em-arch : A system architecture for reproducible and extensible collection of human mobility data (original) (raw)

e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data

Transportation Research Record: Journal of the Transportation Research Board, 2018

GPS-equipped smartphones provide new methods to collect data about travel behavior, including travel survey apps that incorporate automated location sensing. Previous approaches to this have involved proprietary or one-off tools that are inconsistent and difficult to evaluate. In contrast, e-mission is an open-source, extensible software platform that consists of ( a) an app for survey participants to install on their Android or iOS smartphones and ( b) cloud-hosted software for managing the collected data. e-mission collects continuous location data, user-initiated annotations, and responses to contextual, platform initiated survey questions. New studies can be set up using the existing University of California, Berkeley, infrastructure with no additional coding, or the platform can be extended for more complex projects. This paper reviews the requirements for smartphone travel data collection, describes the architecture and capabilities of the e-mission platform, and evaluates its...

An ETL-like platform for the processing of mobility data

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

In this article, we introduce a novel platform dedicated to the extraction, transformation and visualization of mobility data. This platform was developed in the framework of a French regional project (DA3T project) aiming at improving the management and valorisation of touristic cities via the fine-grained analysis of tourist mobility data. The system is totally modular, and non-computer scientists (such as geographers) can make processing pipelines from a variety of modules belonging to different categories (e.g., extraction, filtering, visualization, etc.). Each pipeline is created in order to help fulfil one or several reporting needs. Indeed, the results of those pipelines aim to be presented to local authorities and decision makers to assist them in improving infrastructure and tourism management. Beyond this main use case, the platform is also generic and aims to work with any kind of mobility data (not strictly limited to the tourism field). It is heavily inspired by traditional ETL (Extract, Transform, Load) software and processes. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI); • Information systems → Geographic information systems;

Data4UrbanMobility: Towards Holistic Data Analytics for Mobility Applications in Urban Regions

Companion Proceedings of The 2019 World Wide Web Conference, 2019

With the increasing availability of mobility-related data, such as GPS-traces, Web queries and climate conditions, there is a growing demand to utilize this data to better understand and support urban mobility needs. However, data available from the individual actors, such as providers of information, navigation and transportation systems, is mostly restricted to isolated mobility modes, whereas holistic data analytics over integrated data sources is not sufficiently supported. In this paper we present our ongoing research in the context of holistic data analytics to support urban mobility applications in the Data4UrbanMobility (D4UM) project. First, we discuss challenges in urban mobility analytics and present the D4UM platform we are currently developing to facilitate holistic urban data analytics over integrated heterogeneous data sources along with the available data sources. Second, we present the MiC app-a tool we developed to complement available datasets with intermodal mobility data (i.e. data about journeys that involve more than one mode of mobility) using a citizen science approach. Finally, we present selected use cases and discuss our future work. 1 http://data4urbanmobility.l3s.uni-hannover.de/

A Review of Urban Computing for Mobile Phone Traces: Current Methods, Challenges and Opportunities

2013

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in in-dividual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to tri-angulated mobile phone data for the context of urban computing and modeling applications.

Mobility Atlas Booklet: An Urban Dashboard Design and Implementation

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.

Understanding Urban Human Mobility through Crowdsensed Data

IEEE Communications Magazine

Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. Special emphasis is made on the matching between data types and mobility subjects. Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.

Mobility Analytics for Spatio-Temporal and Social Data

Lecture Notes in Computer Science, 2018

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

MobilityNet: Towards a Public Dataset for Multi-modal Mobility Research

International Conference on Learning Representations, 2020

Influencing transportation demand can significantly reduce CO 2 emissions. Individual user mobility models are key to influencing demand at personal and structural levels. Constructing such models is a challenging task that depends on a number of interdependent steps. Progress on this task is hamstrung by the lack of high quality public datasets. We introduce MobilityNet: the first step towards a common ground for multi-modal mobility research. MobilityNet solves the holistic evaluation, privacy preservation and fine grained ground truth problems through the use of artificial trips, control phones, and repeated travel. It currently includes 1080 hours of data from both Android and iOS, representing 16 different travel contexts and 4 different sensing configurations.

A review of urban computing for mobile phone traces

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing - UrbComp '13, 2013

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.