Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending

Recently published:

• Ermagun, Alireza, and Levinson, D. (2019) Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending Transportation Research part C.  104, July 2019,  38-52 [doi] [free until June 21, 2019] (working paper is always free)

Highlights

• Spatial weight matrix is unstable over time-of-day, while network weight matrix is robust.

• Performance of network weight matrix in non-rush hour is better than rush hour.

• The best look-back time window depends on the travel time between two study detectors.

• The best look-back time window is shorter in uncongested than congested regimes.

Fig. 2. Traffic flow of 140 links for Tuesday January 6th, 2015.

Abstract

This study examines the spatiotemporal dependency between 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 first-order neighboring links are embedded in modeling. In addition, this study proposes a 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.