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In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer's own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers' speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. [ABSTRACT FROM AUTHOR]

The design and operations of signalized intersections are critical for the safety and mobility of all road users. Traditionally, video detection systems have surpassed conventional inductive loops in vehicle detection, offering advantages such as user-friendly interfaces, easy installation, versatile applications, and cost-effective maintenance. Recent technological advancements have expanded the capabilities of video detection systems to include automated pedestrian detection. Pedestrian timing treatment at signalized intersections typically involves two approaches: assuming pedestrian demand during each phase (pedestrian recall operations) or activating pedestrian demand when a button is pressed. Automated Pedestrian Video Detection Systems (APVDS) represent a newer approach where pedestrian calls are automatically initiated upon detecting pedestrians within the detection zone. Both pedestrian push buttons and APVDSs have their pros and cons. While push buttons can cause confusion and incorrect usage among pedestrians, APVDSs theoretically address these issues by autonomously determining pedestrian phase needs. However, challenges arise due to differing pedestrian behaviors compared to vehicles, potentially leading to missed or false calls and unsafe pedestrian actions. This study examines the impact of two APVDS issues: missed pedestrian calls and false pedestrian calls. It also compares the effects of incorrect calls under various operational strategies, including APVDS with and without controller pedestrian recycle, pedestrian recall operations, and push-button operations, with and without pedestrian recycle, across different times of the day to replicate varying vehicular and pedestrian demands. Using Vissim microsimulation software and Surrogate Safety Assessment Model (SSAM), experiments were conducted to replicate field conditions closely. This microsimulation approach enables the measurement and analysis of missed and false calls' impact on surrogate safety measures, providing valuable insights challenging to obtain in real-world conditions. The results contribute to pedestrian traffic signal strategies and enhancing pedestrian safety at signalized intersections in urban environments.

Cycling for transport is a sustainable alternative to using motorised vehicles for daily trips and is a key form of micromobility. Travel time is a critical factor influencing cycling route choice behaviour and uptake. Thus, it is important to understand the factors affecting cycling travel time and speed and their impact on cycling behaviour. In this study, an agent-based transport simulation model with heterogeneous cycling speeds was developed and used for Melbourne to study the impact of a hypothetical traffic signal optimisation intervention along six key cycling corridors. Linear regression and random forest models were used to identify factors affecting cycling speed, which informed the parameters of the agent-based model. Simulation outputs showed, on average, an increase of 4.1 % in the number of cyclists on the corridors, as existing cyclists chose to use these corridors, and an average reduction in cyclists’ moving travel time of 6.2 % for those using the intervention corridors (excluding time spent waiting at traffic signals). The findings provide insights into the effects of road attributes on cycling speed and behaviour, as well as the effectiveness of interventions aimed at reducing cycling delays. These insights are valuable for developing solutions to optimise urban infrastructure for micromobility, enhancing the efficiency and appeal of cycling as a viable transport option.

Existing research has primarily focused on the accuracy of automated pedestrian detection systems, overlooking the consequential environmental impacts arising from false or missed pedestrian detections. To fill these research gaps, this study investigates the emissions and fuel consumption resulting from incorrect pedestrian detection at signalized intersections in microsimulation. To carry out experiments, the authors employ Vissim microsimulation software and the Comprehensive Modal Emission Model (CMEM). For the first time in the literature, missed and false calls are modeled in microsimulation and their environmental impacts are accurately measured. The research highlights the limitations of current automated pedestrian (video) detection systems (APVDSs) technologies in reducing emissions and fuel consumption effectively. While APVDSs offer potential benefits for traffic management, their inability to accurately detect pedestrians undermines their environmental efficacy. This study emphasizes the importance of considering environmental impacts of APVDSs, and challenges the belief that pedestrian recall treatment is the least eco-friendly. Also, the study showed that coupling APVDS or push-button treatments with pedestrian recycle features increases fuel consumption and CO2 by 10% at the intersections with higher pedestrian demand. By understanding the emissions and fuel consumption associated with incorrect detections, transportation agencies can make more informed decisions regarding the implementation and improvement of APVDS technologies. [ABSTRACT FROM AUTHOR]

