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This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management. [ABSTRACT FROM AUTHOR]

In this paper is present the procedure for introducing the coordination of light signals at five intersections on the Boulevard "St. Kliment Ohridski" in Skopje. The primary purpose of a traffic signal is to assign the right-of-way to intersecting traffic streams for the purpose of ensuring that all streams are served safely and without excessive delay. A properly designed and timed signalized intersection will minimize fuel consumption, delay, and stops without having an adverse effect on safety. Travelers will realize one or more of the following benefits at intersections where the traffic signal is needed, properly designed, and well timed: orderly movement of traffic, increase in the traffic-carrying capacity of the intersection, reduction in the frequency and severity of certain types of crashes, progressed traffic when traveling in a coordinated signal system, and interruption of heavy traffic flow to provide safe opportunities for minor movements. Coordinated signal timing synchronizes traffic movements and manages the progression speed of specific modes where uninterrupted flow is desired along a corridor. While traditionally applied to increase vehicular traffic flow and reduce peak-hour delay, coordinated signal timing can also be optimized for slower speeds, creating an uninterrupted flow for bicyclists or low vehicle progression speeds for a pedestrian-friendly downtown. Signals may also be timed to coordinate transit headways along routes where regular transit service is consistent and has low variability. Coordination of traffic signals can reduce the number of stops along a corridor and provide for a continuous flow of traffic at the target speed. Progression speeds should be set at or below the target speed, rather than existing 85th percentile speeds. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Exact Sciences is the property of Master Editora and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents the initial experience (spring-summer 2023) of using artificial neural networks (ANN) in improving traffic management in the large Russian city of Tyumen. Using the example of one of the intersections of the city's road network, it is shown how much transport delays are reduced when the duration of the traffic light cycle phases is quickly adjusted to the actual traffic intensity when compared with the usual previously used traffic light predictive mode. For the specific intersection of Odesskaya and Kotovskogo streets in Tyumen, considered in this article, the traffic light control mode using an ANN can significantly (by 20.6 ... 22.4%) reduce the average delay time of vehicles. It is also important that the reduction in traffic delays, which is possible with the regulation of traffic using ANN, helps to reduce stress for road users and improve road safety. The article presents historical data illustrating the dynamics of changes in the field of traffic management and road safety in Tyumen. This information well confirms the thesis about the dialectic of systemic development and the need for a gradual increase in the intellectual component in traffic management in large cities. The Applications (Appendix A and Appendix B) present the code of the auxiliary procedures and functions module and the code of the main data collection module used to optimize the traffic light control mode at the experimental intersection of the Tyumen road network. The main conclusion of the study is that use of an ANN allows taking into account a much larger number of factors and optimizing the control of the entire object, consisting of several intersections, which is not achievable using predictive modes and local adaptive control. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Road intersections are shared among several conflicted traffic flows. Stop signs are used to control competing traffic flows at road intersections safely. Then, driving rules are constructed to control the competing traffic flows at these stop sign road intersections. Vehicles must apply a complete stop with no motion in front of stop signs. First to arrive, first to go, straight before turns, and right then left are the main driving rules at stop sign intersections. Drivers must be aware of the stop sign's existence, the architecture of the road intersection, and traffic distribution in the competing traffic flows. This is to make the best decision to pass the intersection or wait for other conflicted flows to pass according to the current situation. Due to bad weather conditions, obstacles, or existing heavy vehicles, drivers may miss capturing the stop sign. Moreover, the architecture of the road intersection and the characteristics of the competing traffic flows are not always clear to the drivers. In this work, we aim to keep the driver aware ahead of time of the existing stop signs, the architecture of the road intersection, and the traffic characteristics of the competing traffic flow at the targeted destination. Moreover, the best speed and driving behaviors are recommended to each driver. This is based on his/her position and the distribution of the existing traffic there. A driving assistance protocol is presented in this paper based on vehicular network technology. Real-time traffic characteristics are gathered and analyzed of vehicles around the intersections. Then, the best action for each vehicle is recommended accordingly. The experimental results show that the proposed driving assistant protocol successfully enhances the safety conditions around road intersections controlled by stop signs. This is by reducing the percentage of accident occurrences. Fortunately, the traffic efficiency of these road intersections is also enhanced; the accident percentage is decreased by 25% upon using the proposed protocol. [ABSTRACT FROM AUTHOR]

