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Harisu Muhd Muhd.pptx

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Harisu Muhd Muhd.pptx

  1. 1. BAYERO UNIVERSITY KANO FACULTY OF ENGINEERING DEPARTMENT OF CIVIL ENGINEERING CIV8331: Advanced Traffic Engineering ASSIGNMENT ON Review on Microscopic Traffic Models Using Artificials Intelligence By Harisu Muhammad Muhammad SPS/20/MCE/00053 SUPERVISED BY
  2. 2. Outlines Introduction Of Artificial Intelligence. Review of Microscopic Traffic models for Artificial Intelligence. Self-driving car following Models Applications of Microscopic Traffic Models in Artificial Intelligence Advantage of Microscopic Traffic Models in Artificial Intelligence Disadvantages of Microscopic Traffic Models in Artificial Intelligence
  3. 3. Outlines cont…………….. Current state of Research Future Research Conclusion Recommendation  References
  4. 4. Introduction Of Artificial Intellegence. • Artificial Intelligence (AI) are widely expected to bring a profound revolution in transportation systems. • Although the timeline and the means of introducing (AI) on a massive scale are unknown, many automotive and IT companies already work on self-driving cars (or software/hardware/services for them), with plenty of trials and pilot projects on testing self-driving cars and Connected and automated vehicles (CAVs) Similarly, standards for communication between vehicles (V2V - vehicle-to-vehicle communication, and V2I - vehicle-to-infrastructure) are being developed.
  5. 5. Review of Microscopic Traffic models for Artificial Intelligence. • Microscopic models describe each vehicle's behavior and interactions in the traffic system, making more detailed modeling for each movement of the vehicle . For this reason, microscopic models can be applied with a much higher level of detail.(Halim et al., 2016). • In microscopic traffic models describe the dynamics of traffic flow at the level of each individual Self driving Car., Self driving Car are represented as separate agents, whose motion is governed by specific rules. • Those agents may be in interaction, which also has an impact on their behavior. (Gora et al., 2020) • Those agents may be in interaction, which also has an impact on their behavior
  6. 6. Review Cont…………. There are many well established microscopic models for conventional vehicles, such as Gipps model, Wiedemann model, NagelSchreckenberg model or Intelligent Driver Model. Since introduction of automation and communication between vehicles may significantly change vehicles’ behavior on a microscopic level, it is clear that a need for new microscopic models including CAVs emerged and many new microscopic models have recently appeared, along with studies using such models (Gora et al., 2020) It is believed that autonomous vehicles will replace conventional human drive vehicles in the next decades due to the emerging autonomous driving technology, which will definitely bring a massive transformation in the road transport sector.
  7. 7. Self-driving car following Models • Gipp's model is based directly on self driving car functions and expectancy for vehicles in a stream of traffic and examine the longitudinal movement of each vehicle in front. • Predictive model is a statistical technique using machine learning and data mining where Self driving Car Used to predict and forecast likely future outcomes with the aid of historical and existing data. • Artificial intelligence models are the tools and algorithms used to train computers to process and analyze data – just as humans do. the model incorporates the data gathered from these travels and can give more accurate route information by recognizing changes in traffic flow.
  8. 8. Applications of Microscopic Traffic Models in Artificial Intelligence • Predictive Models the rapid development of intelligent transport systems (ITS) has increased the need to propose advanced methods to Predict traffic information. These methods play an important role in the success of ITS subsystems such as advanced traveler information systems, advanced traffic management systems, advanced public transportation systems, and commercial vehicle operations.(Abduljabbar et al., 2019) • Artificial intelligence (AI) applications are utilized to simulate human intelligence for either solving a problem or making a decision.AI provides the advantages of permanency, reliability, and cost- effectiveness while also addressing uncertainty and speed in either solving a problem or reaching a decision. (Chowdhury & Sadek, 2012)
  9. 9. Advantage of Microscopic Traffic Models in Artificial Intelligence • AVs have the following obvious advantages: smaller gap acceptance, shorter headway, no reaction time in front of the signal system, maintenance of a constant desired speed, and stable acceleration and deceleration. • AI is deemed to be a good fit for transportation systems to overcome the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation.(Abduljabbar et al., 2019) • The future vision for intelligent urban mobility is smarter decision- making based on real-time information, and network optimization by efficient use of infrastructure.
  10. 10. Disadvantages of Microscopic Traffic Models in Artificial Intelligence • Data protection issues. The first problem that arises, is in that, being connected all the time with the whole environment, it can become a cyber-problem of data protection. Even the correct handling of road networks can be compromised. • High cost of implementation. Autonomous vehicle infrastructure revolves around 5G network coverage, which is still expensive, so it may take government’s considerable time to invest in sufficient infrastructure for optimal performance of autonomous vehicles.
  11. 11. Current state of Research • Development of an AI-based for an efficient transportation system is very complicated, due to the creation of a mechanical intelligence along with the proper understanding the human-based information. • The research and development of autonomous driving systems have been developing rapidly. Still, the industry and the governments have not yet reached a clear consensus on how to conduct safety testing and reliable proving in the real world. Because dangerous traffic scenes are difficult to exhaust, there are technical bottlenecks in scene-based actual vehicle testing methods.
  12. 12. Future Research • Future research will be directed towards enhancing predictive operations using more than two features and more than one hidden layer for the structure of the model. Furthermore, it is estimated that if 30% of vehicles were self-driving vehicles by 2030, then the congestion cost will be decreased from 38 $ billion to around 26 $ billion in Australian cities.(Abduljabbar et al., 2019) • AIs techniques can be used to find an optimum and fastest route for the convenient of road users and delivery service purposes. One European company has managed to detect real-time truck performance and driver behavior by analyzing information from sensors on the roads.
  13. 13. Conclusion • The review also focused on a number of application areas which are expected to have more influence in future cities including autonomous vehicles, Microscopic models of CAVs showed a large amount of approaches. Although some models are applied more often, there are no universal methods and it is difficult to compare different models and draw proper conclusions regarding the outcomes. • so it is not obvious which models may eventually become the most accurate and useful
  14. 14. Recommendation • Building a repository of real-world data (e.g., trajectories) for Artificial Intelligences, establishing standards for building, calibrating and validating traffic models of CAV using real- world data, building scenarios (e.g., standard road networks) • to conduct experiments for CAVs, developing a generic, open, software-agnostic benchmarking platform for the evaluation of alternative modelling approaches and conducting further metaresearch to build a database of models and research works with information about their assumptions, inputs, outcomes, scope of applicability, in order to ensure comparability and reproducibility of results.
  15. 15. References • Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189. • Chowdhury, M., & Sadek, A. W. (2012). Advantages and limitations of artificial intelligence. Artificial Intelligence Applications to Critical Transportation Issues, 6(3), 360–375. • Gora, P., Katrakazas, C., Drabicki, A., Islam, F., & Ostaszewski, P. (2020). Microscopic traffic simulation models for connected and automated vehicles (CAVs) – state-of-the-art. Procedia Computer Science, 170, 474– 481. https://doi.org/10.1016/j.procs.2020.03.091 • HUANG, S., & SADEK, A. W. (2012). Artificial intelligence and microscopic traffic simulation models. Artificial Intelligence Applications to Critical Transportation Issues, 65.
  16. 16. . Thank you For Listening

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