The offsets is one of the most important parameters affecting the effectiveness of coordinated traffic signal control. It is used to ensure the continuous movement of the platoon progression on coordinated segments of the road network. In addi)tion, the complexity of the task of finding the optimal values of the advance time of coordinated traffic signals on urban highways is that it is taken into account in: the time of the traffic light cycle and the time of the traffic light offset Expermental evaluation of the accuracy of analytical models for determining the advance time of the traffic signal, based on the average acceleration and the linearly decreasing nature of the change in the acceleration of a car when moving from a standstill from the stop line of the intersection in coordination plan. Using micromodeling tools (VISSIM), a simulation model of car traffic on a one-lane coordinated segment of a city highway was formed. To obtain the basic settings of traffic lights, experiments were conducted on the time of car movement in free conditions for coordinated sections of 300…800 m in length. After that, the progression platoon movement was simulated between two coordinated intersections at different values of the advance time. Based on the deter-mined minimum values of the vehicle travel time along the coordinated segment, the corresponding values of the advance time are determined, which are used to assess the accuracy of its calculation using the proposed analytical models. The results of the experimental evaluation of the analytical models for determining the advance time indicate their sufficiently high accuracy. The highest accuracy is characterized by the analytical model that takes into account the linearly decreasing nature of the change in vehicle acceleration when moving along a coordinated segment of a city high-way. The deviation of the average value of the advance time when it is used on sections whose length varies in the range of 300…800 m is 0.04 s. The presented analytical models are based on the assumption that it is necessary to ensure a smooth movement of the progression platoon and taking into account the influence of different characteristics of vehicle acceleration when driving along coordinated segments of city highways. The use of the developed analytical models makes it possible to obtain an initial starting point for adjusting the advance time of traffic signal cycles in coordination plan. [ABSTRACT FROM AUTHOR]

This paper discusses low-cost approaches capable of ranking traffic intersections for the purpose of signal re-timing. We extracted intersections that are comprised of multiple roads, defined by alphanumeric traffic message channel segment codes per international classification standards. Each of these road segments includes a variety of metrics, including congestion, planning time index, and bottleneck ranking information provided by the Regional Integrated Transportation Information System. Our first approach was to use a ranking formula to calculate intersection rankings using a score between 0 and 10 by considering data for different times of the day and different days of the week, weighting weekdays more heavily than weekends and morning and evening commute times more heavily than other times of day. The second method was to utilize unsupervised machine learning algorithms, primarily k-means clustering, to accomplish the intersection ranking task. We first approach this by checking the performance of basic k-means clustering on our data set. We then explore the ranking problem further by utilizing data provided by traffic professionals in the state of Tennessee. This exploration involves using MATLAB to minimize the mean-squared error of intersection rankings to determine the optimum weights in the ranking formula based on a city's professional data. We then attempted an optimization of our weights via a brute-force search approach to minimize the distance from ranking formula results to the clustering results. All the ranking information was aggregated into an online SQL database hosted by Amazon web services that utilized the PHP scripting language.

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis.

Copyright of Paragraf na Drodze is the property of Jagiellonian University Press 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.)

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis. [ABSTRACT FROM AUTHOR]

Copyright of Paragraf na Drodze is the property of Jagiellonian University Press 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.)

This paper discusses low-cost approaches capable of ranking traffic intersections for the purpose of signal re-timing. We extracted intersections that are comprised of multiple roads, defined by alphanumeric traffic message channel segment codes per international classification standards. Each of these road segments includes a variety of metrics, including congestion, planning time index, and bottleneck ranking information provided by the Regional Integrated Transportation Information System. Our first approach was to use a ranking formula to calculate intersection rankings using a score between 0 and 10 by considering data for different times of the day and different days of the week, weighting weekdays more heavily than weekends and morning and evening commute times more heavily than other times of day. The second method was to utilize unsupervised machine learning algorithms, primarily k-means clustering, to accomplish the intersection ranking task. We first approach this by checking the performance of basic k-means clustering on our data set. We then explore the ranking problem further by utilizing data provided by traffic professionals in the state of Tennessee. This exploration involves using MATLAB to minimize the mean-squared error of intersection rankings to determine the optimum weights in the ranking formula based on a city's professional data. We then attempted an optimization of our weights via a brute-force search approach to minimize the distance from ranking formula results to the clustering results. All the ranking information was aggregated into an online SQL database hosted by Amazon web services that utilized the PHP scripting language. [ABSTRACT FROM AUTHOR]

