Big Data-driven Governance of Smart Sustainable Intelligent Transportation Systems: Autonomous Driving Behaviors, Predictive Modeling Techniques, and Sensing and Computing Technologies.

• Author: Novak, Andrej

1. Introduction

   Connected autonomous vehicles develop on Internet of Things sensing infrastructures, trajectory estimation and computer vision algorithms, and road environment data. The purpose of our systematic review is to examine the recently published literature on big data-driven governance of smart sustainable intelligent transportation systems and integrate the insights it configures on autonomous driving behaviors, predictive modeling techniques, and sensing and computing technologies. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that autonomous mobility technologies integrate traffic congestion monitoring systems, spatial simulation and predictive control algorithms (Androniceanu, 2021; Lazaroiu et al., 2020; Pop et al., 2022), and data modeling tools. The actuality and novelty of this study are articulated by addressing computer vision and route planning algorithms, sensing and computing technologies (Andronie et al., 2021a; Lyons and Lazaroiu, 2020; Popescu et al., 2021), and vehicle and pedestrian detection tools, that is an emerging topic involving much interest. Our research problem is whether real-time data analytics (Andronie et al., 2021b; Nagy and Lazaroiu, 2022; Vatamanescu et al., 2022), environment and vehicle sensors, and virtual simulation modeling and geospatial data mining tools (Bennett, 2022; Glogovetan et al., 2022; Jenkins, 2022) can curtail crash recurrence.

   In this review, prior findings have been cumulated indicating that predictive modeling techniques, urban Internet of Things-sensing tools, and autonomous navigation and spatio-temporal fusion algorithms optimize intelligent transport systems. The identified gaps advance autonomous navigation software, geospatial analytics and image processing algorithms (Blake and Frajtova Michalikova, 2021; Milward et al., 2019; Nica et al., 2022), and virtual simulation and data visualization tools. Our main objective is to indicate that urban Internet of Things-sensing tools, data fusion and 3D virtual simulation technologies, and obstacle avoidance algorithms optimize vehicular traffic flows. This systematic review contributes to the literature on urban computing technologies (Kliestik et al., 2020; Nica and Stehel, 2021), deep learning-based navigating decisions, and remote sensing algorithms by clarifying that geospatial mapping and cognitive automation technologies (de Godoy et al., 2022; Nica, 2021; Zvarikova et al., 2022), optimal trajectory planning tools, and sensor fusion algorithms can decrease traffic death toll.

2. Theoretical Overview of the Main Concepts

   Sensor data processing algorithms, deep reinforcement learning techniques, and urban perception and planning tools shape big data-driven transportation systems. Internet of Things sensing infrastructures, mobile cloud and edge computing systems, and urban big data analytics further self-driving car control algorithms. Route detection and predictive control algorithms, cognitive computing systems, and geospatial data mining tools enable smart urban governance. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), self-driving car perception systems, spatial cognition and route planning algorithms, and crash avoidance technologies (section 4), algorithm-driven sensing devices, mapping and navigation tools, and connected vehicle data (section 5), remote sensing systems, autonomous vehicle interaction control software, and environment mapping and cloud computing algorithms (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   Throughout June 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "big data-driven governance" + "smart sustainable intelligent transportation systems" + "autonomous driving behaviors," "predictive modeling techniques," and "sensing and computing technologies." As research published in 2022 was inspected, only 181 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 37 mainly empirical sources (Tables 1 and 2). Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR (Figures 1-6).

   Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were used that ensure the literature review is comprehensive, transparent, and replicable. The flow diagram, produced by employing a Shiny app, presents the stream of evidence-based collected and processed data through the various steps of a systematic review, designing the amount of identified, included, and removed records, and the justifications for exclusions.

   To ensure compliance with PRISMA guidelines, a citation software was used, and at each stage the inclusion or exclusion of articles was tracked by use of custom spreadsheet. Justification for the removal of ineligible articles was specified during the full-text screening and final selection.

   Figure 6 Screening and quality assessment tools To ensure first-rate standard of evidence, a systematic search of relevant databases including peer-reviewed published journal articles was conducted using predefined search terms, covering a range of research methods and data sources. Reference lists of all relevant sources were manually reviewed for additional relevant citations. Titles of papers and abstracts were screened for suitability and selected full texts were retrieved to establish whether they satisfied the inclusion criteria. All records from each database were evaluated by using data extraction forms. Data covering research aims, participants, study design, and method of each paper were extracted. The inclusion criteria were: (i) articles included in the Web of Science, Scopus, and ProQuest databases, (ii) publication date (2022), (iii) written in English, (iv) being an original empirical research or review article, and (v) particular search terms covered; (i) conference proceedings, (ii) books, and (iii) editorial materials were eliminated from the analysis. SRDR gathered, handled, and analyzed the data for the systematic review, being configured as an archive and tool harnessed in data extraction through transparent, efficient, and reliable quantitative techniques. Elaborate extraction forms can be set up, meeting the needs of research questions and study designs. Distiller SR screened and extracted the collected data. AMSTAR evaluated the methodological quality of systematic reviews. The quality of academic articles was determined and risk of bias was measured by MMAT, that tested content validity and usability of selected studies in terms of screening questions, type of design, corresponding quality criteria, and overall quality score. Dedoose analyzed qualitative and mixed methods research. AXIS evaluated the quality of cross-sectional studies. ROBIS assessed the risk of bias in systematic reviews. Note: Table made from Figure. 4. Self-Driving Car Perception Systems, Spatial Cognition and Route Planning Algorithms, and Crash Avoidance Technologies

   Spatial recognition and urban sensing technologies, image processing and collision avoidance algorithms, and modeling and simulation tools (Gao et al., 2022a; Hakak et al., 2023; Lee et al., 2022; Trotta et al., 2022) configure sustainable urban governance networks. Smart transportation systems leverage tracking control and cooperative navigation algorithms, real-time Internet of Things data, and multi-object detection and virtual reality-based data analytics tools. Geospatial mapping and cognitive automation technologies, optimal trajectory planning tools, and sensor fusion algorithms can decrease traffic death toll.

   Urban computing technologies, deep learning-based navigating decisions, and remote sensing algorithms (Geng et al., 2022; Hieu et al., 2022; Nourinejad and Amirgholy, 2022; Wang et al., 2022a) configure the smart road transport infrastructure. Route detection and...