VEHITS_DC 2024 Abstracts

Short Papers
Short Papers
Paper Nr: 5
Title:

A Consistency Analysis Method for Traffic Sequence Charts
Authors:

Jan Steffen Becker
Abstract: The trend in the development of highly automated vehicles goes towards scenario-based methods. Traffic Sequence Charts are a visual but yet formal language for describing scenario-based requirements on highly automated vehicles. This work presents an approach for finding inconsistencies (conflicts) in a set of scenario-based requirements formalized with Traffic Sequence Charts. The proposed method utilizes satisfiability modulo theories solving on two-sided approximations of possible vehicle behavior. This ensures that found inconsistencies are not caused by approximations, but also occur when applying exact methods. Applicability and scalability of the analysis technique is evaluated in a case study.
Paper Nr: 6
Title:

Novel Criteria Weighting Methodology EFEE: A Case of Micromobility Modes Assessment in İstanbul
Authors:

Esra Çakir
Abstract: This research intends to address a critical gap in the existing literature on the evaluation of micromobility vehicles in large urban centres. While previous studies have explored various aspects of micromobility and Multi-Criteria Decision Making (MCDM) models, there is a lack of research explicitly focusing on: - Distance-based MCDM criterion weighting: Current models often employ static weights for evaluation criteria, failing to account for the dynamic level of users and the effects of criteria on decision system. - Evaluating micromobility vehicles in the context of sustainable urban development goals: There is a need to understand how micromobility vehicles can contribute to achieving carbon neutrality targets set by initiatives like the European Union's (EU) 2030 and 2050 objectives.
Paper Nr: 7
Title:

Decision Support System for Accessing Costs and Risks of Connected and Automated Vehicles as Mobility Service in Urban Contexts
Authors:

Mónica Rodrigues
Abstract: A predicted increase of connected autonomous vehicles (CAVs) in our roads paved the way for new opportunities and challenges towards the promotion of sustainable mobility. However, the impacts of CAVs on the road environment and its implications are widely dependent on technological choices and public policy. Therefore, the main objective of this research is to develop a model-driven decision support system (DSS) that allows assessing the costs and risks of implementing CAVs in urban contexts. First, the reseacher will address what criteria should be considered to define a predictive model to determine, in real conditions, the impacts of implementing CAVs. Secondly, the student intends to determine how CAVs will affect economic, environmental, and time-value costs by assessing the risk of their implementation in different Portuguese cities. Particularly, the analysis on the implementation of CAVs will have a major focus on the reduction of pollutants (such as CO2 and NOx), potentially contributing for the mitigation of climate change effects. The results achieved will integrate a DSS that will support transport systems' planning and implementation of urban strategies for the introduction of CAVs.