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The emerging 5G network has been revolutionizing the data transfer across the networks. Recently, advanced technologies transform the healthcare industry. With the emergence of 5G technology, smart and ubiquitous healthcare systems are interconnected with high speed and low latency. As ubiquitous healthcare applications require stringent network requirements, managing network traffic and resources becomes difficult. Internet of Medical Things (IoMT) devices generates a large volume and variety of data, including physiological signals, images, and videos. As many medical applications require real-time communication and data transfer, this puts high demands on the network, which needs handle low latency, high availability, and high reliability. Achieving consistent QoS is challenging due to the inherent limitations of conventional methods which are susceptible to network congestion and latency issues. The next generation of Software Defined Networking (SDN) in IoMT can play a vital role in supporting traffic management. Reliable classification and forecasting will enable the SDN controller to make optimal routing decisions dynamically. Therefore in this paper, a novel Medical Traffic Forecasting framework based on Weighted Multivariate Singular Spectrum Analysis (MTF-WMSSA) is proposed for an SDN-enabled IoMT healthcare network to analyze and forecast network traffic and ensure accurate real-time medical data transfer. The evaluation was conducted on the EHMS dataset to study the performance of the proposed method. The comparison of the proposed MTF-WMSSA is made with classical methods such as SVR and LSTM. The result shows that the proposed MTF-WMSSA exhibits improved classification accuracy of 93%. The network parameters that are forecasted are average traffic load, average packet arrival rate and average jitter. The MAPE of the multi-step ahead forecast is 13.64 and 11.41 for short and long intervals respectively. The forecasting algorithm proposed in this paper can efficiently determine future flow parameters which help to implement adaptive traffic engineering. The evaluation was conducted on the dataset to study the performance of the proposed method. The result shows that the proposed MTF-WMSSA exhibits better accuracy for multi-step ahead forecast and outperforms the classical methods.

As a result of the ubiquitous network applications and services, exacerbated by the overarching digital revolution the need and demand for efficient and dependable connectivity solutions have surged to unprecedented levels. Quality of Service (QoS)-based routing has emerged as a critical solution, enabling service differentiation, efficient resource allocation, and improved network performance. In this study, we introduce a novel Genetic Algorithm-powered QoS-aware Cross-Network Traffic Engineering framework, GATE-BC, at the confluence of Software Defined Networking (SDN) and Blockchain (BC) technologies. GATE-BC orchestrates end-to-end (E2E) QoS traffic, providing resource-efficient, reliable, and latency-tolerant delivery of intelligent network services in BC-enabled SDNs. Leveraging BC features such as decentralization, transparency, and immutability, GATE-BC eliminates the need for centralized entities in QoS-supported cross-network routing models. We compare GATE-BC framework with three other traffic management and engineering approaches: QoSChain (QC), Hierarchical Routing Approach (HRA), and Distributed Routing Approach (DRA). The extensive simulations reveal that GATE-BC outperforms the other routing strategies in terms of Path Setup Time (PST), Network Message Overhead (NMO), Request Acceptance Ratio (RAR), Network Bandwidth Consumption (NBC), Average Path Length (APL), and Average Network Length (ANL) metrics under various network topologies. Furthermore, GATE-BC employs three different feasible path selection strategies based on bandwidth (GATE-BC_BW), delay (GATE-BC_D), and reliability (GATE-BC_R) QoS parameters to satisfy the service levels requested.

In this paper, we discuss and examine the concept of Midpoint Optimization (MO) for Segment Routing (SR). It is based on the idea of integrating SR policies into the Interior Gateway Protocol (IGP) to allow various demands to be steered into them. We discuss the benefits of this approach when compared to end-to-end SR and potential challenges that might arise in deployment. We further develop a Linear Program-based optimization algorithm to assess the Traffic Engineering capabilities of MO for SR. Based on traffic and topology data from a Tier-1 Internet Service Provider as well as other, publicly available data, we show that, for most problem instances, this algorithm is able to achieve (close to) optimal results with regards to the maximum link utilization, that are on par with state-of-the-art end-to-end SR approaches. However, our MO approach requires substantially less policies to do so. For some instances, the achieved reduction ranges up to more than 99%. Furthermore, we show that latency bounds for individual demands can be incorporated into our algorithm without significantly worsening the quality of solutions. This is a crucial finding as the inclusion of latency bounds is a basically mandatory requirement for traffic engineering algorithms to be used in many real-world networks.

Growing private vehicle use and a limited supply of parking resources have increased competition for parking spaces. Ineffective searching for parking spaces is a significant barrier to smooth traffic flow. Smart technology-generated parking reservations are regarded as an excellent way to increase parking efficiency and lessen traffic. Although there is increased attention on smart parking research and parking reservation optimization, a scoping review is currently lacking. Therefore, the purpose of this paper is to provide a comprehensive review of the research literature related to parking reservations. Out of the original 438 publications identified in this search, 99 were eventually chosen for analysis. The review categorized parking reservations into four research topics: inventory control, allocation decisions, pricing strategies, and performance evaluation. The elements considered in these research themes, modeling techniques, and evaluation criteria are explored in detail. In addition, the challenges and further enhancements of parking reservation optimization are discussed. It is believed that the findings from this study will be beneficial in further research on parking reservations.

