Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

Muhammad Aqib, Rashid Mehmood, Ahmed Alzahrani, Iyad Katib, Aiiad Albeshri and Saleh M. Altowaijri
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Muhammad Aqib: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Rashid Mehmood: High-Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Ahmed Alzahrani: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Iyad Katib: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Aiiad Albeshri: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Saleh M. Altowaijri: Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia

Sustainability, 2019, vol. 11, issue 10, 1-33

Abstract: Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

Keywords: rapid transit systems; metro; London underground; tube; big data; deep learning; TensorFlow; Convolution Neural Networks (CNNs); in-memory computing; Graphics Processing Units (GPUs); transport planning; transport prediction; smart cities; smart transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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