20 research outputs found
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Predicting traffic conditions from online route queries is a challenging task
as there are many complicated interactions over the roads and crowds involved.
In this paper, we intend to improve traffic prediction by appropriate
integration of three kinds of implicit but essential factors encoded in
auxiliary information. We do this within an encoder-decoder sequence learning
framework that integrates the following data: 1) offline geographical and
social attributes. For example, the geographical structure of roads or public
social events such as national celebrations; 2) road intersection information.
In general, traffic congestion occurs at major junctions; 3) online crowd
queries. For example, when many online queries issued for the same destination
due to a public performance, the traffic around the destination will
potentially become heavier at this location after a while. Qualitative and
quantitative experiments on a real-world dataset from Baidu have demonstrated
the effectiveness of our framework.Comment: KDD 2018. The first two authors share equal contribution
Development prediction algorithm of vehicle travel time based traffic data
This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera images and later distinguished vehicles are assigned to the looking at route segment so instantaneous and current velocities are calculated. All data were effectively processed and visualized using both MATLAB and Python programming language and its libraries
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
Traffic forecasting has recently attracted increasing interest due to the
popularity of online navigation services, ridesharing and smart city projects.
Owing to the non-stationary nature of road traffic, forecasting accuracy is
fundamentally limited by the lack of contextual information. To address this
issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network
(H-STGCN), which is able to "deduce" future travel time by exploiting the data
of upcoming traffic volume. Specifically, we propose an algorithm to acquire
the upcoming traffic volume from an online navigation engine. Taking advantage
of the piecewise-linear flow-density relationship, a novel transformer
structure converts the upcoming volume into its equivalent in travel time. We
combine this signal with the commonly-utilized travel-time signal, and then
apply graph convolution to capture the spatial dependency. Particularly, we
construct a compound adjacency matrix which reflects the innate traffic
proximity. We conduct extensive experiments on real-world datasets. The results
show that H-STGCN remarkably outperforms state-of-the-art methods in various
metrics, especially for the prediction of non-recurring congestion