This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.

Are road pricing strategies regressive or progressive? This is a question that has been confronting researchers, practitioners, and policy‐makers who seek to implement new mechanisms to raise funds for transportation while simultaneously managing demand. The theoretical literature is mixed, as is the empirical literature. In part, this has to do with the various types of road pricing strategies that are being debated, different definitions of equity, and alternative assumptions about revenue recycling. Despite this seeming complexity, the literature is clear that equity issues are addressable. This paper provides a synthesis of the literature to date on both the theory of equity, as applied to road pricing, and the findings of empirical and simulation studies of the effects of particular implementations of road pricing, and suggested remedies for real or perceived inequities. To summarize, while there are certainly potential issues with equity associated with road pricing, those issues can be addressed with intelligent mechanism design that provides the right incentives to travellers and uses the raised revenues in a way to achieve desired equitable ends. These include cutting other taxes and investing in infrastructure and services.