Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach

Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study

BACKGROUND

Systematic reviews and meta-analyses are important to evidence-based medicine, but the information retrieval and literature screening procedures are burdensome tasks. Rapid Medical Evidence Synthesis (RMES; Deloitte Tohmatsu Risk Advisory LLC) is a software designed to support information retrieval, literature screening, and data extraction for evidence-based medicine.

OBJECTIVE

This study aimed to evaluate the accuracy of RMES for literature screening with reference to published systematic reviews.

METHODS

We used RMES to automatically screen the titles and abstracts of PubMed-indexed articles included in 12 systematic reviews across 6 medical fields, by applying 4 filters: (1) study type; (2) study type + disease; (3) study type + intervention; and (4) study type + disease + intervention. We determined the numbers of articles correctly included by each filter relative to those included by the authors of each systematic review. Only PubMed-indexed articles were assessed.

RESULTS

Across the 12 reviews, the number of articles analyzed by RMES ranged from 46 to 5612. The number of PubMed-cited articles included in the reviews ranged from 4 to 47. The median (range) percentage of articles correctly labeled by RMES using filters 1-4 were: 80.9% (57.1%-100%), 65.2% (34.1%-81.8%), 70.5% (0%-100%), and 58.6% (0%-81.8%), respectively.

CONCLUSIONS

This study demonstrated good performance and accuracy of RMES for the initial screening of the titles and abstracts of articles for use in systematic reviews. RMES has the potential to reduce the workload involved in the initial screening of published studies.

Towards smart work zones: Creating safe and efficient work zones in the technology era

INTRODUCTION

Work Zones (WZs) have long been identified as a source of traffic fatalities and delays. Despite considerable technological advances that have alleviated many operational challenges associated with a WZ, social concerns about safety and mobility near WZs remain. Notably, the concept of a Smart Work Zone (SWZ) emerged from the compelling need to improve the safety and mobility of traffic and other WZ participants. This study reviewed the literature to assimilate studies related to SWZ Systems (SWZSs), report their findings, and ascertain a future path forward.

METHOD

To accomplish this, the existing WZ-related literature base was clustered into safety and traffic mobility topics using Latent Dirichlet Allocation (LDA) modeling. A thorough investigation of the pivotal inferences for the research topics was undertaken to comprehend current SWZ technologies and the need for further research.

RESULTS

The review uncovered the prominent features of SWZSs reported in the literature and the hindrances to their adoption. The most reported hindrances are the cost and effort associated with development, installation, and relocation. We uncover that Connected Autonomous Vehicles, vehicle-to-vehicle, and vehicle-to-infrastructure communication, along with technology-based worker training are the most promising next frontier for SWZ.

CONCLUSION

Significant research gaps exist in the literature regarding developing and implementing SWZS. Additionally, little effort has been directed toward developing workers' skills and competency. Practical approaches such as Virtual Reality (VR)-based training are necessary to bring workers up to pace with the developing SWZ technologies.

PRACTICAL APPLICATIONS

Future research should be directed towards interconnecting and implementing available safety technologies to automate WZ safety and management. Workers should be trained using more practical techniques. In this context, using VR will enable the simulation of hazardous events in a safe environment while also improving workers' skill retention.

Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation

We live in a complex world characterised by complex people, complex times, and complex social, technological, economic, and ecological environments. The broad aim of our work is to investigate the use of ICT technologies for solving pressing problems in smart cities and societies. Specifically, in this paper, we introduce the concept of deep journalism, a data-driven deep learning-based approach, to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool, DeepJournal (Version 1.0), that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions, reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems that industry and academia seem to ignore. In contrast, academia produces much broader and deeper knowledge on subjects such as pollution that are not sufficiently explored in industry. Our deep journalism approach could find the gaps in information and highlight them to the public and other stakeholders.