Дисертації з теми "Red light running Australia"

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.

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

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.

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

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.

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

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

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

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.

Drivers' mistakes in making immediate decision facing yellow signal interval to stop or go through the intersection is one of main factors contributing to intersection's safety. Incorrect decision might lead to a red light running and a right -angle Collison when passing through the intersection or a rear-end collision when failing to stop safely.Improperly timed traffic signal intervals result in the inability of the drivers to make the right decision and can place them in the dilemma zone. Advance warning systems (AWS) have been used to provide information about the downstream traffic signal change prior to approaching the intersection. On the other hand, advance warning systems increase drivers approach speed according to the literature. However, effect of AWS on dilemma zone has not been studied before. The goal of this thesis is to minimize the number of vehicles caught in dilemma zone by determining more precise boundaries for dilemma zone and to reduce the number of red light violations by predicting the red light runners before arriving to the intersection. Here, dilemma zone boundaries at the presence of AWS has been reexamined with the aid of a large dataset (more than 1870 hours of data for two different intersections). Upper dilemma zone boundaries found to be higher for the intersections with AWS. This is due to vehicles' increasing the speed at the flashing yellow sings to escape the dilemma zone.Moreover, an algorithm for predicting red light runners and distinguishing them from right turners is presented.
Master of Science

碩士
國立交通大學
運輸科技與管理學系
99
The conflict points are much less at a T-junction than at a crossroad, thus making the red light running behavior at these two locations far different from each other. This research chooses 4 T-junctions and one crossroad in Zhongli City and Taoyuan City to observe the behavior. One of the T-junctions is chosen as the control site. Four different environment variables were selected for comparison: T-junction with left-turn lagging phase, T-junction with longer red-light phase, T-junction with red-light count-down displays, and the crossroad. The data were collected by video recording at each intersection, with one site recorded at night time. The result showed that red light running occurred most at the first 10% and the last 10% of a red phase. Drivers who decided to go straight in a crossroad hesitate to run red lights at the intermediate 80% time of a red phase, while about one quarter of drivers who run red lights at T-junctions violated at the intermediate 80% time of a red phase. Left turn in a red phase would disperse. At night, when the traffic is light, red light running rate is higher than that at daytime. Males and drivers who didn‟t accompanied by passengers are the major violators of the red light running. The rate of red light running at the first 10% of a red phase is less at T-junction with left-turn lagging phase, because hook-turn motorcycles would block the cars or motorcycles which intended to cross the junction. The T-junctions with red-light count-down displays give drivers clear forehead time when the light would change to red, thus reduce red light running at the last 10% red phase. Full-size cars and compact cars tend to run the red light during the first 10% of a red phase because of their large momentum. Motorcycles and bicycles, which have higher mobility would take off early, reflected by the result that their red light running often occur during the last 10% of a red phase.

碩士
逢甲大學
交通工程與管理所
91
According to paper review, a substantial of urban motor-vehicle crashes occur at intersection, and many intersection crashes involve drivers running through red light. Although drivers running through red lights constitute a major portion of intersection crashes, little is known about the characteristics of drivers who run red lights. For this reason, the present study used data collected by a self-reported questionnaire, which contained several demographic, driving experience, attitude, and red-light running behavior variables, to provide a profile of red light runners at an urban intersection. Red-light running behaviors were classified by the range of time into four categories, namely red-light running currently, running today, running frequency for the past three months, and running habit. Of the 553 respondents, 52(9.4%) drivers reported that they had run red light when entering the last signalized intersection, and 190(34.4%) respondents had run red light today. About drivers’ red-light running frequency for the past three months, 7.6% of them reported several times a day, 17.7% several times a week, and 55.5% once in a while. Besides, 499(90.2%) drivers have red-light running experience, and 207(41.5%) among these respondents are habitual red-light runners. For understanding the characteristics of red-light running drivers, a multinomial logit method was used. The results show that time of day, road familiarity, ever red light running today, and what kind of frequency affect currently running behaviors and variables such as high running frequency, habit, student, and age between 26 to 35 increase the probability to run the red light today；while gender, age, driving skill, marriage status, and habit influence running frequency for the past three months；finally, drivers who often emotionally run the red light are more likely habitual runners. Research introduces market segmenting method to analyze the mixed effect of two independent variables, and the outcomes reveal that segmenting models can explain the effect of each independent variable to different segmentations more. Furthermore, the same variable which has parameter with significant difference between market segmentations are picked up for the logit model adjustment. Moreover, respondents recommend that red light running can be reduced through strict enforcement, automated cameras, higher penalties, and better traffic signal operation.

