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Advanced Air Mobility (AAM) presents an emerging alternative to traditional car driving for commuting in metropolitan areas. However, its feasibility has not been thoroughly studied nor well understood at the operational level. Given that AAM has not been in place, this study explores the economic, energy, and environmental feasibility of AAM for commuting at an early stage of AAM deployment. We propose a time expanded network model to characterize the dynamics of eVTOL operations between a vertiport pair in different states: in-service flying, relocation flying, charging, and parking, while respecting various operational and commuter time window constraints. By jointly considering eVTOL flying with vertiport access and egress and using real-world data, we demonstrate an application of the model in the Chicago metropolitan area in the US. Different vertiport pairs and eVTOL aircraft models are investigated. We find substantial travel time saving if commuting by AAM. While vehicle operating cost will be higher using eVTOLs than using auto, the generalized travel cost will be less for commuters. On the other hand, with current eVTOL power requirement, the energy consumption and CO2 emissions of AAM will be greater than those of auto driving, with an important contributor being the significance presence of empty flights relocation. These findings, along with sensitivity analysis, shed light on future eVTOL development to enhance the competitiveness of AAM as a viable option for commuting. 

This paper investigates deploying connected and automated vehicle (CAV) lanes in transportation networks with a focus on measuring and preserving equity among travelers. A new metric is proposed to characterize equity based on (1) generalized travel cost per unit origin-destination (OD) distance for travelers on each OD pair and using each vehicle type and (2) maximum deviation of the standardized unit generalized travel cost from system average. A bi-level bi-objective program is developed to simultaneously minimize system travel cost and inequity while deploying CAV lanes. A solution algorithm that combines nondominated sorting genetic algorithm II and variable neighborhood search is designed. Through extensive numerical experiments, we find (1) inequity is more prominent when travel demand is high; (2) human-driven vehicle travelers become more disadvantageous with lower CAV price and higher CAV automation; and (3) subsidy is effective in mitigating inequity, but a fee for using CAV lanes is less promising. 

Electric vertical takeoff and landing aircraft (eVTOLs) are gaining growing interest recently. However, limited attention has been paid to the prospect of using eVTOLs for package delivery. To fill this void, this paper explores the attractiveness of eVTOL-based package delivery in terms of cost, energy consumption, and CO2 emissions. Given that eVTOLs cannot take off/land at customer doorsteps, a two-leg system design is proposed and formulated as an optimization model. To implement the model, we consider multiple plausible eVTOL and ground vehicle types, their cost economics, and energy use and CO2 emission characteristics. Applying the model in the Chicago metro region, we find that the attractiveness of eVTOL-based package delivery depends critically on the eVTOL and ground vehicle types. With an appropriate eVTOL-ground vehicle combination, eVTOL-based delivery can be attractive compared to van-only delivery in terms of total shipping cost, but not necessarily so from the energy and mission perspectives. This highlights the need for future R&D to further enhance the energy efficiency of eVTOLs. When designing eVTOL-based package delivery systems, the importance to account for the potential interactions between eVTOL traffic and commercial air traffic should also be recognized. 

Truck platooning enabled by connected automated vehicle (CAV) technology has been demonstrated to effectively reduce fuel consumption for trucks in a platoon. However, given the limited number of trucks in the traffic stream, it remains questionable how great an energy saving it may yield for a practical freight system if we only rely on ad-hoc platooning. Assuming the presence of a central platooning coordinator, this paper is offered to substantiate truck platooning benefits in fuel economy produced by exploiting platooning opportunities arising from the United States’ domestic truck demands on its highway freight network. An integer programming model is utilized to schedule trucks’ itineraries to facilitate the formation of platoons at platoonable locations to maximize energy savings. A simplification of the real freight network and an approximation algorithm are used to solve the model efficiently. By analyzing the numerical results obtained, this study quantifies the importance of scheduled platooning in improving trucks’ fuel economy. Furthermore, the allowable platoon size, schedule flexibility, and fuel efficiency all play a crucial role in energy savings. Specifically, by assuming that following vehicles in a platoon obtain a 10% energy reduction, an average energy reduction of 8.48% per truck can be achieved for the overall network if the maximum platoon size is seven, and the schedule flexibility is 30 min. The cost–benefit analysis provided at the end suggests that the energy-saving benefits can offset the investment cost in truck platooning technology.