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Real-world traffic flow data often contain missing values, which can limit its usability. Although existing deep learning-based imputation methods have shown promising results by reconstructing observed values, they often overlook certain missing patterns in the dataset and perform worse on filling real missing values. This paper addresses this issue and proposes a novel masking method called PatternwiseMissingMix (M-Mix) for masked modeling-based traffic flow data imputation. M-Mix generates masks by mixing existing missing values in the target datasets to preserve the missing pattern information, thereby enhancing the performance of imputing real missing values. Additionally, a dual-objective loss function is proposed for model optimization, which predicts masked values for higher robustness and reconstructs observed values to maintain semantic correctness. Through extensive experiments on real-world datasets, M-Mix consistently demonstrates superior performance compared to other masking methods. [ABSTRACT FROM AUTHOR]

Urban traffic flow prediction plays a crucial role in intelligent transportation systems (ITS), which can enhance traffic efficiency and ensure public safety. However, predicting urban traffic flow faces numerous challenges, such as intricate temporal dependencies, spatial correlations, and the influence of external factors. Existing research methods cannot fully capture the complex spatio-temporal dependence of traffic flow. Inspired by video analysis in computer vision, we represent traffic flow as traffic frames and propose an end-to-end urban traffic flow prediction model named Spatio-temporal Decoupled 3D DenseNet with Attention ResNet (ST-D3DDARN). Specifically, this model extracts multi-source traffic flow features through closeness, period, trend, and external factor branches. Subsequently, it dynamically establishes global spatio-temporal correlations by integrating spatial self-attention and coordinate attention in a residual network, accurately predicting the inflow and outflow of traffic throughout the city. In order to evaluate the effectiveness of the ST-D3DDARN model, experiments are carried out on two publicly available real-world datasets. The results indicate that ST-D3DDARN outperforms existing models in terms of single-step prediction, multi-step prediction, and efficiency. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) aerial sensors are an important means of collecting ground image data. Through the road segmentation and vehicle detection of drivable areas in UAV aerial images, they can be applied to monitoring roads, traffic flow detection, traffic management, etc. As well, they can be integrated with intelligent transportation systems to support the related work of transportation departments. Existing algorithms only realize a single task, while intelligent transportation requires the simultaneous processing of multiple tasks, which cannot meet complex practical needs. However, UAV aerial images have the characteristics of variable road scenes, a large number of small targets, and dense vehicles, which make it difficult to complete the tasks. In response to these issues, we propose to implement road segmentation and on-road vehicle detection tasks in the same framework for UAV aerial images, and we conduct experiments on a self-constructed dataset based on the DroneVehicle dataset. For road segmentation, we propose a new algorithm C-DeepLabV3+. The new algorithm introduces the coordinate attention (CA) module, which can obtain more accurate segmentation target location information and make the segmentation target edges more continuous. Also, the improved algorithm introduces the cascade feature fusion module to prevent the loss of detail information in road segmentation and to obtain better segmentation performance. For vehicle detection, we propose an improved algorithm S-YOLOv5 by adding a parameter-free lightweight attention module SimAM. Finally, the proposed road segmentation–vehicle detection framework is utilized to unite the C-DeepLabV3+ and S-YOLOv5 algorithms for the implementation of the serial tasks. The experimental results show that on the constructed ViDroneVehicle dataset, the C-DeepLabV3+ algorithm has an mPA value of 98.75% and an mIoU value of 97.53%, which can better segment the road area and solve the problem of occlusion. The mAP value of the S-YOLOv5 algorithm has an mAP value of 97.40%, which is more than YOLOv5's 96.95%, which effectively reduces the vehicle omission and false detection rates. By comparison, the results of both algorithms are superior to multiple state-of-the-art methods. The overall framework proposed in this paper has superior performance and is capable of realizing high-quality and high-precision road segmentation and vehicle detection from UAV aerial images. [ABSTRACT FROM AUTHOR]

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments. [ABSTRACT FROM AUTHOR]

Pedestrian monitoring in crowded areas like train stations has an important impact in the overall operation and management of those public spaces. An organized distribution of the different elements located inside a station will contribute not only to the safety of all passengers but will also allow for a more efficient process of the regular activities including entering/leaving the station, boarding/alighting from trains, and waiting. This improved distribution only comes by obtaining sufficiently accurate information on passengers' positions, and their derivatives like speeds, densities, traffic flow. The work described here addresses this need by using an artificial intelligence approach based on computational vision and convolutional neural networks. From the available videos taken regularly at subways stations, two methods are tested. One is based on tracking each person's bounding box from which filtered 3D kinematics are derived, including position, velocity and density. Another infers the pose and activity that a person has by analyzing its main body key points. Measurements of these quantities would enable a sensible and efficient design of inner spaces in places like railway and subway stations. [ABSTRACT FROM AUTHOR]

The LT‐bridge construction method for the material‐saving and fast construction of post‐tensioned concrete bridges Owners of infrastructure buildings will face enormous challenges in the coming years. Many engineering structures, especially bridges, need to be renovated or rebuilt. They are either no longer able to withstand the current loads or have developed durability problems. Discussions with owners have shown that rapid construction progress is crucial for bridge replacements. The construction sites should disrupt the normal flow of traffic as little as possible. As a result, a new, resource‐saving, and fast construction method for bridges has been developed. Innovative prefabricated elements for the longitudinal girders and the deck slab were developed to fulfill these requirements. The prefabricated elements are connected by reinforcement and an in‐situ concrete layer at the construction site. A comparison of the environmental impact based on the construction materials (life cycle phases A1–A3) between a construction section of an LT‐bridge and a typical German motorway bridge shows a lower global warming potential for the new bridge construction method due to the savings in construction materials. The feasibility of this new construction method was demonstrated by a pilot project in Austria. [ABSTRACT FROM AUTHOR]

Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing trajectory data extracted from an instrumented vehicle study, this research attempts to propose a gated recurrent unit neural network model to predict responses of vehicles interacting with two leading vehicles simultaneously. The recurrent neural network model can illustrate realistic human-like following behavior of drivers, much better than the classical optimal velocity-based models in terms of trajectory reproducing accuracy. The model can also explain the closing-in, shying-away behavior and local stability properties. Results of the study provide insights into the driving behavioral phenomena of disorderly traffic flows and can contribute to the development of a realistic microsimulation model, smarter autonomous systems, and in-traffic safety evaluation as well. [ABSTRACT FROM AUTHOR]

In order to unravel the physical and mathematical mystery of synchronized-flow mechanism and to reveal the fundamental mechanism and origin of synchronized flow produced by nonlinear stochastic processes, we have produced simple stochastic traffic flow models (the gradual-NaSch, Phoenix and mPhoenix models) with nonlinear safe speeds. In the mNaSch model and our gradual-NaSch model, the i th vehicle's speed is the same as or different from the j th vehicle's speed because they are discrete values. In the mNaSch and gradual-NaSch models, the same discrete values of the i th and j th states make it easy to identify synchronized flow with speed-synchronized phase of the i th and j th states. On the other hand, when we deal with synchronized flow in continuous traffic flow models, we face a problem. Continuous values cause the difficulty in identification of synchronization. In order to definitely clarify whether or not synchronized flow occurs in continuous models, we have established a novel idea of Exchange approach to recognizing synchronized flow as speed-synchronized phase. Our idea is generated from the analogical image that the whole system of synchronized metronomes does not change even if the i th and j th synchronized metronomes are exchanged. The Exchange approach (exchanging the i th vehicle's state for any j th vehicle's state in the whole system of traffic flow), which causes distortion such as a collision if non-synchronized vehicle's states are exchanged and makes it possible to definitely clarify whether or not synchronized flow occurs in continuous models, is applied to our simple stochastic continuous model (the mPhoenix model). On the basis of the Exchange approach, we can recognize that the mPhoenix model surely reproduces synchronized flow. In addition, we have proposed mathematical approach to deriving nonlinear safe speeds which guarantee collision free driving and reproduce synchronized flow. [ABSTRACT FROM AUTHOR]

This study explored whether a policy with maximum velocity limitation prior to each jam point in a traffic flow system can mitigate jam situations in a highway. To quantify this question, a new cellular automata traffic model based on the Revised S-NFS model was established. We perused two specific scenarios: a jam naturally brought by a high traffic density, and one brought by a lane-closed section placed as an explicit bottleneck. The result of multi-agent simulation (MAS) reveals that such maximum velocity limitation policy can certainly mitigate jam situations when each jam is naturally bought up not by a bottleneck, as proved by several statistics, and cannot improve the traffic flux. [ABSTRACT FROM AUTHOR]

Starting from a nonlocal version of a classical kinetic traffic model, we derive a class of second-order macroscopic traffic flow models using appropriate moment closure approaches. Under mild assumptions on the closure, we prove that the resulting macroscopic equations fulfill a set of conditions including hyperbolicity, physically reasonable invariant domains and physically reasonable bounds on the speed with which the waves propagate. Finally, numerical results for various situations are presented, illustrating the analytical findings and comparing kinetic and macroscopic solutions. [ABSTRACT FROM AUTHOR]

Because of the high nonlinearity and complexity, it is still a challenge to forecast traffic flow accurately. Most of the existing methods, which lack the ability to model the dynamic spatio-temporal correlations of traffic flows, rely on a priori knowledge of the graph structure to capture static spatial correlations. However, spatial correlation can be dynamic and the physical structure does not reflect the true relationship between roads. To better capture complex spatio-temporal dependencies and predict traffic conditions on road networks, a new forecasting model based on Transformer and Diffusion Graph Attention Network (T-DGAN) is proposed. This model adopts the Transformer encoder-decoder architecture, where the encoder and decoder contain multiple Spatio-Temporal Convolutional Network Blocks (ST-CB) and Diffusion Graph Attention Blocks (DGA-B), which are utilized to capture the spatio-temporal dependency and the dynamic spatial correlation of traffic flow separately. In addition, an information auxiliary module is added to the decoder to summarize the traffic flow information between the encoder and the decoder. Experiments on different datasets verify that the T-DGAN model has achieved higher forecasting performance than the baseline methods. In PEMS03, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of T-DGAN to 60 min are improved by 4.28% and 3.66%, compared with the state-of-the-art model, respectively. [ABSTRACT FROM AUTHOR]

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called 'two-step cluster analysis' is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables. [ABSTRACT FROM AUTHOR]

In the backdrop of an ever-expanding urban transportation road network, the dramatic changes in traffic flow make traffic flow forecasting become a challenge. Which encompass intricate spatial correlations and intricate non-linear temporal relationships. Given a complex time series of traffic flow, accurately predicting traffic flow under such circumstances becomes an arduous task, particularly for long-term sequences. To tackle this conundrum, we put together a traffic prediction framework called the multi-scale attention-based graph convolutional recurrent network (MAGRN). Based on GCN and RNN, this framework skilfully extracts dynamic spatial and temporal features from node attributes. By incorporating multi-scale feature extraction and two attention mechanisms within the attention structure, MAGRN adeptly captures features at varying granularities. This enables MAGRN to diligently capture both the temporal interdependencies and spatial correlations inherent in traffic sequences, thereby minimizing errors. Experimental validation on three real-world traffic datasets unequivocally attests to the superior performance of the MAGRN model. [ABSTRACT FROM AUTHOR]

To enhance the efficiency and stability of traffic flow in the vicinity of traffic signals under mixed traffic, a macroscopic model considering the characteristics of the vicinity of traffic signals is proposed from the perspective of cyber-physical systems (CPS). In this model, the effects of traffic lights and vehicle lane changing behavior are analyzed. Among them, the remaining time of the signal light, the distance between the vehicle and the signal light, and the urgency of lane change are considered to build a macro model. Finally, the simulation results show that compared with the traditional macroscopic model, the proposed model can improve the stability of traffic flow in the vicinity of traffic signals and effectively alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology's efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads. [ABSTRACT FROM AUTHOR]

