System Optimization of Shared Mobility in Suburban Contexts

Shared mobility is a viable choice to improve the connectivity of lower-density neighbourhoods or suburbs that lack high-frequency public transportation services. In addition, its integration with new forms of powertrain and autonomous technologies can achieve more sustainable and efficient transportation. This study compares four shared-mobility technologies in suburban areas: the Internal Combustion Engine, Battery Electric, and two Autonomous Electric Vehicle scenarios, for various passenger capacities ranging from three to fifteen. The study aims to provide policymakers, transportation planners, and transit providers with insights into the potential costs and benefits as well as system configurations of shared mobility in a suburban context. A vehicle routing problem with time windows was applied using the J-Horizon software to optimize the costs of serving existing intra-community demand. The results indicate a similar fleet composition for Battery Electric and Autonomous Electric fleets. Furthermore, the resulting fleet for all four technologies is dominated by larger vehicle capacities. Due to the large share of driver cost in the total cost, the savings using a fleet of Autonomous Electric Vehicles are predicted to be 68% and 70%, respectively, compared to Internal Combustion and Battery Electric fleets.

A Rich Vehicle Routing Problem for a City Logistics Problem

In this work, a Rich Vehicle Routing Problem (RVRP) is faced for solving city logistic problems. In particular, we deal with the problem of a logistic company that has to define the best distribution strategy for obtaining an efficient usage of vehicles and for reducing transportation costs while serving customers with different priority demands during a given planning horizon. Thus, we deal with a multi-period vehicle routing problem with a heterogeneous fleet of vehicles, with customers’ requirements and company restrictions to satisfy, in which the fleet composition has to be daily defined. In fact, the company has a fleet of owned vehicles and the possibility to select, day by day, a certain number of vehicles from the fleet of a third-party company. Routing costs must be minimized together with the number of vehicles used. A mixed integer programming model is proposed, and an experimental campaign is presented for validating it. Tests have been used for evaluating the quality of the solutions in terms of both model behavior and service level to grant to the customers. Moreover, the benefits that can be obtained by postponing deliveries are evaluated. Results are discussed, and some conclusions are highlighted, including the possibility of formulating this problem in such a way as to use the general solver proposed in the recent literature. This seems to be the most interesting challenge to permit companies to improve the distribution activities.

Assessing vehicle fuel efficiency using a dense network of CO₂ observations

Transportation represents the largest sector of anthropogenic CO2 emissions in urban areas. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important. However, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at a neighborhood scale. The BErkeley Air Quality and CO2 Network (BEACO2N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ~1300 km2 multi-city spatial domain. Here, we focus on a 5 km, high volume, stretch of highway in the SF Bay area. We show that inversion of the BEACO2N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO2 emission rate of the average vehicle (g/vkm) are shown to vary by as much as 27 % at different times of a typical weekday because of changes in vehicle speed and fleet composition. The BEACO2N-derived emissions estimates are consistent to within ~3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the Emissions FACtor model (EMFAC) of vehicle fuel efficiency.

Optimization of Capital Distribution and Composition of a Shipping Company Fleet Through Evolutionary Algorithms

The research examines the optimization of fleet management of a shipping company through control algorithms, as finding an algorithm that will reduce a marine company’s exposure to risk by diversifying its fleet composition is one way to make it dominant. The study focused on three companies that, in 2014, invested US$$1 billion in a fleet of tankers, using three different techniques to optimize their fleet composition: equal number of ships of all types, the established risk minimization model and the proposed Risky Asset Pricing maximization model. Seven years of data was used for the synthesis and 4 years of data was used for the evaluation. The research findings show that classic portfolio management through risk minimization is ineffective, as it appears to reduce performance below what is a random or evenly distributed fleet. Comparing the three methods, the superiority of the Risky Asset Pricing model is clear. This algorithm looks for solutions where the demand for ships is low but has enormous fluctuation potential and seeks to identify ships that are at high risk with great potential for price increases to maximize investor returns. The value of the research lies in the identification of methods to optimize capital distribution and composition of a shipping company fleet, which presents valuable insights for the benefit of scholars and maritime companies. Moreover, and contrary to extant works that focus on Markowitz’s theory, this article instead describes how evolutionary algorithms can be used to optimize fleet management.

Theoretical and methodological approaches to the calculation of planned costs for maintenance of service vehicles of the road works customer

Introduction. Effective management of road indstry is particularly important in conditions of limited funding, as it is not possible to provide adequate funding of the economic entity that has the functions of a customer service. The cost of the customer service maintenance depends directly on the compliance with applicable regulations for these works cost determination. One of the aspects that form a considerable part of the costs of customer service is the maintenance of service vehicles.Problem Statement. Road services in the regions (hereinafter RSR) are the recipients and managers of the funds from the State Budget of Ukraine that are directed for the development and maintenance of public roads of state importance, being as well the asset holders of these roads. To perform their functions, the RSR have vehicles on their balance, the use of which involves a number of costs, including fuel and lubricants, repair, maintenance, replacement of tires, etc. The most important of the steps involved in setting the costs of vehicle maintenance is the process of cost planning. Planning has to be based on the structure of the vehicle fleet (own or leased), functional responsibilities and scope of road works.Planning is carried out for vehicles on the balance of the RSR, as well as for leased vehicles. In the case of leasing, the costs mentioned above are added by the vehicles leasing costs. These costs must be forecasted, justified and performed within the approved cost estimates of the customer service expenses.Purpose. The purpose of the article is to highlight the theoretical and methodological approaches to the calculation of planned costs for the maintenance of service vehicles of the road works customer.Materials and methods. Dialectical methods of knowledge, such as analysis and synthesis of fleet composition characteristics and approaches to cost planning methods, as well as induction, were used in writing the article to explore some aspects of the process of calculating the planned cost of maintenance of the road works customer's service vehicles.Results. The study highlights and describes the main approaches to the calculation of planned costs for the maintenance of service vehicles of the road works customer. The methodology of calculation of planned costs for the maintenance of service vehicles is explored.Conclusions. The introduction of a clear methodology and analytical toolkit for calculating all necessary expenditure items for the maintenance of the road works customer's vehicles in the cost planning process will establish the implementation of a clear mechanism for calculating the planned costs of maintenance of the road works customer's vehicles. It will also provide an automated information-analytical complex for planning of cost estimates, with information entry and reporting in standard forms and possibility of prompt response to changes in fleet composition, needs for spare parts, repairs or other expenses for the maintenance of service vehicles and making changes in cost estimates.Keywords: motor vehicles, costs, cost planning, customer service, maintenance of service vehicles

Real-World Contribution of Electrification and Replacement Scenarios to the Fleet Emissions in West Midland Boroughs, UK

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO 6 vehicles on the exhaustive emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions, and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterize the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0 to 37.9%, 44.3 to 48.3%, and 46.9 to 50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO 6 replacement fleet scenario would lead to reductions ranging from 10.0 to 10.4%, 4.0 to 4.2%, and 6.0 to 6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways compared to rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.