Готові списки джерел за темами / Red light running Australia

Добірка наукової літератури з теми "Red light running Australia"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Red light running Australia"

1

Morris, Andrew Paul, Narelle Haworth, Ashleigh Filtness, Daryl-Palma Asongu Nguatem, Laurie Brown, Andry Rakotonirainy, and Sebastien Glaser. "Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries." Behavioral Sciences 11, no. 7 (July 15, 2021): 101. http://dx.doi.org/10.3390/bs11070101.

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Анотація:
(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.
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2

Ainsworth, Claire. "Running the red light." Nature 438, no. 7069 (December 2005): 726–28. http://dx.doi.org/10.1038/438726a.

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3

Gopaul, Chavin, Aruna Singh-Gopaul, and Dave D. Chadee. "Red Light Running in Trinidad." Journal of Transportation Technologies 06, no. 05 (2016): 219–38. http://dx.doi.org/10.4236/jtts.2016.65022.

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4

Hallmark, Shauna, Massiel Orellana, Thomas McDonald, Eric Fitzsimmons, and David Matulac. "Red Light Running in Iowa." Transportation Research Record: Journal of the Transportation Research Board 2182, no. 1 (January 2010): 48–54. http://dx.doi.org/10.3141/2182-07.

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5

Porter, Bryan E., and Kelli J. England. "Predicting Red-Light Running Behavior." Journal of Safety Research 31, no. 1 (March 2000): 1–8. http://dx.doi.org/10.1016/s0022-4375(99)00024-9.

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6

Retting, Richard A., Allan F. Williams, and Michael A. Greene. "Red-Light Running and Sensible Countermeasures: Summary of Research Findings." Transportation Research Record: Journal of the Transportation Research Board 1640, no. 1 (January 1998): 23–26. http://dx.doi.org/10.3141/1640-04.

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Анотація:
Deliberate running of red lights is a common and serious violation that contributes substantially to the more than 1 million motor vehicle collisions that occur at traffic signals each year. Urban-based highway safety research has examined various aspects of the red-light running problem, including the contribution of red-light violations to motor vehicle crashes, the frequency of red-light running, characteristics of red-light runners, and the influence of signal timing on red-light running behavior. A brief summary of recent research efforts to examine the problem of red-light running is provided, and the use of countermeasures, including red-light cameras, to reduce the problem is discussed.
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7

WÄGELE, HEIKE, KRISTINA STEMMER, INGO BURGHARDT, and KATHARINA HÄNDELER. "Two new sacoglossan sea slug species (Opisthobranchia, Gastropoda): Ercolania annelyleorum sp. nov. (Limapontioidea) and Elysia asbecki sp. nov. (Plakobranchoidea), with notes on anatomy, histology and biology." Zootaxa 2676, no. 1 (November 15, 2010): 1. http://dx.doi.org/10.11646/zootaxa.2676.1.1.

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Анотація:
Two new sacoglossan species, belonging to the genus Ercolania Trinchese, 1872 (Ercolania annelyleorum sp. nov.) and the genus Elysia Risso, 1818 (Elysia asbecki sp. nov.) are described from Lizard Island, Great Barrier Reef, Australia. Anatomy of both species was reconstructed by analyzing histological serial sections. Radula morphology was investigated by using light microscopy and scanning electron microscopy. Sequence analyses (NeighborNet; sequence divergence) and tree reconstructions showed for both species their distinction from con-generic species, but also two distinct mitochondrial lines in the new Ercolania species. Adults as well as freshly hatched juveniles of E. annelyleorum sp. nov. have been found in clusters of the ulvophycean alga Boodlea sp., which are sucked out by piercing the cell walls with their radular teeth. This new species differs from other, similar transparent, Ercolania species by its pattern of the green branches of the digestive gland and the presence of two distinct red patches, one in the anterior and the other in the posterior third of the dorsal body part. This coloration and furthermore the combination of following characters distinguishes the new species from all other described Ercolania species so far: rhinophores, elliptic in cross section, with one distinct branch of digestive gland running half way up; cerata not inflated; smooth cutting edge of sabot-shaped tooth; two-lobed prostate gland and presence of two allosperm receptacles with no re-opening of the receptaculum seminis to the outside. According to sequence divergence data of CO1, two mitochondrial lines seem to be present in the new species, which are clearly distinct from all other included Ercolania species. Elysia asbecki sp. nov. differs from other Elysia species by its whitish coloration with orange and dark brown dots and a distinct lighter spot in the neck region of the head. The rhinophores exhibit a black and yellow ribbon at the tip. The species has distinct reddish patches at the anterior base of the parapodia (at the conjunction with the head), one along the middle part of the parapodial edge on both sides and very distinct lateral patches at the end of the foot. CO1 sequences clearly distinguish this species from all closely related Elysia species. The food source of Elysia asbecki sp. nov. could not be verified yet. Measurements of photosynthetic activity within these two new species indicate that E. annelyleorum sp. nov. digests chloroplasts immediately after sequestration, whereas Elysia asbecki sp. nov. shows high maximum quantum yield values, similar to E. timida (Risso, 1818) and E. crispata (Mørch, 1863), both known as long term retention forms.
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8

