like the one which is found within the Transportation Infrastructure Systems (TIS).
Transportation infrastructure is seen as the interconnectivity between the physical and
tangible assets that are required to support and develop a nation. It is therefore essential to
administer the transportation infrastructure efficiently so as to supply continuous, sustainable,
and economic services to the population. A fundamental aspect of this effective

administration is the design of TIS. There are many competing factors currently influencing
the transportation sector, which requires careful system considerations such as, the high
capital cost of design and construction, changes to the legislation, and the impact of the rise in
demand and usage of transport infrastructure. The other areas, which request careful system
consideration, comprise:
a) Transport Infrastructure Planning and Development (TIPD) involves the varied aspects of
building construction, land use planning, and land subdivision that are administered by
governments at all levels. The aim is to achieve high quality and sustainable development
outcomes in the urban and rural areas and taking into account important issues such as
preservation of the physical environment as a part of broader TIPD assessment.
b) Transport Infrastructure Economic Development (TIED). The main aim of major cities'
role as an increasingly dominant regional service center is underpinned by a strong regional
economy based in manufacturing, logistics, wholesale trades, etc. Thus indicating the
importance of regional areas, since their economy is typically dynamic and vibrant, making it
well placed to deal with future economical structural changes resulting from the needs of an
ever-changing demographic profile. As a part of general economic development, a regional
economic development program for transport infrastructure needs to be created and
encompass into any TIS. The suggested TIS, in an economic sense, is powerful, sequential,
more adaptive, and considers more parameters (such as engineering requirements) than the
opposite existing general models.
c) Transport Infrastructure Engineering Requirements (TIER). These requirements provide
fundamental tools for the evaluation of transport infrastructure and their performances. These
are four fundamental aspects, which need to be built-in into the TIS can be represented as four
core elements as follows:-
1- Infrastructure Asset Rehabilitation and Renewal (IARR). This element includes the
assessment and renewal of all assets like bridges, roads, and buildings. As a part of IARR, this
first core element initially includes “Evaluating asset condition and performance”. Once an in-
depth and detailed asset condition and performance evaluation have been applied, the second
phase “developing an asset renewal strategy” commences. This involves the identification of
future asset renewal costs and addressing the prioritization and funding of the standard
liabilities.
2- Structural Performance and Operation (SPO). This element includes damage threshold for
structures, the performance of structures to extreme events such as the act of God including
earthquakes, and/or strategies for the development of the next generation of standards. This
area includes the management and supervision of the Transportation Infrastructure Assets
(TIA) via theories and methods like structural performance analysis. The structure
performance analysis methods are alternatives to avoid the computational complexity problem
related to other techniques like discrete event simulation. In this process albeit a finished
conceptual and technical framework isn't yet obtainable, significant advantages are obtained
not only from performance but also from correctness analysis stages.
3- Sustainable and Tangible Materials (STM). This element includes a thorough investigation
of utilized materials such as Concrete, Timber, Iron, Steel, and Asphalt. In addition, the STM
approach could include developing new materials processes to reinforce sustainability by
decreasing pollution, emissions, energy consumption, and improving Eco-efficiency of
materials processing, and modular construction and advanced materials.
4- Emerging Robust Technologies and Innovative Tools (ERTI). This element includes the
use of software like Real-time Asset Valuation Analysis (RIVA) or sensor applications in
infrastructure monitoring and energy-efficient structures. In addition, this element comprises
the research, recommendation, and implementation of the newly created and derived

technologies (expertise, equipment, and machinery) within the transportation infrastructure
assets. The ERTI will be needed for Transportation Infrastructure Optimization (TIO) in this
new highly technological era to meet the public’s demand for high-quality and convenient
transportation. The implementation in the optimum way of those practices will promote and
enable decision-makers to utilize the new technologies properly to finish the works quickly
with the minimum obstruction to traffic while incorporating quality which will ensure long-
lasting, low-maintenance facilities. All of these three areas (TIPD, TIED, and TIER) need to
be part of any system analysis and design process. The TIS provides this integration and a
comprehensive system development strategy.
Figure 2 A simple neural network
5. ARTIFICIAL INTELLIGENCE APPLICATION AREA IN
TRANSPORTATION
AI application areas are quite diverse. This section lists some of those application areas to
which AI methods have been applied over the years, and explains how these may be relevant
to transportation applications. Among the most important of AI application areas are the
following:
1- System identification and function approximation, which is concerned with building
empirical dynamic models of systems from measured data, or mapping system inputs to
outputs. As previously mentioned, in transportation systems, many of the interrelationships
between the variables or components of a transportation system are not fully understood.
Given this, empirical models are quite common.
2- Nonlinear prediction focuses on the prediction of the behavior of systems where the
relationship between input and output is not linear. This is often the case with transportation
problems including predicting traffic demand or predicting the deterioration of transportation
infrastructure as a function of traffic, construction, and environmental factors.
3- Control focuses on controlling a system so as to achieve the desired output. Control
applications abound in transportation. Examples include signal control of traffic at road
intersections, ramp metering on freeways, dynamic route guidance, positive train control on
railroads, and air traffic control.
Input Layer
Hidden Layer
Output Layer

