published journal article
published journal article
Phd Dissertation
research report
The goal of this research project is to evaluate the benefits and disadvantages of electric vehicles (EVs) in delivery vehicle fleets. We assume fleet operators have both EVs and conventionally fueled vehicles (CFVs) at their disposal for delivery services, and that fleet operators select a mix of EVs and CFVs that minimize overall costs. Moreover, we assume EVs offer a per mile cost advantage over CFVs due to the lower costs of electricity compared to gasoline/diesel, and government subsidies. We also assume that EVs have a shorter range than CFVs. We model the fleet operator’s decision problem as a mixed vehicle routing problem, wherein the decision levers include the routing of EVs and CFVs to serve all delivery locations at minimum cost. Using the Los Angeles (LA) and Orange counties as the study area with a single depot, we develop computational experiments to evaluate the benefits and disadvantages of EVs in delivery vehicle fleets. The results indicate that with EV range less than 100 miles, it is not possible for EVs to serve all the demand in the region. At a 200-mile EV range, and where the EV cost per mile is approximately 60% of the CFV cost per mile, the optimal fleet mix is all EVs. With EV range less than 200, or a tighter gap between EV and CFV costs, the optimal fleet includes both EVs and CFVs. Mostly importantly, the results indicate that increasing EV range is the most important factor, more so than reducing EV costs, in reducing CFVs in medium-duty delivery vehicle fleets, and reducing total emissions.
presentation |
Sep 2023
research report |
Jan 2022
published journal article |
Apr 2024
published journal article
This paper extends a new line of research on urban squatting that focuses on the role of the squatter organizer. The model replaces the benevolent organizer in the model of Brueckner and Selod (2009) with competing, rent-seeking squatter organizers, a structure that may offer a realistic picture of many cities in less-developed countries. The analysis demonstrates that, compared to the benevolent case, rent-seeking behavior puts more land in the hands of squatters, leading to greater squeezing of the formal housing market while extracting more defensive expenditures from each squatter household. Additional results show that competition among squatter organizers has much in common with competition in a traditional industry setting, while illustrating how changes in the squatting climate affect the equilibria. (C) 2013 Elsevier B.V. All rights reserved.
published journal article
published journal article
Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reliably in real-time, requiring between several hundred watts to one kilowatt of power. This power consumption can dramatically reduce vehicles’ driving range and affect emissions. To address this problem, we propose SAGE: a methodology for selectively offloading the key energy-consuming modules of DL architectures to the cloud to optimize edge, energy usage while meeting real-time latency constraints. Furthermore, we leverage Head Network Distillation (HND) to introduce efficient bottlenecks within the DL architecture in order to minimize the network overhead costs of offloading with almost no degradation in the model’s performance. We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge, devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies. Compared to edge-only computation, SAGE reduces energy consumption by an average of 36.13%, 47.07%, and 55.66% for an AV with one low-resolution camera, one high-resolution camera, and three high-resolution cameras, respectively. SAGE also reduces upload data size by up to 98.40% compared to direct camera offloading.
working paper
Most of the equilibrium traffic assignment models used nowadays, are based on aggregate link performance functions. These flow-delay functions represent a crude abstraction of real dependence of travel time on actual traffic volumes and physical conditions of the transportation network elements. These link performance functions reflect the travel impedance associated with the links and intersections. In many applications, especial those which are concerned with detailed microscopic traffic analysis, the performance of these simplified flow-delay relationship might be too crude and thus unsatisfactory. When such analysis is desired, detailed flow-delay models, or simulation models, have to be used. Furthermore in many investigations different levels of detail are necessary for various components of the network. The flow-delay characteristics of some network elements can be represented by crude aggregate relations while other elements need to be represented in great detail and accuracy. When some, or all, of the network elements are not represented by mathematically defined flow-delay function it becomes very difficult to solve for user equilibrium in a transportation network. Similar difficulties might arise in the investigation of system optimum of transportation, communication or other networks. In the framework of this work, a traffic assignment model is developed that can be based on functions, whose exact mathematical form is not known. The proposed solution method applies to steady state network flow problems. This solution will be valid as long as the flow-delay curve is non deceasing when traffic flow increases. The flow-delay function can be numeric pointwise function or a set of simulation-generated values. The empirical analysis and derivation of the proposed solution methods follows the user equilibrium, traffic assignment model, developed by Leblanc [7].
research report
research report |
Sep 2010
conference paper