Wu, Yao, Jian Lu, Hong Chen, and Qian Wan. "Modeling the Frequency of Cyclists’ Red-Light Running Behavior Using Bayesian PG Model and PLN Model." Discrete Dynamics in Nature and Society 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/2593698.

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Анотація:
Red-light running behaviors of bicycles at signalized intersection lead to a large number of traffic conflicts and high collision potentials. The primary objective of this study is to model the cyclists’ red-light running frequency within the framework of Bayesian statistics. Data was collected at twenty-five approaches at seventeen signalized intersections. The Poisson-gamma (PG) and Poisson-lognormal (PLN) model were developed and compared. The models were validated using Bayesianpvalues based on posterior predictive checking indicators. It was found that the two models have a good fit of the observed cyclists’ red-light running frequency. Furthermore, the PLN model outperformed the PG model. The model estimated results showed that the amount of cyclists’ red-light running is significantly influenced by bicycle flow, conflict traffic flow, pedestrian signal type, vehicle speed, and e-bike rate. The validation result demonstrated the reliability of the PLN model. The research results can help transportation professionals to predict the expected amount of the cyclists’ red-light running and develop effective guidelines or policies to reduce red-light running frequency of bicycles at signalized intersections.
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9

Li, Pengfei, Yan Li, and Xiucheng Guo. "A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data." Computational Intelligence and Neuroscience 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/892132.

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Анотація:
The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.
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10

Zhang, Guangnan, Ying Tan, Qiaoting Zhong, and Ruwei Hu. "Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China." International Journal of Environmental Research and Public Health 18, no. 2 (January 11, 2021): 553. http://dx.doi.org/10.3390/ijerph18020553.

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Motorcycles are among the primary means of transport in China, and the phenomenon of motorcyclists running red lights is becoming increasingly prevalent. Based on the traffic crash data for 2006–2010 in Guangdong Province, China, fixed- and random-parameter logit models are used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red light violations and injury severity resulting from motorcyclists’ running red lights in China. Certain factors that affect the probability of motorcyclists running red lights are identified. For instance, while the likelihood of violating red light signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have significantly increased probability of a red light violation during dark conditions. For the resulting severe casualties in red-light-running crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury. Regarding the relationship between red light violations and the severity of injuries in crashes caused by motorcyclists running red lights, this study indicated that driving direction and time period have inconsistent effects on the probability of red light violations and the severity of injuries. On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsignificant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsignificant effects on the probability of motorcyclists’ red light violations in contrast to going straight, but have a very significant impact on the severity of injuries. Moreover, the likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. Measures including road safety educational programs for targeted groups and focused enforcement of traffic policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights.
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Більше джерел

Дисертації з теми "Red light running Australia"

1

Olson, Carl Scott. "Safety Effectiveness of Red Light Treatments for Red Light Running." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/882.

