Visualization of Urban Mobility Data from Intelligent Transportation Systems

Sobral, Thiago; Dias, Teresa Galvão; Borges, José

doi:10.3390/s19020332

Cited by 80 publications

(33 citation statements)

References 86 publications

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“…For MC, we focus more on travel behavior analysis. Even here, the research questions can be quite manifold [44][45][46][47][48]. They reach from traffic count/traffic flow analysis [49] over origin-destination detection [50,51] to predicting trip purpose [52,53].…”

Section: Travel Behavior Analysis Systemsmentioning

confidence: 99%

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

et al. 2021

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Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires.

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Section: Travel Behavior Analysis Systemsmentioning

confidence: 99%

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

et al. 2021

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“…Dividing the study of mobility into three areas, namely behavioural factors, model development and human mobility patterns, the overall aim of the paper is to synergize the work of two groupsone consisting of transport planners/researchers working with small data and the other including computer/data scientists working with big data. Several studies have focused on a visual analytics approach for urban mobility analyses using mobile phone network data (Sobral, Dias, and Borges 2019;Senaratne et al 2017;Andrienko et al 2017;Chen, Guo, and Wang 2015;Sagl, Loidl, and Beinat 2012). Toole et al (2015) used call data records in conjunction with census and road network data with the aim of gaining a better understanding of road usage patterns in five metro regions: Boston, San Francisco, Lisbon, Porto and Rio de Janeiro.…”

Section: Background: Monitoring Mobility Patternsmentioning

confidence: 99%

Monitoring travel patterns in German city regions with the help of mobile phone network data

Fina

Joshi

Wittowsky

2020

International Journal of Digital Earth

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This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning. Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes, a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access. Concepts need to serve the evaluation of policy objectives, for example in regional or local area plans. In this study, we, therefore, extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns. To accomplish this, we link mobile phone records with urban classifications and transport network data, using both visual and computational approaches to mine the data. The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany, testing hypotheses of transit-oriented regional development, as well as testing for congestion risks in the transport network. The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.

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“…One sector in which the visualization of IoT sensors is used is in the Smart Cities domain. These systems generate massive amounts of data that can be analyzed and visualized to better understand people’s dynamics [ 21 ]. Another sector is healthcare: the visualization of data, metadata and sensor networks is becoming one of the most important aspects of the health monitoring process [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production

Lavalle

Teruel

Maté

et al. 2020

Sensors

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Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.

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Visualization of Urban Mobility Data from Intelligent Transportation Systems

Cited by 80 publications

References 86 publications

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

Monitoring travel patterns in German city regions with the help of mobile phone network data

Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production

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