Building a validation measure for activity-based transportation models based on mobile phone data

Abstract

Activity-based micro-simulation transportation models typically predict 24-hour activitytravel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure. Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models. By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.

Characterizing activity sequences using profile Hidden Markov Models

“…Chernbumroong, Cang, Atkins, & Yu, 2013) or from various other big data sources, e.g. mobile call locations (Liu et al, 2014), WIFI traces (Danalet, Bilal, & Bierlaire, 2014) and location-based services (Hasan & Ukkusuri, 2014). However, important differences exist between the established methods and pHMMs in terms of both model building process and the types of sequential information that is focused on.…”

“…, but that weekday behavior generally does not extend into the weekend (e.g. Liu et al, 2014). To each of the clusters, the previously described sequence profile method is applied, with the activity letters being replaced by the location IDs.…”

GIS and Transport Modeling—Strengthening the Spatial Perspective

“…Liu et al [100] pointed at a mismatch in the granularity and abundance of simulation outcomes versus traffic count data, which has usually been used for external validation. Therefore, they suggested using real-time and spatially disaggregated mobile phone data for validation.…”

Semantic Annotation of Mobile Phone Data Using Machine Learning Algorithms

“…If a method can be found which helps to bridge this gap, the potential applications of the semantically enriched phone data are immense. They include inferring people's travel motivations in activity-based transportation modelling, mining individual life styles and activity preferences in urban planning, and providing activity tailored services in the cell phone environment [2].…”