A DEEP TRANSPORTATION MODEL TO PREDICT THE HUMAN MOBILITY FOR AUTONOMOUS VEHICLE With significant demographic expansion and urbanization, transport overcrowding has been a huge significant crisis globally. Urban sprawl triggers major socioeconomic losses annually worldwide associated with fuel consumption, unnecessary environmental damage, loss of opportunity and decreased productivity. Recognizing how people travel and choose method of transportation via a large-scale transportation system is a key to traffic congestions detection and traffic management. Human mobility is a diverse field in physics and computer science and has gained a great deal of interest in recent decades. Such descriptive structures and predictive methods have been formulated for modeling and assessing human movement. Even so, multi-source interdependent information like portable devices, GPS and social networking sites ensure a innovative driving factor for analyzing metropolitan trends of human movement. More and more exponential development has been made across the last decade in self-driving automated vehicles, primarily powered by developments in deep learning and artificial intelligence. This invention acquires broad and diverse information and constructs a cognitive model called Deep transportation model, to simulate and forecast human mobility in the autonomous vehicles. The core feature of deep transportation model is the deep learning framework like LSTM architecture that strives to recognize the aspects of human mobility and transport from broad and streaming data. The deep transportation model dynamically visualize or accurately predict migration of people and their means of transportation in a large-scale transportation system depending on the training paradigm, provided any time span, particular area of the city or observable migration of people. Extensive research and evaluations show the reliability and outstanding quality of our model and indicate that human modes of transport can be projected and interpreted more effectively. A DEEP TRANSPORTATION MODEL TO PREDICT THE HUMAN MOBILITY FOR AUTONOMOUS VEHICLE Drawings: Input SPre-pocessin ModuleDeep _earIng Moduk Visuaiztion and Evalatio L--- .- - - - - - -.- .- - - - - .-.-------2 -4 Figure 1: The architecture of a deep transportation model to predict the human mobility for autonomous vehicle.
A DEEP TRANSPORTATION MODEL TO PREDICT THE HUMAN MOBILITY FOR AUTONOMOUS VEHICLE
2021-03-18
Patent
Electronic Resource
English
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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