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world. Without relying on any infrastructure-based vehicle detectors, the authors present a scalable traffic signal re-timing system that uses a small percentage of connected vehicle trajectories as the only input. Real-world tests demonstrate that the system decreases both delays and number of stops. [ABSTRACT FROM AUTHOR]

Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency. [ABSTRACT FROM AUTHOR]

With the increasing number of urban vehicles, as well as the current situation of non-intelligent traffic control systems, spatiotemporal non-uniform traffic resource occupation, and limited traffic planning and design, existing urban traffic planning methods cannot effectively solve problems such as frequent traffic congestion and uncontrollable commuting time for residents. In order to solve the above problems, this paper first constructs a multi-queue, multi-server queuing model based on the server vacation and a multi-hop cascaded queuing model from the perspective of local intersections and global commuting paths. We analyze the theoretical changes in passage delay costs at local intersections and on global commuting paths as a function of traffic flow and the random duration of traffic signals. On this basis, this article proposes a collaborative intelligent traffic planning algorithm based on artificial intelligence, which utilizes traffic sensors to dynamically perceive traffic congestion status and collaboratively plans the optimal duration of traffic signals and the optimal driving path of vehicles from both local and global perspectives, thereby maximizing the on-time arrival ratio of vehicles while ensuring the required commuting delay. The simulation results show that the proposed method can increase the on-time arrival ratio of vehicles by at least 20% compared to contrast methods while meeting the requirements relating to commuting delays. This verifies that our method can provide support for the improvement in efficiency in future Internet of vehicles. [ABSTRACT FROM AUTHOR]

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase. [ABSTRACT FROM AUTHOR]

The alleviation of traffic congestion relies on efficient traffic control and traffic guidance, which are based on real-time short-term traffic flow prediction. In this paper, the stacked autoencoder (SAE) deep learning model with powerful feature learning capability is selected to predict the traffic flow on road sections. The process of training SAE includes the pre-training phase and the fine-tuning phase, which mainly apply the BP algorithm. However, the process of training SAE is time-consuming and cannot meet the real-time performance of modern application systems. This paper proposes a parallel training strategy for the SAE prediction model based on data parallel mode. The gradient solution process in our algorithm satisfies the conditions of parallel computing, so the training process can be designed in a parallel manner. The original dataset is distributed to some computing nodes, which are work nodes. The work node is responsible for gradient calculation using the local data. The task of the sole master node is to synthesize the gradient calculation results and then broadcast the updated gradient to each work node. The simulation results show that the SAE-based prediction model achieves better results than the traditional model, and the parallel algorithm reduces the running time of training processes. [ABSTRACT FROM AUTHOR]

The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society's constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what causes congestion. The consequences are well-known: delays, air pollution, reduced speed, and dissatisfaction (which may lead to risky maneuvers, reducing pedestrian and other driver safety). Our objective is to simulate the change in traffic patterns brought about by app users in residential areas (using navigational tools like Google Maps and Apple Maps), where the majority of navigational tools provide shortcuts that go through residential areas. In addition to discouraging navigation apps from directing drivers through residential areas during peak hours to mitigate pollution levels, by developing an algorithm based on the technology of Green Light Optimized Speed Advisory (GLOSA) and implementing it in a simulated environment (VISSIM), we can see the effect of changing the duration of red lights while keeping green lights constant. Overall, this solution can be implemented to change the times of traffic lights without the need for supplies, additional equipment, or warning signs because most cities' traffic lights are already remotely controlled. In addition, this procedure is temporary to provide some freedom and does not adhere to the speed specified for drivers who wish to pass through residential areas outside of rush hour. [ABSTRACT FROM AUTHOR]

This paper uses colored Petri nets (PN) to develop a model of vehicle movement in a city. The modeler defines the number and location of crossroads, the connections between them, and how many vehicles are at the given points of the network. The vehicles are recognized by their license plate numbers, and it is possible to determine where they start their journey and where they are going. The algorithm proposed in this paper suggests the shortest vehicle route based on Dijkstra's algorithm. This study focuses on improving route planning by considering road usage, which is determined by the starting and ending locations of vehicles (as if traffic cameras were identifying license plates). This approach will lead to optimal control of traffic lights (or vehicle navigation) to minimize traffic jams and make good use of all roads. Additionally, this paper shares the results of preliminary simulations using both colored and uncolored Petri nets in the GPenSIM environment. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

