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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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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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(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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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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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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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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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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11

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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Анотація:

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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12

Bonneson, James A., and Ho Jun Son. "Prediction of Expected Red-Light-Running Frequency at Urban Intersections." Transportation Research Record: Journal of the Transportation Research Board 1830, no. 1 (January 2003): 38–47. http://dx.doi.org/10.3141/1830-06.

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Statistics consistently indicate that red-light running has become a significant safety problem throughout the United States. Comprehensive guidelines for treating red-light running at problem intersections have been developed. Unfortunately, these guidelines do not include a tool or technique for quantitatively determining if a problem exists and if a countermeasure is truly effective. The objective of this work is to describe the development and calibration of such a tool. The calibrated prediction model developed for this research indicates that red-light running increases with flow rate, speed, and dense platoons arriving at the end of the phase. It was also found that red-light running decreases with increasing cycle length and cross-street width, and when back plates are used on the signal heads. Uses for the calibrated model are described.

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13

Awadallah, Faisal. "A Legal Approach to Reduce Red Light Running Crashes." Transportation Research Record: Journal of the Transportation Research Board 2096, no. 1 (January 2009): 102–7. http://dx.doi.org/10.3141/2096-14.

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14

Alexandros, Kokkalis, Papaioannou Panagiotis, Basbas Sokratis, and Politis Ioannis. "An Intertemporal Evaluation of Highway Intersections Red-light Running." International Journal of Transportation 5, no. 1 (April 30, 2017): 47–58. http://dx.doi.org/10.14257/ijt.2017.5.1.04.

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15

Brasil, Rafael Henrique, and Alexei Manso Correa Machado. "Automatic Detection of Red Light Running Using Vehicular Cameras." IEEE Latin America Transactions 15, no. 1 (January 2017): 81–86. http://dx.doi.org/10.1109/tla.2017.7827891.

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16

Perez, Miguel A., Zachary R. Doerzaph, Suzanne E. Lee, and Vicki L. Neale. "Rapid Prototyping Improves Research on Red-Light-Running Behavior." Ergonomics in Design: The Quarterly of Human Factors Applications 15, no. 4 (October 2007): 23–27. http://dx.doi.org/10.1518/106480407x255215.

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FEATURE AT A GLANCE: Unintentional red-light running is a relatively rare occurrence in everyday driving, but it can lead to serious crashes. While developing systems that warn drivers who are about to run a red light, we conducted tests that allowed us to place naive drivers in unexpected red-light-running scenarios on a test track. By adapting rapid prototyping design concepts, we were able to quickly iterate through research scenarios and develop a method that was repeatable, efficient, and realistic. We discuss lessons learned and pitfalls to avoid in the application of rapid prototyping concepts to human factors research design.

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17

Crofts, Thomas, and Tracey Summerfield. "Red Light on Sex Work in Western Australia." Alternative Law Journal 33, no. 4 (December 2008): 209–13. http://dx.doi.org/10.1177/1037969x0803300404.

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18

WU, MIN, LING ZHU, BINA HU, TIMON CHENG-YI LIU, DONG-LIANG RONG, and TONG-SHENG CHEN. "EFFECTS OF RED LIGHT AT 640 nm FROM LIGHT EMITTING DIODES ON THE RESPIRATORY BURST OF HUMAN NEUTROPHILS." Journal of Innovative Optical Health Sciences 01, no. 02 (October 2008): 285–94. http://dx.doi.org/10.1142/s179354580800008x.

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Photobiomodulation (PBM) has been reported to have effects on respiratory burst of polymorphonuclear neutrophils (PMNs), but little focus was on the individual differences of human PMNs. The latter was investigated in this study. The PMNs were isolated from peripheral blood of 13 volunteers (10 ordinary persons, 3 athletes) and treated by red light (640 ± 15 nm) from light emitting diodes (RLED) at 50, 100, 300, 500 and 1000 J/m2 for 100 seconds, respectively. Blood samples of athletes were extracted at different running stages in 10 km non-interrupted long-distance running, before running, 1 hour after running began, just finishing the running, resting for 1 hour and 2 hours after running. The PMN respiratory burst was assessed by the nitroblue tetrazolium test. It was found that there were three types of RLED PBM on the respiratory burst of 3 types of PMNs, respectively, inducing for the subactivated PMNs, inhibiting for the overactivated PMNs and none for the PMNs in homeostasis. It was then concluded that there may be RLED PBM on dysfunctional human PMNs while none on those in homeostasis, and RLED at 300 J/m2 for 100 seconds may have bi-direction modulation on PMN respiratory burst.

