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57th ISOCARP World Planning Congress in Doha, Qatar
57th ISOCARP World Planning Congress in Doha, Qatar
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Home
Create Account
Introduction
About the Congress
Words of Welcome
ISOCARP President
ISOCARP Secretary General
Minister of Municipality and Environment
General Rapporteur
Congress Team
Committees
Congress Committee
Local Organising Committee
ISOCARP Secretariat
Practical information
Congress Venue
Health and Safety
Exploring Doha
Accommodation
Visa
Programme
Tracks
Track 1
Track 2
Track 3
Track 4
Track 5
Detailed programme
Workshops
Technical Tours
Special Sessions
Women in Planning
Virtual Sessions
Zoom-Presenters
Zoom-Attendees
Zoom-Moderators
Presentation and Recording Guidelines
Speakers
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Proceedings
Congress Recap
Feedback Survey
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Submit your paper
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Submission Guidelines
Submit your presentation file
Registration
Fees
Tickets
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Sponsors
About ISOCARP
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YPP Workshop
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57th ISOCARP World Planning Congress in Doha, Qatar
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A Reinforcement Learning-based Adaptive Control Model for the Future Street Infrastructure Planning: An Algorithm and A Case Study
This submission has open access
Abstract
With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%.
Submission ID :
ISO158
Submission Type
Research Paper
Submission Track
3: Smartness and development. Al-Souq: innovating for performance and management
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Associated Sessions
Hybrid | Track 3 | Session 6. Strategic Planning Methods
Author
Co-Authors
Mr Qiming Ye
PhD Student
,
Imperial College London
YF
Dr Yuxiang Feng
Postdoc, Research Associate
,
Imperial College London
JH
Mrs Jing Han
PhD Student
,
Tongji University
MS
Dr Marc Stettler
Senior Lecturer
,
Imperial College London
PA
Dr Panagiotis Angeloudis
Reader
,
Imperial College London
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