Research on Commuting Mode Split Model Based on Dominant Transportation Distance

In This Issue

Load probability density forecasting by transforming and combining quantile forecasts

With the intensification of climate change and increasingly frequent rainstorms, rainfall-induced shallow landslides have become a widespread geohazard. However, the influence of rainfall patterns on rainfall-induced landslides is often overlooked compared to other factors like rainfall intensity and accumulative rainfall. This study addresses this gap by using rainfall-induced landslides occurred in Futian Town, Chongqing City in 2014 as a case study to examine how different rainfall patterns (uniform, advanced, delayed, intermediate) affect the occurrence of shallow landslides. The TRIGRS model was applied to calculate the hourly factor of safety (FS) for each pattern, with adjacent FS ≤ 1 indicating potential landslides. Their volumes and occurrence times were estimated based on the failure areas, thickness, and specific hours when FS ≤ 1, and probability density curves were used to quantify differences with rainfall patterns. The results show that advanced rainfall induces the greatest number of shallow landslides but with the smallest volumes, whereas intermediate rainfall induces the largest volumes. Temporally, advanced rainfall leads to the shortest occurrence times, indicating the fastest response to rainfall, whereas delayed rainfall corresponds to the slowest response. These insights are valuable for developing early warning models for rainfall-induced landslides.

The generalized cost of a transit user depends mostly on time and money spent per trip. On the other hand, time and distance determine most of the transit agency's cost to provide service. These are important factors to consider in determining the equity performance of service provided to users of different demographic groups. The purpose of this study is to determine significant statistical differences in resources spent by bus users in two metropolitan areas. The study measures inequality among different socioeconomic groups of users in terms of travel time and money consumed per mile in using the service. Inequality is then compared between two case studies.;Transit subsidies represent a transfer of income from taxpayers to bus users. Past studies have found that benefits disproportionately accrue for consumers of long distance trips---mostly higher-income, White, older, and male. Since transit policies seek to attract both, transit dependents and choice riders, the situation raises questions regarding these conflicting objectives.;This study gives a closer look to transit equity by including, not only fare, but also travel time as a way to include quality of service in terms of speed. This approach answers the question: "are faster trips charged cheaper?" Therefore, the analysis focuses on the monetary and temporal resources spent by users of different demographic background.;Information from household travel surveys performed by two metropolitan areas, Columbus and Seattle, is analyzed here. The survey data includes distance, travel time and fare paid per trip, summarized by user and household, and then analyzed along with the demographic information, such as household income, household size, ethnicity, gender, and age. Statistical differences in resources spent are found by using the t-test analysis and inequity is determined by means of the Gini coefficient and the Theil and Atkinson indices of inequality.;Statistical differences were found between demographic groups and inequality was measured. Results support previous findings, showing that---in the two case studies---lower income, minority, younger and female users pay more per mile of service. Furthermore, it was found that in addition to higher fares per mile, they also receive an inferior quality service in terms of speed.;The analysis points to the conclusion that faster trips are charged at cheaper rates of fare per mile. Results found however, are applicable only to the two metropolitan areas analyzed here and during the time when the surveys were taken (1999). The approach can be a useful method to compare transit equity between cities

PurposeIn recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.Design/methodology/approachThe fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work.FindingsOne case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.Originality/valueThe main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

With the shift to many-core chip multiprocessors (CMPs), a critical issue is how to effectively coordinate and manage the execution of applications and hardware resources to overcome performance, power consumption, and reliability challenges stemming from hardware and application variations inherent in this new computing environment. Effective resource and application management on CMPs requires consideration of user/application/hardware-specific requirements and dynamic adaption of management decisions based on the actual run-time environment. However, designing an algorithm to manage resources and applications that can dynamically adapt based on the run-time environment is difficult because most resource and application management and monitoring facilities are only available at the operating system level. This paper presents REEact, an infrastructure that provides the capability to specify user-level management policies with dynamic adaptation. REEact is a virtual execution environment that provides a framework and core services to quickly enable the design of custom management policies for dynamically managing resources and applications. To demonstrate the capabilities and usefulness of REEact, this paper describes three case studies--each illustrating the use of REEact to apply a specific dynamic management policy on a real CMP. Through these case studies, we demonstrate that REEact can effectively and efficiently implement policies to dynamically manage resources and adapt application execution.

The paper reports on a case study regarding the integration of heterogeneous data sources, coming from different institutions, needed to support several studies related to the health status of the population of L’Aquila (Italy) after the earthquake of April 6th 2009. In detail, the paper initially describes all the health studies, then the data sources required to support them, finally proposes a simplified federated architecture and a straightforward technological solution to implement it.