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19

Park, Jeong Soon, Yong Il Jung, and Yun Hwan Kim. "Characteristics and Influencing Factors of Red Light Running (RLR) Crashes." Journal of Korean Society of Transportation 32, no. 3 (June 30, 2014): 198–206. http://dx.doi.org/10.7470/jkst.2014.32.3.198.

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20

Wissinger, Leanne M., Joseph E. Hummer, and Joseph S. Milazzo. "Using Focus Groups to Investigate Issues of Red Light Running." Transportation Research Record: Journal of the Transportation Research Board 1734, no. 1 (January 2000): 38–45. http://dx.doi.org/10.3141/1734-06.

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Red light running (RLR) has been an important issue among transportation officials seeking to make intersections safer for drivers and pedestrians. Many cities in the United States have started programs aimed at reducing the number of red light violations, and many of these programs include the use of automated enforcement utilizing a camera to record violations. Previous research on such enforcement has quantified the rate of its public acceptance through surveys; however, little research has been performed probing the reactions and concerns of the public toward red light cameras. For this study, focus groups were used to investigate the attitudes, beliefs, and perceptions of the public toward RLR and red light cameras. Fifteen focus groups were held throughout North Carolina with representatives from organizations interested in and knowledgeable about traffic safety, traffic engineering, and traffic law enforcement, as well as with people not professionally involved in law enforcement or traffic engineering. Some of the focus group discussions involved such issues as determining an appropriate RLR grace period, developing an educational campaign, addressing financial issues, and determining appropriate penalties for RLR violations. Participants voiced their opinions on both sides of the issues; for instance, many participants said they strongly believed there should be some sort of grace period with automated enforcement, whereas others said they felt a zero-tolerance policy should be used. Also, many participants voiced their unequivocal support for automated enforcement, whereas others expressed concerns.

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21

Kraus, Edgar, and Cesar Quiroga. "Legislative Issues Related to Automated Enforcement of Red-Light Running." Transportation Research Record: Journal of the Transportation Research Board 1830, no. 1 (January 2003): 48–55. http://dx.doi.org/10.3141/1830-07.

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Red-light running is one of the leading causes of crashes in urban areas in the United States. A number of strategies are available to address this problem, including engineering countermeasures, educational campaigns, and improved law enforcement. Law enforcement agencies are increasingly relying on automated systems using photographic devices to enforce red-light-running laws. While automated enforcement systems appear to have wide public support, there is considerable confusion among drivers, engineers, planners, and decision makers as to the legality and constitutionality of those systems. The debate is particularly acute when it comes to issues such as privacy, use of information, and constitutional rights. These issues are analyzed and legal strategies are compared in states that have passed or attempted to pass legislation to regulate automated enforcement. The analysis highlights differences among states depending on their statutory laws and whether red-light violations are treated as civil or criminal offenses. The analysis reveals major differences in the way states legislate program details, which, in turn, affect program implementation. Also included is a review of current European red-light-running legislation, where automated enforcement systems have a longer history than in the United States.

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22

Chen, Xiqun (Michael), Lingxiao Zhou, and Li Li. "Bayesian network for red-light-running prediction at signalized intersections." Journal of Intelligent Transportation Systems 23, no. 2 (October 17, 2018): 120–32. http://dx.doi.org/10.1080/15472450.2018.1486192.

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23

Jahangiri, Arash, Hesham Rakha, and Thomas A. Dingus. "Red-light running violation prediction using observational and simulator data." Accident Analysis & Prevention 96 (November 2016): 316–28. http://dx.doi.org/10.1016/j.aap.2016.06.009.

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24

Wang, Xuesong, Rongjie Yu, and Chujun Zhong. "A field investigation of red-light-running in Shanghai, China." Transportation Research Part F: Traffic Psychology and Behaviour 37 (February 2016): 144–53. http://dx.doi.org/10.1016/j.trf.2015.12.010.