Endocytosis and endolysosomal trafficking are essential for almost all aspects of physiological functions of eukaryotic cells. As our understanding on these membrane trafficking events are mostly from studies in yeast and cultured mammalian cells, one challenge is to systematically evaluate the findings from these cell-based studies in multicellular organisms under physiological settings. One potentially valuable in vivo system to address this challenge is the vitellogenic oocyte in Drosophila, which undergoes extensive endocytosis by Yolkless (Yl), a low-density lipoprotein receptor (LDLR), to uptake extracellular lipoproteins into oocytes and package them into a specialized lysosome, the yolk granule, for storage and usage during later development. However, by now there is still a lack of sufficient understanding on the molecular and cellular processes that control yolk granule biogenesis. Here, by creating genome-tagging lines for Yl receptor and analyzing its distribution in vitellogenic oocytes, we observed a close association of different endosomal structures with distinct phosphoinositides and actin cytoskeleton dynamics. We further showed that Rab5 and Rab11, but surprisingly not Rab4 and Rab7, are essential for yolk granules biogenesis. Instead, we uncovered evidence for a potential role of Rab7 in actin regulation and observed a notable overlap of Rab4 and Rab7, two Rab GTPases that have long been proposed to have distinct spatial distribution and functional roles during endolysosomal trafficking. Through a small-scale RNA interference (RNAi) screen on a set of reported Rab5 effectors, we showed that yolk granule biogenesis largely follows the canonical endolysosomal trafficking and maturation processes. Further, the data suggest that the RAVE/V-ATPase complexes function upstream of or in parallel with Rab7, and are involved in earlier stages of endosomal trafficking events. Together, our study provides s novel insights into endolysosomal pathways and establishes vitellogenic oocyte in Drosophila as an excellent in vivo model for dissecting the highly complex membrane trafficking events in metazoan. Author summary: Endocytosis and endolysosomal trafficking are membrane-based package and delivery systems in eukaryotes for material exchanges intracellularly in-between membrane-enclosed compartments and extracellular with surrounding milieu. Current understanding of these exchange mechanisms are mainly from studies in yeast and cultured mammalian cells, but exactly how they operate in multicellular organisms under physiological conditions remain unclear. Here we focus on vitellogenic oocytes in Drosophila, which uptake large quantity of extracellular lipoproteins by a low-density lipoprotein receptor called Yolkless into the oocyte and package them into large yolk granules, a specialized lysosome, for storage and usage in later development. Using novel fly lines that allows faithful detection and manipulation of endogenous Yolkless receptor and known endosomal regulators, we show that the formation and maturation of yolk granules largely follows the canonical endolysosomal trafficking pathways, including the critical involvement of small GTPases Rab5 and Rab11 as well as distinct phospholipid species and actin networks, although the results also raise questions on the roles of other regulators including Rab4 and Rab7 in granule biogenesis. Together, this study provides novel insights into the highly complex membrane trafficking events in multicellular organisms and supports Drosophila oocyte as a useful in vivo model for such studies in the future. [ABSTRACT FROM AUTHOR]

In order to examine the significant factors of severity in the long and steep downgrade of mountain expressway, firstly, in-depth research was conducted on road traffic accident data of typical mountainous expressway long downhill sections located in Shaanxi Province, the Guangxi Zhuang Autonomous Region and Yunnan Province. A total of 2 559 accident data were obtained. Based on the above accident data, multiple Logit accident severity models were constructed to analyze the direct causes of traffic accidents on mountainous expressway long downhill sections. The analysis results show that speeding, illegal loading exceeding the limit, fatigue driving, improper operation, illegal parking, and vehicle braking failure of the driver in the accident have a significant impact on casualties on long and downhill sections of mountainous highways. The research results provide direction for improving the operational safety of mountainous highways and fundamentally seeking effective ways to prevent frequent accidents on built mountainous highways. [ABSTRACT FROM AUTHOR]

In order to better simulate the car-following characteristics, based on the full velocity difference ( FVD) model, an improved FVD car-following model based on dynamic safety distance was proposed, which considers the headway, velocity difference, velocity and acceleration between the front and rear cars. A variable time headway model was constructed to quantify the influence of front vehicle acceleration on car-following distance. The linear stability analysis of the model was carried out by applying small amplitude perturbation analysis and long-wave expansion, and the critical stability conditions of the improved FVD model were derived. The numerical simulation experiment of micro-disturbance on the annular road was designed. The characteristics of vehicle following behavior after disturbance were analyzed, and the influence of acceleration parameters on the anti-disturbance ability of the model was analyzed. The results show that considering the acceleration information of the front vehicle can reduce the fluctuation amplitude during the disturbance evolution, which is helpful to improve the stability of the traffic flow. [ABSTRACT FROM AUTHOR]

Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to take informed and timely decisions on their operation. Temporal paths are first-class citizens in this model and some classes of them have been identified in the literature. Queries aimed at finding these paths are denoted as (temporal) path queries. In spite of these efforts, many interesting problems remain open and, in this work, we aim at answering some of them. More concretely, we characterize the classes of temporal paths that can be defined in a sensor network in terms of the well-known Allen's temporal algebra. We also show that, out of the 8192 possible interval relations in this algebra, only 11 satisfy two desirable properties that we define: transitivity and robustness. We show how these properties and the paths that satisfy them are relevant in practice by means of a real-world use case consisting of an analysis of salinity that appears close to the Scheldt river in Flanders, Belgium, during high tides occurring in the North Sea. [ABSTRACT FROM AUTHOR]