CONTEXT: Even before the advent of the COVID-19 pandemic, family physicians have reported higher incidence of experiencing burnout, dissatisfaction, and disengagement in their profession than other medical specialties. In 2020, the American Academy of Family Physicians (AAFP) launched the Leading Physician Well-being Certificate Program (LPWCP) to address and promote individual and systemic wellness through informational webinars, activities, and quality improvement projects. OBJECTIVE: This section of the study utilized the Physician Wellness Inventory (PWI) to assess how the program affected the well-being of participating scholars, and if feelings personal distress negatively impacted clinical duties and patient care. STUDY DESIGN: Cross-sectional survey data collected at baseline, midpoint, and endpoint. SETTING OR DATASET: The evaluation study team situated in the Research, Science, and Health of the Public division within AAFP. Survey data was collected digitally through Qualtrics. POPULATION STUDIED: Family physicians and Family Medicine residents who are scholars in the LPWCP (n = 102). INTERVENTION/INSTRUMENT: The PWI sent through Qualtrics at baseline, midpoint, and endpoint of the program to the cohort 1 scholars. OUTCOME MEASURES: Change over time to participant responses in the PWI were analyzed for significant differences between demographic groups, scholars who changed workplaces, and the three survey iterations during the program. RESULTS: Several statistically significant differences between demographic groups (i.e. age, gender, race, ethnicity, employer, and practice type) emerged at all time points. For example, at baseline, scholars born before 1980 were less likely to have been in a patient encounter that distressed them in the past month than those born in or after 1981 (p = 0.023). Additional contrasts were identified in scholars who changed workplaces during the program; at endpoint they reported positive patient relationships outweighed the negative (p = 0.041) compared to those who stayed at their workplace. Multivariate analysis over time also revealed significant changes for the better, demonstrating program efficacy for improving physician wellness. CONCLUSIONS: Results from LPWCP cohort 1 scholars will inform the study team on ways to optimize program potential to maximize physician wellness.

The impact of traffic on equality of urban healthcare service accessibility: A case study in Wuhan, China

pH and base saturation percentage (BSP) are two basic indexes in identifying soil types in Chinese Soil Taxonomy. Some studies proved that there is significant correlation between BSP and pH, thus it could save the cost of laboratory work if we can infer BSP directly from pH. In this study, the measured values of BSP and pH of 162 and 232 horizon samples from 48 and 55 red soil series surveyed from 2009 to 2011 in Fujian and Guangdong respectively were adopted from Soil Series Database to set up the optimal correlation model between BSP and pH. The results showed that: 1) BSP ranged from 2.30% to 94.02% with a mean of 25.07%, while pH from 3.42 to 6.91 with a mean of 4.98 for the total soil samples. 2) There were significant differences in pH between different soil types (R2 were 0.624 for Ferralosols, 0.507 for Ferrosols, 0.515 for Argosols, and 0.456 for Cambosols, p 0), their probability density curves were mainly in flat or normal curves (<0.67). 3) There is significant positive correlation between BSP and pH, and the optimal correlation models are in quadratic form in most circumstances, but the optimal model and the accuracy are different in different circumstances, changed with different regions, parent materials, soil types and land use types. The accuracy of models established in other studies when predicting our soil samples was lower compared with our models. pH < 5.33 or <5.93 could be used roughly to judge BSP < 35% or <50% based on the model of all red soil series (y = 6.84x2 − 45.86x + 81.52, R2 = 0.494, p < 0.01).

The goal of on-device continual learning is to enable models to adapt to streaming data without forgetting previously acquired knowledge, even with limited computational resources and memory constraints. Recent research has demonstrated that weighted regularization-based methods are constrained by indirect knowledge preservation and sensitive hyperparameter settings, and dynamic architecture methods are ill-suited for on-device environments due to increased resource consumption as the structure scales. In order to compensate for these limitations, replay-based continuous learning, which maintains a compact structure and stable performance, is gaining attention. The limitations of replay-based continuous learning are (1) the limited amount of historical training data that can be stored due to limited memory capacity, and (2) the computational resources of on-device systems are significantly lower than those of servers or cloud infrastructures. Consequently, designing strategies that balance the preservation of past knowledge with rapid and cost-effective updates of model parameters has become a critical consideration in on-device continual learning. This paper presents an empirical survey of replay-based continual learning studies, considering the nearest class mean classifier with replay-based sparse weight updates as a representative method for validating the feasibility of diverse edge devices. Our empirical comparison of standard benchmarks, including CIFAR-10, CIFAR-100, and TinyImageNet, deployed on devices such as Jetson Nano and Raspberry Pi, showed that the proposed representative method achieved reasonable accuracy under limited buffer sizes compared with existing replay-based techniques. A significant reduction in training time and resource consumption was observed, thereby supporting the feasibility of replay-based on-device continual learning in practice.

