Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending

Abstract

This study examines the dependency between traffic links using a three-dimensional data detrending algorithm to build a network weight matrix in a real-world example. The network weight matrix reveals how links are spatially dependent in a complex network and detects the competitive and complementary nature of traffic links. We model the traffic flow of 140 traffic links in a sub-network of the Minneapolis -St. Paul highway system for both rush hour and non-rush hour time intervals, and validate the extracted network weight matrix. The results of the modeling indicate: (1) the spatial weight matrix is unstable over time-of-day, while the network weight matrix is robust in all cases and (2) the performance of the network weight matrix in non-rush hour traffic regimes is significantly better than rush hour traffic regimes. The results of the validation show the network weight matrix outperforms the traditional way of capturing spatial dependency between traffic links. Averaging over all traffic links and time, this superiority is about 13.2% in rush hour and 15.3% in non-rush hour, when only the 1 st -order neighboring links are embedded in modeling. Aside from the superiority in forecasting, a remarkable capability of the network weight matrix is its stability and robustness over time, which is not observed in spatial weight matrix. In addition, this study proposes a naïve two-step algorithm to search and identify the best look-back time window for upstream links. We indicate the best look-back time window depends on the travel time between two study detectors, and it varies by time-of-day and traffic link.

Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency

“…We concluded from above observations that for uncongested traffic, the attention scheme is comparatively simple and the model tends to pay attention to most recent information, while congested traffic causes complex attention distribution and the model consults wider range of look-back time steps accordingly. This concurs with the findings of (Ermagun and Levinson, 2019), in which the authors discovered that "the look-back time window for the uncongested traffic regime is shorter than the congested one".…”

Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency

“…We concluded from above observations that for uncongested traffic, the attention scheme is comparatively simple and the model tends to pay attention to most recent information, while congested traffic causes complex attention distribution and the model consults wider range of look-back time steps accordingly. This concurs with the findings of (Ermagun and Levinson, 2019), in which the authors discovered that "the look-back time window for the uncongested traffic regime is shorter than the congested one".…”

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

“…An alternative exogenous feature filtering approach, which is not directly based on connections between spatial locations, has been suggested by Ermagun and Levinson [13][14][15]. The introduced network weight matrix utilises graph characteristics of the road network such as betweenness centrality and vulnerability to discover complementary and competitive spatial links.…”

Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective

“…In Equations (7) and (8), W o represents the weight matrix (Ermagun and Levinson, 2019a , b ; Gunasekaran and Joo, 2019 ). Although the LSTM network is based on the standard RNN, the standard RNN or LSTM can only capture the historical sequential information.…”

Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency

“…We concluded from above observations that for uncongested traffic, the attention scheme is comparatively simple and the model tends to pay attention to most recent information, while congested traffic causes complex attention distribution and the model consults wider range of look-back time steps accordingly. This concurs with the findings of (Ermagun and Levinson, 2019), in which the authors discovered that "the look-back time window for the uncongested traffic regime is shorter than the congested one".…”

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

“…An alternative exogenous feature filtering approach, which is not directly based on connections between spatial locations, has been suggested by Ermagun and Levinson [13][14][15]. The introduced network weight matrix utilises graph characteristics of the road network such as betweenness centrality and vulnerability to discover complementary and competitive spatial links.…”

Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective

“…In Equations (7) and (8), W o represents the weight matrix (Ermagun and Levinson, 2019a , b ; Gunasekaran and Joo, 2019 ). Although the LSTM network is based on the standard RNN, the standard RNN or LSTM can only capture the historical sequential information.…”

Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency

“…We concluded from above observations that for uncongested traffic, the attention scheme is comparatively simple and the model tends to pay attention to most recent information, while congested traffic causes complex attention distribution and the model consults wider range of look-back time steps accordingly. This concurs with the findings of (Ermagun and Levinson, 2019), in which the authors discovered that "the look-back time window for the uncongested traffic regime is shorter than the congested one".…”

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

“…An alternative exogenous feature filtering approach, which is not directly based on connections between spatial locations, has been suggested by Ermagun and Levinson [13][14][15]. The introduced network weight matrix utilises graph characteristics of the road network such as betweenness centrality and vulnerability to discover complementary and competitive spatial links.…”

Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective

“…In Equations (7) and (8), W o represents the weight matrix (Ermagun and Levinson, 2019a , b ; Gunasekaran and Joo, 2019 ). Although the LSTM network is based on the standard RNN, the standard RNN or LSTM can only capture the historical sequential information.…”