Journal of Traffic and Transportation Engineering-English Edition最新文献

Automatic modal identification via automatically interpreting the stabilization diagram provides key technique in bridge structural health monitoring. This paper reviews the progress in the area of automatic modal identification based on interpreting the stabilization diagram. The whole identification process is divided into four steps from establishing the stabilization diagram to removing the outliers in the identification results. The criteria and algorithms used in each step in the existing studies are carefully summarized and classified. Comparisons between typical methods in cleaning and interpreting the stabilization diagram are also conducted. Real structure benchmarks used in the existing studies to validate the proposed automatic modal identification methods are also summarized. Based on the review and comparison, the specific ratio method for cleaning the stabilization diagram, the hierarchical clustering method for interpreting the stabilization diagram and the adjusted boxplot for removing the outliers in the identification results are the most suitable methods for each step. The key point of automatic modal identification based on interpreting the stabilization diagram has also discussed, and it is recommended to pay more attention to cleaning the stabilization diagram. Future study about automatic modal identification under situation with very few sensors deployed should be more concerned. This review aims to help researchers and practitioners in implementing existing automatic modal identification algorithms effectively and developing more suitable and practical methods for civil engineering structures in the future.

Fatigue, corrosion, and bolt loosening are the main causes of structural performance degradation and collapse in steel bridges. Accurate monitoring of steel bridge diseases is a basic premise for ensuring high-quality operation and maintenance of steel bridges. In this regard, a summary and analysis were conducted on the classification of steel bridge diseases, monitoring and detection methods, application statuses, and major difficulties. The main causes, research status, and development trends of steel bridge diseases are discussed. The results showed that, for fatigue crack problems, fatigue crack initiation has a small scale, high difficulty in monitoring and detection, few methods, and low accuracy. As the cracks grow, the difficulty of monitoring and detection decreases, the number of methods increases, and the accuracy improves. Fatigue crack monitoring and detection are affected by the environmental and vehicular loads. Superficial corrosion features are evident in steel bridges, and corrosion identification methods and technologies are rapidly developing. Monitoring and detecting corrosion in concealed areas is difficult and requires further improvements in monitoring and detection technologies and their accuracy. Monitoring and detection methods and supporting equipment for bolt loosening in steel bridges are rapidly developing. The development of intelligent monitoring and detection technologies and supporting equipment is an important research topic that urgently needs to be addressed for the full-lifecycle operation and maintenance of steel bridges and the sustainable development of bridge engineering. Developing new intelligent sensing components based on high-performance materials and sensing element design theory to improve the monitoring and detection perception ability is an important development direction for steel bridge monitoring and detection. Research on intelligent monitoring and detection technologies, standardized indicators, and related topics based on intelligent operations and maintenance provide great support for the development of steel-bridge disease monitoring and detection.

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%–25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues.

Short-term prediction of on-street parking occupancy is essential to the ITS system, which can guide drivers in finding vacant parking spaces. And the spatial dependencies and exogenous dependencies need to be considered simultaneously, which makes short-term prediction of on-street parking occupancy challenging. Therefore, this paper proposes a deep learning model for predicting block-level parking occupancy. First, the importance of multiple points of interest (POI) in different buffers is sorted by Boruta, used for feature selection. The results show that different types of POI data should consider different buffer radii. Then based on the real on-street parking data, long short-term memory (LSTM) that can address the time dependencies is applied to predict the parking occupancy. The results demonstrate that LSTM considering POI data after Boruta selection (LSTM (+BORUTA)) outperforms other baseline methods, including LSTM, with an average testing MAPE of 11.78%. The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance, which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM. When there are more restaurants on both sides of the street, the prediction performance of LSTM (+BORUTA) is significantly better than that of LSTM.

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F₁) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.

The reduction of speed limits in urban roads through traffic calming schemes intends to ensure safer traffic conditions among road users by reducing the probability related to the occurrence of severe accident. Looking it from a different perspective, traffic calming measures can potentially resolve congestion problems at the same time by lowering the overall accessibility and attractiveness of private cars in urban areas. This study proposes a new methodological approach to explore and assess the direct impacts of traffic calming in the transport system efficiency of a metropolitan area. The multi-agent transport simulation (MATSim) and Open-Berlin scenario are utilized to perform this simulation experiment. By developing a new external tool, the free flow speed and road capacity of each network link is updated based on new speed limits and different compliance rates, which are defined per road hierarchy level. The test scenarios that are formulated present radical conditions, where the speed limit in most urban roads of Berlin drops to 30 km/h or even 15 km/h. The findings of this study show a considerably high increase in trips, passenger hours, and passenger kilometers using public transport modes, when traffic calming links are introduced, the reserve change is observed in private cars trips. Although the speed limits are decreased in inner urban roads in most of the scenarios, the decrease of average travel speed of private cars is not so high as it was expected. Surprisingly, private cars are used for longer distances in all test scenarios. Car drivers seem to use already existed motorways and private road to commute. In simulations, driver compliance to the new speed limits seems to be a determinant factor that is strongly influenced by the design interventions applied in a traffic calming area.

