Abstract

Highways play a vivacious role in a country’s economic growth, by facilitating movement of both goods and"br" people from one place to another. Over a short period of time, innovation in automobile and information"br" technology has seen an unprecedented growth and this exploratory research highlights the impact of advent of"br" innovative technologies like Autonomous and Connected Vehicles, Internet of Things applications and Big Data"br" analytics on highway operators, as reflected in the opinions of organizations around the world (highway operators,"br" toll agencies, suppliers, consultants and associations). The opinions were collected on a Likert scale type online"br" survey, which was later tested for its empirical significance with non-parametric Binomial and Wilcoxon signed"br" rank tests, supported by descriptive analysis. The research results clearly indicate that these technologies and"br" products are not far from realization and while on one hand they would facilitate highway operations on the other"br" hand they may pose some serious challenges for operators.

Abstract

This paper aims to develop a user typology which enables user-specific analyses in respect of mobility behavior. It addresses the challenge of integrating unimodal and intermodal travel behavior into a user typology to obtain an overview of intermodal users within the context of their overall mobility behavior. The user typology is based on two cluster analyses (agglomerative hierarchical clustering) which use quantitative survey data on unimodal and intermodal mobility behavior obtained for Berlin, Germany. One cluster analysis was performed for unimodal use and one for intermodal mode use to take into account the users’ relatively low use of intermodal modes as well. The analyses resulted in 6 intermodal and 5 unimodal clusters based on users’ mobility behavior. Since in each case every individual is assigned to one intermodal and one unimodal cluster, the resulting intermodal and unimodal clusters were then combined in order to represent the overall mobility behavior of each individual as mobility types. The mobility types are further characterized by information on socio-demographics and mobility resources obtained from the dataset. These enhanced mobility types (EMT) provide a clearer impression of the users’ characteristics and needs. This user typology takes account of the wide range of mobility options available in cities today and the resulting diversity in people’s mobility behavior. To enable us to address the needs of users who combine several modes of transport within one trip, the proposed procedure approaches the challenge of integrating intermodal behavior into user types. The results provide a user typology which combines intermodal and unimodal travel behavior with personal characteristics and enable researchers and practitioners to work on user-specific research questions and planning tasks. Document type: Article