Intelligent Vehicular Networks, Deep Learning-based Sensing Technologies, and Big Data-driven Algorithmic Decision-Making in Smart Transportation Systems.

• Author: Aldridge, Susan

1. Introduction

   Self-driving cars sense the surrounding environment by inspecting timely and accurate environmental data collected by heterogeneous onboard sensors instantaneously. (Yang et al., 2021) Object detection and tracking, collision avoidance, and trajectory estimation are instrumental in assessing the systematic variable location and behavior of dynamic road users. (Khatab et al., 2021) Public perceptions of self-driving cars in terms of benefits, concerns, and risks configure demographic and socio-economic dissimilarities (Lu et al., 2020) in autonomous vehicle adoption intentions. (Mack et al., 2021)

2. Conceptual Framework and Literature Review

   Smart transportation technologies, by integrated system planning and performance, reduce traffic congestions, parking complications, transportation expenses, and environmental pollution. (Zhao et al., 2021) The energy use performance of autonomous electric vehicles is considerably impacted by the longitudinal navigation monitoring operations. (Zhang et al., 2021a) Elderly persons have reduced requirements and interest in accepting and adopting autonomous vehicles. (Zhang et al., 2021b) Connected and autonomous vehicle platooning can consolidate roadway capacity and decrease energy consumption. (Liu et al., 2021) For object localization and obstacle avoidance, self-driving cars continuously sense the surrounding environment, distribute sensor data for processing to, and receive computing outcomes from, an edge server. (Li et al., 2021) Autonomous vehicles will offer increased mobility options to the older and underprivileged individuals, while decreasing traffic jams and transportation expenses, and optimizing road safety. (Losada-Rojas and Gkritza, 2021)

3. Methodology and Empirical Analysis

   The data used for this study was obtained and replicated from previous research conducted by AAA, Abraham et al. (2017), Accenture, AUVSI, CarGurus, Deloitte, eMarketer, Kennedys, Morning Consult, Perkins Coie, Pew Research Center, SAE, and Schoettle & Sivak (2014). We performed analyses and made estimates regarding how smart transportation technologies can leverage driving data to improve car safety and mobility in addition to road traffic and infrastructure, thus increasing autonomous vehicle adoption intentions by use of instantaneous motion planning and object detection and tracking algorithms to reduce traffic congestions and collisions. Data collected from 6,800 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

4. Study Design, Survey Methods, and Materials

   The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

   Data sources: AAA, Abraham et al. (2017), Accenture, AUVSI, CarGurus, Deloitte, eMarketer, Kennedys, Morning Consult, Perkins Coie, Pew Research Center, SAE, and Schoettle & Sivak (2014). Study participants: 6,800 individuals provided an informed e-consent.

   All data were interrogated by employing graphical and numeric exploratory data analysis methods. Multivariate analyses, and not univariate associations with outcomes, are more likely to factor out confounding covariates and more precisely determine the relative significance of individual variables. Results are estimates and commonly are dissimilar within a narrow range around the actual value. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process.

   Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. The design effect for the survey was 1.3. For tabulation purposes, percentage points are rounded to the nearest whole number. The cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%.

   Groundbreaking computing systems and databases enable data gathering and processing, extracting meaning through robust deployment. The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments.

   Flow diagram of study procedures

5. Statistical Analysis

   This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. Descriptive analyses (mean and standard deviations for continuous variables and counts and percentages for categorical variables) were used. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling.

   Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. AMOS-SEM analyzed the full measurement model and structural model.

   Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements. An Internet-based survey software program was utilized for the delivery and collection of responses. Panel research represents a swift method for gathering data recurrently, drawing a sample from a pre-recruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Non-response bias and common method bias, composite reliability, and construct validity were assessed.

   Flow diagram of statistical parameters and reproducibility

6. Results and Discussion

   Grasping the consequences of separate dissimilarities on requirements can assist in predicting...