Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution.

Urban rail transit, a crucial component of urban public transportation, often experiences increased operational costs and carbon emissions due to low-load operations being conducted during off-peak passenger flow periods. This study aims to develop an optimization method for the daily scheduling of rail train operations with the goal of carbon emission reduction, while comprehensively considering the built environment and travel demand. Firstly, the influence of the urban built environment on residents’ travel demand is analyzed using an XGBoost model. Secondly, a time convolutional travel demand prediction model, Built Environment-Weighted Temporal Convolutional Network (BE-TCN), weighted by built environment factors, is constructed. Finally, an optimization method for rail train operation schedules based on the built environment and travel demand is proposed, with the objective of carbon emission reduction. A case study is conducted using the Hangzhou urban rail transit system as an example. The results indicate that the optimization method proposed in this study can achieve monthly carbon emission reductions of 1524.58 tons, 1181.94 tons, and 520.84 tons for Lines 1, 2, and 4 of the Hangzhou urban rail transit system, respectively. The research findings contribute to enhancing the economic efficiency and environmental sustainability of urban rail transit systems.

With the proposal of the dual carbon goals, it is of great significance to identify the causes of carbon emissions and reduce carbon emissions directly. There is a lack of analysis on the causes of carbon emissions considering the coupling effect of multiple factors and regional heterogeneity. The causes of carbon emissions are examined from multiple perspectives utilizing the panel data spanning from 1997 to 2022, encompassing 30 provinces in China. To further analyze the causes of carbon emissions, an enhanced feature and regularized gradient boosting tree (EG-Tree) model is constructed, and a scoring method for the tree structure is proposed. The coupling effect of multiple factors are analyzed such as coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gas, natural gas, etc., on the carbon emission intensity of various industries and their regional heterogeneity. The results show that: (1) The EG-Tree model constructed in this study could accurately analyze the causes of carbon emissions under the coupling of multiple factors based on the cumulative iterative feature branching contribution values (impact factors), with an average model fitting precision of 0.30. This means the carbon emission intensity values were predicted by various industries in different regions based on different energy consumption levels and industry-specific carbon emissions, compared with the carbon emission intensity values calculated using the carbon emission measurement dataset. (2) The consumption of coal and coke has a significant impact on the average carbon emission factors of various industries, with values of 7139.95 and 7217.05, respectively. The consumption of natural gas and liquefied petroleum gas has a smaller impact on the average carbon emission intensity of various industries under the EG-Tree model with corresponding carbon emission intensity impact factors of 5057.90 and 2789.57, respectively. (3) The Northeast region is a low-carbon area, while the East region is a high-carbon area, with total carbon emissions of 2,238,646.60 million tons and 5,566,314.00 million tons of CO₂, respectively. The Northeast region has the lowest pollution intensity for heating and cooling, with carbon emissions of 155,661.73 million tons of CO₂; the industrial carbon emissions in the East region are relatively high at 1,623,835.62 million tons of CO₂. The research findings of this study are beneficial for relevant departments to focus on the main impact factors of carbon emissions in different regions and industries, and to develop targeted emission reduction policies.