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25

Herath, Chamara, and Niranga Amarasingha. "Factors Affecting Red-Light Running of Pedestrians at Signalized Intersections." Journal of Advances in Engineering and Technology 1, no. 1 (September 30, 2022): 20–32. http://dx.doi.org/10.54389/ykso4631.

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Hundreds of pedestrians have died and many have been injured in the past decades as a result of Red Light Running (RLR) infractions. According to the United States Department of Transportation, 846 pedestrians have died and 143,000 have been injured in 2019 due to RLR violations. The majority of previous studies have focused on pedestrian behavior at an intersection, whereas only a few have looked into pedestrian RLR violations. The main objectives of this research are to find the pedestrians’ RLR rate in Sri Lanka and to find ways to reduce the RLR rate of pedestrians at the signalized crossing. Video observation surveys were conducted to collect data at three signalized intersections within Kandy city limits during weekdays for two hours per site. Pedestrian demographic variables such as gender and age; crossing characteristics such as crossing type, direction, crossing speed etc; and site characteristics such as crossing length, pedestrian green time, etc. were recorded. Chi-square and binary logistic regression tests were done. Results showed that out of 178 females, 130 had compliance with signal phases and out of 386 males, only 215 had compliance with RLR of a pedestrian. Furthermore, other independent variables such as age, crossing type, direction etc. were also associated with compliance RLR of pedestrians under Chi-square results. Based on the results of binary logistic regression, the variables such as gender, crossing type, number of traffic lanes, and pedestrian speed are significant when decreasing the log of probability -0.658, -3.040, -1.022 and -2.556 of compliance for RLR respectively. Variables that crosswalk utilization are also significant when increasing the log of probability of RLR 1.406 of compliance for RLR. The results would help develop safer pedestrian infrastructures and engineering countermeasures as well as assist the researchers and practitioners in better understanding pedestrian crossing behavior at signalized intersections. KEYWORDS:Pedestrian’s violation,Road safety, Signalized crosswalk, Red-light running.

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26

Lindheimer, Tomás E., Eric J. Fitzsimmons, and Steven D. Schrock. "Evaluation of Confirmation Lights on Red Light Running Violations by Time-into-Red Phase." Transportation Research Record: Journal of the Transportation Research Board 2584, no. 1 (January 2016): 24–29. http://dx.doi.org/10.3141/2584-04.

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27

Schleinitz, Katja, Tibor Petzoldt, Sophie Kröling, Tina Gehlert, and Sebastian Mach. "(E-)Cyclists running the red light – The influence of bicycle type and infrastructure characteristics on red light violations." Accident Analysis & Prevention 122 (January 2019): 99–107. http://dx.doi.org/10.1016/j.aap.2018.10.002.

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28

Yang, Xiaobao, Mei Huan, Bingfeng Si, Liang Gao, and Hongwei Guo. "Crossing at a Red Light: Behavior of Cyclists at Urban Intersections." Discrete Dynamics in Nature and Society 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/490810.

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Анотація:

To investigate the relationship between cyclist violation and waiting duration, the red-light running behavior of nonmotorized vehicles is examined at signalized intersections. Violation waiting duration is collected by video cameras and it is assigned as censored and uncensored data to distinguish between normal crossing and red-light running. A proportional hazard-based duration model is introduced, and variables revealing personal characteristics and traffic conditions are used to describe the effects of internal and external factors. Empirical results show that the red-light running behavior of cyclist is time dependent. Cyclist’s violating behavior represents positive duration dependence, that the longer the waiting time elapsed, the more likely cyclists would end the wait soon. About 32% of cyclists are at high risk of violation and low waiting time to cross the intersections. About 15% of all the cyclists are generally nonrisk takers who can obey the traffic rules after waiting for 95 seconds. The human factors and external environment play an important role in cyclists’ violation behavior. Minimizing the effects of unfavorable condition in traffic planning and designing may be an effective measure to enhance traffic safety.

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29

Huan, Mei, and Xiaobao Yang. "A Reliability-Based Analysis of Bicyclist Red-Light Running Behavior at Urban Intersections." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/794080.

