DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Cheng, Xingyi; Zhang, Ruiqing; Zhou, Jie; Xu, Wei

doi:10.1109/ijcnn.2018.8489600

Cited by 85 publications

(45 citation statements)

References 25 publications

(25 reference statements)

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“…e structure has the ability of time processing and sequence learning. RNN is widely used to process nonlinear time series data because of its short-term memory [25,[69][70][71]. e calculation formula of RNN is shown as follows:…”

Section: Arima Modelmentioning

confidence: 99%

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Yang

Zou

Tang

et al. 2020

Journal of Advanced Transportation

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Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

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Section: Arima Modelmentioning

confidence: 99%

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Yang

Zou

Tang

et al. 2020

Journal of Advanced Transportation

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“…In [11], the authors leverage a deep-learning approach in the form of a diffusion convolutional recurrent neural network (DCRNN) to forecast shortterm freeway traffic counts in the LA and San Francisco Bay Area networks. The authors in [3] also propose a deep-learning approach that brings together convolutional neural networks and recurrent neural networks with long short-term memory (LSTM) units, utilizing their architecture for short-term traffic count extrapolation at 349 locations on the Beijing road network. In a set of articles [19,25], the authors leverage data from Bluetooth and GPS probe sensors for travel-time estimation and validation.…”

Section: 1mentioning

confidence: 99%

Street-level Travel-time Estimation via Aggregated Uber Data

Maass

Sathanur

Khan

et al. 2020

2020 Proceedings of the SIAM Workshop on Combinatorial Scientific Computing

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Estimating temporal patterns in travel times along road segments in urban settings is of central importance to traffic engineers and city planners. In this work, we propose a methodology to leverage coarse-grained and aggregated travel time data to estimate the street-level travel times of a given metropolitan area. Our main focus is to estimate travel times along the arterial road segments where relevant data are often unavailable. The central idea of our approach is to leverage easy-to-obtain, aggregated data sets with broad spatial coverage, such as the data published by Uber Movement, as the fabric over which other expensive, fine-grained datasets, such as loop counter and probe data, can be overlaid. Our proposed methodology uses a graph representation of the road network and combines several techniques such as graph-based routing, trip sampling, graph sparsification, and least-squares optimization to estimate the street-level travel times. Using sampled trips and weighted shortest-path routing, we iteratively solve constrained least-squares problems to obtain the travel time estimates. We demonstrate our method on the Los Angeles metropolitan-area street network, where aggregated travel time data is available for trips between traffic analysis zones. Additionally, we present techniques to scale our approach via a novel graph pseudo-sparsification technique.

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“…Great interest is being shown in developing methods for predicting spatio-temporal populations (Zhang, Zheng, and Qi 2017;Hoang, Zheng, and Singh 2016;Zhang et al 2016;Li et al 2018;Cheng et al 2017;Xie et al 2010;Yu et al 2016;Yao et al 2018). For example, Zhang et al 2018proposed a deep learning method for forecasting future populations using spatio-temporal dependence as well as external conditions, such as weather and events.…”

Section: Related Workmentioning

confidence: 99%

Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data

Iwata¹,

Shimizu²

2019

AAAI

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We propose a probabilistic model for estimating population flow, which is defined as populations of the transition between areas over time, given aggregated spatio-temporal population data. Since there is no information about individual trajectories in the aggregated data, it is not straightforward to estimate population flow. With the proposed method, we utilize a collective graphical model with which we can learn individual transition models from the aggregated data by analytically marginalizing the individual locations. Learning a spatio-temporal collective graphical model only from the aggregated data is an ill-posed problem since the number of parameters to be estimated exceeds the number of observations. The proposed method reduces the effective number of parameters by modeling the transition probabilities with a neural network that takes the locations of the origin and the destination areas and the time of day as inputs. By this modeling, we can automatically learn nonlinear spatio-temporal relationships flexibly among transitions, locations, and times. With four real-world population data sets in Japan and China, we demonstrate that the proposed method can estimate the transition population more accurately than existing methods.

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DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Cited by 85 publications

References 25 publications

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Street-level Travel-time Estimation via Aggregated Uber Data

Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data

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