Behavioral responses to pre-planned road capacity reduction based on smartphone GPS trajectory data: A functional data analysis approach

Abstract

Pre-planned events such as constructions or special events lead to road capacity reductions and create bottlenecks in the traffic network. The traffic impact of such events goes beyond local areas, as informed drivers may detour to alternative corridors and consequently the traffic congestion may divert or propagate to other corridors. Due to the lack of real observation data, traditional traffic impact analyses are typically based on simulation models, fixed-location sensor data or survey questionnaires. In this research, we use high-resolution vehicle trajectory data collected via a smartphone app, which is capable of keeping track of individual driver’s behavior before and after road capacity reduction, to investigate travelers’ behavioral responses to pre-planned events and the contribution factors. For this purpose, a functional data analysis (FDA) approach-based clustering method is firstly proposed to cluster trajectory data and identify detour patterns, and two logistic and a least absolute shrinkage and selection operator (LASSO) regression models are used to explain drivers’ detour behavior choice for each pattern with spatial and temporal features of interest. A case study based on a lane closure event on MoPac expressway in Austin, TX is used as an example in this research. The case study demonstrates that: (1) the freeway capacity reduction triggered heterologous behavior responses, (2) driver detour behavior exhibits three major patterns and (3) each detour pattern highly depends on spatial features such as trip length, distance to freeway entrance and distance to other alternative freeways, in addition to the temporal features when the trip happens.