Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation

Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. However, due to the continuous alternations of the road segments and intersections in a path, the dynamic features are supposed to be coupled and interactive. Therefore, modeling one of them limits further improvement in accuracy of estimating travel time. To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments. In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale network architectures for capturing multi-level spatial-temporal information from the dual graph. Finally, we employ the multi-task learning approach to estimate the travel time of a given whole route, each road segment and intersection simultaneously. We conduct extensive experiments to evaluate our proposed model on three real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines

Self supplied navigation systems: microsimulation-based performance assessment

The self-supplied navigation concept is based on the combination of the floating car data technique and the dynamic route guidance service in the same vehicles. The gains obtained by the users of such system are, obviously, closely related to the percentage of equipped vehicles. This study shows concretely this relation relying on the use of a large scale microsimulator urban model, the Lausanne city case. Finally, limitations due to the fact that the same vehicles are providing and exploiting the traffic information are clearly emphasized

Optimizing vehicle profile speed settings based on historic data

This work presents several methods for optimising speed profiles that minimise the traveltime devitations compared to historical traffic data. Defining optimisation models based at different level of detail of the reference data (segment traveltime or total trip traveltime)

Route Travel Time Estimation Using Floating Car Data

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