Situational awareness is crucial for developing sustainable urban planning frameworks for the future. Saudi Arabia’s growing population of 28 million is expected to double by 2032. Riyadh is currently undergoing a radical transformation by introducing a new urban transportation system with the Riyadh Metro and bus networks, the global public urban transportation project. It is currently the largest public urban transportation project in the world. The rapidly growing population demand in Riyadh and the unique cultural and social tapestry of the Kingdom of Saudi Arabia introduce another dimension to the complexity of urban transportation. This project modeled the flows and impact of the Metro on the wider city and look at operational strategies such as pricing, frequency, ridership rules.
Our goal in this project was to develop tools to understand the mobility patterns of the city’s inhabitants to ensure that its services and infrastructures are growing in pace to meet the growing demands of this burgeoning population. This involved extracting reliable mobility data on urban trips and their frequency from passive data, coupled with existing buses and taxi demand to support the transit system’s adoption and operation in Riyadh. We developed algorithms to combine semantically enriched GIS data on infrastructure and economic activity distribution to extract human daily trip chains (urban activity) and identify potential transit demand and frequency.
We developed:
1)Algorithms to combine semantically enriched GIS data on land use, infrastructure, and economic activity distribution, to extract human daily trip chains (urban activities) and to identify potential transit demand and frequency.
2) Derived understanding of human activities, trip-chains and travel times for developing a coupled network approach to optimize the interconnections of vehicle trips car and buses.
We used the Coupled Networks approach: A small network 2 (metro) was coupled with the larger network 1 (road network). We explored various coupling mechanisms that resulted in the best performance in terms of travel times for existing conditions of OD demand and travel times. We also used a data integration approach, collecting data from existing bus and taxi trips using either crowd sourced cell phone applications or obtaining from existing taxi operators and other sources.