• The study investigated the psychological experiences of regular commuters in traffic congestion using a three-phase sequential exploratory mixed design. • Phase 1 established a thematic network of travel adversities, psychological experiences, and consequences with 57 fundamental themes categorized and assigned under the organizing themes of travel adversity, negative affect, fight, flight, and negative road occurrences. • In phase 2, a checklist was developed listing the fundamental themes from phase 1 with Likert-type responses ranging from 0 (never) to 5 (always). • Phase 3 statistically validated the model of the vicious cycle, based on which a mathematical model was formulated for further interdisciplinary inquiries on the prediction and control of traffic behavior in real-world settings. This study investigated the psychological experiences and consequences of travel adversities during traffic congestion using a three-phase sequential exploratory mixed-methods design. Phase 1 explored the travel adversities, psychological experiences, and consequences of a sample of ten (four women and six men) regular commuters of Bangalore's congested roads using semi-structured interviews. In phase 2, a checklist was developed listing the fundamental themes from phase 1 with Likert-type responses ranging from 0 (never) to 5 (always). Phase 3 gathered data in the checklist and tested the statistical validity of the thematic model in a sample of 190 (81 women and 103 men) regular commuters. Attride-Stirling model thematic network was established with 57 fundamental themes categorized and assigned under the organizing themes of travel adversities (n = 6), negative affect (n = 28), fight (n = 7), flight (n = 6), and negative road occurrences (n = 10), in the global theme, psychological experiences and consequences. Structural equation modeling indicated that (1) negative affect significantly predicted fight and flight, (2) fight is a significant predictor of negative road occurrences, and (3) psychological experiences and consequences create a self-perpetuating cycle, with travel adversity triggering negative emotions, which results in fight responses leading to negative road occurrences, further intensifying travel adversity. A mathematical model is established based on this statistical validation, which holds potential applications in real-time traffic algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios. [ABSTRACT FROM AUTHOR]

Developing appropriate signal timing strategies is a primary concern in traffic signal control; however, professionals are constrained by predefined controller options. Existing signal controllers in North America adhere to National Electrical Manufacturers Association (NEMA) standards with ring-barrier control (RBC) design. Due to unique traffic conditions and objectives for each traffic mode, conflicts arise, making it challenging to devise suitable traffic control strategies within current controllers. The limitations include inflexibility in defining pretimed signal diagrams, modifying them, and defining algorithms for modification. This paper introduces the event-based controller (EBC) framework, implemented in DUMKA_E software, capable of replicating NEMA-based RBC functionalities while providing additional flexibility beyond conventional RBC controllers. The EBC eliminates the asymmetry of flexibilities in existing traffic controller frameworks, addressing the challenges mentioned. Overcoming critical limitations of RBC, such as barrier constraints and restricted functionalities to built-in algorithms, the EBC empowers professionals to craft custom traffic signal control strategies. Unlike approaches enhancing RBC with complex computation, the EBC offers flexible functionalities without sacrificing simplicity. The study shows that the EBC's fundamental concepts are no more intricate than RBC's, positioning the EBC as a potential traffic controller framework. The paper establishes the EBC's capability to reproduce RBC controllers' core logic, showcasing advanced abilities for more efficient control logic than RBC. The EBC's functionalities enable context-sensitive traffic signal control strategies, catering to special conditions like multi-modal activities and diverse priority requests, highlighting its full potential. [ABSTRACT FROM AUTHOR]

The first-mile service (FMS) refers to the modal choice for the first part of a commuting journey. A well-designed FMS can provide a seamless travel to access public transport services. This research was driven by the efficiency of different travel modes (e.g. taxi, shared bike, bus) in the FMS. Based on realistic travel experience, an expected utility model for each travel mode was developed. Then, a framework based on evolutionary game theory was designed to deal with the issue of passengers' dynamic interactions during the FMS. Both infinite- and finite-population scenarios were considered. The simulation results showed that walking is the first choice for passengers in comfortable weather and taking a taxi is the first choice in adverse weather. After the implementation of effective strategies, passengers' travel efficiency increases simultaneously. As such, effective strategies such as shared electric bikes, customised buses and taxi-sharing behaviour are encouraged in FMS planning. [ABSTRACT FROM AUTHOR]

Identification and evaluation of traffic congestion are critical for better management of urban traffic, especially at merge sections where two different traffic streams converge and generate shockwaves. This study evaluates the travel time reliability measure (travel time index (TTI)) as a reliable congestion measure by analysing the impact of traffic flow parameters such as traffic volume, capacity, road geometrics and conflicting traffic flows at merge sections on TTI under mixed traffic conditions. The data required for congestion analysis were collected through video recording. Statistical models were developed to: (a) evaluate the relationship between road geometric characteristics and capacity of merge sections; (b) estimate traffic density and its evolution with time and compare it against occupancy values; and (c) correlate the relationship between TTI and traffic behaviour at merge sections and determine the variations in TTI for different levels of service of merge sections. The models developed are statistically validated using secondary data sets from the same study section, as well as from data collected from other multi-lane merge sections. The developed model successfully estimated recurring congestion by considering both space and time. This study highlights the potential usage of TTIs in capturing travel time variations more accurately at merge sections under mixed traffic conditions. [ABSTRACT FROM AUTHOR]

Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control. [ABSTRACT FROM AUTHOR]

Considering the line characteristics and actual situation of Lijiang Rail Transit Line 1, this paper studies and analyzes the control schemes of signaling systems at level crossings of modern trams. The structure, functions and interfaces of the level crossing control system and the level crossing signal equipment layout design of Line 1 are described in detail, and the whole process of trams passing through level crossings is deeply analyzed. By focusing on different types of level crossings, and through the equipment layout and interaction between tram level-crossing control system and municipal traffic control system, trams are able to pass through the level crossings safely and efficiently. This paper puts forward a solution for the scenario where there are several types of level crossings on the same tram line and explains the different processing logic for trams passing through different types of level crossings to fully guarantee the safety and operation efficiency of trams. [ABSTRACT FROM AUTHOR]

