Rapid urbanization levels create serious town road traffic safety conditions in China. The study of town road traffic safety is therefore essential. This paper analyzes the characteristics of town traffic and town road traffic safety. It establishes a town road safety evaluation system based on the PSR model. The town road safety evaluation model is based on Set Pair Analysis. Finally, the model is used to analyze and evaluate the town road traffic safety of Lianyungang city. The results show that the method can make an objective and reasonable evaluation of town road traffic safety. Many factors affect town traffic safety. It is also difficult to appraise the level of town traffic safety scientifically. Each factor often interacts with others and there are multiple traffic safety objectives. Our challenge is to select a reasonable evaluation indicator and to include all the various indicators in the urban traffic safety evaluation.
<abstract><p>Accurately predicting lane-changing behaviors (lane keeping, left lane change and right lane change) in real-time is essential for ensuring traffic safety, particularly in mixed-traffic environments with both autonomous and manual vehicles. This paper proposes a fused model that predicts vehicle lane-changing behaviors based on the road traffic environment and vehicle motion parameters. The model combines the ensemble learning XGBoost algorithm with the deep learning Bi-GRU neural network. The XGBoost algorithm first checks whether the present environment is safe for the lane change and then evaluates the likelihood that the target vehicle will make a lane change. Subsequently, the Bi-GRU neural network is used to accurately forecast the lane-changing behaviors of nearby vehicles using the feasibility of lane-changing and the vehicle's motion status as input features. The highD trajectory dataset was utilized for training and testing the model. The model achieved an accuracy of 98.82%, accurately predicting lane changes with an accuracy exceeding 87% within a 2-second timeframe. By comparing with other methods and conducting experimental validation, we have demonstrated the superiority of the proposed model, thus, the research achievement is of utmost significance for the practical application of autonomous driving technology.</p></abstract>
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.
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.
This paper aims to investigate the characteristics of durations of discretionary lane changes (LCs) on freeways based on an enriched dataset. A comprehensive analysis of LC durations was conducted based on vehicle types, LC directions and navigation speeds. It was found that the heavy vehicle takes longer time to complete LC maneuver. The LC direction significantly influences the durations of passenger cars but has no significant influence on heavy vehicles. The navigation speed was found to have important influence on LC durations. However, it has different impacts according to vehicle types and LC directions. Further analysis of LC durations at different stages showed that drivers of passenger cars might use different strategies to perform LCs when they change lanes to different directions. However, drivers of heavy vehicles in both directions used less time to occupy the target lanes. Results of this study can be beneficial to understand the mechanism of LC process and the influence of LC on traffic flow.
Under the overall strategic guidance of emission peaks and carbon neutrality, an increasing number of cities are focusing on sustainable transportation development as an important measure for sustainable transportation development. Transit-oriented development (TOD) can guide residents to green trip options and reduce the dependence on private cars. Many cities have qualitatively reduced the parking allocation index of office buildings around rail stations, and quantitative research on the influence area and degree of TOD is lacking. This paper selects office buildings in the rail transit station influence area as the research object, puts forward the TOD measurement method of rail transit stations based on the improved “Node-Place” model, and clusters the stations under different measurement indices by the K-means algorithm. For different types of stations, the multinomial logit (MNL) model is used to build different types of trip mode split models to put forward the reduction calculation method of the parking allocation index of office buildings in the rail transit station influence area. Finally, this paper applies the revision of Nanjing’s allocation index in 2019, and the TOD measurement is identified through the “Node-Place-Connection” model. The optimized calculation method of the parking allocation index for office buildings is proposed. The results indicate that the method can reduce parking allocations to encourage the use of green transportation and guide the construction of urban sustainable transportation systems.
Walking, as a healthy and environmentally friendly mode of travel, has been revived in many cities around the world. The mid-block crosswalk (MBC) is a common type of pedestrian facility, and the pedestrian hybrid beacon (PHB) is one of most commonly used signalized MBCs, having a wide range of applications. This study applied an upstream detection (UD) strategy to PHB to reduce the pedestrian waiting time at the crossing. Data were collected through video recordings at two crosswalks at two different periods of the day in the city of Nanjing. First, basic simulation models were developed in VISSIM according to the current layout and the signal control methods of the two crosswalks. Second, signal control logic was adjusted to develop simulation models of PHB. Third, upstream detectors were added to develop simulation models of PHB with a UD strategy. Models for PHB were simulated with 10 different random seeds, and a paired t-test was conducted to evaluate the performance of the UD strategy statistically. The results show that the UD strategy for PHB reduces pedestrian waiting time and increases vehicle delay. However, the reduction in pedestrian waiting time is greater than the increase in vehicle delay. The UD strategy has also been found to be more effective for crosswalks, with relatively short crossing lengths and low pedestrian volume. Finally, a discussion about factors concerned with the application of UD strategies in practice is carried out.
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.
The deviations of straight-going traffic at irregular signalized intersections lead to obvious expansion characteristics of e-bikes. This situation increases the possibility of collisions between motor vehicles and e-bikes. In order to study the change of expansion degree of straight-going e-bike at irregular signalized intersections, the video trajectory extraction technology is used to obtain relevant data of e-bikes during green light release periods at irregular signalized intersections. In addition, we combined the flow and spacing characteristics of e-bikes and used a clustering method to analyze the release stage and release groups. Therefore, the Group 1 of e-bikes in the early green light release was determined to be the main research object of expansion degree. According to the static and dynamic factors, a prediction model for the expansion degree of straight-going e-bikes at irregular signalized intersections was established based on the beetle antennae search–back propagation (BAS-BP) neural network model. Finally, the evaluation indexes were compared and analyzed before and after the beetle antennae search (BAS) algorithm optimization. The results showed that the BAS-BP neural network prediction model was better than that of the back propagation (BP) neural network. The results could provide a theoretical reference for improving the efficiency of mixed traffic flow at irregular signalized intersections.
Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.
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