Railway traffic control (RTC) equipment are technical devices developed for maintaining the safety of rail vehicle traffic operation, and must satisfy certain requirements related to a specific level of traffic safety and effectiveness. They are designed to ensure operational reliability under the intended application conditions throughout their entire service life. Regardless of their type and kind, however, they must behave in accordance with the assumed functional rules and requirements. This is why periodic tests of their correct operation are so crucial. Functional tests are applied to this end. They are employed in the course of device design and approval for use as well as in equipment operational diagnostics. Previous methods for generating test sets and their processing have been intuitive and often rely on the experience of testers that have been acquired in the course of implementing other tests. Such tests may be incomplete or fail to take into account all studied functions. Therefore, this article presents a proprietary method for generating functional tests covering railway traffic control devices that improve their readiness. The authors describe a test determination procedure algorithm and specify the conditions to be met by a test set and by optimization criteria. The article ends with a computer-based exemplification of the developed functional test generation method. [ABSTRACT FROM AUTHOR]

Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants. [ABSTRACT FROM AUTHOR]

The last few decades have been characterised by the rapid development of technology and various industries around the world, as well as increasing levels of urbanisation. The development of technology has resulted in the emergence of many inventions, vehicles and equipment. This has resulted in many unforeseen consequences generating threats to the environment in which humans' function. One of these hazards is the noise emitted by vehicles. For this reason, environmental protection was created in response to threats to human existence (this work is to determine the level of noise resulting from traffic transportation in the city of Pila. In order to achieve the given objective in the city, sound values will be adopted and an acoustic map will be developed on their basis. A large amount of noise in the city of Pila is caused by road transportation, but from the results of the study it can be said that it is minimally exceeded only on two streets by less than five decibels and on three other streets where it is equal to the permissible noise value. The main places where the most noise is generated are two-lane roads and traffic lights and roundabouts. A lot of noise is generated when there are a large number of vehicles slowing down and starting in these places. A very big plus in the fight against noise in Pila is public transportation. [ABSTRACT FROM AUTHOR]

To meet the demand for cooperative signal control at oversaturated heterogeneous traffic flow intersections containing CAVs and HVs, cooperative control including dedicated CAV lanes has been explored to improve intersection safety capacity and reduce vehicle delays while avoiding uncertain HV driving behaviour. However, this approach does not fully exploit CAV network connectivity advantages and intersection spatial and temporal resources. Here, an oversaturated heterogeneous traffic flow signal control model based on a variable virtual waiting zone with a dedicated CAV lane is proposed. Within the model, CAVs going straight or left share a dedicated CAV lane, a CAV variable virtual waiting zone is within the intersection ahead of the dedicated CAV lane, and CAVs and HVs share the straight-through lane. The model framework has three layers. The upper layer optimizes the barrier time using a rolling time domain scheme. The middle layer optimizes the phase duration and variable virtual waiting zone switching time based on the fixed phase sequence, returning the vehicle delay to the upper optimization model. The lower layer performs CAV grouping and trajectory planning in the dedicated CAV lane based on signal timing and variable virtual waiting zone duration, returning the CAV delays to the middle level. [ABSTRACT FROM AUTHOR]

