As we witness a global change occurring with the advent of e-scooters (electric scooters), it is clear that adequate measures need to be taken for the implementation of this means of transport. Bearing in mind that the user should be the focus of the proposed measures, the aim of this paper encompasses the analysis of e-scooter users’ travel patterns, as well as standard scooter users’ willingness to switch to e-scooters for different hypothetical scenarios, and the determination of factors that have an influence on the users’ willingness to switch. The scenarios include the availability of various infrastructure capacities for the city of Belgrade, namely, a separate infrastructure for e-scooters, or sharing the existing infrastructure with cyclists, pedestrians and motor vehicles. Standard methods of descriptive statistics, the McNemar–Bowker test of paired samples, and multinomial logistic regression were used in this paper. The results showed that there was a statistically significant difference in users’ willingness to use an e-scooter, depending on the available infrastructure. The results of multinomial logistic regression showed that the mode of transport used before and during the COVID-19 pandemic, as well as the average distance traveled, had a statistically significant impact on the users’ willingness to switch to e-scooters if they were allowed to use the cycling infrastructure. This paper also identifies additional factors that have a positive (environmental benefits, congestion avoidance) and negative (safety issues, lack of infrastructure, etc.) impact on users’ willingness to switch, which can be of use to decision-makers as a basic guideline for the adequate implementation of e-scooters in transport systems.

Big Data analysis is the process that can help organizations to make better business decisions. Organizations use data warehouses and business intelligence systems, i.e. enterprise information systems (EISs), to support and improve their decision-making processes. Since the ultimate goal of using EISs and Big Data analytics is the same, a logical task is to enable these systems to work together. In this paper we propose a framework of cooperation of these systems, based on the schema on read modeling approach and data virtualization. The goal of data virtualization process is to hide technical details related to data storage from applications and to display heterogeneous data sources as one integrated data source. We have tested the proposed model in a case study in the transportation domain. The study has shown that the proposed integration model responds flexibly and efficiently to the requirements related to adding new data sources, new data models and new data storage technologies.

One of the central decision-making questions in planning road tolling is the selection of the optimal toll collection system (TCS). The question of TCS selection arises in the situation when the existing TCS is to be upgraded or when TCS is selected for a newly constructed road. Considering that there are multiple TCS available nowadays, with their particular advantages and disadvantages, and that there is a range of often conflicting criteria for TCS selection, this decision-making issue belongs to the group of multicriteria decision-making (MCDM) problems. The MCDM-based methodology used in this research integrates Strengths-Weakness-Opportunities-Threat (SWOT) analysis and Fuzzy Preference Ranking Organization Method for Enrichment Evaluations (F-PROMETHEE). The expert-based decision-support framework includes a procedure for defining evaluation criteria and their weights, scoring of alternatives, and sensitivity analysis. Presented decision-support framework is tested with fourteen toll systems. Results indicate that the best-ranked TCS is the dedicated short-range communication multilane free flow. Decision-support framework is developed for transferability to different contexts, where local features can be taken into account by choosing specific alternatives, criteria, and criteria values. Finally, this development opens up opportunities for further analysis of criteria values and considerations of user attitudes in road pricing scheme planning.