Reducing carbon emissions to cope with climate change and short of energy have become a global trend, so it is urgent to accurately measure energy consumption and emissions. As taxi occupies the second highest proportion of domestic roads, it is necessary to study the emission of new energy. However, existing studies often consider the whole region, with low accuracy and no true value, resulting in difficult verification of conclusions. This paper proposed a micro-energy consumption and carbon emission model for taxicabs based on trajectory data and deep learning method to dynamically simulate the real-time energy consumption of taxicabs with different energy sources. First, this paper detected the correlation between driving state and energy consumption carbon emission in portable emissions measurement system (PEMS) environment. Then, a deep learning-based framework was built to learn the vehicle’s energy consumption carbon emission pattern. In particular, Gated Recurrent Unit (GRU) neural network is used to learn current and historical driving habits and the influence of external environment on energy consumption, while life cycle assessment (LCA) method is used to obtain the emission patterns of vehicles with different energy types in the whole life cycle. The measured data are obtained in Wuhan, the precision of our model is higher than that of the existing model. At the same time, we also applied it to the taxicab monthly trajectory dataset to obtain the spatial-temporal energy consumption emission patterns of different types of energy. The results show that pure electric vehicle (PEV) has obvious greenhouse gas emission reduction effect, compared with gasoline and compressed natural gas (CNG) vehicles, the emission reduction is 12.03% and 12.07% respectively, but the total energy consumption achieves little advantage. This model will lay a foundation for the formulation of regional road network emission inventory, so as to provide support for the government to make relevant decisions.

The widespread use of light detection and ranging (LiDAR) data provides a promising source for the automatic detection and inventory of roadside assets. One of the essential elements in roadside furniture is light poles. There is limited research on the mapping of light poles using point-cloud data on rural highways. In this environment, the placement of light poles within roadside clear zones often poses a safety concern, as they are related to an increased risk of collisions. Only a limited number of studies have explored the relationship between light poles and safety because of the time-consuming and labor-intensive practices of collecting light pole assets data using traditional manual methods. This paper proposes an automated approach to mapping the locations of light poles. First, the scanning vehicle trajectory is extracted, smoothed, and then used to segment the point-cloud data into smaller overlapped batches of data. Several filters are applied to extract pole-like objects from the data. The segments are combined back together, and a density-based clustering algorithm is used to group the remaining points into clusters. A geometric filter is finally applied to extract light poles. The model is tested on 28 km of data on three rural highways in Alberta, Canada. The proposed algorithm is found to be accurate relative to previous studies, with average precision, recall, and F1 scores exceeding 98% for the test segments. The proposed work can assist in the automation of light pole inventory and road safety audits by transportation agencies.