MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation

• URL: http://arxiv.org/abs/2601.02943v1
• Date: Tue, 06 Jan 2026 11:40:20 GMT
• Title: MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation
• Authors: Wenzhao Jiang, Jindong Han, Ruiqian Han, Hao Liu,
• Abstract summary: We propose a scalable and adaptive framework that integrates link-level modeling with industrial route-level TTE systems.<n>We construct a stabilized graph mixture-of-experts network to handle heterogeneous traffic patterns.<n>Experiments on real-world datasets validate MixTTE significantly reduces prediction errors compared to seven baselines.
• Score: 10.549493962440804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level dependency modeling, they struggle to capture city-scale traffic dynamics and long-tail scenarios, leading to unreliable predictions in large urban networks. In this paper, we propose \model, a scalable and adaptive framework that synergistically integrates link-level modeling with industrial route-level TTE systems. Specifically, we propose a spatio-temporal external attention module to capture global traffic dynamic dependencies across million-scale road networks efficiently. Moreover, we construct a stabilized graph mixture-of-experts network to handle heterogeneous traffic patterns while maintaining inference efficiency. Furthermore, an asynchronous incremental learning strategy is tailored to enable real-time and stable adaptation to dynamic traffic distribution shifts. Experiments on real-world datasets validate MixTTE significantly reduces prediction errors compared to seven baselines. MixTTE has been deployed in DiDi, substantially improving the accuracy and stability of the TTE service.

Related papers

• Geographically-aware Transformer-based Traffic Forecasting for Urban Motorway Digital Twins [2.0569660530137583]
  This paper introduces a Geographically-aware Transformer-based Traffic Forecasting GATTF model.<n>The model has been evaluated using real-time data from the Geneva motorway network in Switzerland.<n>Results confirm that incorporating geographical awareness through MI enhances the accuracy of GATTF forecasting compared to a standard model.
  arXiv  Detail & Related papers  (2026-02-05T18:33:03Z)
• Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction [0.0]
  This study systematically identifies and evaluates twelve advanced TSF models.<n>It assesses performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy.
  arXiv  Detail & Related papers  (2026-01-06T03:58:57Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks [53.98633183204453]
  In this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network.<n>A world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling.<n>In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions.
  arXiv  Detail & Related papers  (2025-05-03T06:23:18Z)
• TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridors [5.354266007520225]
  Existing deep learning models often lack spatial generalizability, rely on complex architectures, and struggle with real-time deployment.<n>We propose the Temporal Graph-based Digital Twin (TGDT), a scalable framework that integrates Temporal Convolutional Networks and Attentional Graph Neural Networks.
  arXiv  Detail & Related papers  (2025-04-25T01:28:32Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework [0.6554326244334868]
  State-of-the-art models often struggle to consider the data in the best way possible. We propose a novel framework to incorporate travel times between stations into a weighted adjacency matrix of a Graph Neural Network architecture.
  arXiv  Detail & Related papers  (2024-07-17T01:11:07Z)
• Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction [24.26429523848735]
  Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services. We introduce a Congestion Prediction Mixture-of-Experts, CP-MoE, to address the challenges. CP-MoE has been deployed in DiDi to improve the accuracy and reliability of the travel time estimation system.
  arXiv  Detail & Related papers  (2024-06-14T12:57:17Z)
• 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)
• 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.