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Анотація:
Crashes resulting from automobiles running a red light are typically severe in nature. One way to try to reduce the number and severity of these types of crashes is by increasing the red clearance interval of a traffic signal. In Portland, Oregon, eight intersections received a variety of treatments including red extensions. Determining which treatment had what effect can be difficult to weed out. Using a combination of crash analysis and a model simulating an intersection with red extensions, this paper describes the estimated impact of red light running intersection upgrades and red extensions on crashes. By performing a variety of before and after crash analysis, a reduction of angle crashes after treatments was detected, with a crash modification factor of 0.64 +/- 0.28 using the Empirical-Bayes method. Output from the simple simulation also suggest that red light running crashes can be reduced with red extension technology and confirms crash modification values determined from the Empirical-Bayes method.
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2

Elnashar, Dina. "CHARACTERISTICS OF RED LIGHT RUNNING CRASHESIN FLORIDA." Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2717.

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Red light running is one of the main contributing factors of crashes in urban areas in Florida and the United States. Nationwide, according to preliminary estimates by the Federal Highway Administration (FHWA) 2001, there were nearly 218,000 red-light running crashes at intersections. These crashes resulted in as many as 181,000 injuries and 880 fatalities, and an economic loss estimated at $14 billion per year nationwide, According to the Community Traffic Safety Team Florida Coalition (A statewide traffic safety group) there were 9,348 crashes involving red-light running in Florida and 127 fatalities in 1999. This research study focused on studying the red light running crashes and violations in the State of Florida. There were three primary objectives for this research. The first primary objective was to analyze the red light running crashes in Florida from 2002 to 2004. The data for this part was collected from the Crash Analysis Reporting System of the Florida Department of Transportation. These crashes are reported as "disregarded traffic signal" as far as the first contributing cause. The analysis focused on the influences of different factors on red light running crashes including the driver (age group, gender, and DUI history) and the environment (time of day, day of week, type of road, and weather). However, not all red light crashes are reported as "disregarded traffic signal". Therefore, representing red light running crashes only through "disregard traffic signal" noted reports would underestimate the extent of red light running effects at a given intersection. Therefore, the second objective was to review the long form crash reports to determine the actual number of crashes related to red light running. The analysis for a random sample of the crashes on the sate roads of Florida on the year 2004 showed that the percentage of crashes related to red light running reported on the database was found to be (3.13%), and the percentage of crashes related to red light running reported in the original crash repot filled by the police officer are much higher than reported(5.63%), which shows the importance of standardizing the format and coding process for the long form crashes conducted by the police officers to help accurately identify the real cause of the crash at the studied location. The third objective was to analyze the violations data given for five intersections and find if there is a correlation between the average rate of violations per hour and the frequency of red light running crashes. The analysis showed that utilizing the limited number of intersections used in the study, it appears that there is no correlation between the average violations per hour and the red light running crashes at the studied locations.
M.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering MS
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3

Fitzsimmons, Eric John. "The effectiveness of Iowa's automated red light running enforcement programs." [Ames, Iowa : Iowa State University], 2007.

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4

Komol, Md Mostafizur Rahman. "C-ITS based prediction of driver red light running and turning behaviours." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227694/1/Md%20Mostafizur%20Rahman_Komol_Thesis.pdf.

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Анотація:
Red light running is a major traffic violation. Drivers often aggressively or unintentionally violate red signal and cause traffic collisions. Moreover, Vision impairment of turning vehicles by large vehicles and road side static structures near intersections often lead to VRU crashes during their crossing at the intersection. In this research, we have developed models to predict drivers’ red light running and turning behaviour at intersections using Long Short Term Memory and Gated Recurrent Unit algorithms. We have used vehicle kinematic dataset of the C-ITS project: Ipswich Connected Vehicle Pilot, Queensland, taken from the Department of Transport and Main Road, Queensland.
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5

Yan, Xuedong. "SAFETY ISSUES OF RED-LIGHT RUNNING AND UNPROTECTED LEFT-TURN AT SIGNALIZED INTERSECTIONS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3429.

