The interest in tradable mobility credits (TMC) is growing steadily. Compared to existing instruments, its cap-and-trade design for the demand side ensures that a limited quantity, e.g., traffic and related emissions, can by design not be exceeded. However, most TMC schemes are market-based financial instruments that can only be successful, if the market ensures the most efficient allocation of resources and if one can rely on the price. Hence, TMC schemes require trading activity and a liquid market that only emerges when participants are able and willing to trade. In this paper, we systematically review the TMC literature for aspects of trading activity and market liquidity, summarize the literature streams, and discuss determinants of participants’ ability and willingness to trade TMCs. During the literature review we separate those into demand-side, supply-side, and market regulation factors. This first coherent discussion of creating liquid TMC markets with substantial trading activity challenges the instrument and allows us to draw valuable conceptual implications for the TMC scheme design, but also implications for stakeholders beyond concept. Generating trading activity and liquid markets is thoroughly possible, but robustly achieving it can be challenging.

Interactions among different modes or vehicle classes in urban road networks affect the network performance in different and complex ways. Thus, an answer to the question of “how many cars are too many for a city?” is not trivial. However, multi-modal macroscopic fundamental diagrams (MFD) offer a novel opportunity to answer this question. So far, no methodology exists to estimate multi-modal MFDs resulting from arbitrary multi-modal interactions. In this paper, we propose a methodology to capture additional delays in the shape of the MFD and derive an approach for estimating multi-modal MFDs thereof. The influence on the MFD shape is established using the two-fluid theory of urban traffic by defining pairwise copula functions between travel times of each mode. In contrast to many existing approaches, the presented approach retains individual mode's speed information. We show the applicability of the approach with a tri-modal case of bicycles, buses, and cars with empirical data from Amsterdam (The Netherlands) and London (United Kingdom). Although the approach is not limited to this specific tri-modal case, we use the example to discuss the initial policy question by deriving optimal modal splits for a given accumulation of travelers. Last, we compare the new approach to existing estimation methods for bi-modal MFDs describing car and bus traffic.