Intelligent traffic signal control is one of the important means to ensure traffic safety. Effective signal control can make traffic flow fast and smooth, which first needs current and future traffic information. As the control of one intersection may affect adjacent intersections, this paper proposes to predict future traffic flow with consideration of multi-intersections. It can dynamically improve traffic conditions and reduce traffic congestion. Based on various nonlinear spatial relationships at correlated multi-intersections and the potential time-dependent relationship in traffic volume, a traffic flow prediction method named CNNformer which combines transformer with CNN, is proposed. The convolution neural network (CNN) and transformer are used to extract the spatial and temporal features of correlated multiple intersections. The learnable time code is embedded into transformer's location code, and the location information and time information are injected into the model to help it learn the time characteristics of traffic volume. This study also considers the impact of cyclical traffic flow pattern and proposes CNNformer+. In the method, all of the data from the previous time window, as well as the data from the prior week and month, are correspondingly entered into the network. Finally, the output is generated through average pooling, realizing the learning of cyclical traffic flow characteristics. In the experiment, CNNformer+ and the state-of-the-art traffic flow prediction methods are compared using simulated data. The results show that the proposed model outperforms the existing models in prediction accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights' phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles' speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed. [ABSTRACT FROM AUTHOR]

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model's training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections. [ABSTRACT FROM AUTHOR]

Signal timing parameters are essential components in traffic signal control (TSC). It affects not only traffic management, but also traffic safety. However, due to the confidential issues of the traffic management department, or lack of data integration from different signal manufacturers, it is intractable to obtain the city‐scale signal timing data. In the previous studies, some existing estimation methods focused on a single parameter and fixed‐timing scheme. To tackle this issue, this study attempts to develop an integrated parameters inference method based on license plate recognition (LPR) data, considering phase weight, average phase duration information and the overall phases of the intersections. In particular, the proposed method includes phase sequence inference model, cycle length inference model and phase duration inference model. To testify the performance of the proposed method, a real‐world LPR dataset from Guangzhou, China, is applied. Numerical results show that the proposed method performs more efficiently on parameter inferences than the state‐of‐the‐art (SOTA) approach. For instance, in the given research time period, the mean absolute error (MAE) of each phase duration is 2 s averagely (6.32 s in the SOTA approach), and the mean relative error (MRE) of cycle length is 0.91% (11.67% in the SOTA approach). [ABSTRACT FROM AUTHOR]

The article offers information on the campaign precipitates from Mohave County, Arizona, study and discovery of disproportionate rise in crash frequency versus vehicle miles traveled (VMT) in areas common to high tourist and recreation activity.

There are regular developments and changes in cities. Developments in cities have affected transportation, and traffic control tools have changed. Traffic signs and traffic lights have been used to direct pedestrians and vehicles correctly. Traffic light control systems are used to ensure the safety of vehicles and pedestrians, increase the fluency in traffic, guide them in transportation, warn pedestrians and drivers, and regulate and control transportation disruptions. In order to facilitate people's lives, it is desired to control the traffic components autonomously with the developments in autonomous systems. Cyber threats arise due to the active use of the internet and signals or frequencies in the use of modules that will provide communication with traffic lights, traffic signs, and vehicles, which are traffic components at the inter-sections of many roads in the control of central systems. The study is limited to smart traffic lights, which are traffic components. If we examine the cyber-attacks, we can see that Malware Attacks, Buffer Overflow Attacks, DoS attacks, and Jamming Attacks can be made. Network-Based Intrusion Detection Systems and Host-Based Intrusion Detection Systems can be used to detect and stop Malware Attacks, Buffer Overflow Attacks, DoS attacks, and Jamming Attacks. Intrusion detection systems tell us whether the data poses a threat or does not pose after the data passing through the system is examined. In this way, system protection is ensured by controlling the data traffic in the system. [ABSTRACT FROM AUTHOR]

It is of practical importance for individual vehicles to automatically identify traffic signal light status to facilitate their decision makings for traffic safety performance enhancement and operation efficiency maximization when they are approaching intersections, especially in near-future fully-or semi-autonomous vehicle-penetrated traffic systems. Traditional methods aim to directly extract information from the light emitting area of a traffic signal light head, but this can easily result in missed or false signal light status identification due to changes in detection distance and lighting conditions. To address these research gaps, an innovative computer vision-based algorithm is developed for signal control light status identification and countdown time recognition. Based on the unique characteristics of black traffic light boards, color segmentation is conducted in both RGB and HSV channels, and traffic light boards and countdown time display boards are located through cascade filtering algorithms. In the HSV color space, the H component can be extracted and highlighted to determine the current signal status by distinguishing the distribution of the pixel points of the H component. Based on the nearest neighbor interpolation algorithm, an adaptive threading method is developed to improve countdown time recognition. The experimental tests were conducted to verify the effectiveness of the developed approach under various scenarios with acceptable recognition accuracy and execution efficiency. The developed approach can be applied and integrated with other detection and control algorithms to strengthen vehicle-crossing safety performance and operational efficiency at intersections. [ABSTRACT FROM AUTHOR]

Summary. Intersections, for vehicles coming from different directions, are conflict points in road networks. When a driver approaching a signalised intersection encounters the yellow light, he/she is in a dilemma either to safely stop or to pass through the intersection during clearance time. The decision to stop or to pass may change depending on some factors such as duration of yellow light, deceleration and acceleration rate, width of intersection, speed and length of vehicle, etc. This study aims to put forth the effects of some related factors affecting the length of the Type I dilemma zone. To perform this study, five factors including vehicle speed, maximum deceleration rate, perception-reaction time, clearance time, the total intersection width-vehicle length were considered and a total of 648 different traffic cases were investigated. The study results showed that the Type I dilemma zone length increased with the increase of speed, total intersection width-vehicle length and perception-reaction time, but decreased with the increase of clearance time and deceleration rate. [ABSTRACT FROM AUTHOR]

• Thirty autonomous vehicle (AV) intersection pre-crash scenarios were identified. • The proportion of AVs being rear-ended by conventional vehicles was 58.38%. • Contributing factors of AV scenarios were identified using association rules. • Key roadway factors were intersection-related area, signal control, and on-street parking. • Insufficient stop, deceleration, and collision avoidance decisions were important causes for AV crashes. Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations. [ABSTRACT FROM AUTHOR]

• Traffic signal optimization is integrated with a comprehensive and vehicle-level trajectory planning model for mixed traffic conditions. • The low-level vehicle trajectory planning model integrates both car following and lane changing maneuvers. • The high-level model optimizes traffic signal parameters, and arrival time and lane assignment of CAVs. • A customized solution algorithm is designed to solve the complex problem. • The proposed model shows great mobility and energy benefits over fixed-time and adaptive signal control strategies. Infrastructure-assisted cooperative driving, which consists of connected and automated vehicle (CAV) trajectory planning and traffic signal control, can greatly improve the efficiency of signalized intersection operations. In existing studies, the CAV trajectory planning problem is simplified to only modeling longitudinal vehicle behaviors or assuming the lane changing can be executed instantaneously. The generated CAV trajectories are not realistic and can't be implemented at the vehicle level. Instantaneous lane changing process may also lead to safety issues or uncomfortable driving behavior of following vehicles in a mixed traffic environment. To fill this research gap, we propose an optimization framework that integrates a full CAV trajectory planning model with traffic signal control in a cooperative driving environment with both CAVs and human-driven vehicles (HDVs). A two-level optimization model is formulated based on discrete time. The high-level model optimizes traffic signal parameters, CAV arrival time, and lane assignment at the stop bar, to minimize total vehicle delay. Given arrival time and lane assignment, the low-level model generates trajectories with integrated car-following and lane-changing maneuvers to mimic a human driving policy based on imitation learning. Numerical experiments from a real-world intersection show that the proposed model outperforms adaptive and fixed time signal control by as much as 63.06% in terms of reduction of average vehicle delay, due to better utilization of lane capacity and reduced lost time. In addition to mobility benefits, fuel consumption is also significantly reduced under the proposed infrastructure-assisted cooperative driving framework. [ABSTRACT FROM AUTHOR]

