Deep trip generation with graph neural networks for bike sharing system expansion

• URL: http://arxiv.org/abs/2303.11977v1
• Date: Mon, 20 Mar 2023 16:43:41 GMT
• Title: Deep trip generation with graph neural networks for bike sharing system expansion
• Authors: Yuebing Liang, Fangyi Ding, Guan Huang, Zhan Zhao
• Abstract summary: We propose a graph neural network (GNN) approach to predicting the station-level demand based on multi-source urban built environment data. The proposed approach can be regarded as a generalized spatial regression model, indicating the commonalities between spatial regression and GNNs.
• Score: 7.737133861503814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction of the number of trips generated by these new stations across the whole system. Previous studies typically rely on relatively simple regression or machine learning models, which are limited in capturing complex spatial relationships. Despite the growing literature in deep learning methods for travel demand prediction, they are mostly developed for short-term prediction based on time series data, assuming no structural changes to the system. In this study, we focus on the trip generation problem for BSS expansion, and propose a graph neural network (GNN) approach to predicting the station-level demand based on multi-source urban built environment data. Specifically, it constructs multiple localized graphs centered on each target station and uses attention mechanisms to learn the correlation weights between stations. We further illustrate that the proposed approach can be regarded as a generalized spatial regression model, indicating the commonalities between spatial regression and GNNs. The model is evaluated based on realistic experiments using multi-year BSS data from New York City, and the results validate the superior performance of our approach compared to existing methods. We also demonstrate the interpretability of the model for uncovering the effects of built environment features and spatial interactions between stations, which can provide strategic guidance for BSS station location selection and capacity planning.

Related papers

• Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System [0.0]
  Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. In this study, an optimised-temporal graph is constructed using trip data from Bikes Moby, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS.
  arXiv  Detail & Related papers  (2024-03-28T12:29:25Z)
• Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning [2.684349361878955]
  In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. We apply this method to public bicycle rental-and-return data in Seoul, South Korea.
  arXiv  Detail & Related papers  (2024-03-24T07:29:12Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information [0.0]
  This paper exploits simple structures for predicting pedestrian trajectories, based on Transformer Networks. We adapt their use to the problem of vehicle trajectory prediction in urban scenarios in prediction horizons up to 5 seconds. Our model achieves state-of-the-art results and proves to be flexible and adaptable to different types of urban contexts.
  arXiv  Detail & Related papers  (2021-06-01T15:18:55Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study [12.477331187546216]
  The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System. It applies machine learning two levels: network and station.
  arXiv  Detail & Related papers  (2020-09-20T06:46:41Z)
• STDI-Net: Spatial-Temporal Network with Dynamic Interval Mapping for Bike Sharing Demand Prediction [3.7875603451557076]
  In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Interval Network (STDI-Net) The method predicts the number of renting and returning orders of multiple connected stations in the near future by modeling joint spatial-temporal information. Extensive experiments are conducted on the NYC Bike dataset, the results demonstrate the superiority of our method over existing methods.
  arXiv  Detail & Related papers  (2020-06-07T08:52:40Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction [116.66657468425645]
  We propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system. A similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation.
  arXiv  Detail & Related papers  (2020-01-14T16:47:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.