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Анотація:
Crashes categorized as running red light or left turning are most likely to occur at signalized intersections and resulted in substantial severe injuries and property damages. This dissertation mainly focused on these two types of vehicle crashes and the research methodology involved several perspectives. To examine the overall characteristics of red-light running and left-turning crashes, firstly, this study applied 1999-2001 Florida traffic crash data to investigate the accident propensity of three aspects of risk factors related to traffic environments, driver characteristics, and vehicle types. A quasi-induced exposure concept and statistical techniques including classification tree model and multiple logistic regression were used to perform this analysis. Secondly, the UCF driving simulator was applied to test the effect of a proposed new pavement marking countermeasure which purpose is to reduce the red-light running rate at signalized intersections. The simulation experiment results showed that the total red-light running rate with marking is significantly lower than that without marking. Moreover, deceleration rate of stopping drivers with marking for the higher speed limit are significantly less than those without marking. These findings are encouraging and suggesting that the pavement marking may result in safety enhancement as far as right-angle and rear-end traffic crashes at signalized intersections. Thirdly, geometric models to compute sight distances of unprotected left-turns were developed for different signalized intersection configurations including a straight approach leading to a straight one, a straight approach leading to a curved one, and a curved approach leading to a curved one. The models and related analyses can be used to layout intersection design or evaluate the sight distance problem of an existing intersection configuration to ensure safe left-turn maneuvers by drivers.
Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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6

Obeng-Boampong, Kwaku Oduro. "Evaluation of daytime vs. nighttime red-light-running using an advanced warning for end of green phase system." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2746.

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Анотація:
The problem of dilemma zone protection and red-light-running is especially important in certain rural intersections due to the higher speeds at these intersections and their isolated nature. In addition, the presence of a larger percentage of trucks mean that adequate warning and help need to be given to these truck drivers in order to enable them to stop safely, or proceed through the intersection before the onset of red. To curb any potential danger at such intersections, a Texas Department of Transportation (TxDOT) research project on Advanced Warning for End of Green Phase (AWEGS) at high speed intersections deployed AWEGS at two rural intersection sites ?? Tx 6 @ FM 185 near Waco and US 290 @ FM 577 in Brenham. The deployment of AWEGS involved a Level 1 and a later upgrade to a more efficient Level 2 in Waco. Initial results on red-light-running, even though promising, were expressed as observed red-light-running events per day. These resulting rates did not reflect exposure, and the results also raised some concerns with regards to some increase in red-light-running from Level 1 to Level 2. A more detailed analysis of the red-light-running issue at these two sites is provided in this thesis. The main areas of red-light-running analyses presented here are with respect to the reductions in red-light-running rates for the exposure factors of number of cycles and vehicular volumes, the comparison of day and night RLR rates and the nature of speeds of vehicles running the red light at the intersection in Waco. AWEGS was found to reduce the total red-light-running per exposure factor after its deployment. Both Level 1 and Level 2 AWEGS operations were found to reduce red-light-running by up to 60%. Generally, total red-light-running per exposure factor between Level 1 and Level 2 was found to be about the same. Level 2 had lower daytime red-light-running rates and higher nighttime rates than Level 1. Generally, day rates were found to be higher than night rates for all levels of AWEGS deployment. It is recommended that, to better understand the operational aspects of AWEGS and to improve its operations, more implementation of AWEGS and further tests be done. An automated method to collect and analyze data needs to be developed as well as a means of automatically recording video data for calibration and verification It is also recommended that Level 1 technology be implemented in areas where the Level 2 technology may be either too complex or too expensive.
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7

Elmitiny, Noor. "Providing a Better Understanding for the Motorist Behavior Towards Signal Change." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4264.

