TY - GEN

T1 - Mixed traffic trajectory prediction using LSTM–based models in shared space

AU - Cheng, Hao

AU - Sester, Monika

N1 - Funding Information: Acknowledgements The authors cordially thank the funding provided by DFG Training Group 1931 for SocialCars and the participants of the research project MODIS (Multi mODal Intersection Simulation) for providing the dataset of road user trajectories used in this work. Funding Information: The authors cordially thank the funding provided by DFG Training Group 1931 for SocialCars and the participants of the research project MODIS (Multi mODal Intersection Simulation) for providing the dataset of road user trajectories used in this work. Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2018/12/24

Y1 - 2018/12/24

N2 - Real–world behaviors of human road users in a non-regulated space (shared space) are complex. Firstly, there is no explicit regulation in such an area. Users self-organize to share the space. They are more likely to use as little energy as possible to reach their destinations in the shortest possible way, and try to avoid any potential collision. Secondly, different types of users (pedestrians, cyclists, and vehicles) behave differently. For example, pedestrians are more flexible to change their speed and trajectory, while cyclists and vehicles are more or less limited by their travel device—abrupt changes might lead to danger. While there are established models to describe the behavior of individual humans (e.g. Social Force model), due to the heterogeneity of transport modes and diversity of environments, hand-crafted models have difficulties in handling complicated interactions in mixed traffic. To this end, this paper proposes using a Long Short–Term Memory (LSTM) recurrent neural networks based deep learning approach to model user behaviors. It encodes user position coordinates, sight of view, and interactions between different types of neighboring users as spatio–temporal features to predict future trajectories with collision avoidance. The real–world data–driven method can be trained with pre-defined neural networks to circumvent complex manual design and calibration. The results show that ViewType-LSTM, which mimics how a human sees and reacts to different transport modes can well predict mixed traffic trajectories in a shared space at least in the next 3 s, and is also robust in complicated situations.

AB - Real–world behaviors of human road users in a non-regulated space (shared space) are complex. Firstly, there is no explicit regulation in such an area. Users self-organize to share the space. They are more likely to use as little energy as possible to reach their destinations in the shortest possible way, and try to avoid any potential collision. Secondly, different types of users (pedestrians, cyclists, and vehicles) behave differently. For example, pedestrians are more flexible to change their speed and trajectory, while cyclists and vehicles are more or less limited by their travel device—abrupt changes might lead to danger. While there are established models to describe the behavior of individual humans (e.g. Social Force model), due to the heterogeneity of transport modes and diversity of environments, hand-crafted models have difficulties in handling complicated interactions in mixed traffic. To this end, this paper proposes using a Long Short–Term Memory (LSTM) recurrent neural networks based deep learning approach to model user behaviors. It encodes user position coordinates, sight of view, and interactions between different types of neighboring users as spatio–temporal features to predict future trajectories with collision avoidance. The real–world data–driven method can be trained with pre-defined neural networks to circumvent complex manual design and calibration. The results show that ViewType-LSTM, which mimics how a human sees and reacts to different transport modes can well predict mixed traffic trajectories in a shared space at least in the next 3 s, and is also robust in complicated situations.

KW - Long short–term memory

KW - Mixed traffic

KW - Shared space

KW - Trajectory prediction

UR - http://www.scopus.com/inward/record.url?scp=85044822355&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-78208-9_16

DO - 10.1007/978-3-319-78208-9_16

M3 - Conference contribution

AN - SCOPUS:85044822355

SN - 9783319782072

T3 - Lecture Notes in Geoinformation and Cartography

SP - 309

EP - 325

BT - Geospatial Technologies for All - Selected Papers of the 21st AGILE Conference on Geographic Information Science

T2 - 21st AGILE Conference on Geographic Information Science, 2018

Y2 - 12 June 2018 through 15 June 2018

ER -