Visualization Analysis for Urban Human Traveling Behavior Based on Mobile Phone Data

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

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.

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.

In this paper, a map-matching method to integrate and manage trajectory data derived from human travel behavior in an urban environment is introduced. The approach is preliminary based on a conceptual spatio-temporal data model that encapsulates the main semantics of urban trajectories. We introduce several algorithms that provide a computational solution to first a trajectory data integration and association to the urban network, and secondly a sound support to a route generation and semantics enrichment. The whole approach has been experimented applying the Geolife dataset as a testbed in the city of Beijing.

Urban transportation systems and travel behavior vary widely, even among countries with similar per-capita income, technology, and urbanization. This article compares modal—split—how people get from place to place—for 12 countries in Western Europe and North America. Differences in travel behavior arise largely from public policy differences, especially from differences in automobile taxation. In addition, variations in transit subsidies, land use controls, and housing programs significantly influence travel choices, although sometimes only indirectly. The success of public transportation depends more on supportive urban development and automobile taxation policies than on transit subsidies. The absence of such complementary policies in the U.S.—unlike the other countries studied—explains the ineffectiveness of the attempt to revive American public transportation exclusively through large subsidies.

Methodologies for exploring the link between urban form and travel behavior

As a common human behavior, interaction is everywhere in human life. With the rise of big data and human–computer interaction in the 21st century, more and more researchers from different industries and disciplines pay great attention to the human interactive behavior research. From the perspective of computer science, scholars try to use computer technology to make research more meaningful. To the best of our knowledge, this paper is the first study to investigate the potential rules of human interactive behavior in the view of computer science, based on 16 top-tier journals of human interactive behavior from Microsoft Academic Graph dataset. We put forward a topic extraction and clustering model based on word2vec to infer key topics, which can be widely used in different fields of research. We find that the growth of human interactive behavior is in an uptrend on the whole. Besides, the cooperative relationship between authors and countries/regions is closer over time. We also make the mensurable evolution analysis of topics by a statistical method. Some topics are hot all the time, while some are unpopular as time goes by. Finally, we do rankings in the field of human behavior research from a new perspective. All these findings help researchers observe potential patterns and the topic evolution in half a century, which may shed dazzling light on the exploration of human interactive behavior.

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.

Electronic payment systems for public transport, introduced in many cities all over the world during the last decade, are able to capture system transactions and provide comprehensive data records on usage of public transport. Processing and analysis of this data opens new opportunities in transportation and travel behaviour research and is becoming an emerging research topic. This paper presents an analysis of one full day of public transport smart card activity for the entire city-state of Singapore and investigates its potential for characterization of public transport system and urban travel behaviour. In particular, an assessment of spatial and temporal travel behaviour including mode choice, travel- and waiting times is performed using statistical tools and data mining techniques. Thereby data reveals country specific preferences and behaviours as the influence of high passenger volumes and seat availability on route choice decisions and travel times. Furthermore data’s ability to detect travel patterns and infer peoples travel purposes and locations of regular activities is investigated and strategies for modelling of home and work locations are presented. Additionally, household survey data from 2008 is used for comparison of multi modal travel patterns involving public and private transport.

The recent availability of longitudinal data on individual trip making and activity behaviour has provided analysts with new insights into the structures and motives of daily life travel. Multi-week travel diary data-sets and GPS observations are exciting sources of information for the description and modelling of the variability of individual travel patterns. Through an analysis of these strong new data sets, this book questions what are the most suitable methodological tools to represent the structures of long-term travel behaviour. It also examines what the data tells us about the travellers' motives and looks at how planning should translate the findings into forecasting tools and transport strategies. In doing so, the multifaceted and ambiguous character of daily life travel is revealed, illustrating how, while sound routines in time and space seem to dominate daily life, individuals show a considerable amount of variability and flexibility in travel and activity behaviour.

Communities are increasingly looking to land use planning strategies based on a less auto-dependent urban form to reduce the need for travel, especially drive-alone travel. In recent years, several studies have attempted to test the impact urban form has on travel behavior to determine if such designs are warranted. The results of these studies are mixed because of several shortcomings. Some shortcomings can be attributed to data availability; others are a product of the techniques used to characterize urban form or travel. Still other shortcomings are embedded in the strategies employed, using cross-sectional travel data and correlating travel outcomes with urban form. The line of research is being extended, aimed at isolating the influence of urban form on travel behavior; a new research strategy is presented using longitudinal travel data in concert with detailed measures of travel behavior and urban form. Data sources from the Puget Sound are described and a research strategy is presented that permits a pretest-posttest analysis of households’ travel behavior before and after they changed residential location. Early results show few changes in household travel behavior after a move, suggesting that attitudes toward travel are firmly entrenched and postmove travel provides little insight into how changes in urban form affect travel. Although a pretest-posttest makes valiant strides in shedding new light on the matter, the complex phenomenon being addressed requires myriad approaches. More comprehensive research techniques and even research approaches based on different different traditions are much needed to better understand how urban form and travel interact.

