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Telematics, GIS and Artificial Intelligence
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Telematics, GIS and Artificial Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 13357

Special Issue Editors

Dr. Miguel Félix Mata Rivera

E-Mail Website
Guest Editor
Instituto Politécnico Nacional, UPIITA, Mexico City 07340, Mexico
Interests: telematics; AI; mobile computing; GIS
Dr. Roberto Zagal Flores

E-Mail Website
Guest Editor
Instituto Politécnico Nacional, ESCOM, Mexico City GAM 07738, Mexico
Interests: AI; data science; social networks, big data

Special Issue Information

Dear Colleagues,

This Special Issue is related to specific issues addressed at this year’s Conferences and Workshops in Telematics and Computing. Initiated in 2012, the annual WITCOM conference is a forum for exchanging information and research results on telematics, computing, AI, and GIS theory and principles, along with applications of intelligent system technology. The conference traditionally brings together academic and industrial researchers from all areas of telematics, computing, and AI to share their ideas and experiences and learn about the research in telematics, computing, and AI. As its name indicates, the conference is dedicated to interdisciplinary research among these areas.

GIS LATAM is a conference to bring together specialists and interested in the GIS area, mainly in Latin America but also worldwide. It is an annual event to present works and advances related to geographic information systems, both from research communities, industry, and government. It also provides collaborative workshop programs in conjunction with universities, government entities, and industry. This year organizes a track specialized in artificial intelligence with application to the environment and GIS.

However, this year, for WITCOM and GIS LATAM, we would like to put an emphasis on AI and GIS, as it has been used successfully in many applications. Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Dr. Miguel Félix Mata Rivera
Dr. Roberto Zagal Flores
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • telematics
  • artificial intelligence
  • GIS
  • data science
  • informatics security

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Published Papers (6 papers)

