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This paper proposes a new approach to solve the problem of bus network design and frequency setting (BNDFS). Transit network design must satisfy the needs of both service users and transit operators. Numerous optimisation techniques have been proposed for BNDFS in the literature. Previous approaches tend to adopt a sequential optimisation strategy that conducts network routing and service frequency setting in two separate steps. To address the limitation of sequential optimisation, our new algorithm uses Reinforcement Learning for a simultaneous optimisation of three key components of BNDFS: the number of bus routes, the route design and service frequencies. The algorithm can design the best set of bus routes without defining the total number of bus routes in advance, which can reduce the overall computational time. The proposed algorithm was tested on the benchmark Mandl Swiss network. The algorithm is further extended to the routing of express services. The validation includes additional test scenarios which modify the transit demand level on the Mandl network. The new algorithm can be useful to assist transit agencies and planners in improving existing routing and service frequency to cope with changing demand conditions.

In this study, we propose a novel multi-objective nonlinear mixed-integer mathematical programming model for the transit network design and frequency setting problem that aims at designing the routes and determining the frequencies of the routes to satisfy passenger demand in a transit network. The proposed model incorporates the features of real-life transit network systems and reflects the views of both passengers and the transit agency by considering the in-vehicle travel time, transfers, waiting times at the boarding and transfer stops, overcrowding and under-utilization of vehicles, and vehicle fleet size. Unlike previous studies that simplify several aspects of the transit network design and frequency setting problem, the proposed model is the first to determine routes and their frequencies simultaneously from scratch, i.e., without using line and frequency pools while considering the aforementioned issues, such as transfers and waiting. We solve the proposed model using Gurobi. We provide the results of what-if analyses conducted using a real-world public bus transport network in the city of Kayseri in Türkiye. We also present the results of computational tests implemented to validate and verify the model using Mandl benchmark instances from the literature. The results indicate that the model produces better solutions than the state-of-the-art algorithms in the literature and that the model can be used by public transit planners as a decision aid.

As renewable energy resources are gradually connected to the grid, demand response gradually develops and expands. As a response resource with a huge proportion of the load, the role of air-conditioning load is gradually being valued. The paper first describes the equivalent thermal parameter model of the air-conditioning load. Then, an air-conditioning cluster aggregation model is established based on Monte Carlo with uncertainties considered. Finally, the paper proposes a flexible trigger frequency setting method to provide a relatively consistent amount of adjustment in different situations. And the simulation proves the effectiveness of the method.

Modern public transportation systems are increasingly complex: they are operated on a large scale, must support booming urban populations, and run under tight budget constraints. Additionally, passengers are able to make choices between a variety of commuting options. We develop formulations for minimizing system wait time in multimodal networks, while accounting for operator budget constraints, capacity constraints, and passenger preferences. Furthermore, our algorithms run to near optimality in minutes for city-sized networks. We demonstrate the benefit of setting schedule frequencies and prices jointly through case studies on real data from Boston and Tokyo. To our knowledge, ours is the first paper that addresses joint frequency-setting and pricing optimization for public transit networks and at scale.

Appropriate public transport systems are crucial in modern cities. Given the high costs that they represent and the impact they have on people’s lives, effective tools are required to support their design. With this in mind, the Transit Network Design problem (TNDP) and the Transit Network Design and Frequency Setting problem (TNDFSP) have been extensively studied in the domain of Operations Research. However, due to the complexity of these problems, multiple simplifications are typically made when modelling and designing solution algorithms. Therefore, still no optimization techniques are available to address these problems in practice. Moreover, different studies address different versions of the problem, with varying assumptions and constraints, complicating the comparison of results or solution approaches. This paper presents an extensive survey of studies addressing the TNDP and the TNDFSP. It discusses the different assumptions, constraints, objectives, solution approaches and testing instances that have been considered in the literature. Furthermore, a detailed analysis is done regarding the case studies considered for the TNDFSP. Moreover, the variants of the passenger assignment subproblem that have been applied within the TNDP and the TNDFSP are discussed. The analysis shows that extensive research has been done regarding these problems. However, it also identified the significant gap that still exists between theory and practice, even in the studies addressing case studies.

In this work, we develop change-point methods for statistics of high-frequency data. The main interest is in the volatility of an Itô semimartingale, the latter being discretely observed over a fixed time horizon. We construct a minimax-optimal test to discriminate continuous paths from paths with volatility jumps, and it is shown that the test can be embedded into a more general theory to infer the smoothness of volatilities. In a high-frequency setting, we prove weak convergence of the test statistic under the hypothesis to an extreme value distribution. Moreover, we develop methods to infer changes in the Hurst parameters of fractional volatility processes. A simulation study is conducted to demonstrate the performance of our methods in finite-sample applications.