We image the shallow seismic structure across the Southern San Andreas Fault (SSAF) using signals from freight trains and trucks recorded by a dense nodal array, with a linear component perpendicular to SSAF and two 2D subarrays centered on the Banning Fault and Mission Creek Fault (MCF). Particle motion analysis in the frequency band 2–5 Hz shows that the examined traffic sources can be approximated as moving single‐ or multi‐point sources that primarily induce Rayleigh waves. Using several techniques, we resolve strong lateral variations of Rayleigh wave velocities and Q‐values across the SSAF, including 35% velocity reduction across MCF toward the northeast and strong attenuation around the two fault strands. We further resolve 10% mass density reduction and 45% shear modulus decrease across the MCF. These findings suggest that the MCF is currently the main strand of the SSAF in the area with important implications for seismic hazard assessments. Plain Language Summary: Imaging the internal structure of fault zones is essential for understanding earthquake properties and processes. Here we utilize seismic data generated by trains and trucks in the Coachella valley and recorded by a dense seismic array to image the subsurface structure of two main strands of the Southern San Andreas Fault (SSAF). Several types of analyses allow us to resolve seismic velocities, attenuation coefficients, and mass density across the entire San Andreas Fault zone. The results show a clear contrast in physical properties across the Mission Creek strand of the SSAF, highlighting the presence of a bimaterial fault interface and suggesting that it is the main active strand of SSAF. The research opens up possibilities for using common rail and road traffic signals to derive high resolution imaging results of subsurface seismic properties at other locations. Key Points: We detect frequent seismic signals from rail and road traffic in a dense array across the southern San Andreas fault zoneWe use the traffic signals to image shallow structural properties across the Banning and Mission Creek fault strandsThe resolved velocity and density contrasts across the Mission Creek fault suggest it is the main active strand of the Southern San Andreas Fault in the area [ABSTRACT FROM AUTHOR]

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms. [ABSTRACT FROM AUTHOR]

Abstract We image the shallow seismic structure across the Southern San Andreas Fault (SSAF) using signals from freight trains and trucks recorded by a dense nodal array, with a linear component perpendicular to SSAF and two 2D subarrays centered on the Banning Fault and Mission Creek Fault (MCF). Particle motion analysis in the frequency band 2–5 Hz shows that the examined traffic sources can be approximated as moving single‐ or multi‐point sources that primarily induce Rayleigh waves. Using several techniques, we resolve strong lateral variations of Rayleigh wave velocities and Q‐values across the SSAF, including 35% velocity reduction across MCF toward the northeast and strong attenuation around the two fault strands. We further resolve 10% mass density reduction and 45% shear modulus decrease across the MCF. These findings suggest that the MCF is currently the main strand of the SSAF in the area with important implications for seismic hazard assessments.

Fully actuated signal controls are becoming increasingly popular in modern urban environments, attempting to reduce congestion locally, synchronize flows, or prioritize specific types of vehicles. This trend is expected to grow as more vehicles are expected to communicate via Vehicle-to-Infrastructure (V2I) communication. The presented dataset contains cleaned observations from a fully actuated signal control system with priority for public transportation. Time series data of traffic signals that regulate vehicle, public transportation, bicycle, and pedestrian traffic flows are available, showing where a traffic signal operates in a red or green phase. Also, loop detector data representing the occupancy at several locations at an urban intersection in Zurich, Switzerland is available. The data of all traffic signals and loop detectors corresponds to January and February 2019 and has a resolution of 1 s.Recent advances in transportation science show novel approaches for signalized intersections, but most publications assess their methodology on self-collected or simulated data. Therefore, the presented dataset aims at facilitating the development, calibration, and validation of novel methodological developments for modeling, estimation, forecasting, and other tasks in traffic engineering. Furthermore, it can be used as a real-world benchmark dataset for objectively comparing different methodologies.

