Mojtaba Maghrebi

TL;DR: In this paper, a detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income.

TL;DR: This work presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of designing and implementing building information modeling systems (BIM).

TL;DR: The results of this paper open up avenues for travel demand modellers to explore the possibility of using big data (in this case Twitter data) to model short distance (day-to-day or activity based) and long distance (vacation) trips.

TL;DR: A mixed-integer linear programming (MILP) model is presented to minimise the total cost of the ISWM system including transportation costs and facility establishment costs and a stepwise heuristic method is proposed within the frames of two meta-heuristic approaches.

TL;DR: The capability of the support vector machine (SVM) method is analyzed and the results show that the Pearson VII Universal kernel can accurately predict pavement performance in its life cycle.