We introduce a novel framework for estimating eddy fluxes using cross-scalogram smoothing, addressing key limitations of the standard eddy-covariance method. The standard approach suffers from fixed averaging times (typically 30 min) and limited frequency resolution, which can lead to biases and an inability to capture fast dynamics. Our method, based on wavelet transforms, allows for high-resolution analysis of fluxes in both time and frequency domains. It adaptively localises turbulent scales using a metric derived from the vertical component of the Reynolds stress tensor, enabling more accurate flux estimation under varying turbulence conditions. The proposed metric is similar to the u* and σw tests, but it is adapted to the time–frequency setting. By decoupling the filtering of perturbative scales from flux calculations, our approach allows for flexible averaging times. This adaptability makes it particularly suitable for studying rapid ecosystem responses to environmental changes, such as those occurring on timescales shorter than 1 h. We show application of the framework at the beech forest site Hesse (code FR-Hes) and demonstrate its relation with standard eddy-covariance calculations. The proposed method allows for varying the averaging time without impacting the filtering of the perturbative scales. It thus allows for producing estimates of CO2, latent heat, and sensible heat fluxes with faster dynamics (e.g. with 1, 10, and 30 min averaging time). We present statistics of the 10 min averaged fluxes and show that they align well with estimates of the 30 min standard eddy-covariance method. The improved localisation of turbulent scales results in higher estimates of carbon uptake during summer (+2 ± 1 µmolm-2s-1) and a more accurate assessment of nighttime respiration compared to standard eddy-covariance estimates. The methodology is implemented in the Julia package TurbulenceFlux.jl, making it readily accessible for practical applications.

The integration of urban and rural transit networks is a prerequisite for the integration of urban and rural transportation systems. With the promotion of rural revitalization and new urbanization, the existing transit network operated separately in urban and rural areas is insufficient in meeting the travel demands of urban and rural residents. It is necessary to plan the urban and rural transit network rationally and to enhance the overall system performance of the urban and rural transit network. This paper proposes a biobjective model to optimize the integrated urban–rural transit network. The model minimizes both passengers’ and bus operators’ costs by optimizing the bus routes and frequencies simultaneously. Furthermore, we propose a subregional operations model and explore a performance comparison between the integrated and subregional optimization approaches. The genetic algorithm is developed to solve the proposed models. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. The results indicate that the integrated operation of the urban–rural transit network has more optimization space than the subregional operation, and can effectively reduce the number of transfers. Furthermore, under integrated operations, changes in operating costs have a more pronounced impact on total passenger travel time. When the demand is within a particular range, the integrated operation generates a shorter total passenger travel time than the subregional operation for the exact operating cost. In addition, the Pareto‐optimal solution generated under varying interregional demands provides a trade‐off between the total passenger travel time and the operating costs of the bus operator.

Truncated realized quadratic variations (TRQV) are among the most widely used high-frequency-based nonparametric methods to estimate the volatility of a process in the presence of jumps. Nevertheless, the truncation level is known to critically affect its performance, especially in the presence of infinite variation jumps. In this paper, we study the optimal truncation level, in the mean-square error sense, for a semiparametric tempered stable Lévy model. We obtain a novel closed-form 2nd-order approximation of the optimal threshold in a high-frequency setting. As an application, we propose a new estimation method, which combines iteratively an approximate semiparametric method of moment estimator and TRQVs with the newly found small-time approximation for the optimal threshold. The method is tested via simulations to estimate the volatility and the Blumenthal-Getoor index of a generalized CGMY model and, via a localization technique, to estimate the integrated volatility of a Heston type model with CGMY jumps. Our method is found to outperform other alternatives proposed in the literature when working with a Lévy process (i.e., the volatility is constant), or when the index of jump intensity Y is larger than 3/2 in the presence of stochastic volatility.

High-voltage pulsed electric field (HPEF) is one of the technologies applied in the food industry with the principle of non-heat treatment with a short pre-treatment time and no pollution is produced. The main benefit of this tool is to kill microbes in the food industry. In addition to non-thermal sterilization, HPEF can also be used for electroporation. Cell electroporation is the application of an electric voltage to the cell. The cells are damaged (broken/porous) without destroying the bioactive components present in the cells. Previous studies have shown that materials treated with HPEF increase the yield when distilled. It can occur due to the electroporation process, namely the breakdown of the oil cell network, so that the distillation process is efficient (can reduce distillation time and increase the quantity of oil). The care box is designed with dimensions of 60cm x 40cm x 30cm which is made of acrylic material. The electrodes used a 60cm x 40cm plate mounted on a linear drive to adjust the treatment distance. HPEF uses a 15kV neon sign transformer. The transformer input frequency setting is set with a dimmer. A microcontroller is installed to adjust the shock period on the electrodes. Based on trials, the device can be used for various solid materials with different volumes.