01771564

Component

https://doi.org/10.1177/03611981211011169

Predicting entry-traffic flows synchronously could enable inferences about the changing trends and spatial structure of dynamic traffic flows in an expressway network. This research develops a synchronized entry-traffic flow prediction method for regional expressway systems. The new method first organizes numerous entry-traffic flows as a three-dimensional (time slots, spatial locations, and vehicle types) tensor, then applies tensor decomposition to extract their temporally changing features. After forecasting the temporally changing features, predicted values of entry-traffic flows can be calculated synchronously by tensor reconstruction. Data from hourly entry-traffic flows involving nine vehicle types and 201 spatial locations in a regional expressway system of China are used to discuss the performance of this new method. The results show that the new method could obtain prediction results with high overall accuracy. Comparative experiments indicate that the new method and existing methods (autoregressive integrated moving average, or ARIMA, and Holt-Winters) could generate prediction results with similar accuracy. However, the proposed method has the advantage of reducing the number of time series that need to be handled in the prediction of numerous entry-traffic flows for regional expressway systems. This method might be helpful for administrators to guide and manage vehicles so that they enter the expressway system effectively.

© National Academy of Sciences: Transportation Research Board 2021.

English

pp 291-302

2021-10

Transportation Research Record: Journal of the Transportation Research Board

Web

References (34)

Expressways; Mathematical prediction; Motor vehicles; Tensor analysis; Traffic flow

China

Highways; Operations and Traffic Management; Vehicles and Equipment

TRIS, TRB, ATRI

May 13 2021 3:12PM