In the process of promoting the use of the Centralized Traffic Control System 3.0(CTC 3.0) systems, the process management function for the operations of receiving and departing trains is particularly focused on and valued by stations due to the wide coverage of job positions and the need to coordinate multiple such positions in order to facilitate the smooth flow of the process for receiving and departing trains. This paper analyzes the business process of receiving and departing train operations in the station's transportation production process, and combines it with the CTC 3.0 process management function to explore a CTC 3.0 multi-job-position coordination technology scheme based on a wireless private network. The aim is to achieve digitization in the management of the receiving and departing train operation process at CTC 3.0 stations, thereby reducing the workload of station personnel in future practice. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a major problem in this rapidly growing world. Everyone operating a vehicle, as well as the traffic police in charge of managing the traffic, finds it difficult to become stuck in heavy traffic. For this a set, predetermined timing for traffic flow for each direction at the junction is utilized by traditional traffic light controllers. However, the concept of a fixed time traffic signal controller does not work well in places with uneven traffic. A dynamic traffic control system is therefore required, which regulates the traffic signals in accordance with the volume of traffic. This paper proposes a model that uses reinforcement learning (RL) along with deep neural networks (DNN) to manage discretions (signal status) for an environment with the help of Simulation of Urban MObility (SUMO). A simulation of real-world environment consisting a network of Four-way crossroad junction that contains 4 arriving lanes and 4 exiting lanes is used to train the agent. The main objective of this research study is to construct a model that can independently determine the best course of action and aims to provide better traffic management that will decrease the average waiting time, cause lower congestion, and provide a smooth flow of traffic. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) allows transmission, amid moving or stationary vehicles via wireless technology. Amongst several problems, safe transmission is the most important one in smart VANETs in 5G networks. Smart vehicles require integration with advanced road systems encompassing smart payment and traffic control systems. Numerous security mechanisms are used in VANETs to ensure safe communication. One such mechanism is cryptographic digital signatures based on encryption. This study introduces the new seagull optimization algorithm involving share creation with an image encryption scheme (SGOA-SCIES) for secure VANET transmissions. The goal of the SGOA-SCIES technique is to create a considerable number of shares and encrypt them to accomplish security. In the SGOA-SCIES technique, a Multiple Share Creation (MSC) scheme is employed to generate numerous share sets. For the share encryption process, the SGOA-SCIES technique engages the Fractional-Order Chaotic System (FOCS) approach to encrypt the generated shares. The optimal keys of the FOCS method can be chosen by the SGOA usage, which ameliorates the security level. The performance evaluation of the SGOA-SCIES method is examined on benchmark data. The simulations demonstrate the enhanced SGOA-SCIES methodology outcome and compare it with the ones of other existing systems and under the implementation of various measures. [ABSTRACT FROM AUTHOR]

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Network traffic classification has many applications, such as security monitoring, quality of service, traffic engineering, and so on. For the aforementioned applications, Deep Packet Inspection (DPI) is a popularly used technique for traffic classification because it scrutinizes the payload and provides comprehensive information for accurate analysis of network traffic. However, DPI-based methods reduce network performance because they are computationally expensive and hinder end-user privacy as they analyze the payload. To overcome these challenges, bit-level signatures are significantly used to perform network traffic classification. However, most of these methods still need to improve performance as they perform one-by-one signature matching of unknown payloads with application signatures for classification. Moreover, these methods become stagnant with the increase in application signatures. Therefore, to fill this gap, we propose OptiClass, an optimized classifier for application protocols using bit-level signatures. OptiClass performs parallel application signature matching with unknown flows, which results in faster, more accurate, and more efficient network traffic classification. OptiClass achieves twofold performance gains compared to the state-of-the-art methods. First, OptiClass generates bit-level signatures of just 32 bits for all the applications. This keeps OptiClass swift and privacy-preserving. Second, OptiClass uses a novel data structure called BiTSPLITTER for signature matching for fast and accurate classification. We evaluated the performance of OptiClass on three datasets consisting of twenty application protocols. Experimental results report that OptiClass has an average recall, precision, and F1-score of 97.36%, 97.38%, and 97.37%, respectively, and an average classification speed of 9.08 times faster than five closely related state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development. [ABSTRACT FROM AUTHOR]

Traffic signal priority issues have been a research subject for several decades in Poland and worldwide. Traffic control algorithms have evolved considerably during this period and have become increasingly advanced. Most of them operate within coordinated street sequences, which adds to their complexity. Tramway priority affects traffic conditions for other road users, so many aspects must be taken into account when choosing a priority solution. Typically, one of the main criteria for evaluating the effectiveness of priority is reducing travel time for the priority vehicle while ensuring that the travel times of other traffic participants through the intersection are maintained or slightly deteriorated. However, the energy aspects are often overlooked. This publication aims to investigate how local priority for tramways in traffic signals of coordinated streets affects energy consumption for tramway traction needs. The study was conducted using a microscopic modeling method with PTV Vissim software (ver. 2021). The models were built for coordinated sequences with different levels of priority. Real traffic control algorithms with priority were implemented into the model on the sequence of Marymoncka Street and Grochowska Street in Warsaw. Then, by introducing changes to the parameters of the algorithms, their effect on traffic characteristics, including estimated power consumption, was studied. The results obtained from the computer simulation were statistically processed using R software (ver. 4.3.2). The analysis results prove the effectiveness of tramway priority operation, show its impact on electricity consumption, and allow us to determine the limits of its effective application. Thus, they complement the knowledge of the impact of tramway priority on traffic. The research results also have practical value, as they help us to make rational decisions in the process of designing traffic control algorithms at intersections with a multi-criteria approach. [ABSTRACT FROM AUTHOR]

