# Three Pillars Towards Next-Generation Routing System

arXiv: https://arxiv.org/abs/2409.11412 · HTML: https://mlsanity.com/paper/2409.11412/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 119 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version](https://mlsanity.com/paper/2203.15737/) (2022)
- [A Survey on Trajectory Data Management, Analytics, and Learning](https://mlsanity.com/paper/2003.11547/) (2020)
- [A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data](https://mlsanity.com/paper/1512.08580/) (2015)
- [Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting](https://mlsanity.com/paper/2211.16126/) (2022)
- [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://mlsanity.com/paper/2309.13363/) (2023)
- [DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision](https://mlsanity.com/paper/1802.02147/) (2018)
- [Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling](https://mlsanity.com/paper/1304.4661/) (2013)
- [Finding k-Dissimilar Paths with Minimum Collective Length](https://mlsanity.com/paper/1809.06831/) (2018)
- [Query-by-Sketch: Scaling Shortest Path Graph Queries on Very Large Networks](https://mlsanity.com/paper/2104.09733/) (2021)
- [Planting Trees for scalable and efficient Canonical Hub Labeling](https://mlsanity.com/paper/1907.00140/) (2019)
- [Distributed Processing of k Shortest Path Queries over Dynamic Road Networks](https://mlsanity.com/paper/2004.02580/) (2020)
- [Multi-Constraint Shortest Path using Forest Hop Labeling](https://mlsanity.com/paper/2109.04221/) (2021)
- [BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scale](https://mlsanity.com/paper/2204.11012/) (2022)

## 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.