arXiv subjects

Marija Jankovic

Publications and source records attributed to Marija Jankovic.

5 recordsLinked to original sources

arXiv2026

UNCASExt -- A Systematic Computational Framework for Uncertainty Propagation and Scope Consistency in Absolute Environmental Sustainability Assessments (AESA)

Absolute environmental sustainability assessment (AESA) has gained increasing attention in environmental research and policymaking. However, its reliability is challenged by several sources of uncertainty that remain insufficiently accounted for, as well as by scope inconsistencies within the absolute sustainability ratio (ASR), which compares estimated environmental burdens with allocated carrying capacities for a given human activity. This work introduces UNCASExt, an extension of the UNCASE framework for systematically propagating uncertainty and ensuring scope consistency in AESA, together with a supporting open-source Python package, pyaesa. At country and sector levels, the computational framework formalizes allocation procedures that match the scope of allocated carrying capacities with that of estimated environmental burdens across three dimensions: impact pathway modeling; production-based versus consumption-based accounting; business-to-consumer versus business-to-business activities. It also incorporates temporal dynamics, supporting both retrospective and prospective assessments with either static steady-state or dynamic carrying capacities, including greenhouse gas budgets from the Intergovernmental Panel on Climate Change Sixth Assessment Report under Shared Socioeconomic Pathway transition scenarios. The framework is applied to a case study of electricity consumption in France over the period 2019 to 2060. The results show that mismatches between the functional units of estimated environmental burdens and allocated carrying capacities can lead to substantial underallocation, with a median factor of 4.6x across all available sector-region pairs in EXIOBASE 3.10.2. Overall, UNCASExt and pyaesa provide a scalable solution to support AESA harmonization and a versatile way forward to bridge the gap between methodological guidelines and practical application.

cs.CY↗

arXiv2020

A discrete-event simulation model for driver performance assessment: application to autonomous vehicle cockpit design optimization

The latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.

cs.HC↗

arXiv2019

Shared Autonomous Vehicle Simulation and Service Design

Today, driverless cars, as a new technology that allows a more accessible, dynamic and intelligent form of Shared Mobility, are expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars is shared autonomous vehicles (SAVs). This service could merge cabs, carsharing, and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on their operational models, which are linked intrinsically to the service configuration and fleet specification. In addition, any change in operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multi-modal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. Also, the impact of ridesharing and rebalancing strategies on service performance is investigated.Research results suggest that the performance of SAV is strongly correlated with the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the pricing scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results also underline that enabling vehicle-rebalancing strategies may have an important effect on both user and service-related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.

cs.MA↗

arXiv2019

Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.

cs.MA↗

arXiv2019

Shared Autonomous Electric Vehicle Service Performance: Assessing the Impact of Charging Infrastructure and Battery Capacity

Shared autonomous vehicles (SAVs) are the next major evolution in urban mobility. This technology has attracted much interest of car manufacturers aiming at playing a role as transportation network companies (TNCs) in order to gain benefits per kilometer and per ride. The majority of future SAVs will most probably be electric. It is therefore important to understand how limited vehicle range and the configuration of charging infrastructure will affect the performance of shared autonomous electric vehicle (SAEV) services. We aim to explore the impacts of charging station placement, charging type (rapid charging, battery swapping) as well as vehicle range onto service efficiency and customer experience in terms of service availability and response time. We perform an agent-based simulation of SAEVs across the Rouen Normandie metropolitan area in France. The simulation process features impact assessment by considering dynamic demand responsive to the network and traffic. Research results suggest that the performance of SAEVs is strongly correlated to the charging infrastructure. Importantly, faster charging infrastructure and optimized placement of charging locations in order to minimize distances between demand hubs and charging stations result in a higher performance. Further analysis indicates the importance of dispersing charging stations across the service area and how this affects service effectiveness. The results also underline that SAEV battery capacity has to be carefully selected to avoid the overlaps between demand and charging peak times. Finally, the simulation results show that by providing battery swapping infrastructure the performance indicators of SAEV service are significantly improved.

cs.MA↗