碩士
中原大學
電機工程研究所
106
Abstract In this thesis, we will propose a scheme for detecting red light running by license plate recognition based on video surveillance. The main purpose is to detect the license plate of the vehicles passing through the red lights on the road by video surveillance to assist the police to ban irregular vehicle for reducing possibility of the traffic accidents. There are fourth main parts in this thesis. The first part introduces red light detection by using color and graphic detection. YCbCr color space and RGB color space are used to do the color detection. Roundness values range and area size are used to do graphic detection. The second part introduces red light stop line detection by using HSV color space. The reason of using HSV is stated below.HSV color space is not easily affected by light. In addition, HSV color space is independent of each other. In the third part we detect the red light running by license plate recognition. License plate detection system can be divided into three phases which are (1) pre-processing (2)vehicle plate locating and characters extracting and (3) the vehicle plate characters recognition. In the last part, simulation procedures are executed by matlab to present the results. In this thesis, the contributions of the research are as follows: (1) More convenient and efficient scheme: In the worldwide trend, government of countries apply buried pipeline for red light running detection. In this research, we detect red light running by license plate recognition based on video surveillance. It is more convenient and efficient than the former. (2) Low cost: While detecting red light running by license plate recognition based on video surveillance, there is no need to use buried pipeline. The cost of our scheme is much lower than the scheme by using buried pipeline.

碩士
中州科技大學
機械與自動化工程系
105
It is traffic construction and development made Taiwan, a very high population density, more convenience and circulation. Taiwan is the first in the world in terms of the number of motorcycles per square kilometer. Vehicle density is the top few, too. Excessive vehicle density results in higher accident rates even cause casualties and property damage. It's not difficult to find that most of the major causes of accidents are driver violations when we see the news media, newspapers and magazines for traffic accidents. Especially, there are very serious accidents when people running a red light in order to hurry, for the sake of convenience, consider themselves safe. In this paper, we discuss the driving motive of driving a vehicle running red light, and reference the Social Cognitive Theory of Bandura, a Social Psychologist. Through the interactive relationship of three elements: person, environmental, behavior, we explain how the behavior of driving a red light interacts with the external environment, and the importance of changing personal cognition in reducing violations. It will be inhibiting the frequency of violations, reducing traffic accidents and promote traffic safety by analyzing the motivation of red light behavior. This study was conducted by means of questionnaires. The research object is the person who has the road driving experience in Taiwan. Social cognitive theory is used as the theoretical basis, and designed questionnaires refer to the literature on the motives of traffic violations. The initial test questionnaires were distributed and retrieved in a manner that facilitated sampling. After deleting the invalid questionnaire and fixing the inappropriate topic, issue the official questionnaire again. A total of 120 questionnaires were issued and 93 valid questionnaires were retrieved. The exploratory study on red light behavior by questionnaires, the Cronbach's Alpha reliability analysis results were as follows: Environment impact behavior: 0.64. Person impact behavior: 0.775. Environment impact person: 0.711. Behavior impact person: 0.659. Nonparametric statistics results show: age, marital status, and whether or not a locomotive driver's license has a significant impact on red-light behavior, especially marital status. The reason may be that parents have the responsibility of educating their children, the red light behavior will endanger the safety of life.

碩士
國立交通大學
運輸與物流管理學系
107
The motorcycle is the most popular private vehicle and brings the highest traffic accidents in Taiwan. Red-light running violation is one of the most common violation behaviors. The behavior of red-light running (RLR) including psychological status and external stimulus. However, previous studies have only focused on rationality, single dimension of psychological states. Therefore, the present study aims at the related factors influencing RLR intention of motorcyclists, including (a)rationality: based on the theory of planned behavior, (b) irrationality: invulnerability and neuroticism, and (c) external stimulus: time pressure and social influence. A total of 335 motorcyclists successfully completed self-report questionnaires. The hierarchical linear modeling (HLM) was established to evaluate. We found that：(1) rationality such as attitude, subjective norm, and risk perception have significant negative correlations with RLR behavior, while the perceptual behavior control has a significant positive correlation with red light behavior；(2) irrationality such as invulnerability, has a moderating effect on perceptual behavior control and RLR behavior intention, while neuroticism has no moderating effect；(3) external stimulus such as time pressure and social influence have a significant positive correlation with RLR behavior. The results show that the Goodness of Fit of overall model is 0.219, the rational level contains 0.125 is higher than the external stimulus which contains 0.094.