Artificial Intelligence in the Transportation Sector

Introduction

An area of computer science, dealing with creation of intelligent machines which would act and react like humans, is Artificial Intelligence (AI). Activities like speech recognition, learning, planning and problem solving are included in every machine with artificial intelligence. Using AI makes the human work easier and reduce the error rate as compared to humans.

This is the report of project artificial intelligence, provided by the University of Northampton. The introduction part of the report includes introduction of report, task and project background.

After introduction, the report describes the problem domain, which is to be solved using artificial intelligence technique. The potential solution for the problem domain is described in solution phase of report. The overall summary of whole report is presented in the conclusion and different sources from where information was dragged, is referenced in references section.

The major focus of the project was to develop a piece of software which would connect with the concept of artificial intelligence technique for ease and smoothness of transportation.

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Coming from this century, we witness the most traffic jams, road accidents and pollution brought by the unmanaged transportation system. But transportation being the basic industry of national economy development, it’s an origination of progress and development of any nation and society (Jiang, Liu, Niu, 2015). Hence, the task was to come up with solution for the issues that has been rising in transportation sector, by applying proper AI technique.

Background

Using Artificial Intelligence uplifts the transportation sector. The sensors used in motor cars, airplanes, ships and trains are making transportation system smooth.

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Thus, using AI in this sector makes less errors in comparison to human and makes the transportation safer, smarter and more efficient. Besides these, using these techniques help to predict the decision effectively which results in lower cost of labor in the transportation. Hence, it is needed to detect the risks, ease traffic congestions, reduce air pollution along with analyzing the travel demand and pedestrian activities.

Applying AI in transportation ensures the safety of driver and pedestrian, increases the efficiency, and lower the costs too. It makes sure that the system is reliable, predictable, effective and environmental.

To find out the solution for problem for the project provided by the university, following artificial intelligence techniques were applied:

Search algorithms:

Search algorithms if one of the important parts of artificial intelligence. It is considered as essential part of problem solving. The search algorithm is further divided into two parts i.e. blind search and informed search. Blind search includes breadth first search, uniform cost search, depth first search, depth limited search, iterative deeping depth first search and bidirectional search. Similarly, informed search includes best first search and A* search. Out of all searching method, BFS, DFS and A* search method were kept in priority and hence applied to make the system.

Rule based system:

Rule based system is combination of “if-then” statements that uses set of assertions. It’s a knowledge-based system and can be considered as an expert system. This system encapsulates human intelligence like knowledge and hence takes the decision quickly. It is composed of sert of facts and set of rules. Fuzzy logic was applied as rule-based system, to make the system for the project.

Both techniques have its own pros and holds some limitations too. The comparison between the techniques are presented below:

  • Search Algorithm
  • The system is simple, using IF-THEN statements.
  • This technique is probabilistic and thus uses statistical models.
  • It is developed from combined knowledge of human experts.
  • This technique can become slow to implement.
  • Rule based system is easy to write and implement.
  • Writing wrong rule can result in false negatives and false positives.

Problem Domain

Congested roadways, heavy traffic jams, regular accidents and unmanaged scheduling of vehicles, etc. are major transportation issues that public has been facing these days. Due to these problems, public has been struggling on daily basis like delay in work, loss of time, etc. Vehicle scheduling problem is also required to make the transportation effective. Hence, to overcome this issue, a system needs to be developed. The potential solution for the problems mentioned above, is further discussed in solution phase.

Literature review

Finding out the solution for project becomes easier only if background information is researched and studied. Hence, following journals and conference papers were studied to find out the current situation of transportation system. On the use of Artificial Intelligence techniques in Intelligent Transportation Systems, Mirialys Machin, Julio A. Sanguesa, Piedad Garrido, Francisco J. Martinez (2018)

With the increase in population, demand for the vehicles have been increasing. The increasing vehicles is resulting in the unmanaged transportation along with complexity in mobility needs. Hence, the evolution of transportation system is necessary to solve the problems. To solve these problems and to overcome these issues, artificial intelligence techniques should be applied. Improving Intelligent Transportation System is possible after applying AI techniques in different fields like vehicle control, traffic control and prediction, as well as road safety and accident prediction. Implementing Artificial Neural Networks, Genetic algorithms, Fuzzy Logic and Expert system seems promising to analyze and manage the transportation system.