Pedestrian safety is at high stakes due to the non-compliance practices of pedestrians at signalized intersections. Additionally, when pedestrians are hurrying, they deliberately engage in such unsafe behaviour. Therefore, the purpose of this study was to understand how time pressure (i.e., feeling of hurry or saving time) affected pedestrians' decisions to follow traffic rules at signalized junctions. To achieve the study objectives, a pedestrian simulator setup was used to collect the crossing behaviour of forty participants at a four-legged signalized intersection. Non-compliance, one of the riskiest pedestrian behaviours, was examined with respect to three different forms, comprising dangerous temporal non-compliance (D-TNC), non-dangerous temporal non-compliance (ND-TNC), and spatial non-compliance (SNC) behaviour under two distinct conditions: baseline (i.e., no time pressure) and time pressure conditions. The effects of demographics, usual walking features, and time pressure on D-TNC and ND-TNC were investigated using a multinomial regression model, while SNC behaviour was investigated using a binary regression model. It was interesting to note that the majority of the factors related to pedestrians’ usual walking behaviour had an impact on all kinds of non-compliance behaviours. Importantly, the results also showcased that time pressure had a contrasting impact on D-TNC and ND-TNC behaviour whereas SNC behaviour increased under time pressure. Additionally, the varying impacts of D-TNC, ND-TNC, and SNC were also reflected in the occurrence of the crashes, which were probably triggered by discrepancies in the influence of time pressure on non-compliance behaviours. These findings highlight the need for technical solutions, educational outreach, and efficient enforcement practices to reduce pedestrians' non-compliant behaviour.

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time. This is a particular issue along curves with limited available sight, where speed management is necessary to avoid unsafe situations (e.g., driving off the road or invading the other traffic lane). To solve this issue, we proposed a novel intelligent speed adaptation (ISA) system for visibility, called V-ISA (intelligent speed adaptation for visibility). It estimates the real-time safe speed limits based on the prevailing sight conditions. V-ISA comes with three variants with specific feedback modalities (1) visual and (2) auditory information, and (3) direct intervention to assume control over the vehicle speed.

Here, we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances, using a driving simulator. We also considered curve road geometry (curve direction: rightward vs. leftward). Sixty active drivers were recruited for the study. While half of them (experimental group) tested the three V-ISA variants (and a V-ISA off condition), the other half always drove with the V-ISA off (validation group). We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.

All V-ISA variants were efficient at reducing speeds at entrance points, with no discernible negative impact on driver lateral behaviour. On rightward curves, the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations. Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and, consequently, establishes safer driving conditions.

Damage to semi-rigid base asphalt pavement is related to improper matching of the pavement structure moduli. This study mainly focused on the modulus matching of structural layers and the development of a pavement structure optimization method. First, the modulus loss of existing pavement structures was analysed, and a three-dimensional finite element model was established based on the existing pavement. Second, the influence of the modulus of each structural layer on the mechanical response indicators and fatigue life was analysed. Based on the results, a pavement structure design method using the smoothness of the stress-strain curve as the modulus matching criterion of the structural layers was proposed. And it was found that a strain convex point was present and that the stress mutation between the structural layers was significant when the modulus matching of the pavement structure was reasonable. Further, the evaluation indicators were divided into two groups, namely, mechanical indicators and fatigue life indicators. And it was proposed an optimized pavement structure design method based on modulus matching and multi-indicator range analysis. Finally, the optimal modulus combination of pavement structure was determined by this method. The research systematically studied the influence of the modulus of each structural layer on the mechanical response and fatigue life of the pavement, and proposed the concept and specific executive criteria of modulus matching for the first time. Meanwhile, it also provided an effective optimization method for pavement structure design.

The carbon emissions arising from road pavement infrastructures have emerged as critical issue in recent years. The life cycle of a pavement can be divided into five phases, namely raw materials and production, construction, use, maintenance and end of life. While the use phase generates the highest carbon emissions throughout the pavement's life cycle, it is usually neglected in most pavement life cycle assessment (LCA) studies due to its complexity and uncertainty. Therefore, this review selected 126 relevant references, focuses on quantification methods, influential factors and reduction technologies of carbon emissions in pavement use phase. Among the carbon accounting approached, the LCA approach, remains the most widely used for evaluating the environmental impact of pavements. Second, the primary influential factors on the use phase' carbon emission include pavement-vehicle interaction primarily affected by pavement roughness, pavement albedo and climate change. Most influential factors above indirectly cause changes in carbon emissions by influencing the pavement performance and subsequent vehicle emissions. Finally, the review surveys carbon emission reduction technologies during pavement use phase, focusing mainly on reducing pavement rolling resistance and constructing cool pavements. Reflective pavements and permeable pavements are the most widely used cool pavement technologies. Overall, the aspects involved in this paper hold significant promise for quantifying and reducing carbon emissions in the pavement use phase.