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The intersection of a public road with a railway line in one level, called a rail-road crossing, is a particularly dangerous place due to the traffic flows crossing it. The regulations give absolute priority to a rail vehicle over a public road user; in addition, significant differences in the masses and speeds of moving vehicles require traffic regulation consisting of adequate passage protection. To this end, the road on which motor vehicles is equipped with road signs informing drivers that they are approaching an intersection with a railway line. The rail-road crossing, depending on the category, is equipped with signaling devices warning of an oncoming train and barriers closing the entire width of the road or part of it, operated or operating automatically. In Poland, the category of rail-road crossing, the associated method and type of security used at the crossing, is determined on the basis of relevant acts and internal regulations of the railway line manager. The paper presents an overview of the state of safety at rail-road crossings in Poland in the years 2008-2018, presents applicable regulations regarding traffic safety at rail-road crossings, and presents methods of protection of rail-road crossings. In addition, a map of concentration of events on rail-road crossings was presented for the area of the Silesian Voivodeship. The effect of the case study for the Silesian Voivodeship is to identify sensitive rail-road crossings with the highest number of events during the analysis period. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transport sector may be major responsible for directing the urban development of cities, however this possibility is overshadowed by the overuse of individual motorized modes of transport. This situation leads to problems such as congestion, accidents, pollution and increased travel times. In this scenario Traffic Engineering aligned with the perspective of sustainable urban planning appears as an essential tool to mitigate these effects.,. Studies in this area provide important data such as the number of vehicles traveling in a particular region, as well as their composition, their origins/destinations and their speed of operation. Using this information, obtained from collection and analysis of data through recognized methodologies, urban managers can make decisions about traffic intervention measures aiming to increase traffic safety and efficiency. Therefore, the present article demonstrates the planning for developing a traffic study in a traffic light controlled-intersection of Cachoeira do Sul city, as a result of a cooperation between the Federal University of Santa Maria - Campus Cachoeira do Sul and City Council, also involving practical activities of degree subjects as an active teaching/learning resource. [ABSTRACT FROM AUTHOR]

• CAV data enabled adaptive signal control strategies to enhance safety environment. • Innovated mechanism and data models to evaluate CAV data potentials for safety. • SSAM integrated into the developed data fusion models to support safety evaluation. • The proof-of-concept study suggests positive benefits from operation and safety. Given the connected and autonomous vehicle (CAV) generated trajectories as a "floating sensor" data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision. [ABSTRACT FROM AUTHOR]

The optimization of intersection signal control can improve traffic efficiency, reduce congestion degree, and improve traffic safety. Aiming at implementing the coordinated adaptive traffic signal control (ATSC) across large-scale arterial network, multi-agent reinforcement learning (MARL) has been widely concerned and lucubrated. Nevertheless, the existing MARL-based ATSC studies suffers from several limitations: (1) While most existing researches focused on the mobility performance of controlled corridor, there calls for a methodology that aims at combine multi-objective performance on traffic safety, efficiency, and network coordination simultaneously; (2) Most methods ignore the correlations between multiple agents, nor considers the spatial–temporal dependencies among the corelated neighboring intersections due to high communications requirements, which can hardly be achieved in real adaptive coordination control. To overcome the aforementioned difficulties, a multi-objective reinforcement learning model (NACRL) for network-wide coordinated signal control is proposed. Firstly, to enforce a coordinated network control with safety and efficiency considerations, a reward mechanism inspecting both traffic safety and traffic efficiency indicators was designed to achieve ideal performance in terms of mobility, safety and smooth. Secondly, the proposed NACRL conducted a centralized training-decentralized execution framework, this overcomes the critical limitation of data transmission in the field implementation while explicitly analyzing the traffic state over the entire network instead of examining each isolated intersection. Last but not least, the proposed algorithm utilized the attention mechanism to dynamically capture the sophisticated spatial–temporal dependencies over the complex arterial network, which aids the better coordinated control over multi-agents deployed at the intersections across the corridor. To testify the effeteness of the proposed algorithm, extensive experiments were implemented in both large-scale synthetic traffic grid and real-world arterial network. The experiment demonstrated that the proposed NACRL algorithm outperforms other state-of-the-art baselines with simultaneously improved performance in terms of traffic safety, traffic efficiency and network coordination, as well as improved algorithm convergence and interpretability. [ABSTRACT FROM AUTHOR]