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Анотація:
This research explores the red light running phenomena and offer a better understanding of the factors associated with it. The red light running is a type of traffic violation that can lead to angle crash and the most common counter measure is installing a red light running cameras. Red light running cameras some time can reduce the rates of red light running but because of the increased worry of the public towards crossing the intersection it can cause an increase in rear end crashes. Also the public opinion of the red light running cameras is that they are a revenue generator for the local counties and not a concern of public safety. Further more, they consider this type of enforcement as violation of privacy. There was two ways to collect the data needed for the research. One way is through a tripod cameras setup temporarily placed at the intersection. This setup can collect individual vehicles caught in the change phase with specific information about their reactions and conditions. This required extensive manual analysis for the recorded videos plus data could not be collected during adverse weather conditions. The second way was using traffic monitoring cameras permanently located at the site to collect red light running information and the simultaneous traffic conditions. This system offered more extensive information since the cameras monitor the traffic 24/7 collecting data directly. On the other hand this system lacked the ability to identify the circumstances associated with individual red light running incidents. The research team finally decided to use the two methods to study the red light running phenomena aiming to combine the benefits of the two systems. During the research the team conducted an experiment to test a red light running countermeasure in the field and evaluate the public reaction and usage of this countermeasure. The marking was previously tested in a driving simulator and proved to be successful in helping the drivers make better stop/go decisions thus reducing red light running rates without increasing the rear-end crashes. The experiment was divided into three phases; before marking installation called "before", after marking installation called "after", and following a media campaign designed to inform the public about the use of the marking the third phase called "after media" The behavior study that aimed at analyzing the motorist reactions toward the signal change interval identified factors which contributed to red light running. There important factors were: distance from the stop bar, speed of traffic, leading or following in the traffic, vehicle type. It was found that a driver is more likely to run red light following another vehicle in the intersection. Also the speeding vehicles can clear the intersection faster thus got less involved in red light running violations. The proposed "Signal Ahead" marking was found to have a very good potential as a red light running counter measure. The red light running rates in the test intersection dropped from 53 RLR/hr/1000veh for the "before" phase, to 24 RLR/hr/1000veh for the "after media" phase. The marking after media analysis period found that the marking can help the driver make stop/go decision as the dilemma zone decreased by 50 ft between the "before" and the "after media" periods. Analysis of the traffic condition associated with the red light running it revealed that relation between the traffic conditions and the red light running is non-linear, with some interactions between factors. The most important factors included in the model were: traffic volume, average speed of traffic, the percentage of green time, the percentage of heavy vehicles, the interaction between traffic volume and percentage of heavy vehicles. The most interesting finding was the interaction between the volume and the percent of heavy vehicles. As the volume increased the effect of the heavy vehicles reversed from reducing the red light running to increasing the red light. This finding may be attributed to the sight blocking that happens when a driver of a passenger car follows a larger heavy vehicle, and can be also explained by the potential frustration experienced by the motorist resulting from driving behind a bigger vehicle.
Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering PhD
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8

Doerzaph, Zachary R. "Intersection Stopping Behavior as Influenced by Driver State: Implications for Intersection Decision Support Systems." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/9935.

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Анотація:
It is estimated that as many as 2.7 million crashes occur each year at intersections or are intersection related; resulting in over 8500 fatalities each year. These statistics have prompted government and corporate sponsored research into collision countermeasure systems that can enhance safety at intersections. Researchers are investigating technologies to provide an infrastructure-based or infrastructure-cooperative Intersection Decision Support (IDS) systems. Such systems would use pre-specified algorithms to identify drivers that have a high likelihood of violating the traffic signal and thus increase the risk of a collision. The system would subsequently warn the violating driver to stop though an in-vehicle or infrastructure-mounted interface. An IDS algorithm must be designed to provide adequate time for the driver to perceive, react, and stop the vehicle, while simultaneously avoiding a high false alarm rate. Prior to developing these algorithms, scientists must understand how drivers respond to traffic signals. Little research has focused on the influence of driver state on red-light running behavior or methods for distinguishing red light violators from non-violators. The objective of the present study was to define trends associated with intersection crossings under different driver states and to explore the point detection method of predicting red light running upstream of the intersection. This was accomplished through a test-track mixed-factor experiment with 28 participants. Each participant experienced a baseline (complete a full stop at the red light), distracted (misses signal phase change due to inattention), and willful (driver knowingly makes a late crossing in an attempt to 'beat the light') driver state conditions. To provide the opportunity for red-light running behavior from participants, the amber change interval began at five different distances from the intersection. These distances were located near and within the dilemma zone, a region in which drivers have a difficult time deciding whether to go or to stop. Data collected from in-vehicle sensors was statistically analyzed to determine significant effects between driver states, and to investigate point detection algorithms.
Master of Science
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9

Harb, Rami Charles. "THE USE OF THE UCF DRIVING SIMIULATOR TO TEST THE CONTRIBUTION OF LARGER SIZE VEHICLES (LSVs) IN REAR-END COLLISIONS AND RED LIGHT RUNNING ON INTERSECTIONS." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3878.

