Abstract
Realistically modelling behaviour and interaction of heterogeneous road users (pedestrians and vehicles) in mixed-traffic zones (a.k.a. shared spaces) is challenging. The dynamic nature of the environment, heterogeneity of transport modes, and the absence of classical traffic rules make realistic microscopic traffic simulation hard problems. Existing multi-agent-based simulations of shared spaces largely use an expert-based approach, combining a symbolic (e.g. rule-based) modelling and reasoning paradigm (e.g. using BDI representations of beliefs and plans) with the hand-crafted encoding of the actual decision logic. More recently, deep learning (DL) models are largely used to derive and predict trajectories based on e.g. video data. In-depth studies comparing these two kinds of approaches are missing. In this work, we propose an expert-based model called GSFM that combines Social Force Model and Game theory and a DL model called LSTM-DBSCAN that manipulates Long Short-Term Memories and density-based clustering for multi-agent trajectory prediction. We create a common framework to run these two models in parallel to guarantee a fair comparison. Real-world mixed traffic data from shared spaces of different layout are used to calibrate/train and evaluate the models. The empirical results imply that both models can generate realistic predictions, but they differ in the way of handling collisions and mimicking heterogeneous behaviour. Via a thorough study, we draw the conclusion of their respective strengths and weaknesses
Original language | English |
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Title of host publication | Multi-Agent-Based Simulation XXI |
Subtitle of host publication | 21st International Workshop, MABS 2020, Auckland, New Zealand, May 10, 2020, Revised Selected Papers |
Editors | S. Swarup, B.T.R. Savarimuthu |
Place of Publication | Cham |
Publisher | Springer |
Pages | 13-27 |
Number of pages | 15 |
ISBN (Electronic) | 978-3-030-66888-4 |
ISBN (Print) | 978-3-030-66887-7 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 21st International Workshop on Multi-Agent-Based Simulation, MABS 2020 - Virtual, Auckland, New Zealand Duration: 10 May 2020 → 10 May 2020 Conference number: 21 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12316 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 21st International Workshop on Multi-Agent-Based Simulation, MABS 2020 |
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Abbreviated title | MABS |
Country/Territory | New Zealand |
City | Auckland |
Period | 10/05/20 → 10/05/20 |
Keywords
- ITC-CV
- n/a OA procedure