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Most research on within-day dynamic traffic equilibrium with information provision implicitly considers travel time information, often assuming information to be perfect or imperfect based on travelers’ perception error. However, lacking explicit formulation of information limits insightful analysis of information impact on dynamic traffic equilibrium and the potential benefits of leveraging information provision to improve system-level performance. To address this gap, this paper proposes a within-day dynamic traffic equilibrium model that explicitly formulates strategic information provision as an endogenous element. The proposed model considers travelers’ reactions to the information, creating an interdependent relationship between provided information and traffic dynamics. In this framework, two classes of travelers receive different types of travel time information: one class receives instantaneous travel time reflecting the prevailing traffic conditions, while the other class receives strategic forecasts of travel times, generated by accounting for travelers’ reactions to instantaneous information based on strategic thinking from behavioral game theory. The resulting multi-class within-day dynamic equilibrium differs from existing models by explicitly modeling information provision and consideration of information consistency. The inherent dynamics of real-time updated traffic information, traffic conditions, and travelers’ choice behaviors are analytically modeled, with the resulting dynamic equilibrium formulated as a fixed-point problem. The theoretical propositions and numerical findings offer rich insights into the impact of information on the traffic network, strategic forecast information penetration, the relationship between the proposed equilibrium and traditional dynamic traffic equilibria, and information accuracy. This research contributes to a deeper understanding of the interplay between information and traffic dynamics, paving the way for more effective traffic management strategies. 

Not AvailableThe growing conflict between parking demand and limited curbside space highlights the need for efficient on-street parking management. Freight and service vehicles, in particular, pose high societal costs when parking is unavailable, often leading to double parking, congestion, and safety risks. Given their essential services, prioritising these vehicles in parking allocation is critical. However, research in this area remains limited. This paper proposes an optimisation model that allocates on-street parking among passenger cars, freight, and service vehicles, considering their distinct impacts. Using a queuing theory framework, the model ensures parking availability for prioritised vehicles and optimises car parking time limits. The objective is to minimise negative societal externalities caused by insufficient parking. A case study shows that the model effectively prioritises freight and service vehicles and adjusts car parking limits to meet the goal, along with rich managerial insights. The model offers planners a flexible tool to balance parking utilisation and traffic efficiency. 

Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a label-free, non-invasive approach to monitor the viability ofex vivomouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDS_e), and late OCT correlation decay speed (OCDS_l) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary information relevant to tissue viability, in addition to the morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making.