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This paper describes the red-light running behavior of bicyclists at urban intersections based on reliability analysis approach. Bicyclists’ crossing behavior was collected by video recording. Four proportional hazard models by the Cox, exponential, Weibull, and Gompertz distributions were proposed to analyze the covariate effects on safety crossing reliability. The influential variables include personal characteristics, movement information, and situation factors. The results indicate that the Cox hazard model gives the best description of bicyclists’ red-light running behavior. Bicyclists’ safety crossing reliabilities decrease as their waiting times increase. There are about 15.5% of bicyclists with negligible waiting times, who are at high risk of red-light running and very low safety crossing reliabilities. The proposed reliability models can capture the covariates’ effects on bicyclists’ crossing behavior at signalized intersections. Both personal characteristics and traffic conditions have significant effects on bicyclists’ safety crossing reliability. A bicyclist is more likely to have low safety crossing reliability and high violation risk when more riders are crossing against the red light, and they wait closer to the motorized lane. These findings provide valuable insights in understanding bicyclists’ violation behavior; and their implications in assessing bicyclists’ safety crossing reliability were discussed.

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Campisi, Tiziana, Giovanni Tesoriere, Antonino Canale, Socrates Basbas, Panagiotis Vaitsis, Andreas Nikiforiadis, and Miltiadis Nikolaidis. "Comparison of Red-Light Running (RLR) and Yellow-Light Running (YLR) traffic violations in the cities of Enna and Thessaloniki." Transportation Research Procedia 45 (2020): 947–54. http://dx.doi.org/10.1016/j.trpro.2020.02.072.

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31

Smith, David M., John McFadden, and Karl A. Passetti. "Automated Enforcement of Red Light Running Technology and Programs: A Review." Transportation Research Record: Journal of the Transportation Research Board 1734, no. 1 (January 2000): 29–37. http://dx.doi.org/10.3141/1734-05.

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Automated enforcement involves the use of image capture technology to monitor and enforce traffic control laws, regulations, or restrictions. The increase in aggressive driving and the high percentage of crashes that occur at intersections led to the development and implementation of automated enforcement technology to detect and cite motorists who enter a signalized intersection in violation of the red phase. The primary focus of this research was to establish how well the automated enforcement system achieves its principal objective: reducing crashes and red light running (RLR) violations at signalized intersections. Evaluations of automated enforcement programs at three locations in the United States were performed as part of this research. The automated enforcement programs in New York City; Polk County, Florida; and Howard County, Maryland, were reviewed as a part of this research. Some of the major findings from this research are as follows: ( a) In 1997 there were over 789,000 crashes at signalized intersections, of which 97,000 were attributed to RLR; ( b) In 1997, 961 deaths were attributed to RLR; ( c) Electronic enforcement is a proven technique used globally to curb RLR violations and crashes; ( d) A synthesis of automated RLR enforcement programs in the United States showed promising results; ( e) New York City has the oldest automated RLR enforcement program in the United States and has yielded a 20 percent reduction in violations since 1993; ( f) Although additional data quantifying the effect of automated RLR enforcement campaigns are needed for Polk and Howard counties, preliminary findings are promising; ( g) A 10-step process for successful implementation of an automated RLR enforcement program was illustrated.

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32

Li, Meng, Xiqun (Michael) Chen, Xi Lin, Dingyuan Xu, and Yinhai Wang. "Connected vehicle-based red-light running prediction for adaptive signalized intersections." Journal of Intelligent Transportation Systems 22, no. 3 (August 23, 2016): 229–43. http://dx.doi.org/10.1080/15472450.2016.1215921.

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33

Shaaban, Khaled, Dina Elnashar, and Essam Radwan. "Influence of Crash Report Forms on Red Light Running Crash Data." Journal of Traffic and Logistics Engineering 1, no. 2 (2013): 197–201. http://dx.doi.org/10.12720/jtle.1.2.197-201.

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34

Huang, Kuang-Shih, Po-Jui Chiu, Hsin-Mu Tsai, Chih-Chung Kuo, Hui-Yu Lee, and Yu-Chiang Frank Wang. "RedEye: Preventing Collisions Caused by Red-Light Running Scooters With Smartphones." IEEE Transactions on Intelligent Transportation Systems 17, no. 5 (May 2016): 1243–57. http://dx.doi.org/10.1109/tits.2015.2502142.

