A map-matching approach for travel behavior analysis

During the last decades, concepts of Intelligent Transportation Systems (ITS) were continuously adapted and improved based on new insights into human travel behavior. Drivers for improvements are the quantity and quality of available mobility data, which increased significantly in recent years. Based on travel behavior, literature proposes a large number of different solutions for next step or future location prediction. However a holistic spatio-temporal prediction, which could further improve the quality of ITS, creates a more complex task. The prediction of medium-term mobility for one to seven days is challenging in particular for atypical travel behavior, since the weekdays’ order delivers no reliable indication for the next day’s travel behavior. With our contribution, we explore the benefits of various prediction approaches for medium-term mobility prediction and combine them dynamically to predict individual mobility behavior for a period of one week. The derived framework utilizes an exhaustive search approach to benefit from a machine learning based clustering method on location data. In conjunction with an Artificial Neural Network, the prediction framework is robust against prediction errors created by atypical behavior. With two data sets consisting of smartphone and vehicle data, we demonstrate the framework’s real-world applicability. We show that clustering an individual’s historical movement data can improve the prediction accuracy of different prediction methods that will be explained in detail and illustrate the interrelation of entropy and prediction accuracy.

Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study

With the fast development of data acquisition techniques and storage capabilities, more and more human travel related data has been collected. These “Big Data” bring both opportunities and challenges for human travel behavior research. In this paper, millions of residents' daily mobile phone records collected in Shanghai are analyzed by four visualization methods to characterize urban human travel behavior patterns. Particularly, this paper studies: 1) the travel OD map that reveals the spatial structure of travel demand, 2) the two-dimensional population density map that characterizes the continuous spatial distribution of residents, 3) the vector field map of resident movement that depicts the local movement feature of residents, 4) the particle graph of resident flow that demonstrates the smooth dynamic movement process of residents. Both the design objectives and the animation techniques for each visualization method are discussed. The findings on residents' travel behaviors could help the government make the appropriate urban planning and help residents make the better daily travel planning.

The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

Fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) data models. This paper reviews fuzzy conceptual data models proposed in the literature, where fuzzy ER/EER, IFO and UML data models are mainly discussed, and reviews the applications of fuzzy conceptual data models.

The expansion, densification and proliferation of urban areas around the world is currently occurring at a rate that is unprecedented in human history. It is predicted that global urban land cover will triple between 2000 and 2030, with some regions (including biodiversity hotspots) experiencing a ninefold increase in urban land cover over the same time period (Seto, G€ uneralp & Hutyra 2012). Accompanying the expansion of urban landscapes, it is anticipated that the human population living in cities and towns globally will increase from 3 5 to 5 billion people within the next 20 years (Fragkias et al. 2013). Thus, the demands of an expanding and urbanizing human population are one of the pressing ecological problems our world is facing (Sanderson et al. 2002), alongside, and in combination with, global climate change and changes to biodiversity at local and global scales (Pimm et al. 2014). Yet, urban environments also present a unique opportunity to expand our fundamental knowledge related to ecology and evolution due to the presence of intense and often novel selection pressures. In the inaugural issue of this journal, Calow (1987) defined functional ecology as the sum of three interactive processes: (i) those occurring between organisms and their environment, (ii) biotic interactions between organisms and (iii) adaptive processes driven by natural selection. The same three processes were highlighted 1 year earlier by Jared Diamond in Nature, when he called for biologists to pay more attention to the potential of using the unprecedented environmental conditions that exist within towns and cities to develop and test evolutionary and ecological theory (Diamond 1986). There is thus a natural synergy between functional ecology and urban ecology, as exemplified by some of the classic papers that have appeared in this journal, such as Rydell (1992) who demonstrated that the form of echolocation system determined the impact of light pollution on bat foraging behaviour. The potential of combining functional ecological research with urban ecology is, however, a long way from being fully realized. This is in part explained by the youth of urban ecology as a discipline. Scientific enquiry into the ecological consequences of urban environments has been underway for over half a century, although most of the momentum emerged after the mid 1990s (McDonnell 2011; Wu 2014). Thus, the focus of much urban research to date has involved describing patterns along environmental gradients (Gagn e 2013; McDonnell & Hahs 2013) rather than investigating the mechanistic processes that lie at the heart of functional ecology. To be effective in addressing the global challenges of urbanization, a much better understanding of how the urban environment affects the ecology and evolution of organisms needs to be developed (Grimm et al. 2008; Marzluff 2012; Gil & Brumm 2014; McDonnell & Hahs 2015). The purpose of this special feature is to draw attention to the plethora of opportunities that await researchers investigating the ecology and evolution of organisms in urban environments. The combination of environmental stressors and conditions within urban areas provides a novel opportunity to test and expand our theories related to ecology and evolution of organisms, and some intriguing insights are already beginning to emerge. For example, the detailed understanding of the molecular, genetic and developmental mechanisms of beak evolution that has arisen from studying Galapagos finches has been significantly advanced by studying beak evolution in the house finch Carpodacus mexicanus in response to novel urban food sources and its consequences for acoustic communication (Badyaev 2010, 2014). Thus, urban ecology has the potential to extend our understanding of extremely well-studied ecological and evolutionary problems.

