Carbon emissions from the logistics industry have been rising year after year. Correct handling of the relationship between economic development and environmental protection is of great significance to the implementation of green logistics, which is an important component of China’s strategy for strong transportation. This paper focuses on the evaluation of the carbon emissions efficiency of logistics industry from a new strong transportation strategy perspective. A super-efficiency slack-based measurement (Super-SBM) model and Malmquist index are combined to evaluate the static and dynamic carbon emissions efficiency of the logistics industry. The results indicate that compared with the SBM model, the Super-SBM model can more effectively measure the carbon emissions efficiency of the logistics industry. Pilot regions for the strong transportation strategy were divided into two categories, namely regions with slow carbon emission growth rates but high efficiency, and regions with high carbon emission growth rates but low efficiency. Some policy recommendations from the strong transportation strategy perspective were proposed to improve the carbon emissions efficiency of the logistics industry, especially for the second category of pilot regions. This study is expected to provide a basis for decision-making for efficient emissions reduction measures and policies, and to encourage the pilot regions to take the lead in achieving the goal of China’s strategy for transportation.

This paper aims to investigate the characteristics of discretionary lane change (LC) duration on freeways based on an enriched dataset that contains the LC vehicle trajectories of 2905 passenger cars and 433 heavy vehicles. The LC duration is comprehensively analysed, and four stochastic LC duration models are established according to vehicle type and LC direction. LC duration varies with vehicle type and LC direction. Considering driver heterogeneity, accelerated failure time (AFT) models with fixed parameters, latent classes, and random parameters were established in this paper. The results show that drivers of heavy vehicles display greater heterogeneity and that vehicle types and LC directions have significant influence on the LC duration. The results of this study are helpful for understanding the mechanism of the LC process and the influence of LC on traffic flow and improving the safety of lane-changing behaviours of connected and autonomous vehicles.

The literature has offered much evidence regarding associations between the built environment (BE) and commuting behavior. However, most prior studies are conducted based on cross-sectional samples from developed countries, and little is known about the longitudinal link between BE and commuting behavior. Based on two rounds of survey data from China, this study examines relationships of BE with commuting mode choice from both cross-sectional and longitudinal perspectives. The effects of life-cycle events are considered within a unified framework. Results of the longitudinal examination of BE and commuting mode shift largely support the cross-sectional analysis. Specifically, promoting more balanced land use and improving residential density are important for car use reductions and active travel initiatives. Meanwhile, more balanced land use improves the probability of commuting by motorcycle and electric bike, but reduces the probability of commuting by public transit. This study also highlights the remarkable role played by life-cycle events in affecting commuting mode shifts.

Through an urban tunnel-driving experiment, this paper studies the changing trend of drivers’ visual characteristics in tunnels. A Tobii Pro Glasses 2 wearable eye tracker was used to measure pupil diameter, scanning time, and fixation point distribution of the driver during driving. A two-step clustering algorithm and the data-fitting method were used to analyze the experimental data. The results show that the univariate clustering analysis of the pupil diameter change rate of drivers has poor discrimination because the pupil diameter change rate of drivers in the process of “dark adaptation” is larger, while the pupil diameter change rate of drivers in the process of “bright adaptation” is relatively smooth. The univariate and bivariate clustering results of drivers’ pupil diameters were all placed into three categories, with reasonable distribution and suitable differentiation. The clustering results accurately corresponded to different locations of the tunnel. The clustering method proposed in this paper can identify similar behaviors of drivers at different locations in the transition section at the tunnel entrance, the inner section, and the outer area of the tunnel. Through data-fitting of drivers’ visual characteristic parameters in different tunnels, it was found that a short tunnel, with a length of less than 1 km, has little influence on visual characteristics when the maximum pupil diameter is small, and the percentage of saccades is relatively low. An urban tunnel with a length between 1 and 2 km has a significant influence on visual characteristics. In this range, with the increase in tunnel length, the maximum pupil diameter increases significantly, and the percentage of saccades increases rapidly. When the tunnel length exceeds 2 km, the maximum pupil diameter does not continue to increase. The longer the urban tunnel, the more discrete the distribution of drivers’ gaze points. The research results should provide a scientific basis for the design of urban tunnel traffic safety facilities and traffic organization.

Walking has a positive impact on people’s emotional health. However, in the case of serious air pollution, it is controversial whether walking exercise can still improve individuals’ emotional health. Using data from the 2014 wave of the China Labor-Force Dynamics Survey, this study explored the relationship between walking and emotional health with different levels of environmental pollution. The results indicated that respondents who took regular walks had better emotional health than those who did not walk regularly. For those whose main mode of physical exercise was walking, the average number of walks per week was significantly and positively correlated with their emotional health; however, the average duration of the walk had no significant impact on their emotional health. Moreover, for those whose main mode of physical exercise was walking and who lived in neighborhoods with a polluted environment, regular walking still had a positive impact on their emotional health. This suggests that even if environmental pollution is serious, walking still plays an important role in regulating individuals’ mental health. We propose that in order to promote the emotional health of residents, it is necessary to create more public spaces for outdoor activities and simultaneously increase efforts to control environmental pollution.

To alleviate intersection traffic congestion and reduce carbon emissions at intersections, research on exploiting reinforcement learning for intersection signal control has become a frontier topic in the field of intelligent transportation. This study utilizes a deep reinforcement learning algorithm based on the D3QN (dueling double deep Q network) to achieve adaptive control of signal timings. Under a mixed traffic environment with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), this study constructs a reward function (Reward—CO₂ Reduction) to minimize vehicle waiting time and carbon dioxide emissions at the intersection. Additionally, to account for the spatiotemporal distribution characteristics of traffic flow, an adaptive-phase action space and a fixed-phase action space are designed to optimize action selections. The proposed algorithm is validated in a SUMO simulation with different traffic volumes and CAV penetration rates. The experimental results are compared with other control strategies like Webster’s method (fixed-time control). The analysis shows that the proposed model can effectively reduce carbon dioxide emissions when the traffic volume is low or medium. As the penetration rate of CAVs increases, the average carbon dioxide emissions and waiting time can be further reduced with the proposed model. The significance of this study lies in its dual achievement: by presenting a flexible strategy that not only reduces the environmental impact by lowering carbon dioxide emissions but also enhances traffic efficiency, it provides a tangible example of the advancement of green intelligent transportation systems.