Identifying mismatch between urban travel demand and transport network services using GPS data: A case study in the fast growing Chinese city of Harbin

The world's urban population growth and economic development have led to the reshaping of metropolitan space layouts among residential, employment and shopping locations, generating growing mismatch between travel demand and transport services. A reliable method to accurately analyze mobility demand and underlying transport network systems and to identify areas with serious mismatch problems is important for the design of effective policy measures. In this paper, we make use of the wide deployment of GPS devices in vehicles in many cities today, to develop such a method. This approach is developed using GPS data collected from all taxis operating in the Chinese city of Harbin between July and September in 2013. It consists of four major steps. First, city-wide mobility patterns are modeled based on GPS trajectories. This model captures a set of key traffic characteristics for each pair of regions in the entire urban network, including travel demand, travel speed and route directness of travel paths. From this model, a set of indicators is then built to measure the road transport performance between the regions, and the areas with serious mismatch problems are subsequently pinpointed. Finally, the identified problematic regions are further examined and specific transport problems are analyzed. By applying the proposed method to the city of Harbin, the potential and effectiveness of this method are demonstrated. Moreover, with more and more urban vehicles being equipped with GPS devices, the designed method can be easily transferred to other cities, thus paving a way for the adoption of the presented approach for an up-to-date and spatial-temporal sensitive road network analysis approach that supports the establishment of a more sustainable urban transport system.