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Анотація:
Driving safety has been an issue of great concern in the United States throughout the years. According to the National Center for Statistics and Analysis (NCSA), in 2003 alone, there were 6,267,000 crashes in the U.S. from which 1,915,000 were injury crashes, including 38,764 fatal crashes and 43,220 human casualties. The U.S. Department of Transportation spends millions of dollars every year on research that aims to improve roadway safety and decrease the number of traffic collisions. In spring 2002, the Center for Advanced Traffic System Simulation (CATSS), at the University of Central Florida, acquired a sophisticated reconfigurable driving simulator. This simulator, which consists of a late model truck cab, or passenger vehicle cab, mounted on a motion base capable of operation with six degrees of freedom, is a great tool for traffic studies. Two applications of the simulator are to study the contribution of Light Truck Vehicles (LTVs) to potential rear-end collisions, the most common type of crashes, which account for about a third of the U.S. traffic crashes, and the involvement of Larger Size Vehicles (LSVs) in red light running. LTVs can obstruct horizontal visibility for the following car driver and has been a major issue, especially at unsignalized intersections. The sudden stop of an LTV, in the shadow of the blindness of the succeeding car driver, may deprive the following vehicle of a sufficient response time, leading to high probability of a rear-end collision. As for LSVs, they can obstruct the vertical visibility of the traffic light for the succeeding car driver on signalized intersection producing a potential red light running for the latter. Two sub-scenarios were developed in the UCF driving simulator for each the vertical and horizontal visibility blockage scenarios. The first sub-scenario is the base sub-scenario for both scenarios, where the simulator car follows a passenger car, and the second sub-scenario is the test sub-scenario, where the simulator car follows an LTV for the horizontal visibility blockage scenario and an LSV for the vertical visibility blockage scenario. A suggested solution for the vertical visibility blockage of the traffic light problem that consisted of adding a traffic signal pole on the right side of the road was also designed in the driving simulator. The results showed that LTVs produce more rear-end collisions at unsignalized intersections due to the horizontal visibility blockage and following car drivers' behavior. The results also showed that LSVs contribute significantly to red light running on signalized intersections and that the addition of a traffic signal pole on the right side of the road reduces the red light running probability.
M.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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10

Amer, Ahmed. "Statistical and Behavioral Modeling of Driver Behavior on Signalized Intersection Approaches." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77995.

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Анотація:
The onset of a yellow indication is typically associated with the risk of vehicle crashes resulting from dilemma-zone and red-light-running problems. Such risk of vehicle crashes is greater for high-speed signalized intersection approaches. The research presented in this dissertation develops statistical as well as behavioral frameworks for modeling driver behavior while approaching high-speed signalized intersection approaches at the onset of a yellow indication. The analysis in this dissertation utilizes two sources of data. The main source is a new dataset that was collected as part of this research effort during the summer of 2008. This experiment includes two instructed speeds; 72.4 km/h (45 mph) with 1727 approaching trials (687 running and 1040 stopping), and 88.5 km/h (55 mph) with 1727 approaching trials (625 running and 1102 stopping). The complementary source is an existing dataset that was collected earlier in the spring of 2005 on the Virginia Smart Road facility. This dataset includes a total of 1186 yellow approaching trials (441 running and 745 stopping). The adopted analysis approach comprises four major parts that fulfill the objectives of this dissertation. The first part is concerned with the characterization of different driver behavioral attributes, including driver yellow/red light running behavior, driver stop-run decisions, driver perception-reaction times (PRT), and driver deceleration levels. The characterization of these attributes involves analysis of variance (ANOVA) and frequency distribution analyses, as well as the calibration of statistical models. The second part of the dissertation introduces a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone). Lookup tables are developed to assist practitioners in the design of yellow timings that reflects the stochastic nature of driver PRT and deceleration levels. An extension of the proposed approach is presented that can be integrated with the IntelliDriveSM initiative. Furthermore, the third part of the dissertation develops an agent-based Bayesian statistics approach to capture the stochastic nature of the driver stop-run decision. The Bayesian model parameters are calibrated using the Markov Chain Monte Carlo (MCMC) slice procedure implemented within the MATLAB® software. In addition, two procedures for the Bayesian model application are illustrated; namely Cascaded regression and Cholesky decomposition. Both procedures are demonstrated to produce replications that are consistent with the Bayesian model realizations, and capture the parameter correlations without the need to store the set of parameter realizations. The proposed Bayesian approach is ideal for modeling multi-agent systems in which each agent has its own unique set of parameters. Finally, the fourth part of the dissertation introduces and validates a state-of-the-art behavioral modeling framework that can be used as a tool to simulate driver behavior after the onset of a yellow indication until he/she reaches the intersection stop line. The behavioral model is able to track dilemma zone drivers and update the information available to them every time step until they reach a final decision. It is anticipated that this behavioral model will be implemented in microscopic traffic simulation software to enhance the modeling of driver behavior as they approach signalized intersections.
Ph. D.
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Книги з теми "Red light running Australia"

1

Lawson, S. D. Red-light running: Accidents and surveillance cameras. Basingstoke: AA Foundation for Road Safety Research and Birmingham City Council, 1991.

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2

Eccles, Kimberly A. Automated enforcement for speeding and red light running. Washington, D.C: Transportation Research Board, 2012.

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3

Eccles, Kimberly A., Rebecca Fiedler, Bhagwant Persaud, Craig Lyon, and Glenn Hansen. Automated Enforcement for Speeding and Red Light Running. Washington, D.C.: Transportation Research Board, 2012. http://dx.doi.org/10.17226/22716.

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4

McFadden, John. Synthesis and evaluation of red light running automated enforcement programs in the United States. Washington, D.C: U.S. Dept. of Transportation, Federal Highway Administration, Office of Highway Safety Infrastructure, 1999.

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5

Utilization and impacts of automated traffic enforcement: Hearing before the Subcommittee on Highways and Transit of the Committee on Transportation and Infrastructure, House of Representatives, One Hundred Eleventh Congress, second session, June 30, 2010. Washington: U.S. G.P.O., 2010.

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6

Browning, Kelsey. Running the Red Light. Harlequin Enterprises ULC, 2014.

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7

Running the Red: An Evaluation of the Strathclyde Police Red Light Camera Initiative (Running the Red). The Stationery Office Books (Agencies), 1995.

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8

Association of selected intersection factors with red-light-running crashes. McLean, Va: U.S. Dept. of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2000.

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9

Ali, Kamyab, Iowa. Dept. of Transportation., and Iowa State University. Center for Transportation Research and Education., eds. Red light running in Iowa: The scope, impact, and possible implications. Ames, Iowa: Center for Transportation Research and Education, Iowa State University, 2000.

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10

The Curvy The Curvy Girl Code Press. One Year Running Logbook for Plus Size Runners: Light Skin, Red Ponytail Edition. Independently Published, 2021.

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Частини книг з теми "Red light running Australia"

1

Carnis, Laurent. "Red Light Running." In Encyclopedia of Law and Economics, 1–9. New York, NY: Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4614-7883-6_588-2.

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2

Zahid, Muhammad, Arshad Jamal, Yangzhou Chen, Tufail Ahmed, and Muhammad Ijaz. "Predicting Red Light Running Violation Using Machine Learning Classifiers." In Lecture Notes in Electrical Engineering, 137–48. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5429-9_10.

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3

Wang, Jinmei, Zhaoan Wang, and Jianguo Yang. "Modeling and Simulation of Red Light Running Violation at Urban Intersections." In Lecture Notes in Computer Science, 376–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30585-9_42.