In the era of big data, with the development of computer technology, especially the comprehensive popularization of mobile terminal device and the gradual construction of the Internet of Things, the urban physical environment and social environment have been comprehensively digitized and quantified. Computational thinking mode has gradually become a new thinking mode for human beings to recognize and govern urban complex system. Meanwhile computational urban science has become the main discipline development aspect of modern urban planning. Computational thinking is the thinking of computer science using algorithms based on time complexity and space complexity, which provides a new paradigm for the construction of index system, data collection, data storage, data analysis, pattern recognition, dynamic governance in the process of scientific planning and urban management. Based on this, this paper takes the computational thinking mode of urban planning discipline in big data era as the research object, takes the scientific construction of computational urban planning as the research purpose, and adopts literature research methods and interdisciplinary research methods, comprehensively studies the connotation of the computing thinking mode of computer science. Meanwhile, this paper systematically discusses the system construction of urban computing, model generation, the theory and method of digital twinning, as well as the popularization of the computational thinking mode of urban and rural planning discipline and the scientific research of computational urban planning, which responds to the needs of the era of the development of urban and rural planning disciplines in the era of big data.

The research on human spatial-temporal behavior has always been the focus of urban geographic studies. In today’s society, social networks, smart phones and smart cards can provide massive data for researchers to analysis human behavior. From mobile phone trajectories, this paper presents algorithms for mining human spatial-temporal behavior pattern, which includes not only the regular movement pattern of individuals, but also the co-occurrence pattern among user groups. The parallel algorithms in this paper are proposed for big data of user trajectories, and the experimental results show that the proposed algorithms are effective. The research on human spatial-temporal behavior pattern can be applied to urban spatial-temporal behavior analysis, smart city construction and other areas.

Over the last decade, tourism industry is exploring the latest technologies to improve the customer experience and enhance customer satisfaction. In order to address the customer demands and business, tourism industry embraces to redefine their products and services using deep learning models and big data analytics. Deep learning-based model can be used to measure customer satisfaction, perception and behavior utilizing sentiment analysis, emotion analysis, and data analysis. Big data analytics in tourism can improve the overall tourism business operations and services. Deep learning techniques with big data analytics not only helps in developing the economical tourism models but also analyze the impact of various environmental factors on tourism planning and travel demand. In addition, these techniques are helpful in estimation of tourist seasonal demands, market price strategies and data analysis automation. Deep learning using data analytics can bring a paradigm shift in the tourism industry by offering personalized recommendations with budget specific packages based on customer past travel, reviews and experience. In this chapter, we will briefly analyze the impact of deep learning in tourism sector in terms of innovation, commercialization and profitability of the business. In addition, we will also review the advancement and potential of the deep learning-based methods with big data analytics in tourism industry in terms of customer overall experience.

Urban rail transit passenger travel behavior exhibits pronounced spatio-temporal heterogeneity, which poses significant challenges for accurately capturing passenger flow dynamics and enabling precise short-term travel forecasting. Unlike most existing studies that focus on station-level predictions, this research innovatively explores the spatio-temporal characteristics of travel behavior from the perspective of individual passengers. Leveraging one-card transaction data from Shanghai’s rail transit system between April 1 and 30, 2015, we extract individual travel trajectory data in the form of OD chains through an OD relationship matching algorithm. A 13-dimensional clustering feature is constructed using a three-dimensional framework encompassing temporal, spatial, and travel intensity attributes. Notably, we introduce frequent travel OD share data as a critical metric to quantify the regularity of passenger travel patterns. To address the complexity of mixed-type data (including categorical and numerical variables), the K-Prototypes clustering algorithm is employed, demonstrating superior performance in handling heterogeneous datasets compared to traditional methods. The clustering results categorize passengers into four distinct groups, with the commuter category—accounting for 40% of the total—exhibiting the strongest spatial regularity. Further analysis of travel patterns across categories provides empirical evidence for identifying the primary sources of metro passenger flow, offering actionable insights for optimizing urban transit planning and demand management.