Detecting Urban Dynamics with Taxi Trip Data for Evaluation and Optimizing of Spatial Planning:The Example of Xiamen City, China

Commonly, it is very hard to examine underlying urban dynamics due to rapid spatial expansion and land use variations. In this paper, the origin-destination (OD) data extracted from taxi trip data collected in Xiamen, China, covering 30 days was utilized to detect the underlying dynamics of Xiamen City. Specifically, we discretized the study area into 400m*400m grids so that the number of originating points and destination points of the taxi trips could be counted separately within each single grid. Then, heat maps of the taxi mobility were made to achieve a general understanding of urban dynamics. Secondly, we took advantage of the concept of complex networks to analyze the daily taxi trip data. Using a method of community detection, we divided the study area into six main sub-regions called functional self-sufficient zones (FSZs) in which spatial associations are tight and dense. The features of these FSZs helped us to gain a deeper understanding of urban dynamics. Finally, based on this understanding, we further evaluated and optimized the urban spatial planning of Xiamen. Balancing land use allocation was suggested to enhance the multicentric structure and reduce congestion. This study provides a relevant contribution by exploring the potential of applying taxi trip data to identify urban dynamics revelations and urban planning optimization solutions

Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel Geographic Information System (GIS) assisted optimal design method for renewable powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities