Transportation sound carries critical environmental situational awareness, yet it remains an underutilized modality in Intelligent Transportation Systems (ITS). To fully exploit this acoustic dimension, this paper proposes a Physics-Aware Safety Framework for Transportation Sound (PASF-TS). This framework aims to establish an active safety defense line for autonomous agents through pure acoustic parsing, specifically targeting extreme conditions where vision is severely degraded (e.g., dense fog or unlit night environments). First, the proposed framework quantifies the underlying acoustic signal structure across three dimensions—type, magnitude, and duration —to construct models for kinematic perception, spatial field perception, and energy morphology, thereby generating a multidimensional physical risk field. Second, to overcome the limitations of singular physical metrics and achieve a category-agnostic global assessment, this study introduces a physical confidence vector that reflects the real-time reliability of each module, dynamically outputting a continuous total system safety risk value. Subsequently, by integrating reinforcement learning, this risk value is mapped into absolute safety boundaries for the autonomous agent. An acoustic-driven risk aversion model based on the Soft Actor-Critic (SAC) algorithm is developed, effectively resolving control dilemmas in Non-Line-of-Sight (NLOS) environments. Finally, in-depth quantitative risk analyses are conducted on common transportation sounds (braking, honking, sirens, and collisions). The framework's feasibility for active collision avoidance is comprehensively validated in typical game-theoretic scenarios, including straight single-lane roads and unsignalized intersections under visually constrained conditions. This research provides a novel paradigm for the active safety of autonomous vehicles and offers robust theoretical and engineering support for the intelligent management of multi-modal transportation systems.

This paper presents a coarse-to-refined method for segmenting road curbs from Mobile Laser Scanning (MLS) point clouds, encompassing both organized and unorganized data. The proposed method consists of four parts: point cloud pre-processing, coarse curb segmentation, refined curb segmentation, and global curb verification. Part I aims to reduce the number of irrelevant points and establish appropriate local coordinate systems for subsequent curb segmentation tasks. Part II extracts two-dimensional (2D) projection features to obtain coarse curbs. Part III introduces the universal conic section model and proposes k-neighborhood straight-line features for refined curb segmentation. Part IV develops the curb matching and segmentation rationality judgment rules to establish correspondences between left and right curbs, thereby accurately extracting and distinguishing curbs on both sides of the road. Experimental results demonstrate that the proposed method achieves desirable curb segmentation within a broad range of parameter settings in both typical and complex road scenarios.

3D road reconstruction plays an essential role in road information extraction; however, the existing 3D point cloud registration methods cannot provide high accuracy and robustness for 3D road reconstruction. We propose a multi-direction registration framework for 3D road reconstruction using vehicle-borne LiDAR and integrated navigation system (INS) data. The framework includes five modules. Module I acquires road point clouds with rough geographical locations. Module II presents a novel key point matching algorithm to reduce the displacement deviation along the travel direction. Module III constructs a dual-plane sliding window to extract curb and pavement point clouds. Module IV develops a plane-to-plane registration algorithm for rough pavement registration. Module V extracts the intersection line between the pavement and curb for curb registration. Experimental results demonstrate that our method can achieve state-of-the-art performance in reconstruction accuracy and technical robustness.

Automated pavement crack detection is crucial to supporting fine pavement maintenance and ensuring safety for road facilities. Due to the complex pavement condition and crack features, it is still a critical challenge in intelligent pavement surveys. This paper proposed a novel pixel-level pavement crack segmentation network, PCSNet, to provide a solution to this challenge. The network has richer attention and hybrid pyramid structures, which implement full-process crack feature fusion and enhancement. The richer attention module consists of cascaded self-attention and attention gate modules. It captures the crack spatial dependence information and prunes the feature response. The hybrid pyramid structures consist of a multistage convolutional pyramid module and a pyramid pooling module. It integrates contextual information at multiple receptive field scales to enhance the potential crack feature representation. The proposed structure enriches the crack details and optimises the scene parsing on the global geometry of the cracks. A sizeable 3D pavement crack dataset is built for training and testing. The proposed network exhibited the best performance, achieving F1-score, mean intersection of union, and mean pixel accuracy of 81.21%, 77.13%, and 87.17%, respectively. The network can reconstruct the complete crack geometry, preserve the crack edges well, and optimises the detection of shallow and complex cracks. The method exhibits superior and robust performance, facilitating accurate pavement technical condition assessment and maintenance decisions.