Abstract A driver of a vehicle is a main part of "driver – automobile – road – environment" (DARE) system, sustainable functioning of which determines the effectivity and safety of the road traffic. The driver as an operatof of DARE system gets most of the information from the road, scilicet from moving and stationery objects on it, traffic signs, traffic lights, condition of the surface and traffic environment. Analysis of the majority of road accidents demonstrates that the weakest part of human-machine DARE system limiting its efficency and reliability is a human. To provide necessary reliability and safety the driver of any vehicle has to be cautious. It is maintained by the appropriate psychophysiological condition which is in turn affected by many factors. In the article the analysis of research works considering the effect of different factors on the vehicle driver's reliability is presented. The means and methods of research conducting are described. The recomendations on constructing the stand for research of the effect of driver's psychophysiological condition on road traffic safety are given. [ABSTRACT FROM AUTHOR]

• Systematic review on urban traffic control under mixed traffic with CAVs. • Cover signal timing, CAV trajectory/route planning, and their cooperative control. • Summarize mixed traffic control methods at intersections with CAV-dedicated facilities. • Outline promising research directions and challenges. Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. [ABSTRACT FROM AUTHOR]

Objective: The aims of this study were to extend the current literature on school climate that is focused on understanding how teacher, administrator, and student perceptions about driving-focused aspects of the social, educational, and institutional climate of schools can affect students' achievement, behavior, and adjustment toward the development of the concept of a school safe driving climate (SSDC) and initiate the development of tools and processes for assessing SSDC.Methods: A mixed methods approach was used to develop an initial version of a survey-based measure of SSDC that involved self-report surveys (students) and in-depth interviews (teachers). Exploratory factor analytic procedures identified SSDC constructs and a regression framework was used to examine associations among SSDC constructs and self-reported driving behaviors. Qualitative data were subjected to inductive analysis, with a goal of elucidating teachers' perspectives on SSDC and an SSDC intervention.Results: The study sample consisted of 947 adolescents (48% male) from one large high school and 44 teacher advisors. Participants were recruited from a school participating in a state-wide effort to promote transportation safety through peer-led programming. Two SSDC factors were identified-Administrative contributions to school safety and value of school safety-that were associated with adolescents' perceptions of their driving behaviors. Adolescents perceived that the intervention affected administrative safety. Teacher interviews contextualized these results and provided guidance on program revisions.Conclusions: Safe driving climate may be an important, modifiable, and measurable aspect of school climate. Additional research is needed to refine the assessment tool and to use it in longitudinal and experimental studies. [ABSTRACT FROM AUTHOR]

Objective: This study intended to investigate the interactions between accident rate and traffic signs on state roads located in Croatia and accommodate the heterogeneity attributed to unobserved factors. Data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia's Ministry of the Interior.Methods: To address the heterogeneity, a panel quantile regression model was proposed, in which a quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, and the panel data model accommodates the heterogeneity attributed to unobserved factors.Results: Results revealed that (1) low visibility of material damage (MD) and death or injury (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3) the average speed limit and the number of invalid traffic signs per kilometer exhibited a high accident rate.Conclusions: To our knowledge, this study is the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by including visibility, the present study demonstrates that low visibility causes a relatively higher risk of MD and DI. It is noteworthy that average speed limit positively corresponds with accident rate; the number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; and the number of invalid traffic signs per kilometer is significant for the accident rate; thus, regular maintenance should be performed for a safer roadway environment. [ABSTRACT FROM AUTHOR]

