Maximal regularity of the spatially periodic Stokes operator and application to nematic liquid crystal flows

summary:We consider the dynamics of spatially periodic nematic liquid crystal flows in the whole space and prove existence and uniqueness of local-in-time strong solutions using maximal $L^p$-regularity of the periodic Laplace and Stokes operators and a local-in-time existence theorem for quasilinear parabolic equations à la Clément-Li (1993). Maximal regularity of the Laplace and the Stokes operator is obtained using an extrapolation theorem on the locally compact abelian group $G:=\mathbb R^{n-1}\times \mathbb R / L \mathbb Z$ to obtain an $\mathcal {R}$-bound for the resolvent estimate. Then, Weis' theorem connecting $\mathcal {R}$-boundedness of the resolvent with maximal $L^p$ regularity of a sectorial operator applies

Efficient Algorithms for Fully Multimodal Journey Planning

We study the journey planning problem for fully multimodal networks consisting of public transit and an arbitrary number of non-schedule-based transfer modes (e.g., walking, e-scooter, bicycle). Obtaining reasonable results in this setting requires multicriteria optimization, making the problem highly complex. Previous approaches were either limited to a single transfer mode or suffered from prohibitively slow running times. We establish a fully multimodal journey planning model that excludes undesirable solutions and can be solved efficiently. We extend existing efficient bimodal algorithms to our model and propose a new algorithm, HydRA, which enables even faster queries. On metropolitan and mid-sized country networks with walking and e-scooter as transfer modes, HydRA achieves query times of around 30 ms, which is fast enough for interactive applications

Efficient Algorithms for Fully Multimodal Journey Planning

We study the journey planning problem for fully multimodal networks consisting of public transit and an arbitrary number of non-schedule-based transfer modes (e.g., walking, e-scooter, bicycle). Obtaining reasonable results in this setting requires multicriteria optimization, making the problem highly complex. Previous approaches were either limited to a single transfer mode or suffered from prohibitively slow running times. We establish a fully multimodal journey planning model that excludes undesirable solutions and can be solved efficiently. We extend existing efficient bimodal algorithms to our model and propose a new algorithm, HydRA, which enables even faster queries. On metropolitan and mid-sized country networks with walking and e-scooter as transfer modes, HydRA achieves query times of around 30 ms, which is fast enough for interactive applications

Faster Multi-Modal Route Planning With Bike Sharing Using ULTRA

We study multi-modal route planning in a network comprised of schedule-based public transportation, unrestricted walking, and cycling with bikes available from bike sharing stations. So far this problem has only been considered for scenarios with at most one bike sharing operator, for which MCR is the best known algorithm [Delling et al., 2013]. However, for practical applications, algorithms should be able to distinguish between bike sharing stations of multiple competing bike sharing operators. Furthermore, MCR has recently been outperformed by ULTRA for multi-modal route planning scenarios without bike sharing [Baum et al., 2019]. In this paper, we present two approaches for modeling multi-modal transportation networks with multiple bike sharing operators: The operator-dependent model requires explicit handling of bike sharing stations within the algorithm, which we demonstrate with an adapted version of MCR. In the operator-expanded model, all relevant information is encoded within an expanded network. This allows for applying any multi-modal public transit algorithm without modification, which we show for ULTRA. We proceed by describing an additional preprocessing step called operator pruning, which can be used to accelerate both approaches. We conclude our work with an extensive experimental evaluation on the networks of London, Switzerland, and Germany. Our experiments show that the new preprocessing technique accelerates both approaches significantly, with the fastest algorithm (ULTRA-RAPTOR with operator pruning) being more than an order of magnitude faster than the basic MCR approach. Moreover, the ULTRA preprocessing step also benefits from operator pruning, as its running time is reduced by a factor of 14 to 20

An Efficient Solution for One-To-Many Multi-Modal Journey Planning

We study the one-to-many journey planning problem in multi-modal transportation networks consisting of a public transit network and an additional, non-schedule-based mode of transport. Given a departure time and a single source vertex, we aim to compute optimal journeys to all vertices in a set of targets, optimizing both travel time and the number of transfers used. Solving this problem yields a crucial component in many other problems, such as efficient point-of-interest queries, computation of isochrones, or multi-modal traffic assignments. While many algorithms for multi-modal journey planning exist, none of them are applicable to one-to-many scenarios. Our solution is based on the combination of two state-of-the-art approaches: ULTRA, which enables efficient journey planning in multi-modal networks, but only for one-to-one queries, and (R)PHAST, which enables efficient one-to-many queries, but only in time-independent networks. Similarly to ULTRA, our new approach can be combined with any existing public transit algorithm that allows a search to all stops, which we demonstrate for CSA and RAPTOR. For small to moderately sized target sets, the resulting algorithms are nearly as fast as the pure public transit algorithms they are based on. For large target sets, we achieve a speedup of up to 7 compared to a naive one-to-many extension of a state-of-the-art multi-modal approach

Integrating ULTRA and Trip-Based Routing

We study a bi-modal journey planning scenario consisting of a public transit network and a transfer graph representing a secondary transportation mode (e.g., walking or cycling). Given a pair of source and target locations, the objective is to find a Pareto set of journeys optimizing arrival time and the number of required transfers. For public transit networks with a restricted, transitively closed transfer graph, one of the fastest known algorithms solving this bi-criteria problem is Trip-Based Routing [Witt, 2015]. However, this algorithm cannot be trivially extended to unrestricted transfer graphs. In this work, we combine Trip-Based Routing with ULTRA [Baum et al., 2019], a preprocessing technique that allows any public transit algorithm that requires transitive transfers to handle an unrestricted transfer graph. Since both ULTRA and Trip-Based Routing precompute transfer shortcuts in a preprocessing phase, a naive combination of the two leads to a three-phase algorithm that performs redundant work and produces superfluous shortcuts. We therefore propose a new, integrated preprocessing phase that combines the advantages of both and reduces the number of computed shortcuts by up to a factor of 9 compared to a naive combination. The resulting query algorithm, ULTRA-Trip-Based is the fastest known algorithm for the considered problem setting, achieving a speedup of up to 4 compared to the fastest previously known approach, ULTRA-RAPTOR

Optimal Regularity in Time and Space for Nonlocal Porous Medium Type Equations

A broad class of possibly non-unique generalized kinetic solutions to hyperbolic-parabolic PDEs is introduced. Optimal regularity estimates in time and space for such solutions to nonlocal, and spatially inhomogeneous variants of the porous medium equation are shown in the scale of Sobolev spaces. The optimality of these results is shown by comparison to the nonlocal Barenblatt solution. The regularity results are used in order to obtain existence of generalized kinetic solutions