Conclusions and key knowledge gaps Sample Clauses

Conclusions and key knowledge gaps. ‌ In this chapter we explored the state-of-the-art for (dis)incentive mechanisms in mobility management. In these concluding remarks we summarise our guiding questions, briefly detailing what was found from literature and which knowledge gaps we have identified. - What are the fundamental objectives of incentive schemes in mobility management? Incentive schemes have been employed across multiple levels of the planning pyramid, with objectives ranging from highly strategic (reduce car ownership, promote sustainable modes) to downright operational (buffer traffic outside of an incident area). Across the three axes employed for our literature review evaluation (centralisation/decentralisation, stasis/dynamism and responsiveness/unresponsiveness), a clear trend seems to appear: the closer the objectives are to operational planning, the more the approaches tend to exhibit responsive/dynamic properties. Decentralisation efforts, if present, appear exclusively in operational approaches. - How are optimal values for (dis)incentives determined? Methods for determining optimal values show considerable dependency on both i) the objectives of interest and ii) whether the (dis)incentive approach is monetary. Considerable research effort has been spent in developing single- objective approaches for monetary disincentive schemes, with optimality guarantees or conditions. Behavioural approaches, such as nudges, have received comparatively less attention in methodological research, although a clear trend for further development of both theoretical and practical applications of these approaches can be observed in the last ~5 years. When considering multiple objectives, potentially conflicting, various approaches have been proposed, ranging from computational heuristics to nonlinear optimization approaches. The issue of pareto-optimality when including directly conflictual objectives (e.g., maximising both efficiency and fairness) remains an open challenge. - How can this arbitration be carried out through decentralized decision- making? Decentralisation remains an unresolved challenge in literature. While some facets of incentive mechanisms are naturally decentralised (e.g. enforcement), we could not identify any efforts in literature concerning either decentralised computation of the optimal (dis)incentive values nor network-wide objective (re)formulation on the basis of decentralised actions. The fundamental challenge of DIT4TraM is, indeed, filling this gap in literature...