With the widespread application of large-section quasi-rectangular pipe-jacking tunnels in urban road traffic engineering in China, higher requirements have been put forward to control the influence of their construction on the surrounding environment. To scientifically evaluate the stability of large-section quasi-rectangular pipe-jacking tunnels under-passing existing box culverts, we proposed a novel viscoelastic–plastic model coupling Biot consolidation with non-stationary parameter shear creep (NPSCBCVPM) to fully characterize the coupling effect of consolidation and rheology of saturated soft soil. NPSCCBVPM was developed in Fortran as an ABAQUS user material subroutine. In addition, the NPSCBCVPM was compared with the creep tests of undisturbed soft soil and the generalized Nishihara creep model (GNCM). Finally, the proposed model was applied to the large-section quasi-rectangular pipe-jacking tunnel under-passing existing box culverts of Songhu Road in Shanghai. The results show that NPSCBCVPM are in good agreement with the creep tests of soft soil, and NPSCBCVPM can better reflect the nonlinear rheological characteristics of soft soil than GNCM. Furthermore, the proposed model can scientifically evaluate the viscoelastic–plastic stability analysis of large-section quasi-rectangular pipe-jacking tunnel under-passing box culvert. Further research should focus on developing three-dimensional NPSCBCVPM to better evaluate the spatial response of the box culvert structure and surrounding soil to the entire construction process of large-section quasi-rectangular pipe-jacking tunnels. [ABSTRACT FROM AUTHOR]

When active noise control technology is applied to traffic noise control, additional sources are often added to the facade of the barrier, and error sensors are placed in the protected area. The noise reduction effect in the area without error sensors is often ignored. In this paper, the effect of the additional source configuration on the sound field in the space without error sensors is researched. By analyzing the directivity and distribution of the sound field at the top of the barrier under various conditions, it is believed that the optimal location of the additional source is related to the height of the primary source and the barrier. An approximate model is established to evaluate the optimal location of the additional source for achieving a good noise reduction effect in a large space. Experiments are also carried out to verify the model. The conclusions are beneficial for improving the noise reduction effect in the area higher than the barrier and without error microphones. [ABSTRACT FROM AUTHOR]

Summary: Road accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning. [ABSTRACT FROM AUTHOR]

The research of traffic congestion control has been developed rapidly in recent years. Researching the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can provide some new methods for relieving the traffic congestion. In this paper, we study the bifurcation characteristics and nonlinear control of traffic flow based on the continuum model considering the new energy vehicles effect. First, a macroscopic traffic flow model considering the number of new energy vehicles and their range is proposed. Then, we transform it into a traffic flow stability model suitable for branching analysis using the traveling wave transformation. Therefore, we can transform the traffic flow problem into the stability analysis problem of systems and reflect unstable traffic characteristics such as congestion. Based on the stability model, the existence conditions of the Hopf bifurcation are proved and some bifurcation points of the traffic system are found. Second, we study the congestion and stability abrupt change behaviors near the equilibrium point and branching point to seek for the formation mechanism of traffic congestion. Finally, for the unstable bifurcation point, the control scheme is designed by Chebyshev polynomial approximation, stochastic feedback control, and other methods to delay or disappear the unstable bifurcation point and alleviate traffic congestion. It has important theoretical significance and application value. [ABSTRACT FROM AUTHOR]

the dynamic states of the road networks. In order to solve this problem, this paper proposed a deep reinforcement learning traffic light control strategy combined with fuzzy control, used SAC deep reinforcement learning to jointly optimize the phase selection and timing of traffic lights at two intersections, while considering multiple influencing factors, used fuzzy control to process the penalty function of SAC. The experimental results demonstrate that compared with the fixed cycle strategy, SAC control strategy and DDPG control strategy, the proposed traffic signal control strategy can obtain faster vehicle speed, and the fuel consumption and exhaust emissions of the vehicle are also improved. [ABSTRACT FROM AUTHOR]

Recently, the Non-Standalone Architecture option 3 standard is proposed to facilitate the deployment of 5th Generation (5G) New Radio (NR) cellular systems. According to this standard, 5G NR base stations can utilize the 4th Generation (4G) Long-Term Evolution (LTE) core network to provide network services. Additionally, the network can be configured to support LTE-NR dual connectivity enhancement, which means that a User Equipment (UE) can connect to a 4G LTE and a 5G NR base station at the same time. In such a dual connectivity network, the 4G LTE base station dispatches downlink traffic flows between the 4G LTE and 5G NR base stations for UEs. As investigated in previous works, traffic flow controls between 4G LTE and 5G NR base stations will affect network performance. In this work, we propose a downlink flow control algorithm based on the concept of Genetic Algorithm, with the objectives of maximizing throughput and fairness of throughput. We verify our designs through simulation programs and experiments on real platforms. The results indicate that the proposed solution effectively achieves the designed objectives. [ABSTRACT FROM AUTHOR]