Seismic ambient noise contains rich information about the physical properties of the critical zone. From the motor vehicle noise, we extract the daily seismic attenuation by quantifying the linear relationship between the frequency and amplitude ratio of paired instantaneous spectra. After verifying the reliability of the proposed method, we apply it to seismic ambient noise data collected at three urban sites. The estimated attenuation is compared against three environmental variables: rainfall, temperature, and traffic volume. The results show that the estimated seismic attenuation correlates most strongly with precipitation with positive time lags, suggesting a high attenuation sensitivity to changes in soil moisture and groundwater system. Furthermore, differences in attenuation‐precipitation correlations indicate that near‐surface physical properties may vary significantly due to local site conditions. Our statistical method extracts reliable near‐surface information from highly complex urban ambient noise data, achieving unprecedented spatial and temporal resolution compared to existing ambient noise imaging methods. Plain Language Summary: Seismic waves travel through Earth's layers and contain information about the underground physical properties. As the seismic waves travel longer distances, their amplitudes decay due to geometrical spreading and medium attenuation, the latter of which could change rapidly due to changes in subsurface compaction and pore fluid saturation. By measuring the seismic amplitude differences between waves emitted from the same motor vehicle at two different locations, we estimate daily seismic attenuation statistically from hundreds of isolated traffic signals recorded by a single seismic sensor. By comparing the estimated time‐varying seismic attenuation with rainfall, temperature, and traffic volume, we find that precipitation dominates the correlation, suggesting a causal relation between rainfall and seismic attenuation through changes of soil moisture content and groundwater level fluctuations. Key Points: A new method is proposed for extracting seismic attenuation from moving vehicle sources recorded by a single seismic stationWe statistically resolve the daily seismic attenuation from hundreds of isolated traffic signalsVariations in seismic attenuation highly correlate with rainfall, suggesting rainfall's causal effect through the groundwater system [ABSTRACT FROM AUTHOR]

The goal of the project is to construct a density-based traffic signal management system that can automatically modify the timing of the signal based on the volume of traffic at the crossing. Traffic signal regulation is becoming a major issue that causes congestion and has a major role in the delay of emergency vehicles because of the rise in automobiles on the road these days. The majority of urban areas in the world have serious traffic problems, thus it's time to switch from more human or fixed modes to automated systems with decision-making skills. The fixed-based traffic signalling system of today may become ineffective if only one lane is used at a time. In this work, Arduino-based intelligent traffic control system framework helps to reduce this issue.

Abstract Seismic ambient noise contains rich information about the physical properties of the critical zone. From the motor vehicle noise, we extract the daily seismic attenuation by quantifying the linear relationship between the frequency and amplitude ratio of paired instantaneous spectra. After verifying the reliability of the proposed method, we apply it to seismic ambient noise data collected at three urban sites. The estimated attenuation is compared against three environmental variables: rainfall, temperature, and traffic volume. The results show that the estimated seismic attenuation correlates most strongly with precipitation with positive time lags, suggesting a high attenuation sensitivity to changes in soil moisture and groundwater system. Furthermore, differences in attenuation‐precipitation correlations indicate that near‐surface physical properties may vary significantly due to local site conditions. Our statistical method extracts reliable near‐surface information from highly complex urban ambient noise data, achieving unprecedented spatial and temporal resolution compared to existing ambient noise imaging methods.

The objective of this study was to establish strategic guidelines to solve the existing vehicular mobility problems in the District of Riohacha, proposing the adoption of advanced technologies to optimize traffic management in the city. The methodology of the study consisted in the application of surveys and the review of relevant bibliography. The results allowed the identification of various intelligent traffic control tools used in different regions of the world, determining their applicability and benefits for the context of Riohacha, where there was a notable lack of traffic signals. It was concluded that the implementation of the technological tools proposed in this study could offer effective solutions to the mobility challenges faced by the District of Riohacha. [ABSTRACT FROM AUTHOR]

In order to improve the traffic capacity of the intersection and shorten the traffic delay, a new urban intelligent traffic signal coordination control system based on machine learning is proposed in this paper. The hardware of the system is designed, including annunciator, vehicle detection module, traffic flow statistics module and signal control module. The reinforcement learning algorithm in machine learning is used to train and learn the traffic vehicle data, so as to extract the spatial characteristics of the traffic environment state. Under the constraint of the minimum loss function, the training objective is solved and the output result of the optimal coordinated control of traffic signals is output. The experimental results show that compared with the traditional control system, the maximum traffic capacity of the intersection under the control of this system is 2400 vehicles/hour, and the maximum traffic delay is no more than 30s. [ABSTRACT FROM AUTHOR]

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon’s entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.

