Dynamic Spatial-Temporal Representation Leaning for Crowd Flow
Prediction
release_yujdpduht5dz5at2fowts32yt4
by
Lingbo Liu, Jiajie Zhen, Guanbin Li, Geng Zhan, Liang Lin
2020
Abstract
As a crucial component in intelligent transportation systems, crowd flow
prediction has recently attracted widespread research interest in the field of
artificial intelligence (AI) with the increasing availability of large-scale
traffic mobility data. Its key challenge lies in how to integrate diverse
factors (such as temporal laws and spatial dependencies) to infer the evolution
trend of crowd flow. To address this problem, we propose a unified neural
network called Attentive Crowd Flow Machine (ACFM), which can effectively learn
the spatial-temporal feature representations of crowd flow with an attention
mechanism. In particular, our ACFM is composed of two progressive Convolutional
Long Short-Term Memory (ConvLSTM) units connected with a convolutional layer.
Specifically, the first ConvLSTM unit takes normal crowd flow features as input
and generates a hidden state at each time-step, which is further fed into the
connected convolutional layer for spatial attention map inference. The second
ConvLSTM unit aims at learning the dynamic spatial-temporal representations
from the attentionally weighted crowd flow features. Further, we develop two
deep frameworks based on ACFM to predict citywide short-term/long-term crowd
flow by adaptively incorporating the sequential and periodic data as well as
other external influences. Extensive experiments on two standard benchmarks
well demonstrate the superiority of the proposed method for crowd flow
prediction. Moreover, to verify the generalization of our method, we also apply
the customized framework to forecast the passenger pickup/dropoff demands and
show its superior performance in this traffic prediction task.
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