Study on the Vehicle Scheduling Problem in Transportation System, Bingqiang Situ, Wenzhou Jin, (2009)

One of the major issues that transportation system is facing is poor operational planning of public transportation system. Thus, this classical optimization problem should be solved by preparing departure time model along with departure interval model. Heuristic algorithms help to solve the problems by optimizing the departure time.

Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel-Time Prediction, Yongchang Ma, Mashrur Chowdhury, Adel Sadek, and Mansoureh Jeihani (2012)

This paper focuses on online traffic measurements available from vehicle infrastructure integration, where mobile devices and vehicles communicate to improve mobility and safety in transportation system. The paper also focuses on the proposed intelligent techniques i.e. artificial neural networks and support vector regression. The techniques would help to determine future travel time and current travel time.

Solution

The possible solution for the problem provided by the university is discussed in this phase of report. In order to solve the vehicle scheduling problem, heuristic algorithms are used. Using this algorithm optimizes the departure time and interval. The headway time is formulated as following: where ‘xj’ denotes the departure time of vehicles, ‘i’ denotes any station, ‘tij’ denotes time taken to reach station ‘i’ from original station, ‘J’ denotes the transit routes, ‘I’ denotes set of transit stations, ‘j’ and ‘k’ denotes any stations. Then, where pik is number of routes that could arrive in station ‘i’ with route i.e. ‘j’, and Yi is number of routes at the same time. Then, the vehicle scheduling problem is formulated as follow:

The equation above is later formulated as:

In the above equation, ‘Dj’ denotes departure interval of route where ‘hj’ is minimum interval of two vehicles departing, ‘Hj’ is maximum interval of two vehicles departing, ‘Bj’ denotes operating cost of bus in route ‘j’, ‘Cj’ denotes the capacity of any bus. By applying heuristic algorithm in above equations, the departure interval of any transport can be calculated and hence the schedule can be calculated easily. A good and maintained schedule of vehicles helps to reduce the traffic jam and prevents from loss of time.

Conclusion

To sum up, AI techniques heuristic algorithm was used to reach the solution for the problems. Using heuristic algorithm led to calculate the departure interval of transports and also led to display the schedule of transportation. As mentioned in the literatures which were reviewed above, applying AI techniques in transportation really helps to lower the issues that have been occurring recently. Techniques like Neural networks, Fuzzy logic, Genetic algorithms, Expert system helps to make the transport system better.

Using artificial intelligence in transportation holds both strengths and weaknesses. It will help to increase safety and reduce the traffic accidents. Use of this technology will also reduce the production costs. It helps to increase the processing and predicting ability than humans and reduces the error rate while performing any type of work, compared to human. However, it also has some limitations. More implementation and use of AI everywhere invite unemployment. The human works will be covered by AI. Along with this, it’ll require more power to execute and hence results in deterioration natural resources.

References

  1. Jiang, Liu, Niu, M., (2019). The Evaluation Studies of Regional Transportation Accessibility Based on Intelligent Transportation System: Take the Example in Yunnan Province of China. In International Conference on Intelligent Transportation, Big Data and Smart City. Halong Bay, Vietnam, 19-20 Dec. 2015. Vietnam: IEEE. 127.
  2. Machin, M., Sanguesa, J., Garrido, P. and Martinez, F. (2018). On the use of artificial intelligence techniques in intelligent transportation systems. 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).
  3. Bingqiang Situ and Wenzhou Jin (2009). Study on the vehicle scheduling problem in transportation system. 2009 2nd IEEE International Conference on Computer Science and Information Technology.
  4. www.javatpoint.com. (2020). Search Algorithms in AI - Javatpoint. [online] Available at: https://www.javatpoint.com/search-algorithms-in-ai [Accessed 18 Jan. 2020].
  5. Ma, Y., Chowdhury, M., Sadek, A. and Jeihani, M. (2012). Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel-Time Prediction. IEEE Transactions on Intelligent Transportation Systems, 13(3), pp.1369-1382.
  6. Online sciences. 2019. Artificial Intelligence in transportation, Advantages, Disadvantages. [ONLINE] Available at: https://www.online-sciences.com/robotics/artificial-intelligence-in-transportation-advantages-disadvantages-applications/. [Accessed 10 January 2020].
Updated: May 30, 2022
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Artificial Intelligence in the Transportation Sector. (2022, May 30). Retrieved from https://studymoose.com/artificial-intelligence-in-the-transportation-sector-essay

Artificial Intelligence in the Transportation Sector essay
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