Personal Mobility Service System in Urban Areas: The IRMA Project

Motta, Gianmario; Sacco, Daniele; Ma, Tianyi; You, Linlin; Liu, Kaixu

doi:10.1109/sose.2015.15

Cited by 32 publications

(23 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research topics on mobility services are divided into two types: mobility service platforms [7,8,17,18] and mobility [9,[19][20][21][22][23][24]. As mentioned earlier, the studies conducted on mobility have contributed to the invention of various means of transportation (e.g., electric kickboards and electric bicycles), which shorten the necessary duration to arrive at a certain destination.…”

Section: Mobility Servicementioning

confidence: 99%

“…Such means have been used at relatively lower prices compared with taxis since late 2010. Additionally, several researchers have investigated Mobility as a Service (MaaS) [6][7][8] and other mobility services that depend on autonomous driving technology [9][10][11], which is one of the essential components in the artificial intelligence industry. By following the trend of the mobility-related technology development up to date, there were two possible expectations: the diversification of the types of mobility and the unification of all the mobility services using one platform.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

et al. 2020

View full text Add to dashboard Cite

Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.

show abstract

Section: Mobility Servicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The IRMA project [10] seeks to implement a software framework that targets personal mobility that is green oriented, shared and public transportation and provide an Integrated Real-time Mobility Assistant. IRMA is an extendible modular platform using multiple sources of data (crowd, open, social, and sensor data) and it provides traffic services to several stakeholders, which includes the municipality, citizens, and transport providers.…”

Section: Related Workmentioning

confidence: 99%

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

Babau¹,

Marcu²,

Tihu³

et al. 2017

Acta Cybern

View full text Add to dashboard Cite

Traffic optimization is a subject that has become vital for the world we live in. People these days need to get from a starting point to a destination point as fast and as safe as possible. Traffic congestion plays a key role in the frustration of people and it results in lost time, reduced productivity and wasted resources. In our study we seek to address these issues by proposing a real-time road traffic planning system based on mobile context and crowd sourcing efforts. The first step toward this goal is real-time traffic characterization using data collected from mobile sensors of drivers, pedestrians, cyclists, passengers, etc.. We started developing a data collection and analysis system composed of a mobile application in order to collect user context data and a Web application to view and analyze the data. This new system will eventually give the users an automatically optimized route to the destination and predict the users' traveling route based on live traffic conditions and historical data.

show abstract

“…The heterogeneity of OSM data can be examined from various aspects such as element sizes, the number of edits, and the number of users for each element (Ma et al 2015). For example, the element size ranges from 3 up to 5,000,000, the number of edits for each element can go up from 1 to 2,000.…”

Section: European Handbook Of Crowdsourced Geographic Informationmentioning

confidence: 99%

“…The world population makes more than 64% of all travel kilometers within urban environments, which is expected to triple by 2050. Thus, a high priority for cities around the world is to support citizens' mobility within the urban environment based on safety, efficiency and environmental protection (Motta et al 2015). Mobile crowd sensing paradigm will enable key methods, techniques and systems in that direction.…”

Section: Introductionmentioning

confidence: 99%

European Handbook of Crowdsourced Geographic Information

2016

View full text Add to dashboard Cite

Part II: Quality: Criteria and methodologiesChapter 5. Handling quality in crowdsourced geographic information (Laura Criscuolo, Paola Carrara, Gloria Bordogna, Monica Pepe, Francesco Zucca, Roberto Seppi, Alessandro Oggioni and Anna Rampini) 57Chapter 6. Data quality in crowdsourcing for biodiversity research: issues and examples (Clemens Jacobs) 75Chapter 7. Semantic Challenges for Volunteered Geographic Information (Andrea Ballatore) 87Chapter 8. Quality analysis of the Parisian OSM toponyms evolution (Vyron Antoniou, Guillaume Touya and Ana-Maria Raimond) 97Chapter 9. Tackling the thematic accuracy of areal features in OpenStreetMap (Ahmed Loai Ali) 113iv European Handbook of Crowdsourced Geographic InformationFive years later, in 2012, the ENERGIC action started exploring new VGI sources, sharing and developing data retrieval software, assessing VGI quality, defining standardization criteria for interoperability with other datasets, identifying applications and transferring them for business implementation (market analysis, risk management, advertising, etc. 1 ).The action is based on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet. People now consume and produce geographic information on the go via platforms such as Facebook, Twitter, Flickr, Instagram and others. The availability of cheap GPS allows everyone to survey and map and contribute to projects like Wikimapia and OpenStreetMap. The exploitation, integration and application of these sources, termed crowdsourced or user generated information, offer to multidisciplinary scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales.The most popular definition of such content that possesses a geographic reference data is Volunteered Geographic Information ( The growing volume of scientific production on the topic cover multiple domains but some major threads may be identified, although intertwined and often coexisting, to build a narrative on the development of VGI. After an initial phase concerned mostly with conceptualizing and defining the new phenomenon (Coleman 2010; Elwood,2008; Capineri & Rondinone 2011;See et al. 2016;Sui et al. 2012) In conclusion, a very wide panorama which proves the broad penetration of VGI in the scientific community and the diverse research paths.The collection of the papers in this book touch on many of these threads. The Book StructureThe book includes peer-reviewed chapters, organised in six parts, which try to address some PART I: Theoretical and social aspectsPart I deals with the nature and features of crowdsourced geographic information: the different sources and typologies of VGI, the fundamental aspect of participation. Capineri (Chapter 2) discusses the main features of crowdsourced geographic information by focusing on the components of such data: the geographical reference, the contents and the producers' profile in order to show the potentialities and crit...

show abstract

Personal Mobility Service System in Urban Areas: The IRMA Project

Cited by 32 publications

References 22 publications

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

European Handbook of Crowdsourced Geographic Information

Contact Info

Product

Resources

About