Context information, which plays a crucial role in many computer vision tasks, can benefit deep networks to construct better cognitive competence from more comprehensive surrounding. However, most existing crowd counting methods often overlook the importance of context information extracted from both global and local views. To address or alleviate this problem, this paper proposes a novel Context Attention Fusion Network, which is abbreviated as CAFNet for crowd counting. The core idea behind CAFNet is the interaction of multiple context information, including local context, cross-level context, and cross-layer context. To explore local context, we design a local context aggregation module to extract hierarchically local semantic information and then integrate them adaptively. To utilize cross-level context, a guidance attention fusion module is designed to fuse low-level feature map as the guidance of high-level context information so that the spatial details can be effectively compensated. To make full use of cross-layer features, a multi-layer context fusion module is developed to exchange the potentiality of multi-layer information to generate a high-resolution density map. Experimental results on four challenging datasets manifest that the newly proposed CAFNet can deliver impressive results compared with other state-of-the-art crowd counting models.

This study aims to utilize the hybrid solid-state LiDAR (SSL) point cloud to achieve pavement 3D reconstruction and overcome the stratification problem of the pavement point cloud. A low-cost Mobile Laser Scanning (MLS) system based on the hybrid SSL is constructed beforehand. Then, a region of interest (ROI) extraction algorithm is designed to filter the useless point cloud. Finally, a plane-based global registration (PGR) approach is proposed to address the stratification problem of vertical direction. PGR includes two parts: coarse registration and refined registration. The coarse registration extracts the pavement plane to reduce the vertical error. The refined registration utilizes the Iterative Closest Point (ICP) algorithm to reduce the registration error further. Compared with the mainstream registration algorithms, PGR shows the highest registration accuracy in different road scenarios and maintains excellent real-time performance. Furthermore, the reconstruction accuracy is kept within 1 cm, sufficient to extract pavement information.

Risky driving behaviors are one of the key contributors to traffic accidents. The rapid and accurate identification of them is important to improve the safety of the driving environment. This study introduces a context-aware framework for the evaluation of risky driving behaviors based on trajectory data. It consists of three models to identify the context, determine risky maneuvers, and evaluate risky driving behaviors. We first propose a surrogate-based method to label risky maneuvers considering context factors. Then, the features of driving trajectories are extracted as the input features for the evaluation of risky behavior. Based on the labeling result and maneuver features, supervised machine learning algorithms are leveraged to model their relationships for evaluations. Three feature extraction methods and five classifiers are compared in this article to select the most suitable one. Last, a context-aware evaluation framework is proposed to recognize risky driving behaviors incorporating context. The trajectory data extracted from unmanned aerial vehicles are used to validate the proposed framework. The results show that the accuracy of risky driving behaviors evaluation could reach 97%. The proposed framework in this study can effectively evaluate risky driving behaviors based on trajectory data with the consideration of context factors.

为了准确、快速地识别路面多病害,采用一种基于多分支框架的深度学习方法,提取并融合路面图像的大、小尺度特征,将路面二维图像和三维图像作为网络输入,增强病害特征.采集裂缝、条状修补、块状修补、坑槽、松散等沥青路面病害图像共计10 562张,进行人工标注.结果表明:500次训练后该方法的平均交并比为0.83,准确率和召回率的调和平均数F值为0.90,优于U-net、PSPNet、DeepLabv3+等方法;在单一类别上,对条状修补、坑槽、松散、桥接缝等分割效果最优,对裂缝、块状修补的识别展现出较强的鲁棒性;所提方法的识别效果高于仅使用单一输入或者单一分支的方法.因此,双通道和多分支的设计方法可以显著提升网络对多类别路面病害的识别精度.