Highway traffic control signs are commonly used to regulate, warn, and guide road users. It is widely believed that traffic signs comprehension has a tremendous effect on traffic safety. The primary objective of this research is to investigate the relationship between drivers’ personal characteristics and their familiarity/comprehensibility with a thirty nine posted traffic signs. To this end, 400 surveys were distributed among Jordanian drivers. The results showed that the familiarity level of traffic signs is higher than comprehensibility level. On average 79%, 77%, and 83% of the drivers were familiar with regulatory, warning, and guidance traffic signs, respectively. On the other hand, only 61%, 66%, and 75% of the drivers comprehended regulatory, warning, and guidance traffic signs, respectively. “Narrow Bridge”, “Divided Roadway a Head”, “Dead End” and “Highway” received the lowest comprehensibility scores among drivers. Participants with commercial driving license had higher familiarity and comprehensibility levels than those with regular license. Drivers with a driving experience more than 11years show more familiarity and comprehensibility for traffic signs than those with less than 2 years driving experience. The number of traffic violations did not have a significant effect on traffic signs familiarity and comprehensibility. [ABSTRACT FROM AUTHOR]

A fundamental objective of traffic signal operations is the development of phasing plans that reduce delays while maintaining a high level of safety. One issue of concern is the treatment of left-turn phasing, which can operate as a protected movement, a permitted movement yielding to conflicting traffic, or a combination protected-permitted movement. Protected-only movements can improve safety of the turning movement, but they can also increase delays and congestion at intersections. Most states use criteria for left-turn phasing selection based on a threshold crash values and do not account for traffic volumes or intersection features that may influence crash frequency. This research leverages conflict points as an indicator of potential safety estimation to assist in the selection of the left-turn phasing and relates them to historical crash records. Prediction models of potential conflicts were developed through microsimulation for 200 existing intersections; hourly volume data resulted in approximately 2,300 hours of observations. The number of leftturn- related conflicts was obtained through SSAM and related to the number of crashes at each intersection. The proposed models offer a simple but realistic approach for determining the boundary conditions that influence safety when left-turn decisions are required. The models can be used to develop nomographs, which practicing traffic engineers can use for left-turn phasing decisions. [ABSTRACT FROM AUTHOR]

Urban road networks may benefit from left turn prohibition at signalized intersections regarding capacity, for particular traffic demand patterns. The objective of this paper is to propose a method for minimizing the total travel time by prohibiting left turns at intersections. With the flows obtained from the stochastic user equilibrium model, we were able to derive the stage generation, stage sequence, cycle length, and the green durations using a stage-based method which can handle the case that stages are sharing movements. The final output is a list of the prohibited left turns in the network and a new signal timing plan for every intersection. The optimal list of prohibited left turns was found using a genetic algorithm, and a combination of several algorithms was employed for the signal timing plan. The results show that left turn prohibition may lead to travel time reduction. Therefore, when designing a signal timing plan, left turn prohibition should be considered on a par with other left turn treatment options. [ABSTRACT FROM AUTHOR]

A detailed analysis of traffic conditions at the intersection allows us to obtain information about the efficiency of its operation. This type of analysis usually involves assessing the capacity and scale of the intersection and applying classical measures to assess its quality. In Poland, for this purpose, methods developed by the General Directorate for National Roads and Motorways are used to estimate the capacity of intersections without traffic lights, intersections with traffic lights, and roundabouts. The paper presents the results of the analysis of traffic conditions at the intersection of Brzezinska and Nowochrzanowska Streets, located in Myslowice (Silesian Province, Poland). The paper also examines the findings on traffic safety at this intersection. [ABSTRACT FROM AUTHOR]

In order to increase mobility and promote modal shift to walking, intersections in the city of Aarhus, Denmark, have been equipped with intelligent management of green light for pedestrians. This allows adjustment of green time based on radar detection of pedestrians in the crossing and prolongation of the green time for the pedestrians if required. The effect is examined in a before/after study of a twostage pedestrian crossing with a centre refuge island in an intersection of four-lane roads. The data consists of responses from an on-site questionnaire including 72+53 individuals and 266+318 hours of video registration of the pedestrian crossing in the before and after study, respectively. The study includes the pedestrians' experiences, the measured time of crossing and the share of pedestrians crossing at red light. The analyses of video recordings are carried out with the video analysing software RUBA. The share of pedestrians who often or frequently only reached the refuge island before red light decreased from 42% to 13%. Video data show that the pedestrians reduced their crossing at red light from 44% to 16% in the remote lane, while the percentage remained virtually unchanged on the closest lane with 16%. The mean crossing time was also reduced by 5 and 6 seconds in- and outside of the peak hour period, respectively. The reductions in mean crossing time were not statistically significant. [ABSTRACT FROM AUTHOR]