Abstract Background Good vision through sunglasses is important to safety when driving and ISO 12312-1:2013 sets requirements for luminous transmittance and the transmittance of traffic signals. Methods We measured the spectral transmittances, 380–780 nm in 5-nm steps, of 232 sunglasses lenses and calculated the luminous transmittance, category (1–4) and transmittance of red, yellow, green and blue traffic signals (Q values). Furthermore, we developed a prototype for the general public to self-check sunglasses regarding safety for driving. We combined a white LED, a photodetector, and calculations to measure luminous transmittance, traffic signal transmittance, category, and Q-factors in sunglasses. Results Spectroscopy shows that 75% of sunglasses on the Brazilian market comply with ISO 12312-1:2013 requirements to be suitable for driving. The prototype was validated by testing 232 samples by trained users. Additionally, 60 other samples were tested by untrained users and results were compared to spectrophotometric measurements. Bland–Altman analyses showed no significant biases and 95% agreement of limits within the pre-defined tolerances for all measurements. Conclusions Our prototype offers the general public a way to check whether their sunglasses are suitable for driving. As tested, 24.6% of sunglasses are not appropriate for driving and consumer must be more attentive to this information. Significance Immediate attention regarding checking sunglasses for driving conditions is needed for non-certified sunglasses.

The article describes the problem of incorrect U-turns at intersections with traffic lights. Statistical data on road incidents related to U-turns are presented. Then, the international, Polish and foreign regulations concerning u-turning at intersections with traffic lights were analysed. The situations in which U-turns are allowed or prohibit-ed are presented. The differences in design rules for junctions with U-turns in different countries have been taken into account. A literature review was also carried out that outlined various current U-turns around the world, including the design of turning places, the location of turning points, road safety when turning, and the impact of U-turns on traffic conditions. The further part of the article presents the results of field tests of the U-turn at 6 intersections located in Warsaw. The research was conducted by video observation. The results were broken down by age, gender, place of regis-tration of the vehicle, type of vehicle, and the effect of incorrect turning. Data on road incidents at the examined intersections were also analysed. Data from the database kept by the Police were compared with the measure-ment data. A regression analysis was performed between the types of recorded incorrect manoeuvres and the number of accidents at the intersection. The results of statistical analysis carried out do not indicate the existence of a relationship between the number of identified incorrect U-turns and the number of road incidents at inter-sections. Based on the research, it was found that the phenomenon of incorrect U-turns at intersections with traffic lights is common, and the use of directional (protected) signals does not eliminate this phenomenon. The conclusions indicate practical solutions to reduce the number of illegally U-turning vehicles. The recommended actions are related to the stage of shaping the road network, designing the road geometry and organizing traffic and traffic lights, and auditing road safety, as well as the stage of road operation.

Integrated management of travel corridors comprising of freeways and adjacent arterial streets can potentially improve the performance of the highway facilities. However, several research gaps exist in data collection and performance measurement, analysis tools and control strategies. In this project first we analyzed high resolution data consisting of time-stamped records of every event involving vehicles, together with the signal phase at real-world signalized intersections and developed procedures for estimating performance measures. Next, we assessed the performance of a new microscopic simulator for signalized arterials. The model predictions were in close agreement with the predictions from widely used models in practice. We also developed and applied control strategies for freeway-arterial coordinated control to avoid queue override and developed a methodology to provide estimates of the amount and impacts of freeway diverted traffic in case of no-recurrent (incident related) congestion.

Copyright of Journal of Tikrit University for Humanities is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

This study proposes a novel distributed predictive cruise control (PCC) algorithm based on reinforcement learning. The algorithm aims at reducing idle time and maintaining an adjustable speed depending on the traffic signals. The effectiveness of the proposed approach has been validated through Paramics microscopic traffic simulations by proposing a scenario in Statesboro, Georgia. For different traffic demands, the travel time and fuel consumption rate of vehicles are compared between non‐PCC and PCC algorithms. Microscopic traffic simulation results demonstrate that the proposed PCC algorithm will reduce the fuel consumption rate by 4.24% and decrease the average travel time by 3.78%.

