Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

Advances in Experimental Social Psychology continues to be one of the most sought after and most often cited series in this field. Containing contributions of major empirical and theoretical interest, this series represents the best and the brightest in new research, theory, and practice in social psychology. This serial is part of the Social Sciences package on ScienceDirect. Visit info.sciencedirect.com for more information. Advances in Experimental Social Psychology is available online on ScienceDirect - full-text online of volume 32 onward. Elsevier book series on ScienceDirect gives multiple users throughout an institution simultaneous online access to an important complement to primary research. Digital delivery ensures users reliable, 24-hour access to the latest peer-reviewed content. The Elsevier book series are compiled and written by the most highly regarded authors in their fields and are selected from across the globe using Elsevier's extensive researcher network. For more information about the Elsevier Book Series on ScienceDirect Program, please visit store.elsevier.com. * One of the most sought after and most often cited series in this field* Contains contributions of major empirical and theoretical interest* Represents the best and the brightest in new research, theory, and practice in social psychology

/pdf/advances-in-experimental-social-psychology-1uimh1of8o.pdf

Advances in experimental social psychology

Travel behaviour has been studied for decades to guide transportation development and management, with the support of traditional data collected by travel surveys. Recently, with the development of information and communication technologies (ICT), we have entered an era of big data, and many sources of novel data, including mobile phone data, have emerged and been applied to travel behaviour research. Compared with traditional travel data, mobile phone data have many unique features and advantages, which attract scholars in various fields to apply them to travel behaviour research, and a certain amount of progress has been made to date. However, this is only the beginning, and mobile phone data still have great potential that needs to be exploited to further advance human mobility studies. This paper provides a review of existing travel behaviour studies that have applied mobile phone data, and presents the progress that has been achieved to date, and then discusses the potential of mobile phone data in advancing travel behaviour research and raises some challenges that need to be dealt with in this process.

Applying mobile phone data to travel behaviour research: A literature review

Understanding urban functions and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we present a novel approach to uncover...

Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns

The rise of research into shared mobility systems reflects emerging challenges, such as rising traffic congestion, rising oil prices and rising environmental concern. The operations research community has turned towards more sharable and sustainable systems of transportation. Shared mobility systems can be collapsed into two main streams: Those where people share rides and those where parcel transportation and people transportation are combined. This survey sets out to review recent research in this area, including different optimization approaches, and to provide guidelines and promising directions for future research. It makes a distinction between prearranged and real-time problem settings and their methods of solution, and also gives an overview of real-case applications relevant to the research area.

https://hal.archives-ouvertes.fr/hal-02014342/document

A survey of models and algorithms for optimizing shared mobility

The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called "travel behavior analysis" or "human mobility analysis"). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.

The promises of big data and small data for travel behavior (aka human mobility) analysis.

In this paper, the authors argue that spatial proximity, measured in residential distance, though still matters, probably is not the most important facilitator for social interactions. People’s mobility patterns, both at an individual level and at a pair level, can be an equally important factor if not more. The authors tested this hypothesis by utilizing a unique mobile phone dataset with both location information and calling information for a large urbanized city in China. Through this dataset, the authors are able to identify social ties as well as calculate various measures on spatial proximity and mobility patterns. The analyses were carried out both at the individual level for the number of social ties and at the pair level for the strength of a tie. The results appear to confirm the hypothesis: social ties, both in terms of scope (number of social ties) and strength, are closely related to mobility patterns attributes; more than to the spatial proximity measure. The authors also evaluate the role of the built environment in affecting social ties and show that though living in a dense area can potentially increase the number of social ties, but the effect is very small. There are two simple takeaways from the paper: first, if you want to have more friends, visit new places often; you do not need to travel far though; second, if you want to strengthen an existing tie, visit new places often and those places that will bring your mobility pattern closer to that of the other party.

Yu Liu

Papers

The promises of big data and small data for travel behavior (aka human mobility) analysis.

Does Distance Still Matter in Facilitating Social Ties? The Roles of Mobility Patterns and the Built Environment