Public Transit Arrival Prediction: a Seq2Seq RNN Approach

• URL: http://arxiv.org/abs/2210.01655v2
• Date: Sat, 31 Aug 2024 18:00:40 GMT
• Title: Public Transit Arrival Prediction: a Seq2Seq RNN Approach
• Authors: Nancy Bhutani, Soumen Pachal, Avinash Achar,
• Abstract summary: Bus arrival time prediction (BATP) is a challenging problem especially in the developing world. A novel data-driven model based on recurrent neural networks (RNNs) is proposed for BATP (in real-time) in the current work.
• Score: 1.9294297881760765
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation etc. The developing world in particular is plagued by additional factors like lack of lane discipline, excess vehicles, diverse modes of transport and so on. This renders the bus arrival time prediction (BATP) to be a challenging problem especially in the developing world. A novel data-driven model based on recurrent neural networks (RNNs) is proposed for BATP (in real-time) in the current work. The model intelligently incorporates both spatial and temporal correlations in a unique (non-linear) fashion distinct from existing approaches. In particular, we propose a Gated Recurrent Unit (GRU) based Encoder-Decoder(ED) OR Seq2Seq RNN model (originally introduced for language translation) for BATP. The geometry of the dynamic real time BATP problem enables a nice fit with the Encoder-Decoder based RNN structure. We feed relevant additional synchronized inputs (from previous trips) at each step of the decoder (a feature classically unexplored in machine translation applications). Further motivated from accurately modelling congestion influences on travel time prediction, we additionally propose to use a bidirectional layer at the decoder (something unexplored in other time-series based ED application contexts). The effectiveness of the proposed algorithms is demonstrated on real field data collected from challenging traffic conditions. Our experiments indicate that the proposed method outperforms diverse existing state-of-art data-driven approaches proposed for the same problem.

Related papers

• ArrivalNet: Predicting City-wide Bus/Tram Arrival Time with Two-dimensional Temporal Variation Modeling [10.788986436621364]
  This paper proposes textitArrivalNet, a two-dimensional temporal variation-based multi-step ATP for buses and trams. It decomposes the one-dimensional temporal sequence into intra-periodic and inter-periodic variations, which can be recast into two-dimensional tensors. The transformed 2D blocks in different frequencies have an image-like feature representation that enables effective learning with computer vision backbones.
  arXiv  Detail & Related papers  (2024-10-17T10:17:23Z)
• Multiple Areal Feature Aware Transportation Demand Prediction [2.996323123990199]
  We propose a novel multi-feature-aware graph convolutional recurrent network (ST-MFGCRN) that fuses multiple areal features during-temproal understanding. We evaluate the proposed model on two real-world transportation datasets.
  arXiv  Detail & Related papers  (2024-08-23T07:51:10Z)
• Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations [20.174094418301245]
  Traffic flow forecasting is a fundamental research issue for transportation planning and management. In recent years, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) have achieved great success in capturing spatial-temporal correlations for traffic flow forecasting. However, two non-ignorable issues haven't been well solved: 1) The message passing in GNNs is immediate, while in reality the spatial message interactions among neighboring nodes can be delayed.
  arXiv  Detail & Related papers  (2024-02-02T08:55:23Z)
• 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)
• Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments [110.79706180350507]
  We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality. Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
  arXiv  Detail & Related papers  (2022-03-11T09:46:22Z)
• Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting [11.495992519252585]
  We argue that such correlations are universal and play a pivotal role in traffic flow. We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor. The proposed STNN model can be applied on any unseen traffic networks.
  arXiv  Detail & Related papers  (2021-09-11T09:04:35Z)
• Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation [99.54200992904721]
  We propose a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM) to jointly learn the evolutionary patterns of OD and DO ridership. An OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices. A DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously.
  arXiv  Detail & Related papers  (2021-07-02T10:11:51Z)
• SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network [2.524966118517392]
  We have designed a simplified S-temporal GNN(SST-GNN) that effectively encodes the dependency by separately aggregating different neighborhood. We have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets.
  arXiv  Detail & Related papers  (2021-03-31T18:28:44Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention [88.33372574562824]
  We propose a novel framework based on feed-forward network (FFN) for ETA, FFN with Multi-factor self-Attention (FMA-ETA) The novel Multi-factor self-attention mechanism is proposed to deal with different category features and aggregate the information purposefully. Experiments show FMA-ETA is competitive with state-of-the-art methods in terms of the prediction accuracy with significantly better inference speed.
  arXiv  Detail & Related papers  (2020-06-07T08:10:47Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.