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35

Ahmed, Mohamed M., and Mohamed Abdel-Aty. "Evaluation and spatial analysis of automated red-light running enforcement cameras." Transportation Research Part C: Emerging Technologies 50 (January 2015): 130–40. http://dx.doi.org/10.1016/j.trc.2014.07.012.

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36

Fraboni, F., V. Marín Puchades, M. De Angelis, L. Pietrantoni, and G. Prati. "Red-light running behavior of cyclists in Italy: An observational study." Accident Analysis & Prevention 120 (November 2018): 219–32. http://dx.doi.org/10.1016/j.aap.2018.08.013.

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37

Retting, Richard A., Susan A. Ferguson, and Charles M. Farmer. "Reducing red light running through longer yellow signal timing and red light camera enforcement: Results of a field investigation." Accident Analysis & Prevention 40, no. 1 (January 2008): 327–33. http://dx.doi.org/10.1016/j.aap.2007.06.011.

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38

Yan, Xuedong, Essam Radwan, and Elizabeth Birriel. "Analysis of Red Light Running Crashes Based on Quasi-Induced Exposure and Multiple Logistic Regression Method." Transportation Research Record: Journal of the Transportation Research Board 1908, no. 1 (January 2005): 70–79. http://dx.doi.org/10.1177/0361198105190800109.

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According to recent national statistics, red light running crashes represent a significant safety problem at signalized intersections. To examine the overall characteristics of red light running crashes, this study used the 1999 to 2001 Florida crash database to investigate the crash propensity related to traffic environments, driver characteristics, and vehicle types. The quasi-induced exposure concept and multiple logistic regression technique were used to perform this analysis. The results showed that traffic factors including number of lanes, crash time, weather, highway character, day of week, urban or rural location, speed limit, driver age, alcohol or drug use, physical defect, driver residence, and vehicle type were significantly associated with the risk of red light running crashes. Furthermore, it confirmed that there were significant interaction effects between the risk factors, including crash time and highway character, number of lanes and urban or rural location, weather condition and driver age, driver age and gender, alcohol or drug use and gender, and type of vehicle and gender.

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39

Huang, Jianchang, Guohua Song, Jianbo Zhang, Zufen Li, Yizheng Wu, and Lei Yu. "The Impact of Pedestrian and Nonmotorized Vehicle Violations on Vehicle Emissions at Signalized Intersections in the Real World: A Case Study in Beijing." Journal of Advanced Transportation 2021 (March 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/8849234.

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Emission around intersections has become an issue in the urban traffic network. This paper aims to investigate the impact of pedestrian and nonmotorized vehicle violations on emissions at mixed-traffic flow intersection based on the volumes of vehicles, nonmotor vehicles, and pedestrians. Also, it focuses on the arterial and collector intersections with high vehicle volume and limited space. Running red light and crossing intersection diagonally are two critical violations, accounting for 91.75% of effective violations (interference with vehicles’ operation). In this context, a violation blocking model is developed to estimate the blocking probability for each vehicle based on the volumes of pedestrians and nonmotor vehicles. The model includes two scenarios. (1) Through phase: the violation blocking model of running red light is developed based on the survival curve (the relationship between waiting time and running red light probability). (2) Left-turn phase: the violation blocking model at this phase includes two parts: (i) crossing the intersection diagonally model is developed for the first vehicle and (ii) running red light model is developed for subsequent vehicles. The existing emission model can estimate the emissions based on the blocking positions. In the case study, emissions increase with the vehicle volume approaching the saturated flow rate and the volumes of nonmotor vehicles and pedestrians increasing. Results show that the maximum emission increase of CO (carbon monoxide) for through phase and left-turn phase can reach 16.7% and 36.4%.

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40

Wang, Wencheng, Zhenzhou Yuan, Yanting Liu, Xiaobao Yang, and Yang Yang. "A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections." Journal of Advanced Transportation 2019 (July 8, 2019): 1–13. http://dx.doi.org/10.1155/2019/2345903.

