Smart Mobility Trends: Open Data and Other Tools

Soriano, Francisco Ridocci; Zapater, José Javier Samper; Martínez-Durá, Juan José; Gimeno, Ramón Vicente Cirilo; Plumé, Javier Martínez

doi:10.1109/mits.2017.2743203

Cited by 25 publications

(20 citation statements)

References 2 publications

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“…In this sense, a match between an emerging technology and private interests is evident. In particular, cities' mobility [14] and the future of the transportation systems are often one of the main concerns when talking about SC [15]. Zhuadar et al [16] emphasized a next wave of SC intelligent systems, in which humans' ability to connect with machines is advocated.…”

Section: Introductionmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

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Section: Introductionmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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“…Thus, for example, according to [16], informational interventions are vital to encourage changes in attitudes and perceptions of people. Along the same lines, the work presented in [17] emphasizes that "open data can impact positively on citizens in particular and society in general".…”

Section: Sharing Smart City Traffic Datamentioning

confidence: 94%

Semantic Traffic Sensor Data: The TRAFAIR Experience

Desimoni

Ilarri

et al. 2020

Applied Sciences

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Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

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“…Intelligent technologies allow industries and governments to serve citizens better through disruptive applications. Among them, we highlight the ones that are related to Intelligent Transportation Systems (ITS), which are responsible for ensuring efficient and sustainable transportation [1]. Many of these applications explore data from several sources to better understand the city dynamics and adapt to them.…”

Section: Introductionmentioning

confidence: 99%

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

et al. 2021

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Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode.

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Smart Mobility Trends: Open Data and Other Tools

Cited by 25 publications

References 2 publications

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Semantic Traffic Sensor Data: The TRAFAIR Experience

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

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