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Research

33 pages, 48967 KiB  
Article
Medical Support Vehicle Location and Deployment at Mass Casualty Incidents
by Miguel Medina-Perez, Giovanni Guzmán, Magdalena Saldana-Perez and Valeria Karina Legaria-Santiago
Information 2024, 15(5), 260; https://doi.org/10.3390/info15050260 - 3 May 2024
Viewed by 1289
Abstract
Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical [...] Read more.
Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions. Full article
(This article belongs to the Special Issue Telematics, GIS and Artificial Intelligence)
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13 pages, 4966 KiB  
Article
Erlang-U: Blocking Probability of UAV-Assisted Cellular Systems
by Mario E. Rivero-Angeles, Iclia Villordo-Jimenez, Izlian Y. Orea-Flores, Noé Torres-Cruz and Angel Pretelín Ricárdez
Information 2024, 15(4), 192; https://doi.org/10.3390/info15040192 - 31 Mar 2024
Viewed by 1045
Abstract
In modern and future communication systems, we expect peaks of traffic that largely exceed the capacity of the system, since they are originally designed to support normal traffic loads. Such peaks can be caused by emergency events and cultural or sporting gatherings, among [...] Read more.
In modern and future communication systems, we expect peaks of traffic that largely exceed the capacity of the system, since they are originally designed to support normal traffic loads. Such peaks can be caused by emergency events and cultural or sporting gatherings, among others. Indeed, implementing more channels than the ones required in normal traffic conditions would entail higher costs and energy consumption. As such, when a traffic peak arrives, the system performance is greatly affected. To this end, we propose the use of mobile channels that assist cellular systems to increase the capacity of the network for a certain period. In this paper, we derive the blocking probability of a UAV (Unmanned Aerial Vehicle)-assisted cellular system to temporarily increase the capacity of the communication network in case of a traffic overload. The analysis presented in this work allows a careful design of future communication systems requiring fewer channels, that can serve users in normal traffic load conditions while using UAVs to maintain an adequate blocking probability when the traffic load increases. To this end, we develop the ErlangU formula, similar to the ErlangB formula for a conventional voice service cellular system. Full article
(This article belongs to the Special Issue Telematics, GIS and Artificial Intelligence)
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18 pages, 6294 KiB  
Article
Location Analytics of Routine Occurrences (LARO) to Identify Locations with Regularly Occurring Events with a Case Study on Traffic Accidents
by Yanan Wu, Yalin Yang and May Yuan
Information 2024, 15(2), 107; https://doi.org/10.3390/info15020107 - 9 Feb 2024
Viewed by 2240
Abstract
Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of [...] Read more.
Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings. Full article
(This article belongs to the Special Issue Telematics, GIS and Artificial Intelligence)
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21 pages, 6263 KiB  
Article
Collaboration System for Multidisciplinary Research with Essential Data Analysis Toolkit Built-In
by Laura I. Garay-Jiménez, Jose Fausto Romero-Lujambio, Amaury Santiago-Horta, Blanca Tovar-Corona, Pilar Gómez-Miranda and Miguel Félix Mata-Rivera
Information 2023, 14(12), 626; https://doi.org/10.3390/info14120626 - 21 Nov 2023
Viewed by 2061
Abstract
Environmental research calls for a multidisciplinary approach, where highly specialized research teams collaborate in data analysis. Nevertheless, managing the data lifecycle and research artifacts becomes challenging because the project teams require techniques and tools tailored to their study fields. Another pain point is [...] Read more.
Environmental research calls for a multidisciplinary approach, where highly specialized research teams collaborate in data analysis. Nevertheless, managing the data lifecycle and research artifacts becomes challenging because the project teams require techniques and tools tailored to their study fields. Another pain point is the unavailability of essential analysis and data representation formats for querying and interpreting the shared results. In addition, managing progress reports across the teams is demanding because they manage different platforms and systems. These concerns discourage the knowledge-sharing process and lead to researchers’ low adherence to the system. A hybrid methodology based on Design Thinking and an Agile approach enables us to understand and attend to the research process needs. As a result, a microservices-based architecture of the system, which can be deployed in cloud, hybrid, or standalone environments and adapt the computing resources according to the actual requirements with an access control system based on users and roles, enables the security and confidentiality, allowing the team’s lead to share or revoke access. Additionally, intelligent assistance is available for document searches and dataset analyses. A multidisciplinary researchers’ team that uses this system as a knowledge-sharing workspace reported an 83% acceptance. Full article
(This article belongs to the Special Issue Telematics, GIS and Artificial Intelligence)
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19 pages, 2131 KiB  
Article
Addressing Vehicle Sharing through Behavioral Analysis: A Solution to User Clustering Using Recency-Frequency-Monetary and Vehicle Relocation Based on Neighborhood Splits
by Nicolo’ Brandizzi, Samuele Russo, Gaspare Galati and Christian Napoli
Information 2022, 13(11), 511; https://doi.org/10.3390/info13110511 - 25 Oct 2022
Cited by 20 | Viewed by 1641
Abstract
In many developed cities around the world, vehicle sharing is becoming an increasingly popular form of green transportation. While such services are associated with lower emissions and easier mobility, their management poses a significant challenge. In this paper, we examine a dataset collected [...] Read more.
In many developed cities around the world, vehicle sharing is becoming an increasingly popular form of green transportation. While such services are associated with lower emissions and easier mobility, their management poses a significant challenge. In this paper, we examine a dataset collected in Barcelona during the months of august and september 2020 in order to investigate relocation strategies and user clustering. By proposing a neighborhood area split and relating it to user demand, we propose two different areas based on majority demand and users’ requests and provide interpretations of both. We then aim to identify groups of similar users using a variant of Recency Frequency Monetary/Duration (RFM or RFD) clustering that extends to GPS coordinates of voyages in order to differentiate scores based on economic and geographical factors; furthermore, a user-based clustering approach was used to maximize client preferences. As a result of our analysis, the sharing company may be able to make more informed decisions regarding where to focus its resources. In fact, we find that the majority of the demand is concentrated in an area that represents 7.47 percent of the city’s area. Additionally, we propose a discount-based approach in order to influence the user’s behavior in parking the vehicle where it is most needed. Full article
(This article belongs to the Special Issue Telematics, GIS and Artificial Intelligence)
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14 pages, 2074 KiB  
Article
Improving Map Matching of Floating Car Data with Artificial Intelligence Techniques
by Georgia Ayfantopoulou, Marios Nikolaos Militsis, Josep Maria Salanova Grau and Socrates Basbas
Information 2022, 13(11), 508; https://doi.org/10.3390/info13110508 - 24 Oct 2022
Cited by 1 | Viewed by 3029
Abstract
Map matching is a crucial data processing task for transferring measurements from the dynamic sensor location to the relevant road segment. It is especially important when estimating road network speed by using probe vehicles (floating car data) as speed measurement sensors. Most common [...] Read more.
Map matching is a crucial data processing task for transferring measurements from the dynamic sensor location to the relevant road segment. It is especially important when estimating road network speed by using probe vehicles (floating car data) as speed measurement sensors. Most common approaches rely on finding the closet road segment, but road network geometry (e.g., dense areas, two-way streets, and superposition of road segments due to different heights) and inaccuracy in the GNSS location (up to decades of meters in urban areas) can wrongly allocate up to 30% of the measurements. More advanced methods rely on taking the topology of the network into account, significantly improving the accuracy at a higher computational cost, especially when the accuracy of the GNSS location is low. In order to both improve the accuracy of the “closet road segment” methods and reduce the processing time of the topology-based methods, the data can be pre-processed using AI techniques to reduce noise created by the inaccuracy of the GNSS location and improve the overall accuracy of the map-matching task. This paper applies AI to correct GNSS locations and improve the map-matching results, achieving a matching accuracy of 76%. The proposed methodology is demonstrated to the floating car data generated by a fleet of 1200 taxi vehicles in Thessaloniki used to estimate road network speed in real time for information services and for supporting traffic management in the city. Full article
(This article belongs to the Special Issue Telematics, GIS and Artificial Intelligence)
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