A gain enhancement technique in pyramidal horn antenna using a slot type band pass frequency selective surface (FSS) is presented. The horn antenna without FSS has achieved a measured gain of 10.5 dBi at 10 GHz, while the antenna with the single layer slotted FSS yields an enhanced measured gain of 14.2 dBi (an improvement of 3.7 dB or 134%). Conventionally, such a gain enhancement would 5 times increase in its dimensions (flaring). However, the authors achieve the same with only 2 times increase in dimensions. Another interesting value addition of the proposed idea is that an FSS sheet can be added to the existing horn antenna to increase its gain without altering the antenna itself. Such a scheme is highly beneficial in strategic applications. The proposed gain enhanced horn antenna can be recommended to be utilised in several X‐band applications, such as radar systems (weather, surveillance, traffic control and military), satellite communications and point–point microwave links that require high gain and small form factor. [ABSTRACT FROM AUTHOR]

Short-term traffic volume prediction is crux for alleviating traffic gridlock. Considering the insufficiently extracted spatiotemporal and periodic characteristics of traffic stream in existing traffic volume forecast studies, this research presents a short-term traffic volume forecast model (WRNCLTCL) that considers spatiotemporal and periodic characteristics. Firstly, the wavelet threshold is used to denoise the initial traffic stream data. Secondly, the CNN-LSTM model is employed to capture the spatiotemporal features of the traffic stream. Considering the degradation problem that may be caused by the model with the increase of network depth, add residual neural units based on CNN. We employ the TCNLSTM model to acquire the periodic characteristics of the traffic stream. Finally, we combine the extracted spatiotemporal and periodic features, and utilize the fully connected layer to obtain the ultimate forecasted results. WRNCL-TCL is applied to real data sets in two different scenarios to validate the forecasting capability of the suggested model. Compared to the benchmark model and the ablation experiment, the consequences suggest that the proposed model exhibits favorable predictive capabilities and can serve as a theoretical foundation for traffic control. [ABSTRACT FROM AUTHOR]

In the dynamic digitalization landscape, the fusion of Internet of Things (IoT) technology with Programmable Logic Controllers (PLCs) has emerged as a pivotal avenue, promising enriched practical experiences and heightened comprehension. This paper navi-gates through recent studies elucidating the applications of IoT-based PLCs in diverse con-texts, from smart home automation to traffic control and resource management. Notable contributions include remote monitoring systems, smart traffic control, water level monitoring, and automated greenhouses. Acknowledging persistent challenges in understanding and implementing PLCs, this research introduces an innovative IoT-based PLC Trainer Kit. The kit aims to elevate prac-tical learning by providing hands-on experiences, refining skills, and optimizing applica-tions across various domains. Emphasizing the pivotal role of practical training in PLC education, the study conducts a needs analysis, followed by the design, development, and testing phases of the IoT-based PLC Trainer. The prototype, employing an Omron CPM1A PLC, pneumatic components, sensors, and an intuitive interface, undergoes meticulous testing, affirming its readiness for deployment. The development of the IoT-based PLC Trainer marks a significant achievement, ad-dressing the gap between theoretical knowledge and practical application. Through metic-ulous design, material selection, and testing, the trainer demonstrates optimal functionali-ty and user-friendliness. Anticipating its deployment in educational institutions and industrial setups, the trainer is poised to contribute to the realm of industrial automation, offering a practical understanding of PLC applications within the Internet of Things con-text. This project not only signifies technical expertise but also bridges the gap between theory and practice in the dynamic domain of IoT-driven programmable logic control-lers. [ABSTRACT FROM AUTHOR]

Intelligent Transport Systems (ITS) have emerged as an efficient solution for enhancing road utilization efficiency, ensuring convenient and safe transportation, and reducing energy consumption. This research addresses the challenging problem of accurately estimating road traffic congestion from videos recorded in low visibility and adverse weather conditions such as rainy, overcast, and sunny weather. To solve this problem, we propose a method based on the use a custom Deep Convolutional Neural Network (DCNN) that we have designed and trained to classify nighttime road traffic videos into three distinct categories. To train and test this DCNN, we used nighttime videos from the UCSD (University of California San Diego) public dataset. For comparison, we have also tested a second method, which uses an existing CNN that we have trained on the same dataset to classify road traffic into the same three categories. The performances of the two methods were assessed and compared using the UCSD nighttime dataset. The first method achieves an impressive Correct Classification Rate (CCR) of 98.91%, which is higher than the 96.18% CCR achieved by the second method. These rates surpass the 89.47% state-of-the-art CCR obtained with this dataset. This exceptional precision in estimating road traffic congestion in challenging low visibility conditions is achieved thanks to the integration of advanced deep learning techniques and CNNs. The proposed method can be integrated as part of a traffic monitoring system, thus contributing to the advancement of Intelligent Transport Systems and their potential to create cleaner, safer, and more efficient transportation networks. [ABSTRACT FROM AUTHOR]

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This paper studies the specific schemes and steps of adding three grades of protection to LTE system carrying signal services based on Xi'an Metro Line 14. Two feasible access schemes of grading protection are proposed according to the characteristics of Line 14, and the optimal grading protection access scheme is selected through network architecture and performance evaluation and comparison to ensure the security and stability of network transmission. At the same time, through the description of the relevant interface configuration and precautions of the grading protection system, the important steps in the process of adding grading protection to LTE system involving the safety of train operation are elaborated to ensure network security, provide important guarantee for traffic control of rail transit signaling system, and provide reference value for subsequent rail transit grading protection access. [ABSTRACT FROM AUTHOR]