Understanding activity-travel patterns and their determinants with regard to multi-purpose commuters is essential for enhancing commuting efficiency and ensuring equal participation in activities. This study applies sequence analysis and hierarchical clustering to identify distinct activity-travel patterns of Kunming commuters using 2016 Household Travel Survey data. Subsequently, a multinomial logistic regression model (MNL) examines the factors influencing these patterns. The results reveal significant heterogeneity across four activity-travel patterns: the fixed commuter pattern (FCP), characterized by pronounced morning and evening peaks with minimal non-commuting activities; the balanced commuter pattern (BCP), where commuters participate in non-commuting activities after afternoon work; the restricted commuter pattern (RCP), with non-commuting activities occurring after midday work; and the flexible commuter pattern (FLCP), featuring a late-start work pattern where some commuters go to work after 5 pm. Additionally, the study finds that female commuters and those with longer commuting and working hours tend to have simpler time allocation. Conversely, male commuters, those from complex family structures, car-owning households, and residents in areas with abundant activity opportunities actively engage in non-commuting activities. These findings can help policymakers optimize travel services and develop heterogeneous commuting and transportation policies.

IBM's networking broadband services (NBBS) is a unique network control point architecture capable of managing both asynchronous transfer mode (ATM) networks as well as more generic fast packet networks with variable size packets. The NBBS traffic management functions provide value-added enhanced variable bit rate (VBR) services based on statistical traffic descriptors in addition to providing ATM Forum compliant VBR services based on deterministic rule-based traffic descriptors. In this paper, we first summarize NBBS traffic management functions that are relevant to our study. These include traffic estimation, monitoring, policing and dynamic bandwidth adaptation procedures. The NBBS traffic estimation and adaptation module has a novel feature that continuously monitors the source traffic and dynamically adjusts the bandwidth reserved in the network links for the network connection when it detects a significant change in the connection traffic characteristics. The main contribution of this paper is to integrate all these functions in a trace driven simulation experiment to study their aggregate effect in a general network setting using actual traffic traces. Based on observed SNA, TCP, and compressed video traces we observed that NBSS dynamic bandwidth adaptation function provides a three-fold savings in bandwidth use compared to static peak bandwidth allocation in our case study. Our study also provides useful insights into network dimensioning problem in order to achieve a desired level of network availability for different services. Finally, we provide a simple formula for estimating the amount of bandwidth savings achieved through dynamic bandwidth adaptation versus static peak bandwidth allocation in a general network setting by making a comparison on the individual traces in isolation.

The question of interest groups influence is fundamental to our understanding and evaluation of political systems and processes. The definition and measurement of influence is however one of the most serious challenges to empirical studies of interest groups. Some argue that it is a mission impossible to observe the diffuse concept of influence. Nevertheless, more innovative methods have been developed to manage this challenge, but we do not know to what extent these different measurement methods agree, and therefore to what extent we can compare different studies and accumulate knowledge about which groups are influential and why. This article argues that, if different measures of influence correlate, we can have more faith in our measures and perhaps by triangulation come even closer to measuring at least important aspects of interest group influence. Therefore, the article is set out to test measurement agreement across different measures of interest groups’ influence. It focuses on studies of many cases (large-N studies) and uses measures based on (i) survey data and (ii) documentary data. The Danish Parliament is the empirical setting of the study. First, the article reviews different definitions of influence and outlines the definition of this study. Here, influence is understood as control over political outputs, such as bills or parliamentary debates. Second, the article discusses strengths and weaknesses of different measures of influence. Survey data can illuminate informal venues of influence but may be biased by strategic replies from the interest groups. Documentary data can track the impact of group activity on political outputs in a more unbiased manner but can only uncover formal venues of influence. Put together, these indicators may provide a more valid measure of influence. This, however, requires the indicators to correlate. The article tests the agreement between these two sources of data across three different measures: (i) group activity; (ii) agenda-setting influence; and (iii) legislative influence. Agreement is strongest with relation to activities and weakest with relation to legislative influence, which was also expected. In general, the analyses show that even though measurement agreement is low, it is promising for future studies that different measures of influence are strongly and significantly correlated. The article finds no clear indications of some group types being less ‘honest’ in their responses. We do not need to be very suspicious towards specific categories of groups, such as business for instance. The article does obtain results indicating that the formulation of response categories is very important to the answers we obtain and also important to the agreement between survey and documentary data. These findings implicate that measuring – aspects – of influence is not necessarily a mission impossible. As different measures correlate, we can compare different studies and thereby arrive at more general conclusions, and we may improve our studies by combining more measures of influence and also by paying even more attention to the design of the surveys we use.

Until now, a large amount of effort has been spent on structuring educational repositories. Standard metadata for learning objects have been provided and large databases have been deployed both from academia, and private organizations following mainly a content-centric approach. Vice-versa, a pedagogy-centric approach to the collection and sharing of learning resources still remains under-investigated. In this paper, we provide a different perspective on repositories for education, having as a central point the “educational experience”, i.e. a detailed and structured case study by which teachers and researchers in the educational field can understand where, when and how the digital material was used, and what educational benefits were obtained. We describe a framework by which educational experiences conducted in real classes of all levels of schooling can be gathered and shared. We provide readers with an empirical validation and a numerical evaluation of the approach.