Visualization of Urban Mobility Data from Intelligent Transportation Systems

Analysing environmental opinion using highly customisable visualisation tools to understand citizens' attitudes and barriers

Knowledge of public opinion is key to understanding citizens' attitudes towards environmental policies. However, large polls and surveys generate complex datasets from which it is not always easy to draw conclusions. In addition, tailor-made solutions for analysing public opinion face the challenge of handling too many layers of information, which can easily lead to an overwhelming user experience and impair decision-making. Service design methodologies can support the design of ad hoc visualisation tools focused on user needs. We present Op-e-nion, a case study of a visualisation tool for the analysis of public opinion regarding environmental issues, aimed at administrations and public institutions. The involvement of experts from different fields allowed for the identification of the main metrics necessary to target the least engaged socio-demographic groups as well as the barriers that limited their environmental actions. Experts also highlighted useful aspects of the design process and the final prototype to help them define more effective campaigns and policies to address social challenges and promote citizen action. An innovative step was introduced in the methodology by involving non-state actors in the evaluation of the tool, ensuring problem detection and enhancing the sustainability of the final product. Important aspects for the visualisation of multi-categorical data included simplifying the interaction with the tool while prioritising relevant information, and using highly customizable visualisations to answer specific user requirements and changing needs (i.e. analytical vs. managerial tasks). Improved visualisations of public opinion data will, in turn, better support the development of policies shaped by citizens' concerns.

Measuring the Effectiveness of Static Maps to Communicate Changes Over Time

Both in digital and print media, it is common to use static maps to show the evolution of values in various regions over time. The ability to communicate local or global trends, while reducing the cognitive load on readers, is of vital importance for an audience that is not always well versed in map interpretation. This study aims to measure the efficiency of four static maps (choropleth, tile grid map and their banded versions) to test their usefulness in presenting changes over time from a user experience perspective. We first evaluate the effectiveness of these map types by quantitative performance analysis (time and success rates). In a second phase, we gather qualitative data to detect which type of map favors decision-making. On a quantitative level, our results show that certain types of maps work better to show global trends, while other types are more useful when analyzing regional trends or detecting the regions that fit a specific pattern. On a qualitative level, those representations which are already familiar to the user are often better valued despite having lower measured success rates.

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

Mapping of Orthopaedic Fractures for Optimal Surgical Guidance

Orthopaedic fractures may be difficult to treat surgically if accurate information on the fracture propagation and its exit points are not known. Even with two-dimensional (2D) radiographic images, it is difficult to be completely certain of the exact location of the fracture site, the fracture propagation pattern and the exit points of the fracture. Three-dimensional (3D) computerised tomographic models are better in providing surgeons with the extent of bone fractures, but they may still not be sufficient to allow surgeons to plan open reduction and internal fixation (ORIF) surgery.Fracture patterns and fracture maps are developed to be visual tools in 2D and 3D. These tools can be developed using fractured bones either before or after fracture reduction. Aside from being beneficial to surgeons during pre-surgical planning, these maps aid bioengineers who design fracture fixation plates and implants for these fractures, as well as represent fracture classifications.Fracture maps can be either created ex silico or in silico. Ex silico models are created using 3D printed bone models, onto which fracture patterns are marked. In silico fracture models are created by tracing the fracture lines from a fractured bone to a healthy bone template on a computer. The points of interest in both of these representations are the path of fracture propagation on the bone's surface and exit zones, which eventually determine the surgeon's choice of plate and fracture reduction. Both ex silico and in silico fracture maps are used for pre-surgical planning by the surgeons where they can plan the best way to reduce the fracture as well as template various implants in a low-risk environment before performing the surgery.Recently, fracture maps have been further digitised into heat maps. These heat maps provide visual representations of critical regions of fractures propagating through the bone and identify the weaker zones in the bone structure. These heat maps can allow engineers to develop optimal surgical plates to fix an array of fracture patterns propagating through the bone. Correlation of fractured regions with the mechanisms of injury, age, gender, etc. may improve fracture predictability in the future and optimise the intervention, along with making sure that surgeons do not miss fractures of the bone that may otherwise be hidden from plain sight.

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

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

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.

Intelligent Total Transportation Management System for Future Smart Cities

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

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

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.