Role of travel information in supporting travel decision adaption: exploring spatial patterns

Abstract

How consumers acquire dynamic traveller information to adjust their travel behaviour is a key component of Intelligent Transportation Systems (ITS). The association between information acquisition/adjustments and various contextual factors can be captured in traditional statistical models, referred to as a global model. These models provide one overall set of estimated coefficients. However, associations between correlates may vary over space. Specifically, people living in different locations may have different information acquisition patterns and may respond differently to dynamic information, which cannot be fully captured in a global model. Consequently, this study uses Geographically Weighted Regression (GWR) -a locally-based model as an alternative, to answer the following questions: (1) which factors are associated with traveller information acquisition and decision adaption, (2) whether these associations are the same over the entire study region and (3) how are these associations distributed spatially? The modelling results indicate that there is spatial heterogeneity in information acquisition and use decisions, and GWR provides a more complete depiction. The models capture how correlates vary over space. The broader implication of the study is to consider spatial heterogeneity in transportation models.

Entangled communities and spatial synchronization lead to criticality in urban traffic

“…Alongside traffic flow theory, the impact of different travel behaviours and traffic control schemes have been studied in depth10, for example in connection with the effect of pre-travel and real time travel information and variable travel demands1112, real and perceived information uncertainty1314 and its cognitive cost15, inequality among drivers16 or different user responses to travel information17.…”

The Influence of Information Services on Public Transport Behavior of Urban and Rural Residents

“…The research shows that the information service has a positive influence on these public transport travel behaviors. The second is the influence of information services on the objective effects of travel behavior [30][31][32][33][34]. These studies show that information services can attract travelers, reduce travel time, and improve public transport service levels; The third is the influence of information services on public transport time and psychological activities at public transport stations [35,36].…”

Cluster-based lognormal distribution model for accident duration

“…The information on the predicted accident duration can be used to alert drivers of the necessity to reroute or reschedule their trips. Hence, accurate accident duration prediction is a key step towards choosing efficient accident management strategies, providing reliable traffic information and improving travel time reliability (Lyman and Bertini 2008;Wang and Khattak 2013).…”