3,958 research outputs found
Identifying shifts in multi-modal travel patterns during special events using mobile data: Celebrating Vappu in Helsinki
Large urban special events significantly contribute to a city's vibrancy and
economic growth but concurrently impose challenges on transportation systems
due to alterations in mobility patterns. This study aims to shed light on
mobility patterns by utilizing a unique, comprehensive dataset collected from
the Helsinki public transport mobile application and Bluetooth beacons. Earlier
methods, relying on mobile phone records or focusing on single traffic modes,
do not fully grasp the intricacies of travel behavior during such events. We
focus on the Vappu festivities (May 1st) in the Helsinki Metropolitan Area, a
national holiday characterized by mass gatherings and outdoor activities. We
examine and compare multi-modal mobility patterns during the event with those
during typical non-working days in May 2022. Through this case study, we find
that people tend to favor public transport over private cars and are prepared
to walk longer distances to participate in the event. The study underscores the
value of using comprehensive multi-modal data to better understand and manage
transportation during large-scale events.Comment: 6 pages, 12 figures, Submitted to ITSC202
A Framework for Integrating Transportation Into Smart Cities
In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities.
This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe
The Need of Multidisciplinary Approaches and Engineering Tools for the Development and Implementation of the Smart City Paradigm
This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art
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