A very important class of models widely used nowadays to describe and predict, at least in stochastic terms, the behavior of many-particle systems (where the word "particle" is not meant in the purely mechanical sense: particles can be cells of a living tissue, or cars in a traffic flow, or even members of an animal or human population) is the Kinetic Theory for Active Particles, i.e., a scheme of possible generalizations and re-interpretations of the Boltzmann equation. Now, though in the literature on the subject this point is systematically disregarded, this scheme is based on Markov Chains, which are special stochastic processes with important properties they share with many natural processes. This circumstance is here carefully discussed not only to suggest the different ways in which Markov Chains can intervene in equations describing the stochastic behavior of any many-particle system, but also, as a preliminary methodological step, to point out the way in which the notion of a Markov Chain can be suitably generalized to this aim. As a final result of the discussion, we find how to develop new very plausible and likely ways to take into account possible effects of the external world on a non-isolated many-particle system, with particular attention paid to socio-economic problems. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model's performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets. [ABSTRACT FROM AUTHOR]

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a "teacher-student" distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the "parallel-fusion" structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns. [ABSTRACT FROM AUTHOR]

• Pedestrians present a "last-second" check before crossing initiations. This head-turning pattern in front of AVs aligns with real-world observations, suggesting the potential for AVs to predict pedestrians' crossing intent using head movements. • Absence of a zebra crossing leads to a more conservative pedestrian behaviour, with significantly increased head-turning before crossing initiations. • Pedestrians present fewer head turns when the eHMIs are present, suggesting that eHMIs play a role in conveying extra information and reducing the necessity for pedestrians to seek additional confirmation of the AV's intentions. • A learning effect is observed, that pedestrians show a decreased number of head turns over time. • A higher rate of head-turning is seen for AV-non-yielding scenarios, indicating more active head movements during potential hazards. In the future, Automated Vehicles (AVs) may be able to use pedestrians' head movement patterns to understand their crossing intentions. This ability of the AV to predict pedestrian crossing intention will improve road safety in mixed traffic situations and may also enhance traffic flow, allowing the vehicle to gradually reduce its speed in advance of a yield, eliminating the need for a complete and erratic halt. To date, most of the work conducted on studying pedestrian head movements has been based on observation studies. To further our understanding in this area, this study examined pedestrians' head movements when interacting with AVs during a range of road crossing scenarios, developed in a VR environment. Thirty-eight participants took part in this CAVE-based pedestrian simulator study. Head movements were recorded using stereoscopic motion-tracking glasses, as pedestrians crossed the road in response to an AV which approached from the right (UK-based road). A zebra crossing was included in half of the trials to understand how it affected crossing behaviour. The effect of different approaching speeds of the AV, and the presence of an external Human-Machine Interface (eHMI), on head movements and crossing behaviour was also studied. Results showed that the absolute head-turning rate (change in pedestrians' head-turning angle, per frame) increased significantly at around 1 s before a crossing initiation, reaching a peak at the crossing initiation, where pedestrians presented a "last-second check" before the crossing decision. Another increase in absolute head-turning rate to the right was seen at the end of the crossing (∼1.5 s after crossing initiation), to check the proximity of the approaching vehicle. A higher rate of head-turning was also seen for AV-non-yielding scenarios. Finally, the least number of head turns was seen for the yielding conditions which included an eHMI, in the presence of the zebra crossing. These results show the value of infrastructural and vehicle-based cues in assisting pedestrians' crossing decisions and provide an insight into how head-turning behaviour can be used by AVs to better predict pedestrians' crossing intentions in urban settings. [ABSTRACT FROM AUTHOR]