4- Pattern recognition or classification describes a broad range of problems where the goal is
to classify an object or put it in its right class or category. Pattern recognition is often
associated with image processing, although many prediction problems can also be regarded as
a pattern classification problem. Examples of pattern recognition or classification problems in
transportation include automatic incident detection (i.e., classifying the traffic state as incident
or incident-free), image processing for traffic data collection and for identifying cracks in
pavements or bridge structures. Another example of a transportation pattern recognition
problem involves the vital area of transportation equipment diagnosis.
5- Clustering refers to the problem of grouping cases with similar characteristics together and
identifying the number of groups or classes. For transportation, clustering might be wont to
identify specific classes of drivers supported driver behavior, for instance.
6- Planning refers to the act of formulating a program for a definite course of action intended
to achieve the desired goal. In transportation, the goal of the transportation planning process is
to identify the transportation needs of a community and to recommend the best course of
action required to meet those demands, while taking into account the economic, social, and
environmental impacts of transportation. AI-based decision support systems for transportation
planning might be quite useful, especially when accurate analytical models are lacking, and
when problems involve multiple stakeholders with often conflicting objectives.
7-Design is a key activity of the transportation engineering profession, including geometric
design of highways, interchange design, structural design of pavements and bridges, culvert
design, retaining walls design, and guardrail design, to list a few examples. AI methods could
add a lot to the value and capabilities of computer-aided design which is now commonly used
for engineering design applications, by providing additional decision-support capabilities.
8- Decision making refers to the cognitive process of selecting a course of action from among
multiple alternatives. Transportation officials are continuously faced with challenging
situations where a decision needs to be made. Examples of these situations include deciding
whether to create a replacement road, what proportion money should be allocated to
maintenance and rehabilitation activities and which road segments or bridges to maintain, and
whether to divert traffic to an alternate route in an event situation.
9- Optimization refers to the study of problems in which one seeks to minimize or maximize a
function by choosing values for a set of decision variables while satisfying a set of
constraints. Optimization problems abound in transportation. Examples include designing an
optimal transit network for a given community, developing an optimal shipping policy for a
company, developing an optimal work plan for maintaining and rehabilitating a pavement
network, and developing an optimal timing plan for a gaggle of traffic signals.
6. CONCLUSION
This paper presents an overview of the applications of AI in transportation with a
concentration in the methods used in this field. The review focused on in explanation two
methods of AI, the first method is KBS which representing the symbolic category of AI and
the second is the NN method which representing the computational intelligence category.
KBSs are now routinely used in thousands of real-world applications. Most such applications
involve relatively small knowledge bases, containing hundreds instead of thousands of units
(objects, rules, frames, cases). The next generation of KBSs could involve knowledge bases
containing many thousands or maybe many units. They will need to perform well in
increasingly complex, time-critical environments. This is a daunting task, but it promises
huge benefits in terms of the safe and efficient transportation of our traveling public. In the
last couple of decades, NNs have been widely used to solve various transportation problems
that defy traditional modeling approaches. A plethora of research efforts have shown that NNs

can be most efficient and effective when addressing complex problems for which an accurate
and complete analytical description is often too difficult to get and yet are often easily
represented by examples or patterns. NNs are particularly useful in applications of function
approximation, pattern recognition, and pattern classification, to name a few. There exists a
wide spectrum of architectures as presented earlier, each suited for specific applications (e.g.,
pavement management, short-term traffic prediction, incident detection, etc.) Flexibility and
adaptability are two of the most powerful features in neural network architectures, which
continue to expand this computational paradigm and its potential to tackle a large number of
problems in the area of transportation engineering. The last part of the paper entitled in the AI
application area in transportation tried to give examples and guidelines of AI applications in
the transportation field. It can be concluded that the application of AI in transportation is a
wide range and very useful to improve the transportation sector if it is used in a proper way
with good knowledge using suitable data.
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