We studied trip purpose imputation using data mining and machine learning techniques based on a dataset of GPS-based trajectories gathered in Switzerland. With large number labeled activities eight categories, we explored location information hierarchical clustering achieved classification accuracy 86.7% random forest approach as baseline. The contribution this study is summarized below. Firstl...

Predicting net induced demand can be helpful for better feasibility study before investigating transportation infrastructure. The net induced demand can be defined as newly generated trips, but not shifted trips, by some improvements in transportation systems. This study only focused on the net induced demand rather than any other concepts belong to the general term “induced demand”. The net in...

Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that highly suitable in the current era climate change. This study proposes methodology for inferring bike trip purpose based on bike-share point-of-interest (POI) data. Because involves decision-making, its inference necessitates an understanding spatiotemporal complexity...

This paper explores the effects of trip purpose on price elasticity of bus mode. Data for the research was collected through passenger field survey in Isfahan, Iran. Due to the nature of the data, nonlinear regression and nonparametric statistics tools were used for analysis. It was found that the logarithmic function best explains the relationship between percentage of change in demand and per...

Departments of Transportation (DoT) at the state and federal level are consistently try to log travel behavior of individuals to help with urban planning. This is done by placing a Global Positioning System (GPS) in a users’ vehicle, and augmenting the electronic data with a travel diary to classify the purpose of each trip. Travel diary’s are typically kept to log the time, length, and purpose...