• Digital billboards can greatly impact drivers' behavior, leading them to neglect the leading vehicle, pedestrian crossings, red lights, and traffic flow when overtaking. • Four factors significantly influenced the likelihood of driver distraction: the driver's crash history, time of day, driver's age, and road type. • Drivers with prior crash experience approached DBs with more caution than those without such a history. • Young adults were more susceptible to distraction from DBs compared to older drivers. • Drivers were more prone to distraction at night compared to daytime, and more likely to be distracted at intersections compared to other road types. The objective of this current study was to evaluate the impact of digital billboards (DBs) on self-reported and observed driving behavior, given their established association with distracted driving. This investigation focused on driver behavior in Iran using a dual-pronged approach. Initially, self-reported driving behavior was analyzed using a Driving Behavior Questionnaire (DBQ), which was completed online by 453 drivers. The factor analysis of the questionnaire data emphasized the significant role of DBs in generating driving errors, lapses, unintentional violations, and intentional violations. The DBQ questions exhibited a clear factor structure, demonstrating high factor loadings and satisfactory internal stability. The findings indicated that advertising signages notably influenced drivers' behavior, particularly in instances of neglecting the behavior of the leading vehicle's driver (Lapse), disregarding pedestrian crossings (Error), disregarding red lights (Intentional violation), and overtaking without considering traffic flow behind (Unintentional violation). Subsequently, participants engaged in an Instrumented Vehicle Study (IVS) to explore observed driver behavior when encountering DBs (899 samples). Four factors were identified as significantly influencing the likelihood of driver distraction: the driver's crash history, time of day, driver's age, and road type. A logistic regression analysis was conducted using the IVS data, revealing that drivers with prior crash experience approached DBs with 8.8 times more caution than those without such a history. Moreover, young adults were 8.25 times more susceptible to distraction from DBs compared to their older counterparts. Notably, the findings suggested that drivers were nearly four times more prone to distraction at night compared to daytime. Additionally, drivers were twice as likely to be distracted at intersections compared to other road types. The outcomes of this study can offer insights for policy interventions regarding the content and placement of DBs, aiming to minimize their impact on road safety while still enabling advertisers to target their intended audience. [ABSTRACT FROM AUTHOR]

• Stated preference survey among 1,000 respondents in the Philadelphia and the Seattle metro areas, two areas with a wide range of built environments and pedestrian behavior. • Descriptive data analysis shows pedestrians felt less safe with driverless cars on the road. • Pedestrians preferred disabling driverless function and capping speeds of driverless cars in urban core to increasing enforcement of jaywalking laws. • Respondents' current crossing behavior might serve as an indicator of their cross behavior in the presence of driverless cars. • Multilevel binomial logit shows pedestrians were more likely to jaywalk in the presence of driverless cars in high visibility and low traffic volume, and were deterred by bigger, faster streets. When interacting with driverless cars, pedestrians might be emboldened to jaywalk knowing the vehicles will slow down for them automatically. Increased jaywalking might disrupt traffic flow and pose challenges for traffic law enforcement. Few studies examined pedestrians' attitudes toward driverless cars' safety implication, management strategies for the interactions between pedestrians and driverless cars, and how pedestrians would expect their street crossing behavior to change in the presence of driverless cars. We conducted an online stated preference survey of 1,000 residents in the Philadelphia and the Seattle metropolitan areas to investigate respondents' attitudes toward driverless cars and their preferences of jaywalking under different driverless car saturation levels. Through descriptive data analysis and multilevel binomial logit analysis, we found that overall pedestrians felt less safe with driverless cars on the road. Pedestrians preferred limiting the speeds of driverless cars or disabling driverless functions in areas with heavy pedestrian traffic to increasing enforcement for jaywalking laws. Multilevel binomial logit analysis showed pedestrians were more likely to jaywalk in the presence of driverless cars in high visibility and low traffic volume, and were deterred by bigger, faster streets. Respondents' current crossing behavior might serve as an indicator of their crossing behavior in the presence of driverless cars. Our findings remind cities that sound street design strategies could help to ensure safer interactions between pedestrians and driverless cars and greater acceptance of driverless cars on urban streets. [ABSTRACT FROM AUTHOR]

Network traffic classification is a fundamental and intricate component of network management in the modern, high-tech era of 5G architectural design, planning of resources, and other areas. Investigation of traffic classification is a key responsibility of traffic engineering in SDN. SDN is a network programmability technology used in 5G networks that divides the control plane from the data plane. It also points the way for autonomous and dynamic network control. SDN needs data from the classification system's flow statistics to apply the appropriate network flow policies. To control the volume of heterogeneous network traffic data in 5G network service, the network administrator must implement a carefully supervised traffic investigation system. This study uses machine learning techniques to examine alternative ways of handling heterogeneous network traffic. The suggested approach is Ensemble Learning for Automated Network Traffic Categorization. i.e., CatBoosting for Automated network traffic classification for multiclass (Cat-ANTC) predicts traffic categorization and offers a higher prediction accuracy than individual models and a more regularized model formalization to decrease over-fitting and boost efficiency. The Cat-ANTC is evaluated using benchmark network traffic datasets that are openly accessible and contrasted with current classifiers and optimization methods. It is clear that when compared to the currently used ensemble techniques, the suggested ensemble methodology produces promising outcomes. Additionally, the proposed method is tested and shown to perform better than the classification of traffic flow using the current model. [ABSTRACT FROM AUTHOR]