This study aims to investigate the effects of risk factors affecting high-speed railway delay using Bayesian networks. At first, we determined risk factors based on real time data about high-speed railway delay in China. Then, the Bayesian network structure model of the delay of high-speed railway was established according to expert experience and Dempster-Shafer evidence theory. The established Bayesian network structure model was modified by test for conditional independence in the next step. Finally, the posterior probability of each contributing factors in the Bayesian network was calculated based on the real time data. Compared to other statistical analysis methods, Bayesian network took interaction of risk factors into consideration by analyzing the correlation and influence degree of influencing factors under incomplete information. It has been found that the device failure is the most significant factor leading to the delay of the train, among which, failure of automatic train protection, platform door and catenary are the three specific factors that affect the delay most. The findings in this study provide useful and valuable information for high-speed railway operation managers to take effective countermeasures to reduce the delay rate of high-speed railway.

Traffic safety for hazardous material (hazmat) transportation has not been studied well at a macro level in recent years. A Bayesian negative binomial conditional autoregressive safety model was used within Chinese provinces and cities. A total of 1,229 hazmat transportation crashes in China were collected from the years 2015 to 2017. The frequency of hazmat transportation crashes and the frequency of severe crashes including fatalities and serious injuries were studied in relation to socioeconomic factors, road classification, and the scale of hazmat transportation. The results show that higher crash frequencies are associated with a greater gross domestic product index, increasing road densities, and number of hazmat transportation vehicles and hazmat drivers per vehicle. The frequency of severe crashes tends to be higher in provinces with greater populations, increasing road densities, mileage of low-grade roads, and number of companies. The urban road mileage and number of hazmat loaders are negatively associated with the total number of hazmat crashes and severe crashes. Additionally, the hospital density also has a negative correlation with the frequency of severe traffic crashes. These results could help hazmat transportation managers and planners determine the risk factors of hazmat crashes on a macro level and develop appropriate measures for improving hazmat transportation safety.

Autonomous vehicles (AVs) have made significant progress in recent years, including entering the testing stage on urban roads. However, there still is a relative lack of knowledge on the public acceptance of AV road tests in their cities. This paper fills this gap by modeling the survey data collected by BikePGH in Pittsburgh. Special attention was paid to two types of typical vulnerable road users, bicyclists and pedestrians. An ordered probit model was built to investigate the factors associated with bicyclists’ and pedestrians’ willingness to support their city as an AV proving ground. The model results indicated that adults aged 65 years and older were more likely to support AV testing than those aged 25 - 44 years old. Bicyclists’ and pedestrians’ attitudes on the perceived safety and benefits of AVs were positively associated with the willingness to support their city as an AV proving ground. Also, we applied a mediation model to explore the suppressor variable, finding that the influence of interactive experience on bicyclists’ and pedestrians’ willingness to support AV tests in their city was mainly suppressed by the perceived safety of AVs. We recommend AV companies protect the rights of vulnerable road users when testing their AVs on proving grounds.

Revealing urban community structures of a city is of great importance for investigating urban development and sprawl behind the movement dynamics. However, most studies focus on delineating urban community structure and its variations with a single transit mode without covering hierarchical travel distances. This paper proposes an overarching framework to reveal urban community structures by fusing multisource spatiotemporal transportation data. Network science methods and community detection are applied to construct spatially embedded networks and uncover the urban structure from different perspectives, using 1-week transportation data derived from the metro, taxi, and dockless bike-sharing systems (BSSs) of Shanghai, China, in year 2016. Our finding shows that Shanghai can be clustered into six primary communities and exhibits polycentric patterns with strong monocentric characteristics. Shanghai's urban structure moves toward an embedded hierarchical pattern: the dispersed monocentric structure and the centralized polycentric structure. It reflects poor functional interdependence and horizontal connectivity between communities. Beneath the complex and coupled travel-flow system, the metro dominants the basic framework of the urban community structure and contributes to form the prototype of the core community, while the taxi and BSS tend to play complementary roles like expanding, enhancing, and refining the structure. This research not only provides a promising bridge from the complex urban transportation networks to urban community structures, but also implies potential urban planning policies from an internal and comprehensive perspective.

