RESUMEN
Scholars and practitioners believe that the large-scale deployment of charging piles is imperative to our future electric transportation systems. Major economies ambitiously install charging pile networks, with massive construction spending, maintenance costs, and urban space occupation. However, recent developments in technology may significantly reduce the necessary charging capacity required by the system. This paper develops a linear programming model to characterize the effects of likely scenarios where vehicle-to-vehicle (V2V) charging is available via vehicle modularization or wireless charging. Specifically, we consider scenarios in which vehicles can transmit energy to each other (coordinated by a central platform) while traveling closely on the same road. We first estimate the number of charging piles needed for completing the travel plan of 73 cars from data, assuming a battery capacity of 400 km's range and no V2V charging. Our results show that once V2V charging technologies with an efficiency of 50% are available, more than 2/3 of the charging piles investment would be wasted. Additionally, if the efficiency of V2V charging increases to 75%, we can easily reduce the battery capacity of vehicles to 200 km, which will reduce production costs and improve energy efficiency. These results may reveal us an alternative pathway towards transportation electrification.
RESUMEN
The construction industry has been severely affected by the COVID-19 pandemic and the associated restrictions on person-to-person contacts issued by the government. A construction site usually has a high number of workers working at the same time; therefore, the question of how to ensure their safety during the pandemic-that is, how to protect them from getting infected-has become an urgent problem. In this study, we propose a bi-objective integer programming model to establish the optimal schedule plan under COVID-19 regulations. We develop a solution method and conduct numerical experiments to solve and validate our model. The optimal schedule plan can avoid contacts between workers of different groups while minimizing the total costs of complying with government policy. Our proposed model can be applied in practice to help project managers establish a reasonable and cost-effective schedule plan. This study contributes to reducing the operating costs of contractors and protecting the health of construction workers.
RESUMEN
Maritime transport plays a key role in global trade. The safeguard of maritime transport is the Port State Control (PSC) inspection implemented all over the world. The outbreak of the Coronavirus disease (COVID-19) in 2020 presents new and unprecedented impacts on global supply chains and the ports as well as the entire shipping industry. Various measures were adopted by the countries and regions to halt the spread of the pandemic, mainly by reducing face-to-face interactions. As PSC inspections involve getting onboard vessels and in-person communications between the inspectors and the crew, its procedure and results are highly likely to be influenced by the COVID-19. This study aims to explore whether, how, and why the global and regional PSC inspection statuses are influenced by the pandemic through analyzing real inspection data. Specifically, three general indicators, namely inspection number, average deficiency number per inspection, and detention rate, are considered. Moreover, a detailed and comprehensive analysis of the inspection data at the Hong Kong port is conducted, including the number of inspections conducted, the average deficiency number and detention rate, the types of inspections conducted and ships inspected, the detailed deficiency and detention conditions, the relationship between the local pandemic situation and the PSC inspection status, and regression analysis on the influencing factors on inspection outcome. It is found that the COVID-19 pandemic indeed has an impact on PSC. Meanwhile, pragmatic and flexible measures are adopted by the port states, and the PSC has always been acting as a 'safety net' to guarantee maritime safety, promote the marine environment, and protect the seafarers' rights even under the difficult times during the COVID-19 pandemic.
RESUMEN
In this paper, we propose a novel methodology to define and estimate a surrogate measure. By imposing a hypothetical disturbance to the leading vehicle, the following vehicle's action is represented as a probabilistic causal model. After that, a tree is built to describe the eight possible conflict types under the model. The surrogate measure, named Aggregated Crash Index (ACI), is thus proposed to measure the crash risk. This index reflects the accommodability of freeway traffic state to a traffic disturbance. We further apply this measure to evaluate the crash risks in a freeway section of Pacific Motorway, Australia. The results show that the proposed indicator outperforms the three traditional crash surrogate measures (i.e., Time to Collision, Proportion of Stopping Distance, and Crash Potential Index) in representing rear-end crash risks. The applications of this measure are also discussed.