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4

Wang, Zhiqiang, Xiaodong Sun, Xiaoxu Zhang, Ti Han, and Fei Gao. "Algorithm Improvement of Pedestrians’ Red-Light Running Snapshot System Based on Image Recognition." In Lecture Notes in Electrical Engineering, 1718–26. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_207.

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5

Nguyen Van, Nam, Hanh Le Thi, Minh Phan Nhat, and Long Lai Ngoc Thang. "Red-Light Running Violation Detection of Vehicles in Video Using Deep Learning Methods." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 214–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08878-0_15.

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6

Arhin, S. A., E. C. Noel, L. Williams, and M. Lakew. "Development of a red-light running violation index model for signalized intersections." In Intersections Control and Safety, 41–52. WIT Press, 2013. http://dx.doi.org/10.2495/978-1-84564-764-3/004.

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7

Latour, Bruno. "The Strange Entanglement of Jurimorphs." In Latour and the Passage of Law. Edinburgh University Press, 2015. http://dx.doi.org/10.3366/edinburgh/9780748697908.003.0013.

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Bruno Latour closes out this volume by taking hold of several threads running through the preceding chapters. In addition to responding to a few of the criticisms that have cropped up, Latour offers remarks on the specific analyses developed in several of the chapters in order to shed light on crucial elements of the AIME project and his view of the legal mode of existence, addressing among other things domains, institutions, normativity, jurimorphs and a few modal crossings stimulated by the work of the book’s contributors. The outlines of a new concept – the red letter of the law – even begin to take shape as Latour moves between and among the compelling and original arguments of the individual chapters.
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Тези доповідей конференцій з теми "Red light running Australia"

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Elias, Sardar, Moojan Ghafurian, and Siby Samuel. "Effectiveness of Red-Light Running Countermeasures." In AutomotiveUI '19: 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3342197.3344542.

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Qian, Hong-bo, and Yu-pu Dong. "Engineering Countermeasures to Reducing Red-Light Running." In 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009. IEEE, 2009. http://dx.doi.org/10.1109/case.2009.22.

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Kim, Namkyu, Jisu Kim, Hongki Kim, Kimuk Lim, Youlim Ko, Nulee Jeong, Anthony H. Smith, and Helen A. McNally. "Red Light Running Prediction System using LIDAR." In 2019 IEEE Sensors Applications Symposium (SAS). IEEE, 2019. http://dx.doi.org/10.1109/sas.2019.8706098.

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4

Baughman, Carl. "A Data Collection Method for Red Light Running." In Seventh International Conference on Applications of Advanced Technologies in Transportation (AATT). Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40632(245)27.

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5

Caballero-Gil, Pino, Cándido Caballero-Gil, and Jezabel Molina-Gil. "Ubiquitous Collision Avoidance System for Red Light Running." In the 15th ACM International Symposium. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3243046.3243054.

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6

Wang, Lanjun, Liping Zhang, Wei-Bin Zhang, and Kun Zhou. "Red light running prediction for dynamic all-red extension at signalized intersection." In 2009 12th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2009. http://dx.doi.org/10.1109/itsc.2009.5309545.

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Hu, Zhe, Zhong-xiang Feng, Fei-yu Meng, and Qu Huang. "Differences of Influence Factors for Pedestrian Running a Red Light." In 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.256.

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Zhang, Ya-Ping, He-jiang Li, and Yi-Ping Deng. "A Review of “3E” Countermeasures to Reduce Red Light Running." In 16th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479896.168.

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9

Jahangiri, Arash, Hesham A. Rakha, and Thomas A. Dingus. "Adopting Machine Learning Methods to Predict Red-light Running Violations." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.112.

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10

Amarasingha, Niranga, and MN Mohammed Ilhaam. "PW 0298 Investigating red-light-running rates in sri lanka." In Safety 2018 abstracts. BMJ Publishing Group Ltd, 2018. http://dx.doi.org/10.1136/injuryprevention-2018-safety.133.

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Звіти організацій з теми "Red light running Australia"

1

Olson, Carl. Safety Effectiveness of Red Light Treatments for Red Light Running. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.882.

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