• This study defends against cyber-attacks by designing robust signal control plans. • Biobjective worst-case attack models are developed from the aspect of cyber-attacker. • Bi-level robust signal control SO models are developed under direct cyber-attacks. • An improved biobjective robust SO method is proposed to solve the bi-level models. In the cyber age, modern traffic signal control systems are inevitably exposed to a wide range of cyber-attacks, which would negatively impact traffic system performance. To defend against direct cyber-attacks that alter signal settings directly and maliciously by hacking into the wireless connected traffic control center or signal controllers, this study applies a precautionary and inexpensive measure of designing the robust signal control plan. This is realized by modelling the biobjective traffic signal control simulation-based optimization (SO) problem under cyber-attacks in a bi-level framework, with the aim of minimizing the negative impact of potential cyber-attacks on traffic efficiency and safety performance. Depending on the type of attacker with complete or incomplete knowledge on traffic signal control system, two typical bi-level models exist. Then, we propose an improved biobjective robust simulation-based optimization (IBORSO) algorithm to solve the two models, during which the sequential interaction between the attacker and the defender is seen as the Stackelberg game, and the resulting equilibrium solutions are the desired robust signal control plans to withstand cyber-attacks. An urban road network in Changsha, China, is modelled as the experimental site and numerical results show the effectiveness of IBORSO in solving the two bi-level models. Meanwhile, the solved robust signal control plans can defend against two typical attacks satisfactorily and achieve better or comparable biobjective performance compared with the field-implemented one and optimized ones by biobjective efficient global optimization (BOEGO). [ABSTRACT FROM AUTHOR]

In general, the use of superelevation and changes in a road's transverse slope are typically based on the road's design speed to provide safety and comfort for a vehicle driving on a horizontal curve. However, due to the difference between operating and design speeds, there has always been uncertainty in determining the margin of safety using superelevation. This paper discusses the assessment of the safety margins obtained from the application of superelevation in horizontal-curve design. The assessment uses geometric design guides and a reliability index to determine any uncertainties in the design parameters. In addition, adjusted design charts of superelevation values related to the radius of horizontal curves at various levels of probability of noncompliance are provided. The results show that the adjusted superelevation values are generally greater than those derived from current standards. The adjusted design charts can help designers select an appropriate superelevation value for a particular horizontal curve for highways that have geometric constraints. These adjusted charts may also aid designers in predicting the consequences and safety margins associated with the selection of superelevation alternatives required to approve geometric designs that involve violations of the standard requirements due to environmental constraints. [ABSTRACT FROM AUTHOR]

• Proposed a semi-actuated control logic for superstreet intersection. • The model uses a fuzzy-logic type 2 system and derives control parameters. • A VISSIM simulation estimates the quality of the proposed control algorithm. • The model is tested on a field superstreet under various traffic demand patterns. The paper presents a novel approach to RCUT control, based on the interval Type-2 fuzzy system (IT2FS). RCUT design, among other various alternative intersections, proposes dislocation of left and through movements from the main intersection for an increase in safety and efficient traffic conditions. This concept assumes the rise of two new U-turn intersections downstream from the main intersection that can be controlled by traffic lights. RCUT design is justified when U-turn traffic demands, along with demands from and to minor streets, are significantly lower than arterial street ones. The semi-actuated traffic control seems the most appropriate control mode that should be applied at the RCUT. Evaluation of semi-actuated traffic control based on IT2FS is compared with other traffic control modes. The results show that semi-actuated traffic control, based on IT2FS, can generate statistically better results than other well-known controls. [ABSTRACT FROM AUTHOR]