The aim of the current research is to identify (the effect of the five fingers and traffic lights strategies on the achievement of intermediate first-grade students of mathematics and the development of their motivation towards it), by verifying the following two zero hypotheses: . There are no statistically significant differences at the significance level (0.05) between the mean scores of the three groups' students in the mathematics achievement test. 2. There are no statistically significant differences at the level of significance (0.05) between the mean scores of students of the three groups in the motivation scale towards learning mathematics. The research sample consisted of (99) students who were divided into three groups, the first experimental group (35) students, the second experimental group (33) students, and the control group (31) students. for the previous year, intelligence test, and pre-mathematics motivation scale). The sample was intentionally tested from (Omar bin Abdul Aziz Intermediate School for Boys), affiliated to al-Rusafa al-Ola Education Directorate, and the researcher prepared an achievement test in mathematics, which consisted in its final form of (30) items of the type (multiple choice), and the researcher relied on the motivation scale. Towards Learning Mathematics prepared by Al-Zuhairi (2014), consisting of (20) paragraphs with five alternatives (always, often, sometimes, rarely, never), and validity and reliability were extracted for both.

• Queue length estimation models for connected vehicles are developed. • Revised models are developed when range measurement sensor are present. • Models with fusion are evaluated using microscopic traffic simulations and field data. • Knowing back of queue improves the accuracy as much as 25% over without sensor. • Accuracy was improved especially at low market penetration rates up to 30%. This paper presents a method for queue length estimation from connected vehicles equipped with range measurement sensors such as light detection and ranging, stereo camera, and ultrasonic. Simple plug-and-play models are developed for queue length estimations that do not require ground truth queue lengths. The proposed method is simple to implement and can be adopted to cyclic queues like traffic signals with known phase lengths. The models are evaluated with microscopic traffic simulations and connected vehicle field test data. Compared to queue length estimations without range sensors, the numerical experiments performed using microsimulations show that the model with range sensors improves the errors by 25% in variance-to-mean ratio and 5% in coefficient of variation at less than 20% market penetration rates. Using the connected vehicle field test data, it is observed that the fusing range sensor can improve close to 10 % without sensor estimators, 30 % of the queue back, and 48 % of the Highway Capacity Manual's control delay-based estimations at about 25 % market penetration rate. [ABSTRACT FROM AUTHOR]

Background: Good vision through sunglasses is important to safety when driving and ISO 12312-1:2013 sets requirements for luminous transmittance and the transmittance of traffic signals.Methods: We measured the spectral transmittances, 380-780 nm in 5-nm steps, of 232 sunglasses lenses and calculated the luminous transmittance, category (1-4) and transmittance of red, yellow, green and blue traffic signals (Q values). Furthermore, we developed a prototype for the general public to self-check sunglasses regarding safety for driving. We combined a white LED, a photodetector, and calculations to measure luminous transmittance, traffic signal transmittance, category, and Q-factors in sunglasses.Results: Spectroscopy shows that 75% of sunglasses on the Brazilian market comply with ISO 12312-1:2013 requirements to be suitable for driving. The prototype was validated by testing 232 samples by trained users. Additionally, 60 other samples were tested by untrained users and results were compared to spectrophotometric measurements. Bland-Altman analyses showed no significant biases and 95% agreement of limits within the pre-defined tolerances for all measurements.Conclusions: Our prototype offers the general public a way to check whether their sunglasses are suitable for driving. As tested, 24.6% of sunglasses are not appropriate for driving and consumer must be more attentive to this information.Significance: Immediate attention regarding checking sunglasses for driving conditions is needed for non-certified sunglasses. [ABSTRACT FROM AUTHOR]

Documented evidence shows that anchor nut loosening on highway ancillary structures has been a problem in the transportation industry since at least the 1990s. There have been reported cases in the past, where loose anchor nuts were found to be partially responsible for the failure of ancillary structures. Resonant vibrations in ancillary structures as a result of variable frequency winds are believed to be one of the potential causes of loose anchor nuts. The current research primarily focused on investigating the effect of wind-induced vibrations on the loosening of anchor nuts in double-nut moment connections in ancillary structures. The research involved large-scale vibration testing of a traffic signal on the basis of vibration stress results from a 4-month field monitoring program. The purpose of the large-scale testing was to establish the relationship between the number of vibratory cycles, rod pretension, and nut loosening. A small-scale vibration test was also performed to validate the large-scale testing results. The testing results showed that anchor rods tightened in a double-nut moment connection above a pretension of 38 MPa (5.5 Ksi) do not experience nut loosening under vibrations. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) offers information on traffic networks like the position of intersections and streets, road categories, speed limits, number of lanes etc. Based on this information, network input data for transport simulations, e.g. the agent-based transport simulation MATSim can be generated. A MATSim network is modeled as a graph with nodes (intersections) and links (streets). Previously, data about traffic signals and lanes could only be provided to MATSim simulations by manual data preparation or had to be emulated by reducing flow capacities of links that lead towards signalized intersections. To be able to model inner-city traffic more realistically, this study extends the input generation of MATSim. Information about traffic signals and lanes is extracted from OSM and automatically processed to create signal and lane input files for the simulation. Processing data about traffic signals and lanes is required to optimize fixed-time plans, as well as to develop and validate algorithms for traffic-adaptive signals. This study provides the basis for further development of traffic lights in the transport simulation MATSim. An application of a MATSim simulation including traffic signals and lanes in the Berlin metropolitan area verifies the developed method. [ABSTRACT FROM AUTHOR]