Predicting lane change maneuvers is critical for autonomous vehicles and traffic management as lane change may cause conflict in traffic flow. Most existing studies do not consider the effect of traffic context (i.e., traffic level and vehicle type) on lane change maneuvers. Therefore, these models cannot adapt to different traffic environments. This study aims to address this problem and establish an integrated lane change prediction model incorporating traffic context using machine learning algorithms. In addition, lane change decisions and lane change trajectories are both predicted to capture the whole process, which have been less studied. The framework of the proposed model contains two parts: the traffic context classification model, which is used to predict traffic level and vehicle type, and the integrated lane change prediction model, which is used to predict lane change decision with XGBoost and lane change trajectories with LSTM incorporating context information. Instead of considering lane change, we establish trajectory prediction models for left lane change and right lane change, further improving the prediction accuracy. The naturalistic trajectories of the highD dataset are used to train and validate the model. The results show that the proposed model improves the accuracy from 97.02% to 98.20% when predicting lane change decision that incorporate traffic context. In addition, the MSE decreases from 11.21 to 6.62 when predicting trajectories. The proposed models are also validated on NGSIM dataset, proving the adaptability of the model. The proposed model can be applied to different environments to reduce collision risks caused by lane change maneuvers and improve traffic management and driving safety.

现行国家标准未对非错台型桥头沉降的检测方法作出规定,而现有方法主观低效、普适性低且易受路面病害干扰.针对上述问题,利用车载式检测系统快速采集道路纵坡数据与路面图像,对纵坡数据和行驶距离进行数据预处理后形成道路纵断面线,并将其与图像位置进行了对应.基于此建立了一种非错台型桥头沉降计算模型与算法,其中提出沉降点占比SPR这一计算指标.在3种车速下,分别对各15组距离梯度进行了试验.结果表明:车载式检测系统符合国家规程有关纵坡自动化检测的误差要求.同时对试验对象选取和计算模型取值作出了规定,在宁波境内对100座桥开展了试验.结合试验数据,利用所建模型与算法输出了200组(上、下桥)SPR,并将其与人工测量的桥接缝前后200组纵坡差绝对值进行了相关性和回归分析,两者的相关系数为0.9574,回归模型决定系数为0.9166,表明所提方法的输出结果与人工试验结果间有很强的相关性,且回归模型精度较高.因此,检测方法对于检测和定量描述非错台型桥头沉降状况是科学合理的,有效填补了桥头沉降检测的研究空白.

考虑中转旅客在换乘过程中的流程时间、捷运时间及步行时间建立了"换乘紧张度"的参量,评价登机口分配对于中转旅客换乘的影响.以中转旅客总换乘紧张度最小、航班分配失败率最低及登机口使用数量最少为优化目标,并考虑航班分配的登机口类型约束、航班冲突约束等条件,建立多目标多约束的中转旅客登机口分配优化模型,并采用生物地理学优化算法求解模型.研究结果表明:登机口分配优化模型与先到先服务原则下的登机口调度方案相比,成功分配航班的数量增加54架次,提高了12％,且宽体机分配成功率为100％;旅客换乘紧张度在1.0之内比例为70％,成功换乘人数增加了30％;总换乘紧张度降低了25％.

Autonomous vehicles (AVs) are expected to eliminate many driver errors to save thousands of lives. As vulnerable road users, pedestrians and bicyclists are more likely to be injured or killed in accidents involving cars. Therefore, understanding the perceptions of vulnerable road users is crucial for developing and deploying AVs and solving public concerns because the acceptance of AVs relies on both AV users and other road users, such as vulnerable road users. This paper analyzed two surveys collected in 2017 and 2019 by BikePGH in Pittsburgh to understand vulnerable road users' perceptions of AVs in different years. The analysis showed that vulnerable road users' interactive experiences with AVs increased from 2017 to 2019 in Pittsburgh, and the interactive experiences with AVs positively affected vulnerable road users' perceived safety and receptivity toward AVs in 2017 and 2019. Specifically, vulnerable road users' perceived safety of AVs significantly increased from 2017 to 2019, while their receptivity toward AVs did not change significantly during that period. Additionally, autonomous driving accidents negatively affected vulnerable road users' perceptions of AVs. Therefore, we recommend that policymakers provide opportunities for the public to interact with AVs and guarantee vulnerable road users' safety and benefits in AV testing.

