DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

Hanyuan Zhang · Hao Wu · Weiwei Sun · Baihua Zheng

180 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Data Management and Algorithms

Abstract

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to leverage the natural abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches.

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References

2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 1979ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES 2014Travel time estimation of a path using sparse trajectories 2018When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks 2004A Simple and Effective Method for Predicting Travel Times on Freeways 2008Traffic Estimation And Prediction Based On Real Time Floating Car Data

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2020A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges 2019Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends 2021A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation 2019Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system 2019Nei-TTE: Intelligent Traffic Time Estimation Based on Fine-Grained Time Derivation of Road Segments for Smart City

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