Innovations in Electric Vehicle Technology: A Review of Emerging Trends and Their Potential Impacts on Transportation and Society

The adoption of electric vehicles (EVs) has gained significant momentum in recent years, driven by the need to reduce greenhouse gas emissions, improve air quality, and achieve sustainable transportation. This study presents a comprehensive review of emerging trends in EV technology and their potential impacts on transportation and society. The study explores various areas of innovation in the field of EVs, including battery technology, wireless charging, vehicle-to-grid (V2G) communication, lightweight materials, autonomous driving, vehicle-to-everything (V2X) communication, circular economy approaches, advanced charging infrastructure, energy storage, and social and behavioral innovations. This study reveals that battery technology advancements are driving the adoption of EVs. Lithium-ion batteries have improved energy density, charging speed, and lifespan. Alternative battery technologies, like solid-state and lithium-sulfur batteries, show promise for even higher energy density, faster charging, and increased safety. Wireless charging technology is emerging, with high-power and high-efficiency systems potentially addressing concerns about charging infrastructure and range anxiety. V2G communication allows EVs to serve as mobile energy storage units, contributing to grid stability, load balancing, and renewable energy integration. Lightweight materials, like advanced composites and lightweight metals, can significantly reduce the weight of EVs, improving energy efficiency and overall performance. Autonomous driving technologies have the potential to improve safety, reduce congestion, and optimize energy use. V2X communication enables a wide range of applications, like intelligent traffic management and enhanced safety features. Circular economy approaches, including designing EVs with recyclability and reusability in mind, using recycled materials in manufacturing, and developing end-of-life recycling and repurposing strategies, can minimize the environmental impact of EVs and contribute to their sustainability

An Empirical Study of the Factors Influencing the Adoption of Electric Vehicles

The adoption of electric vehicles (EVs) has become increasingly important in recent years due to concerns about climate change and the need to reduce greenhouse gas emissions. The widespread adoption of EVs is critical to achieving global climate goals and reducing air pollution. Therefore, there is a need for empirical research to understand the factors that affect the adoption of EVs. The purpose of this study was to empirically investigate the factors affecting the adoption of EVs. The study used a sample of 425 individuals, and multiple regression analysis was conducted to analyze the data. The independent variables in the study were Economic Factors, Technological Factors, Social Factors, and Regulatory Factors. All the variables were found to be significant in explaining the adoption of EVs. The results of the study show that Economic Factors were the most important factor affecting the adoption of EVs, followed by Technological Factors, Social Factors, and Regulatory Factors. The findings suggest that cost and financial incentives play a significant role in the decision to adopt EVs. Technological factors, such as the availability and performance of charging infrastructure and battery technology, also influence the adoption of EVs. Additionally, social factors, such as social norms and attitudes towards EVs, and regulatory factors, such as government policies and regulations, also affect the adoption of EVs. The study's findings have important implications for policymakers, industry leaders, and other stakeholders in the transportation sector. The results suggest that policies aimed at reducing the cost of EVs and providing financial incentives can encourage greater adoption of EVs. Additionally, efforts to improve charging infrastructure and battery technology can increase the attractiveness of EVs. Social and regulatory factors should also be considered in efforts to promote the adoption of EVs

A Review of Connected and Automated Vehicle Traffic Flow Models for Next-Generation Intelligent Transportation Systems

Connected and Automated Vehicle (CAV) technology is a rapidly developing field that is expected to transform the transportation industry. This study provides an overview of traffic flow models for Connected and Automated Vehicles (CAVs). The study explores the different levels of automation in CAVs and discuss the strengths and limitations of three categories of traffic flow models: microscopic, mesoscopic, and macroscopic. The article highlights that while microscopic models provide a high level of detail and accuracy, they require significant data input and computational resources, making them difficult to scale up to large networks or regions. Mesoscopic models are more computationally efficient but still provide useful detail and can simulate traffic flow over a larger area than microscopic models. Macroscopic models, while most computationally efficient, may not capture the effects of specific traffic management strategies or provide the level of detail necessary to capture individual vehicle movements and driver behaviors. The study emphasizes the need to take into account other factors that can influence CAV traffic flow, such as human-driven vehicles, road infrastructure, and communication protocols. By providing insights into the strengths and weaknesses of each approach, this article aims to facilitate the development of next-generation Intelligent Transportation Systems (ITS) that effectively manage traffic flow and fully realize the potential of CAVs

Sustainable Transportation Planning: Strategies for Reducing Greenhouse Gas Emissions in Urban Areas

Sustainable transportation is a crucial aspect of reducing greenhouse gas emissions and promoting a more sustainable future. This study aimed to explore strategies for reducing greenhouse gas emissions in urban areas through sustainable transportation planning. A comprehensive review of literature was conducted to identify effective strategies and policies that can be implemented to achieve this goal. The findings revealed that promoting the use of public transportation, non-motorized transportation, and electric vehicles can significantly reduce greenhouse gas emissions in urban areas. In addition, implementing a congestion charge and improving urban planning by promoting mixed-use development and walkability can also contribute to this goal. Furthermore, promoting telecommuting was found to be an effective strategy for reducing the need for car travel, which can in turn reduce greenhouse gas emissions. The study suggests that sustainable transportation planning requires a comprehensive approach that takes into account the needs of all stakeholders, including government officials, transportation planners, businesses, and residents. The findings of this study have important implications for policymakers and transportation planners seeking to develop sustainable transportation plans that can contribute to a more sustainable future