Effort and cost estimation is crucial in software management. Estimation of software size plays a key role in the estimation. Line of Code (LOC) is still a commonly used software size measure. Despite the fact that software sizing is well recognized as an important problem for more than two decades, there is still much problem in existing methods. Conceptual data model is widely used in the requirements analysis for information systems. It is also not difficult to construct conceptual data models in the early stage of developing information systems. Much characteristic of an information system is actually reflected from its conceptual data model. We explore into the use of conceptual data model for estimating LOC. This paper proposes a novel method for estimating LOC for an information system from its conceptual data model through the use of multiple linear regression model. We have validated the method through collecting samples from both the industry and open-source systems.

A data base design aid, SYDADA, is introduced. Input to SYDADA is a requirements specification, expressed in a language called SYSDOC. A SYSDOC specification consists of two main parts: a conceptual data model and a transaction description. The conceptual data model is a high level, implementation independent data description. The transaction description is expressed in a very high level language which uses the conceptual data model. SYDADA analyzes the requirements specification by modelling the specified system. In the system model the conceptual data model is the data base, and the transactions specified can be executed using this data base. The most important result of the analysis is a set of statistics showing how, and how frequently, the various parts of the conceptual data model are used. We have shown how these statistics can be used as a basis for logical data base design.

Defining and validating metrics for assessing the understandability of entity–relationship diagrams

We present an open source software, named as TrajAnalytics, which models, transforms, and then visualizes urban trajectory data for transportation and urban study. It allows researchers, administrations, and practitioners to understand the population mobility data and to discover knowledge intuitively. A conceptual data model is presented which integrates trajectory data with geo-structures through a variety of data access queries. The model guides us to develop data processing and management capability and support various visual queries through a Web-based visualization interface. A set of visualization widgets and interaction functions promote easy user engagement. A primary user study shows the usability of the software.

This chapter links urban landscape and residential travel behavior. Travel behavior can be measured by modal choice, car ownership rates, trip generation, commuting time and distance, and trip chaining. This chapter focuses on active travel, including walking and cycling, promoted by researchers. Alternative travel modes are compared between Germany and the United States as well as policies increasing public transport demand and improving road safety. Germany’s land-use and public policies promote compact, mixed-use development, and active travel. Other countries might build upon Germany’s experience. To control traffic congestion, impact travel behavior, and drive cars less, urban development strategies are pursued including transit-oriented development (TOD). Research findings on the links between neighborhood characteristics and non-motorized transport can be useful to investigate the effects of neighborhood environment on residential health.

Efficient data warehouse (DW) design depends on the quality of a conceptual model. In this paper, a set of quality metrics have been proposed along with the theoretical validation for quality measurement of conceptual level object-oriented multidimensional data model. The paper also describes the designer level and user level viewpoints of quality evaluation through the criteria like complexity, completeness, expressiveness and analysability. Finally, the work focuses on the empirical validation of the set of metrics and measurements. The empirical validation process shows the usefulness of the proposed metrics for the assessment of operability factor of conceptual level multidimensional data model. Significant correlations exist among the proposed quality measurements and operability factors. The proposed quality evaluation framework is verified in context of a graph semantic-based object-oriented multidimensional data model GOOMD.

Despite a large body of research suggesting that the built environment influences individual travel behavior, uncertainty remains about the true nature, size, and strength of any causal relationships between the built environment and travel behavior. Residential self-selection, the phenomenon whereby individuals or households select a residential area based on their transport attitudes, is a frequently proposed alternative explanation for the reported associations. To resolve the issue of residential self-selection, longitudinal studies are often recommended. In this paper, we argue that intervention study designs are insufficient to fully resolve the problem and that intervention studies on the built environment and travel behavior may still be biased by residential self-selection. The aim of this paper is to extend existing conceptualizations of the relationships between the built environment, travel behavior, and attitudes and to provide suggestions for how a causal relationship between the built environment and travel behavior may be ascertained with more accurate estimates of effect sizes. We discuss the complexities of determining causal effects in intervention studies with participants who relocate, and the biases that may occur. We illustrate the complexities by presenting extended conceptualizations. Based on these conceptualizations, we provide considerations for future research. We suggest repeating analyses with and without individuals who relocated during the study, and with and without statistical controls for residential relocation. Additional quantitative and qualitative analyses will be necessary to obtain more accurate effect size estimates and a better understanding of the causal relationships.

In this short paper we will briefly introduce a data warehouse conceptual data model, which gracefully extends the standard entity-relationship (ER) conceptual data model with multi-dimensional aggregated entities. The conceptual data model has a clear model-theoretic semantics grounded on the extension of the standard ER semantics with the QMD logic-based multi-dimensional data model.