Personas: A Market Segmentation Approach for Transportation Behavior Change

Abstract

This paper proposes a market segmentation method applied in the field of transportation behavior change using GPS trajectories and socio-demographic data collected from the advanced demand management system “GoEzy” designed by Metropia. User attributes are extracted using several statistical methods such as dynamic time warping, density-based spatial clustering of applications with noise (DBSCAN), and signal processing method to infer users’ sensitivity to incentives, temporal, and spatial travel patterns. Ten personas were generated by K-means clustering, representing different types of people with various travel patterns and sensitivity to incentives. The experiment was conducted on 24 new users to test if the persona could be used as a tool to predict their willingness to change. The results showed that after creating personas for new users and providing them with new incentives, their modified departure time pattern according to the new incentives matched expectations from analysis of the 10 personas.

Which Factors Affect User Satisfaction with ETC? Evidence from Shanghai and Beijing

“…Zhao et al applied the K-means clustering algorithm to classify spatial traffic hot spots by camera locations while exploring the weekly travel patterns of private cars [43]. Arian et al proposed a market segmentation method applied to the field of traffic behavior change, in which 10 personas were generated by using K-means clustering, representing different types of people with different travel patterns and sensitivity to incentives [44,45]. Eltved et al used K-means clustering to group passengers according to their travel behavior before and after public transport service closures, allowing them to observe how different passenger groups change their travel behavior after public transport service disruptions [46].…”

Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas

“…The second aspect of the review focused on a deeper understandi ers' perspectives through a synthesis of findings in the previous res hand, the synthesis focused on understanding usage frequency, aimin into specific user types. On the other hand, the synthesis focused on de for gaining a deeper understanding of underlying reasons for users' originated from design research [39], and similar ideas have been use studies previously for market segmentation [40]. For example, Eldeeb lized a persona-based approach to understand the preferences of the The second aspect of the review focused on a deeper understanding of e-scooter users' perspectives through a synthesis of findings in the previous research.…”

“…On the other hand, the synthesis focused on developing personas for gaining a deeper understanding of underlying reasons for users' behavior. Personas originated from design research [39], and similar ideas have been used in transportation studies previously for market segmentation [40]. For example, Eldeeb and Mohamed utilized a persona-based approach to understand the preferences of the key transit market groups and estimate their willingness to pay for service improvements [41].…”

User value identification based on an improved consumer value segmentation algorithm

“…In addition, as respect to the segmentation methods, the clustering algorithms, as a kind of current mainstream market segmentation method, have been widely applied in tourism, transportation behavior change and other fields to implement an effect market segmentation (Knuth et al. , 2019; Arian et al. , 2021; Mauricio et al.…”

“…In addition, as respect to the segmentation methods, the clustering algorithms, as a kind of current mainstream market segmentation method, have been widely applied in tourism, transportation behavior change and other fields to implement an effect market segmentation (Knuth et al, 2019;Arian et al, 2021;Mauricio et al, 2021); they can be broadly divided into two categories: partitioning and hierarchical clustering algorithms (Kashyap and Bhattacharya, 2017). In terms of the partition clustering, the K-means algorithm (KMA) is the most popular and frequently used market segmentation algorithm (Arunachalam and Kumar, 2018;France and Ghose, 2019), which first conducts a comprehensive investigation about consumer information from the perspective of the consumers' geographical, demographic, psychological, behavioral and other characteristics, then clusters these consumers according to the similarity between consumers, and finally obtains differentiated segments (Casas-Rosal et al, 2021).…”