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Three Pillars Towards Next-Generation Routing System

arXiv 2409.11412 ↗First posted 3 Sep 2024cs.NI cs.ET cs.LGcited by 0

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Abstract (arXiv)

The routing results are playing an increasingly important role in transportation efficiency, but they could generate traffic congestion unintentionally. This is because the traffic condition and routing system are disconnected components in the current routing paradigm. In this paper, we propose a next-generation routing paradigm that could reduce traffic congestion by considering the influence of the routing results in real-time. Specifically, we regard the routing results as the root cause of the future traffic flow, which at the same time is identified as the root cause of traffic conditions. To implement such a system, we identify three essential components: 1) the traffic condition simulation that establishes the relation between traffic flow and traffic condition with guaranteed accuracy; 2) the future route management that supports efficient simulation with dynamic route update; 3) the global routing optimization that improves the overall transportation system efficiency. Preliminary design and experimental results will be presented, and the corresponding challenges and research directions will also be discussed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 119 references that are in the map. First posted on arXiv 3 Sep 2024.

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References · 17

  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  2. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  3. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  4. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  5. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  6. 2020A Survey on Trajectory Data Management, Analytics, and Learningcited by 41
  7. 2015A Simple Baseline for Travel Time Estimation using Large-Scale Trip Datacited by 28
  8. 2022Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecastingcited by 22
  9. 2023MLPST: MLP is All You Need for Spatio-Temporal Predictioncited by 22
  10. 2018DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervisioncited by 21
  11. 2013Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labelingcited by 7
  12. 2018Finding k-Dissimilar Paths with Minimum Collective Lengthcited by 4
5 more
  1. 2021Query-by-Sketch: Scaling Shortest Path Graph Queries on Very Large Networkscited by 3
  2. 2019Planting Trees for scalable and efficient Canonical Hub Labelingcited by 1
  3. 2020Distributed Processing of k Shortest Path Queries over Dynamic Road Networkscited by 1
  4. 2021Multi-Constraint Shortest Path using Forest Hop Labelingcited by 1
  5. 2022BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scalecited by 1

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