This paper provides a detailed discussion on the implementation and effect of CHN Energy dispatching system in Xinshuo Railway. The successful deployment of this system has achieved a deep integration of CTC system and the dispatch information system, allowing them to operate in a unified manner, and improving the efficiency of transportation organization. To ensure the stability and safety of the system, potential safety risks of CTC system are thoroughly analyzed, and improvement strategies and preventive measures are proposed. In the practical application of the system, the problems are analyzed, and specific solutions and suggestions are put forward. Finally, the future development of the system in Xinshuo Railway is prospected, including centralized control, integrated interlocking, and modernized communication, etc. [ABSTRACT FROM AUTHOR]

Traffic engineering of software-defined networks (SDNs) refers to network traffic monitoring and network status analysis to improve network performance, which can be achieved by addressing a variety of issues such as routing, congestion control, flow control, and load balancing. In this paper, a new traffic engineering approach is proposed that attempts to manage the traffic of SDN by adjusting the retransmission timeout. In the proposed approach, the initial retransmission timeout is determined based on transmission and propagation delays, and then this time is adaptively adjusted according to the priority of the packets, which depends on the type of packet and the number of elapsed hops. The conducted simulations in the Mininet tool have validated the effectiveness of the proposed approach in comparison to counterpart methods in terms of average throughput, average bandwidth utilization, end-to-end delay, and packet loss ratio. Eventually, analysis of the time complexity and message complexity demonstrates that the overhead of the proposed approach is negligible. [ABSTRACT FROM AUTHOR]

The article presents the problem of the cargo transport with the example of a selected mode of transport in Poland. The volume of goods transported by air transport in 2010-2021 was analysed. Cargo transport in air transport is very popular. From year to year, there is an increase in the volume of transported goods. The coronavirus pandemic, and the related economic crisis did not significantly affect cargo transport, as was the case with passenger transport, the number of which decreased significantly during the pandemic. [ABSTRACT FROM AUTHOR]

The article describes the problem of excessive traffic concentration at signalized pedestrian crossings. The main drawback of such crossings is the inappropriate duration of green signals for vehicles. Too long green times for vehicular traffic significantly increase time lost by pedestrians, while too short ones cause short-term congestion, resulting in long queues of vehicles and a deteriorated level of service. The negative effects of both of these situations also include dangerous behavior of road users, such as red light running or crossing. Recent (June 2021) changes in the road traffic law in Poland have privileged unprotected road users at pedestrian crossings. New models and technical and design standards proposed in this country (March 2021) recommend the installation of pedestrian priority crossings at particularly dangerous spots and in urban areas with a high proportion of pedestrian traffic. All this is done mainly for traffic-calming purposes. However, previously these measures were not a common practice in Poland. Therefore, the objective of this article was to develop a simple method of green signal timing, ensuring, firstly, priority to pedestrian traffic at such places, and, secondly, sustained capacity of vehicular traffic lanes. The developed models are practical, universal and easy to adapt in other countries. They ensure a very good or good quality of vehicle traffic service, with average lost time not exceeding 25 s in a vast majority of analyzed traffic level cases. The article also describes a traffic control algorithm dependent on traffic detection. [ABSTRACT FROM AUTHOR]

This study aims to develop a methodology for evaluating the potential of passenger and delivery transportation. The method uses facility location and population distribution information to represent terminal traffic and home delivery trips, and extracts human flow trips that can be combined with home delivery trips using the Delivery Efficiency Criteria. The evaluation showed that efficiency can be maximized by selecting consolidation areas within a delivery efficiency criterion value of 0.8 to 1.0. The method allows for the visualization of Origin-Destination of difficult-to-move trips, aiding in route setting for freight and passenger consolidation transportation. [ABSTRACT FROM AUTHOR]

The article offers information on the Dominican government's presentation of the Santo Domingo Integrated Transportation System (SIT-SD), a project addressing traffic congestion and mobility issues in the capital city, with an estimated investment of 3.2 billion dollars. Topics include the initiative's focus on reducing private vehicle traffic and promoting collective transportation by connecting the entire metropolitan region of Greater Santo Domingo through a public-private partnership model.

Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications. [ABSTRACT FROM AUTHOR]

Objective: A referendum took place in Greece on the 5th of July 2015 to decide whether the suggested bailout would be accepted. Since this was the first one since 1974, the period between the referendum and the subsequent national elections was characterized by increased uncertainty and had spillover effects in many aspects of everyday life. We take advantage of this quasi-experiment to investigate the short-term impact of the referendum on vehicle collisions casualties. Methods: We use data from the daily number of injuries and fatalities caused by vehicle collisions in 2015 and employ a difference-in-differences approach, comparing trends before and after the referendum. Results: We reveal that the referendum had a short-term impact on road traffic casualties (4.14 more casualties per day), compared to what would have been expected in the absence of the referendum. Conclusions: The study provides evidence that negative emotions and anxiety, due to uncertainty, could promote dangerous driving behavior. Preventive and traffic control measures may need to be considered by policy makers during periods of uncertainty. [ABSTRACT FROM AUTHOR]