In urban Vehicular Ad-hoc Networks (VANETs), disseminating traffic data efficiently is challenging due to the dynamic and complex nature of the network. Multi-hop-based broadcasting approaches are commonly used to address this issue. However, selecting the optimal relay nodes poses a challenge and directly impacts network performance. Existing relay selection strategies, such as beacon-based methods, have limitations in scaling under different traffic conditions. This paper proposes a multi-attribute relay selection strategy for urban multi-hop VANETs to overcome these challenges. The strategy evaluates the rebroadcast capability of each receiving node based on its real-time status. It utilizes a fuzzy-BCM-based weight estimation strategy to determine the contribution of each attribute to the node's capability. The node with the highest broadcasting capability is given priority to access the channel and broadcast the data packet. The proposed scheme is evaluated through simulation tests in VANET simulation environments, considering various traffic flow and speed variations. Performance comparison with four benchmark methods is conducted. The results show that the proposed scheme improves the overall dissemination efficiency by 51.1% compared to the benchmarked methods. [ABSTRACT FROM AUTHOR]

Real-time traffic prediction uses past data to anticipate traffic volume. The volume of traffic in the region may be estimated using interpolation and extrapolation from library data by the trend structure. It is based on a prediction model with a linear function. From this, the distance-relevant procedures are used to conduct the vehicle traffic flow system. To improve forecast accuracy for managing traffic flow and representing the traffic pattern in the trip route, an adaptive model of optimum learning was presented for missing traffic flow predictions. To categorize and forecast the traffic flow from the database for this model, Adaptive Wildfire Optimization (AWO) with the AI method is suggested. It chooses the best features from the database's overall properties to outperform the conventional classification model in making predictions. Spatial Pattern Super Learning (SPSL), a paradigm for enhancing pattern learning, is presented to increase learning accuracy. By comparing the suggested model's overall outcomes with those of other cutting-edge techniques using statistical factors, the findings may be confirmed. [ABSTRACT FROM AUTHOR]

The short-term traffic flow prediction can help to reduce flight delays and optimize resource allocation. Using chaos dynamics theory to analyze the chaotic characteristics of en-route traffic flow is the basis of short-term en-route traffic flow prediction and ensuring the orderly and smooth state of the en-route. This paper takes the time series of en-route traffic flow extracted from Automatic-Dependent Surveillance Broadcast (ADS-B) measured data as the research object, uses the improved C–C method to reconstruct the phase space, and uses the improved small data volume method to calculate the Lyapunov index to identify the chaos phenomenon of en-route traffic flow. In order to avoid the interference of chaos phenomenon on traffic prediction, the Wavelet Neural Network (WNN) model is established to predict the traffic flow at en-route points. The experimental shows that when the number of iterations is 10,000, the average accuracy of WNN prediction is 0.87173, and the average running time is 6.9335334 s. According to the experimental results, it can be seen that the smaller number of iterations has more advantages in running time, which greatly reduces the overall running time. At the same time, it indicates that appropriately increasing or reducing the number of iterations in this experiment has little effect on the results. [ABSTRACT FROM AUTHOR]

The hydrodynamic model can be used to describe traffic problems in transport. When the speed of the first car is less than the speed behind it, it leads to traffic jams. When the first car's speed is faster than the cars behind it, it leads to traffic evacuation. If we consider the first car to be a piston, then the speed of the piston will cause traffic jams and traffic evacuation. In this paper, we study the piston problem for the hydrodynamic model. The formation and propagation of shock wave, rarefaction wave, delta-shock wave, and vacuum can describe the phenomena of traffic jams, traffic evacuation, severe traffic jams, and traffic evacuation with traffic volume of zero, respectively. Therefore, for different traffic phenomena, we prove the existence of shock solution, rarefaction solution, delta shock solution, and vacuum solution. In addition, we perform some representative numerical simulations. [ABSTRACT FROM AUTHOR]

