Regularity of vehicle trips in urban areas

The regularity of trips has been a fundamental assumption for estimating or forecasting travel demand, which is an essential part of the analysis for traffic operation and planning. For this type of analysis, a survey method has been traditionally used to understand personal trip behavior in detail. However, surveys have limitations in collecting accurate traffic data on a large scale because they depend purely on participants’ memories. Recently, as the development of ICT facilitates collection of various traffic information, research using this new information has drawn attention, which discovers human mobility pattern on a wide range. In this study, a month of trajectory data collected from on-board transponders equipped in the vehicles in Daegu metropolitan city, South Korea was used to unveil the individual trip regularity in trip chain level. We applied dynamic time warping and the inter-spike interval algorithms to examine these data and to quantitatively measure spatial and temporal regularity separately. The outcomes showed that i) the degree of trip regularity can be properly estimated using the indices, ii) spatial and temporal regularities are correlated - drivers who made trips at regular times also used similar paths in space across days, and iii) commuters and non-commuters have different distributions of regularity scores - commuters made more regular trips. This finding is intriguing because the trip regularity can prove the predictability of human mobility. In addition, if regularity indices are used to measure historically collected trip behavior, this method can provide an alternative way of estimating or forecasting travel demand at the individual trip level.