道路坡度是建立车辆排放模型和选择生态路线的重要因素,然而由于坡度数据采集难度较大,在车辆排放模型构建和车辆导航系统的路径优化中往往被忽略.文中以CO2和NOx两种典型的车辆尾气为例,分别研究了路面坡度对轻型车和重型车尾气排放的影响,论证了路面坡度的重要性.车辆尾气排放数据由OBEAS-3000便携式排放测试仪采集,道路坡度使用车载GPS记录的高程数据进行估计.研究结果表明:在-5％ ～5％ 的坡度范围内,两种车型的CO2和NO x排放量均随路面坡度的增大呈现上升趋势;特别是当路面坡度大于1％ 时,两种气体的排放量随坡度增大快速增长.此外,轻型车辆的排放对道路坡度更为敏感,而重型车辆尾气排放的绝对增量远大于轻型车辆,忽略道路坡度会导致车辆尾气排放估计误差,尤其是对轻型车尾气排放影响较大.因此,在车辆尾气排放评估和生态路径选择规划中,不应忽略道路坡度的影响.

Escalator-related injuries have become an important issue in daily metro operation. To reduce the probability and severity of escalator-related injuries, this study conducted a probability and severity analysis of escalator-related injuries by using a Bayesian network to identify the risk factors that affect the escalator safety in metro stations. The Bayesian network structure was constructed based on expert knowledge and Dempster-Shafer evidence theory, and further modified based on conditional-independence test. Then, 950 escalator-related injuries were used to estimate the posterior probabilities of the Bayesian network with expectation-maximization (EM) algorithm. The results of probability analysis indicate that the most influential factor in four passenger behaviors is failing to stand firm (p = 0.48), followed by carrying out other tasks (p = 0.32), not holding the handrail (p = 0.23), and another passenger's movement (p = 0.20). Women (p = 0.64) and elderly people (aged 66 years and above, p = 0.48) are more likely to be involved in escalator-related injuries. Riding an escalator with company (p = 0.63) has a relatively high likelihood of resulting in escalator-related injuries. The results from the severity analysis show that head and neck injuries seem to be more serious and are more likely to require an ambulance for treatment. Passengers who suffer from entrapment injury tend to claim for compensation. Severe injuries, as expected, significantly increase the probability of a claim for compensation. These findings could provide valuable references for metro operation corporations to understand the characteristics of escalator-related injuries and develop effective injury prevention measures.

With the increasing demand of hazardous material (Hazmat), traffic accidents occurred frequently during Hazmat transportation, which had caused widespread concern in communities. Therefore, a good understanding of Hazmat transportation accident characteristics and contributing factors is of practical importance. In this study, 1721 Hazmat accidents that have occurred during road transportation for the period 2014–2017 in China were examined, and a random-parameters ordered probit model was established to explore the influence of contributing factors on the severity of accidents by accounting for unobserved heterogeneity in the data. Both the injuries and the number of people evacuated were considered as the indicator of accident severity and investigated, respectively. Results show that higher injury severity is likely to be associated with type of Hazmat (compressed gas, explosive, and poison), misoperation, driver fatigue, speeding, tunnel, slope, county road, dry road surface, winter, dark, more than two vehicles, rear end crash, and explosion. As for the correlation between risk factors and the severity of evacuation, type of Hazmat (compressed gas, explosive, and poison), quantity of Hazmat (10–39 t), misoperation, county road, dry road surface, weekdays, dusk, explosion significantly contribute to increasing the severity of evacuation of Hazmat accidents.

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.