Span-wire traffic signals are vulnerable to extreme wind events such as hurricanes and thunderstorms. In past events in the Southeastern Coast of the United States, many failures of span-wire traffic signals were reported. In order to identify their dynamic behavior during extreme wind events and investigate their buffeting response, a large-scale aeroelastic testing was conducted at the NHERI Wall of Wind (WOW) Experimental Facility (EF) at Florida International University (FIU). The WOW is a large-scale open jet wind testing facility, comprised of 12 fans, and capable of simulating winds at speeds up to 70 m/s, corresponding to a Category 5 hurricane. Following the Froude number criterion, a 1:10 aeroelastic model of a span-wire traffic signal system consisting of two 3-section and one 5-section signals was designed and constructed, based on the properties of its full-scale counterpart. In the testing protocol, various wind directions ranging between 0° and 180° were considered at full-scale wind speeds ranging between 21 and 43 m/s. The results of the aeroelastic tests show a similar behavior compared with previous full-scale tests conducted at the WOW. However, an increase in the RMS of accelerations was observed in comparison with those from the full-scale tests. This is attributed to the fact that the aeroelastic model enabled better simulation of low-frequency eddies in the turbulence spectrum compared to the full-scale testing turbulence spectrum.

Kentsel alanlardaki trafik akışı, hem sürücü hem de yayalar için temel problemlerden biridir. Trafik yoğunluğu ve trafik ışıkları büyük zaman kayıplarına yol açmaktadır. Trafikte beklemelerle oluşan bu zaman kayıpları, ülkeler için boşa harcanmış önemli yakıt miktarlarına ve dolayısıyla önemli maliyet kayıplarına neden olmaktadır. Bu çalışmada, İzmir, Türkiye’de bir anayoldaki trafik akışı incelenmiştir. Kentsel trafikte boşa harcanmış kaynakları azaltmak üzere; sinyal kontrollü trafik akışını etkileyen faktörleri dikkate alan bir deneysel tasarım çalışması yapılmıştır. Tasarım faktörleri; trafik ışıklarının sinyal süreleri, trafik yoğunluğu ve araçların hızı olarak belirlenmiştir. Bu faktörlerin, sistemde geçirilen süre, kırmızı ışıkta bekleme süreleri ve sistemden çıkabilen araç sayısı amaçları üzerindeki etkileri incelenmiştir. Seçilen 486 tasarım noktasının sonuçları, oluşturulan benzetim modelinden elde edilmiştir. Sonuç olarak, tasarım noktaları içinden toplam bekleme süresini en küçükleyen en iyi faktör düzeyleri belirlenmiştir.

Traffic flow in urban areas is one of the major problems both for drivers and pedestrians. Traffic congestion and traffic lights constitute a large portion of the time spent in traffic. This wasted time for waiting in traffic also costs countries considerable amount of wasted fuel and hence considerable amount of money. In this study, traffic flow of a road in Izmir, Turkey is considered. In order to decrease all the wasted resources in urban traffic, an experimental design is conducted on the factors affecting the signal controlled traffic flow. The design factors are determined to be signal times of traffic lights, traffic intensity and the speed of vehicles. The effects of these factors on the three performance measures of time in system, waiting time in red light and number of vehicles going out of the system are analyzed. A fractional factorial design is carried out on the 486 design points evaluated using simulation modeling. In results, among the design points, best level of factors to minimize total waiting time in traffic flow are determined.