Simulation evaluation of emerging estimation techniques for multinomial probit models

A simulation evaluation is presented to compare alternative estimation techniques for a five-alternative multinomial probit (MNP) model with random parameters, including cross-sectional and panel datasets and for scenarios with and without correlation among random parameters. The different estimation techniques assessed are: (1) The maximum approximate composite marginal likelihood (MACML) approach; (2) The Geweke-Hajivassiliou-Keane (GHK) simulator with Halton sequences, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; (3) The GHK approach with sparse grid nodes and weights, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; and (4) a Bayesian Markov Chain Monte Carlo (MCMC) approach. In addition, for comparison purposes, the GHK simulator with Halton sequences was implemented in conjunction with the traditional, full information maximum likelihood approach as well. The results indicate that the MACML approach provided the best performance in terms of the accuracy and precision of parameter recovery and estimation time for all data generation settings considered in this study. For panel data settings, the GHK approach with Halton sequences, when combined with the CML approach, provided better performance than when implemented with the full information maximum likelihood approach, albeit not better than the MACML approach. The sparse grid approach did not perform well in recovering the parameters as the dimension of integration increased, particularly so with the panel datasets. The Bayesian MCMC approach performed well in datasets without correlations among random parameters, but exhibited limitations in datasets with correlated parameters.Civil, Architectural, and Environmental Engineerin

Traffic assignment models : applicability and efficacy

This dissertation is concerned with the traffic assignment problem (TAP), an important tool in transportation planning. We first study a theoretical extension of TAP which incorporates symmetric interactions in the link travel costs. In particular, we prove the theoretical convergence of conventional solution methods for this problem, and analyze convergence behavior for these methods. We also show how a set of real world interactions such as merge models can be modeled using these type of symmetric interactions. Second, we apply these findings to a practical case study of railroad electrification, formulated as a rail network design problem. We solve this problem on a large network representing the North American railroad network, and analyze the solutions to provide policy recommendations. We model the interactions between diesel- and electric-goods flow as a symmetric congestion cost and a separable fuel/crew cost. Third, we study the empirical behavior of TAP under input uncertainty. Specifically, we analyze the effects of three types of input errors (uniform, origin- or destination- specific, spatially correlated) on network metrics, such as total system travel time or congestion, at equilibrium. Empirical bounds for these output metrics are identified for various levels of input error. We apply these findings to a case study to demonstrate potential usage for planning purposes. Lastly, we conduct a comprehensive empirical study on the convergence behavior of traffic assignment convergence metrics. We analyze five commonly used network metrics at various convergence levels, for different solution algorithms, and identify concrete thresholds for convergence. We also show the relationship between different convergence criteria metrics, allowing for transfer of these thresholds to different metrics.Operations Research and Industrial Engineerin

Integration of charging behavior into infrastructure planning and management of electric vehicles: A systematic review and framework

Increasing electric vehicle (EV) sales have shifted the focus of researchers from EV adoption to new operational challenges such as charging infrastructure deployment and management. These challenges require an accurate characterization of EV user charging behavior, especially with evolving battery technology. This study critically reviews approaches and data sources used to elicit EV charging behavior and patterns from a demand-side perspective and investigates how supply-side studies on charging infrastructure deployment and management incorporate charging behavior. We observe a noticeable disconnect between both strands of the literature, as supply-side studies still rely on simplistic assumptions about charging behavior and focus on a handful of aspects in isolation. More specifically, several studies either consider personal EVs or ride-hailing services with only public fast-charging infrastructure while ignoring available home/work charging infrastructure. We recommend shifting from this silo approach to a system-level dynamic planning framework where future charging demand is forecasted by combining charging behavior models with the models to forecast travel demand and EV adoption, followed by an integration of demand information into supply-side optimization. The framework can thus capture complex supply–demand interactions and inform the charging infrastructure planning policies, laying out a roadmap for emerging and mature EV markets

Adaptive Routing Behavior with Real-Time Information Under Multiple Travel Objectives

Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes during their travel in order to optimize their own objective of travel. Various travel objectives are captured in mathematical models via disutility functions. The focus of this research was to study the behavior of travelers with multiple trip objectives when they are provided real-time information, and assess their ability to determine “optimal” routing policies, compared to exact solutions based on the online shortest path problem. A web-based experiment was carried out to simulate a traffic network with limited information provision. The decision strategies of participants were analyzed and compared to a variety of decision policies established in the literature – optimal, greedy, and a priori – and a general model to describe the observed travelers’ decision strategies was calibrated from over 40,000 decision points extracted from the collected data. Apart from trip objective, other factors such as relative position in the network and experience gained were found to influence user decisions. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes

Adaptive routing behavior with real-time information under multiple travel objectives

Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes during their travel in order to optimize their own objective of travel. Various travel objectives are captured in mathematical models via disutility functions. The focus of this research was to study the behavior of travelers with multiple trip objectives when they are provided real-time information, and assess their ability to determine “optimal” routing policies, compared to exact solutions based on the online shortest path problem. A web-based experiment was carried out to simulate a traffic network with limited information provision. The decision strategies of participants were analyzed and compared to a variety of decision policies established in the literature – optimal, greedy, and a priori – and a general model to describe the observed travelers’ decision strategies was calibrated from over 40,000 decision points extracted from the collected data. Apart from trip objective, other factors such as relative position in the network and experience gained were found to influence user decisions. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes