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Urban air quality and the impact of mobile source pollutants on human health are of increasing concern in transportation studies. Existing research often focuses on reducing traffic congestion and carbon footprints, but there's a notable gap in understanding the health impacts of traffic from an environmentally-just perspective. Addressing this, our paper introduces an integrated simulation platform that models not only traffic-related air quality but also the direct health implications at a microscopic level. This platform integrates five modules: SUMO for traffic modeling, MOVES for emissions modeling, a 3D grid-based dispersion model, a Matlab-based visualizer for pollutant concentrations, and a human exposure model. We emphasize the transportation-health pathway, examining how different mobility strategies impact human health. Our case study on multimodal on-demand services demonstrates that a distributed pickup strategy can reduce cancer risk from PM 2 . 5 exposure by 33.4% compared to centralized pickup. This platform offers insights into traffic-related air quality and health impacts, providing valuable data for improving transportation systems and strategies with a focus on health outcomes. 

We present a new general framework for constructing com- pact and adaptively secure attribute-based encryption (ABE) schemes from k-Lin in asymmetric bilinear pairing groups. Previously, the only construction [Kowalczyk and Wee, Eurocrypt ’19] that simultaneously achieves compactness and adaptive security from static assumptions sup- ports policies represented by Boolean formulae. Our framework enables supporting more expressive policies represented by arithmetic branching programs. Our framework extends to ABE for policies represented by uniform models of computation such as Turing machines. Such policies enjoy the feature of being applicable to attributes of arbitrary lengths. We obtain the first compact adaptively secure ABE for deterministic and non-deterministic finite automata (DFA and NFA) from k-Lin, previously unknown from any static assumptions. Beyond finite automata, we obtain the first ABE for large classes of uniform computation, captured by deterministic and non-deterministic logspace Turing machines (the complexity classes L and NL) based on k-Lin. Our ABE scheme has compact secret keys of size linear in the description size of the Turing machine M. The ciphertext size grows linearly in the input length, but also linearly in the time complexity, and exponentially in the space complexity. Irrespective of compactness, we stress that our scheme is the first that supports large classes of Turing machines based solely on standard assumptions. In comparison, previous ABE for general Turing machines all rely on strong primitives related to indistinguishability obfuscation.