15 research outputs found
RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
Real-time traffic accident forecasting is increasingly important for public
safety and urban management (e.g., real-time safe route planning and emergency
response deployment). Previous works on accident forecasting are often
performed on hour levels, utilizing existed neural networks with static
region-wise correlations taken into account. However, it is still challenging
when the granularity of forecasting step improves as the highly dynamic nature
of road network and inherent rareness of accident records in one training
sample, which leads to biased results and zero-inflated issue. In this work, we
propose a novel framework RiskOracle, to improve the prediction granularity to
minute levels. Specifically, we first transform the zero-risk values in labels
to fit the training network. Then, we propose the Differential Time-varying
Graph neural network (DTGN) to capture the immediate changes of traffic status
and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and
region selection schemes to highlight citywide most-likely accident subregions,
bridging the gap between biased risk values and sporadic accident distribution.
Extensive experiments on two real-world datasets demonstrate the effectiveness
and scalability of our RiskOracle framework.Comment: 8 pages, 4 figures. Conference paper accepted by AAAI 202
Graph Input Representations for Machine Learning Applications in Urban Network Analysis
Understanding and learning the characteristics of network paths has been of
particular interest for decades and has led to several successful applications.
Such analysis becomes challenging for urban networks as their size and
complexity are significantly higher compared to other networks. The
state-of-the-art machine learning (ML) techniques allow us to detect hidden
patterns and, thus, infer the features associated with them. However, very
little is known about the impact on the performance of such predictive models
by the use of different input representations. In this paper, we design and
evaluate six different graph input representations (i.e., representations of
the network paths), by considering the network's topological and temporal
characteristics, for being used as inputs for machine learning models to learn
the behavior of urban networks paths. The representations are validated and
then tested with a real-world taxi journeys dataset predicting the tips using a
road network of New York. Our results demonstrate that the input
representations that use temporal information help the model to achieve the
highest accuracy (RMSE of 1.42$)
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks