Time-Series Analysis of the Causal Effects Among Perceived Quality, Satisfaction, Loyalty, and Frequency of Public Transportation Use

Abstract

How to drive modal shift is one of the primary issues in creating a sustainable society. By encouraging people to migrate from private car use to public transport, city planners can prepare for a super-aged society, reduce greenhouse gas emissions, and mitigate negative externalities of private car use such as congestion, accidents, and noise. To achieve these goals, city planners are required to understand whether public transport usage can be increased by improving the service quality and what roles user satisfaction and loyalty play in the process. The relations between service quality, user satisfaction, loyalty, and user frequency have been studied for a long time. However, most of the studies are based on cross-sectional analysis. Cross-sectional analysis is less powerful in detecting causality as it does not show pre-post relations or lagged effects between variables. To investigate causal and non-simultaneous relations among quality, satisfaction, loyalty, and user frequency of public transport, we used data of the Benchmarking in European Service of Public Transport survey from 2001 to 2015 in four European cities, and applied vector auto regressive (VAR) analysis. The result shows that improvement of service quality has positive effects on user's satisfaction, loyalty, and user frequency and that better satisfaction leads to higher loyalty and user frequency. More importantly, the result also shows that behavioral modification (change in user frequency) occurs with time lag while psychological modifications (changes in satisfaction and loyalty) occur immediately after changes in service quality. The findings suggest that city/transport planners ought to keep improving the service quality of public transport and monitoring the achievement from a long-term perspective.

Examining the causal relationship between bike-share and public transit in response to the COVID-19 pandemic

“…For example, several researchers examined the impact of upstream and downstream traffic conditions on traffic flows ( Chen et al, 2016 ; Deng, 2016 ) and public transit ridership spillover effects on nearby regions ( Li et al, 2020 ). Also, the causal relationship between perceived service and public transit use has been analyzed using the VAR model with public survey and ridership data over multiple years ( Kawabata et al, 2020 ). Results show that improving public transit service quality leads to higher user frequency with time lags.…”

Passenger Transport Trends in the V4 Countries

“…The authors demonstrate this statement in the example of Beijing, which has become a "public transport city" with its public transport system as part of a broader plan to build humanistic, scientific, technological, and green public transport systems. Kawabata et al [3] state that it is necessary to encourage people to switch from their private cars to public transport, to reduce greenhouse gas emissions and to mitigate negative effects of using private cars, such as traffic jams, accidents, and noise level. To investigate the causal relationships between quality, satisfaction, loyalty and user frequency of public transport, the study uses the vector autoregressive analysis (VAR) on time series of data from four European cities.…”