Special Issue "Big Data and Transportation"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Big Data Mining and Analytics".

Deadline for manuscript submissions: closed (31 October 2021).

Special Issue Editor

Prof. Dr. Lee D. Han
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA
Interests: multisource sensor data processing and mining; accuracy and precision of big data; real-time non-recurrent event detection and management; management of connected and automated vehicles; mass evacuation operations; parallel simulation of microscopic traffic network; traffic flow theory; big data-based human behavior analysis and modification; dynamic traffic modeling and simulations
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Special Issue Information

Dear Colleagues,

During the past decade, “Big Data” has become a trendy ubiquitous term poised to transform every field, including transportation. The Informatics Editorial Office has launched a new Special Issue to provide a forum for looking into the promises, challenges, theories, tools, applications, success stories, lessons learned, etc. of Big Data in the vast realm of transportation. This Special Issue in Informatics welcomes papers in the field of big data in transportation. If your paper is well-prepared and approved for further publication, you might be eligible for discounts for your publication.

We welcome submissions on big data in transportation related to following topics:

  • Big Data—quantity vs quality;
  • Automated large-scale data imputation;
  • Real-time traffic operations using Big Data;
  • Big Data for user behaviors;
  • Transportation Big Data and privacy;
  • Big Data and travel behaviors in the time of pandemic and other large-scale disasters;
  • Reliabilities and costs of various transportation Big Data sources;
  • Maturing and emerging transportation Big Data applications;
  • The paradigm shift in transportation statistics collection and analysis approaches;
  • Big Data experience in different transportation modes?
  • Freight transportation and Big Data;
  • Better long-term planning with Big Data;
  • Big Data and AI;
  • The upcoming era of CAV and Big Data;
  • Transferable success in Big Data applications;
  • Big Data, GIS, and transportation;
  • Visualization of Big Data analytics;
  • Big Data and transportation logistics;
  • Small applications with Big Data;
  • Improving transportation safety with Big Data;
  • Research needs in transportation Big Data;
  • Security with Big Data;
  • Big Data for mass evacuation planning and monitoring;
  • Highway capacity manual in the time of Big Data.

Prof. Dr. Lee D. Han
Guest Editor

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 papers will be 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. Informatics is an international peer-reviewed open access quarterly 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.

Published Papers (2 papers)

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Research

Article
A Simplified and High Accuracy Algorithm of RSSI-Based Localization Zoning for Children Tracking In-Out the School Buses Using Bluetooth Low Energy Beacon
Informatics 2021, 8(4), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040065 - 25 Sep 2021
Cited by 1 | Viewed by 534
Abstract
To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a [...] Read more.
To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a high accuracy child monitoring system for locating students if they are inside or outside a school bus using the Internet of Things (IoT) via Bluetooth Low Energy (BLE) which is suitable for a signal strength indication (RSSI) algorithm. The in/out-bus child tracking system alerts a driver to determine if there is a child left on the bus or not. Distance between devices is analyzed for decision making to affiliate the zone of the current children’s position. A simplified and high accuracy machine learning of least mean square (LMS) algorithm is used in this research with model-based RSSI localization techniques. The distance is calculated with the grid size of 0.5 m × 0.5 m similar in size to an actual seat of a school bus using two zones (inside or outside a school bus). The averaged signal strength is proposed for this research, rather than using the raw value of the signal strength in typical works, providing a robust position-tracking system with high accuracy while maintaining the simplicity of the classical trilateration method leading to precise classification of each student from each zone. The test was performed to validate the effectiveness of the proposed tracking strategy which precisely shows the positions of each student. The proposed method, therefore, can be applied for future autopilot school buses where students’ home locations can be securely stored in the system used for references to transport each student to their homes without a driver. Full article
(This article belongs to the Special Issue Big Data and Transportation)
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Article
Estimating Freeway Level-of-Service Using Crowdsourced Data
Informatics 2021, 8(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8010017 - 05 Mar 2021
Cited by 1 | Viewed by 987
Abstract
In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to [...] Read more.
In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to monitor freeways. The Highway Capacity Manual (HCM) provides analytical methods to assess LOS based on traffic density and highway characteristics. Generally, obtaining reliable density data on every road in large networks using traditional fixed location sensors and cameras is expensive and otherwise unrealistic. Traditional intelligent transportation system facilities are typically limited to major urban areas in different states. Crowdsourced data are an emerging, low-cost solution that can potentially improve safety and operations. This study incorporates crowdsourced data provided by Waze to propose an algorithm for LOS assessment on an hourly basis. The proposed algorithm exploits various features from big data (crowdsourced Waze user alerts and speed/travel time variation) to perform LOS classification using machine learning models. Three categories of model inputs are introduced: Basic statistical measures of speed; travel time reliability measures; and the number of hourly Waze alerts. Data collected from fixed location sensors were used to calculate ground truth LOS. The results reveal that using Waze crowdsourced alerts can improve the LOS estimation accuracy by about 10% (accuracy = 0.93, Kappa = 0.83). The proposed method was also tested and confirmed by using data from after coronavirus disease 2019 (COVID-19) with severe traffic breakdown due to a stay-at-home policy. The proposed method is extendible for freeways in other locations. The results of this research provide transportation agencies with a LOS method based on crowdsourced data on different freeway segments, regardless of the availability of traditional fixed location sensors. Full article
(This article belongs to the Special Issue Big Data and Transportation)
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