Computing the CO₂ Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City

The intensive usage of vehicles has led to many urban problems including the traffic jams and the environmental pollutions. To handle these problems, most previous studies have used macro-based models to estimate and analyze the CO₂ emission inventories, but very few studies have focused on computing vehicle CO₂ emissions using a micro-based model. This paper presents an in-depth study on computing the CO₂ emissions from taxi trajectory data and further analyzing their spatiotemporal patterns from three aspects. Taking the Wuhan city as a case study, this paper uses the comprehensive modal emission model to quantitatively compute the CO₂ emissions from the taxi trajectory, and statistical results suggest that CO₂ emissions of the entire city has experienced a remarkable regularity in times of the day and days of the week. Specifically, the spatiotemporal patterns of CO₂ emissions from taxi trajectories are analyzed from three different aspects. Firstly, a spatial clustering algorithm is used to aggregate the taxi trajectory points with high CO₂ emissions, and the number of clusters displayed a regular change pattern. For example, the cluster number is gradually increased from workdays to weekends or holidays and from normal time period to peak time period in one day. Secondly, the data field model is used to allocate the CO₂ emissions of taxi trajectory to the individual streets, and the emission from street network exhibited a remarkable distribution in space and time. For instance, streets with high CO₂ emission tend to appear in the morning peak time period of workdays and evening peak time period of weekends or holidays, and they are spatially adjacent to the universities, railway stations, or central business districts. Thirdly, a spatiotemporal autocorrelation technique is adopted to examine the concentration of taxi trajectory emissions in space, and different regions are positively auto-correlated with each other in both times of the day and days of the week. These results can help to the proposal of efficient CO₂ emissions reduction strategies, and they can provide guidance for taxis management, low-carbon traveling, and so on.