When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks
By Junbo Zhang, Wei Cao, Jian Li, Yu Zheng. AAAI Conference on Artificial Intelligence, 2018
357 citations recorded by the source corpus.
Estimating the travel time of any path (denoted by a sequence of connected road segments) in a city is of great importance to traffic monitoring, route planning, ridesharing, taxi/Uber dispatching, etc. However, it is a very challenging problem, affected by diverse complex factors, including spatial correlations, temporal dependencies, external conditions (e.g. weather, traffic lights). Prior work usually focuses on estimating the travel times of individual road segments or sub-paths and then summing up these times, which leads to an inaccurate estimation because such approaches do not consider road intersections/traffic lights, and local errors may accumulate. To address these issues, we propose an end-to-end Deep learning framework for Travel Time Estimation called DeepTTE that estimates the travel time of the whole path directly. More specifically, we present a geo-convolution operation by integrating the geographic information into the classical convolution, capable of capturing spatial correlations. By stacking recurrent unit on the geo-convoluton layer, our DeepTTE can capture the temporal dependencies simultaneously. A multi-task learning component is given on the top of DeepTTE, that estimates the travel time of both the entire path and each local path simultaneously during the training phase. The extensive experiments on two large-scale datasets shows our DeepTTE significantly outperforms the state-of-the-art methods.
Subfields: Building and Construction, Transportation
View Paper: https://www.semanticscholar.org/paper/b3ecec764cd665b4c1c149a98539f2b8dfcb8738
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Citations in the corpus
- A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “For example, the authors in [132, 133] first regard the given route as a sequence of segments.”
- Ridesourcing systems: A framework and review (2019, background): “…under diverse settings, such as pricing for dial-a-ride problem ( Sayarshad and Chow, 2015 ); pricing for last-mile shared rides ( Chen and Wang, 2018a, 2018b ); auction pricing for ridesourcing ( Asghari et al., 2016; Asghari and Shahabi, 2017 ); dynamic pricing with dispatching ( Chen…”
- Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, methodology): “89% [116] – Occupancy PeMSD-SF 60min 7 rolling time windows (24 time-points at a time) 16.”
- High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, background): “A trajectory-based convolution operator has also been proposed to capture the spatial dependency in the geo-location sequences [27].”
- GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction (2018, methodology): “Inspired by the previous works [ Liang et al. , 2017; Wang et al. , 2018 ] focusing on the effects of external factors in spatio-temporal applications, we design a simple yet effective component to handle these factors.”
- A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “[155] aimed to estimate the travel time of a path from the mobility trajectory data.”
- ETA Prediction with Graph Neural Networks in Google Maps (2021, background): “Earlier work modifies convolutional neural networks to be mindful of the spatial properties of the trajectory [28] and employs graph neural networks coupled with recurrent mechanisms [13].”
- Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, background): “[138] proposed an end-to-end Deep learning framework for Travel”