Alternative intersection designs can provide cost-effective solutions to overcome the proven inadequacy of conventional approaches. Several studies have assessed the performance of alternative designs against a range of traffic volumes and geometric design aspects, each in isolation, but a model which can factor in multiple variables into the analysis is the identified research gap. The displaced left-turn – DLT intersection design was found to be the most versatile, efficient, and transferable to locations elsewhere in the world. In this paper, a displaced right-turn intersection – a variant of DLT, was modelled for a range of traffic flows and design conditions. Regression models were developed for Practical Reserve Capacity and Delay as dependent variables with traffic flow, proportion of right-turning traffic, signal cycle time and length of displaced turn as explanatory variables. These models can provide relatively quick preliminary estimates of the performance indicators before committing to resource-consuming junction remodelling works. [ABSTRACT FROM AUTHOR]

When a work zone is located at an intersection, it greatly reduces the capacity and change the traffic flow characteristics. However, the impacts of work zones have not attracted much attention, and the signal control method of the intersection with a work zone has not been investigated yet. This paper focuses on a specific type of work zone, which is located within the area of an intersection. The saturation flow rate model, traffic wave model, traffic delay model, and emptying time model are proposed to capture the traffic flow characteristics of the intersection with an island work zone, and a signal control model is further put forward. The real data is collected to calibrate, validate, and evaluate the proposed models. The results indicate that the proposed signal control model can reduce about 15.6% of queue length and 17.2% of traffic delay for the intersection with an island work zone. [ABSTRACT FROM AUTHOR]

The weaving section on the freeway will be an active bottleneck due to vehicles' mandatory or discretionary lane-changing (LC) conflicts. In the early stages of the development of intelligent connected transportation, destination information collection of weaving vehicles is possible, which provides an opportunity to improve performance in weaving traffic. In this paper, a simulation system with coordinated strategies embedded to satisfy the LC needs is presented under the weaving traffic flow. First, an algorithm is proposed to evaluate the balance between the demand for LC vehicles and the available gaps provided in weaving traffic, which can be adjusted by variable speed limit (VSL) and ramp metering strategies. Second, to validate the proposed algorithm, a typical weave section with a merge and a diverge connected through a single auxiliary lane is researched, and a simulation platform based on cellular automata has been developed. The optimal control strategies are obtained by model predictive control (MPC) and taking the minimum travel time as an objective. Third, the advantages of coordinated control strategies are studied and discussed in traffic efficiency improvement and the success of LC proportions under different combinations of weaving ratios and large vehicle traffic mix. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is pivotal when constructing intelligent cities to enhance urban mobility and to efficiently manage traffic flows. Traditional deep learning-based traffic prediction models primarily focus on capturing spatial and temporal dependencies, thus overlooking the existence of spatial and temporal heterogeneities. Heterogeneity is a crucial inherent characteristic of traffic data for the practical applications of traffic prediction. Spatial heterogeneities refer to the differences in traffic patterns across different regions, e.g., variations in traffic flow between office and commercial areas. Temporal heterogeneities refer to the changes in traffic patterns across different time steps, e.g., from morning to evening. Although existing models attempt to capture heterogeneities through predefined handcrafted features, multiple sets of parameters, and the fusion of spatial–temporal graphs, there are still some limitations. We propose a self-supervised learning-based traffic prediction framework called Traffic Prediction with Self-Supervised Learning (TPSSL) to address this issue. This framework leverages a spatial–temporal encoder for the prediction task and introduces adaptive data masking to enhance the robustness of the model against noise disturbances. Moreover, we introduce two auxiliary self-supervised learning paradigms to capture spatial heterogeneities and temporal heterogeneities, which also enrich the embeddings of the primary prediction task. We conduct experiments on four widely used traffic flow datasets, and the results demonstrate that TPSSL achieves state-of-the-art performance in traffic prediction tasks. [ABSTRACT FROM AUTHOR]

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called "progressive pretraining (PPT)." We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, N O x , P M 2.5 , and weather data for Oslo, Norway. I considered a total of six datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution. [ABSTRACT FROM AUTHOR]