Connected and Networked Driving: Smart Mobility Technologies, Urban Transportation Systems, and Big Data-driven Algorithmic Decision-Making.

• Author: Allen, Margaret
• Position: Report

1. Introduction

   Self-driving cars can monitor their state and the surrounding environment by harnessing massive volumes of smart connected sensors, networking with the traffic infrastructure and the other road users by deploying wireless vehicle-to-everything interaction. (Ersal et al., 2020) Comprehensive data as regards distinct vehicle performance and driver behavior can be generated instantaneously. (Leiman, 2020) Users' acceptance and adoption attitudes in relation to self-driving vehicles are associated with trust and sustainability issues, configuring links between perceived convenience and accessibility, together with behavioral intention. (Dirsehan and Can, 2020)

2. Conceptual Framework and Literature Review

   As a result of gathered data, self-driving cars can make decisions, design their journey, and follow it by harnessing groundbreaking controllers. (Ersal et al., 2020) The regulation of autonomous driving technology is crucial to improve its upsides while preventing the risks. (Xue et al., 2020) The fusion of coordinated vehicle driving behavior of deep learning-based sensing and computing technologies (Andrei et al., 2016; Kliestik et al., 2018; Kowo and Akinbola, 2019; Lazaroiu et al., 2020; Olsen, 2019; Svabova et al., 2020) and supporting data infrastructure (Dusmanescu et al., 2016; Kijek and Matras-Bolibok, 2019; Krizanova et al., 2019; Nica et al., 2014; Peters et al., 2020) may result in optimized capacity of smart transportation systems, leading to travel time savings. (Szimba and Hartmann, 2020) Ride-sharing services can be advanced by cutting-edge applications (Englund, 2019; Kliestik et al., 2020a, b; Lazaroiu et al., 2017; Nica, 2018; Popescu et al., 2018; Popescu et al., 2019) for self-driving cars shaping urban mobility. (Liu et al., 2020) Shared autonomous vehicles may cut down car crashes, greenhouse emissions, the volume of road users and the need for parking spots. (Pakusch et al., 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from AAA, Adobe Analytics, ANSYS, Atomik Research, AUVSI, Axios, Deloitte, EY, Ipsos, McKinsey, Perkins Coie, SAE, and Schoettle & Sivak (2014), I performed analyses and made estimates regarding smart mobility technologies, urban transportation systems, and big data-driven algorithmic decision-making. Data were analyzed using structural equation modeling.

4. Results and Discussion

   Self-driving cars react to traffic stimuli with superior control precision and diminished response time in contrast to human drivers, thoroughly shaping the traffic determinants throughout intelligent vehicular networks. Vehicle-to-everything connectivity assists self-driving cars in collecting connected and networked road data from smart transportation systems. (Ersal et al., 2020) Decision-making self-driving car control algorithms relieve the user from steering the vehicle, enabling the conduct of other activities throughout the ride. The roadway capacity increase may decrease snarl-ups, cut down energy consumption and emissions, while enhancing road safety. (Szimba and Hartmann, 2020) Ride-shared and self-driving cars from real or virtual stations can be flawlessly allocated to accommodate users' trip demands, while taking into account the typical overcrowding in capacitated networks. (Liu et al., 2020) (Tables 1-12)

   Self-driving cars will shape the development of sustainable smart cities and communities. (Pakusch et al., 2020) By decreasing stop-and-go snarl-ups, self-driving cars may optimize the traffic flux and cut down the energy use and emissions extensively. (Ersal et al., 2020) The consequences on travel time by harnessing connected and autonomous vehicle mobility are associated with improved infrastructure capacity and superior fluidity of traffic flows, while searching for a parking lot is not performed by the user. (Szimba and Hartmann, 2020) Updating and devising regulations or carrying out tests in various urban settings facilitate the transition to self-driving car acceptance, adoption, and implementation. (Gonzalez-Gonzalez et al., 2020) Vehicle tracking systems displaying models of behavior (e.g. harsh braking or accelerating) may indicate certain increased driving risks. (Leiman, 2020)

   Users in a connected setting will make informed decisions as regards safe driving. (Ali et al., 2020) Self-driving cars improve their situation awareness in conformity with the perceptual data gathered by use of smart connected sensors, then...