01629541

Component

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. The authors synthesize 130 extracted research papers from two perspectives: (1) methodological framework, and (2) approach for capturing and incorporating spatial information. From the methodology side, spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. There is a broad and longstanding agreement that non-parametric methods outperform the naive statistical methods such as historical average, real time profile, and exponential smoothing. However, to make a conclusion regarding the performance of neural network methods against space-time autoregressive integrated moving average (STARIMA) family models, more research is needed in this field. From the spatial dependency detection side, the authors believe that a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks. This systematic review highlights that the field is approaching its maturity, while it is still as crude as it is perplexing. It is perplexing in the conceptual methodology, and it is crude in the capture of spatial information.

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

01618707

17-05855

English

29p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Methodology; Network links; Spatial analysis; Traffic flow; Traffic forecasting

Planning and Forecasting; Transportation (General)

Transportation Research Board Annual Meeting 2017 Paper #17-05855

PRP, TRIS, TRB, ATRI

Dec 8 2016 12:21PM