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It is a dangerous behaviour for pedestrians and nonmotorized vehicles to cross intersections without waiting when they arrive at intersections during the red-light period. This paper investigates three typical signalized major-major intersections in the center of Beijing, by collecting and analyzing 1368 samples of pedestrians and nonmotorized vehicles. A random parameter logit model (RPLM) is established, with immediate red-light running (IRLR) behaviour as the dependent variable. The results show that the number of people waiting upon arrival, number of people crossing upon arrival, traffic mode, motor vehicle phase upon arrival, and speed change upon arrival have significant effects on IRLR behaviour. Accordingly, we suggest enforcing education administration on cyclists to reduce cyclists’ IRLR behaviour. Thus, people’s red-light running (RLR) behaviour will further decrease with fewer cyclists’ IRLR behaviour.

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41

Schattler, Kerrie L., Tapan K. Datta, and Colleen L. Hill. "Change and Clearance Interval Design on Red-Light Running and Late Exits." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (January 2003): 193–201. http://dx.doi.org/10.3141/1856-21.

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Red-light violations (RLV) have been an ongoing concern to many engineering professionals, because a large portion of crashes that occur at signalized intersections involve red-light running and such crashes often result in injuries and fatalities. It has been estimated that in the United States, about 260,000 traffic crashes occur per year that involve drivers who run red lights, of which 750 are fatal. A before-and-after evaluation of the impacts in terms of RLV and late exits at signalized intersections was performed with a change and clearance interval calculated according to ITE guidelines. The study included three signalized intersections located in Oakland County, Michigan. RLV data were collected with video cameras at intersection approaches before and after implementation of the change, and clearance intervals were calculated according to ITE guidelines. The results of the before-and-after study on RLV indicated mixed results. At one of the study intersections, the RLV rates were reduced after the modified change and clearance intervals were installed. However, at the other two study locations, no significant differences were found in RLV rates in the before and after periods. The rates of late exits significantly decreased after installation of the test change and clearance intervals at all three study intersections. Therefore, the effects of implemented all-red clearance intervals were effective in reducing the opportunity and risk of late-exiting vehicles being exposed to oncoming traffic at the three study intersections.

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42

Wang, Lanjun, Liping Zhang, Kun Zhou, Wei-Bin Zhang, and Xiqin Wang. "Prediction of Red-Light Running on Basis of Inductive-Loop Detectors for Dynamic All-Red Extension." Transportation Research Record: Journal of the Transportation Research Board 2311, no. 1 (January 2012): 44–50. http://dx.doi.org/10.3141/2311-04.

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43

Zhang, Liping, Kun Zhou, Wei-bin Zhang, and James A. Misener. "Prediction of Red Light Running Based on Statistics of Discrete Point Sensors." Transportation Research Record: Journal of the Transportation Research Board 2128, no. 1 (January 2009): 132–42. http://dx.doi.org/10.3141/2128-14.

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44

Mohammed, Hameed A., Masoud Ghodrat Abadi, and David S. Hurwitz. "Red-light running violation during car following at high-speed signalized intersections." Transportation Engineering 8 (June 2022): 100110. http://dx.doi.org/10.1016/j.treng.2022.100110.

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45

Abdul Manan, Muhammad Marizwan, Syed Tajul Malik Syed Tajul Arif, Ho Jen Sim, Norfaizah Mohamad Khaidir, Hawa Mohamed Jamil, and Muhammad Ruhaizat Abd Ghani. "Red light running motorcyclists at signalized intersection in Malaysia: An empirical study." IOP Conference Series: Materials Science and Engineering 512 (April 24, 2019): 012019. http://dx.doi.org/10.1088/1757-899x/512/1/012019.

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46

Guo, Yanyong, Pan Liu, Lu Bai, Chengcheng Xu, and Jun Chen. "Red Light Running Behavior of Electric Bicycles at Signalized Intersections in China." Transportation Research Record: Journal of the Transportation Research Board 2468, no. 1 (January 2014): 28–37. http://dx.doi.org/10.3141/2468-04.

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47

Lavrenz, Steven M., Christopher M. Day, Jay Grossman, Richard Freije, and Darcy M. Bullock. "Use of High-Resolution Signal Controller Data to Identify Red Light Running." Transportation Research Record: Journal of the Transportation Research Board 2558, no. 1 (January 2016): 41–53. http://dx.doi.org/10.3141/2558-05.

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48

Zhang, Ya Ping, Yan Liu, Lan Liu, and Chuan Yun Fu. "Type Classification of Motorized Vehicles Red Light Running and Their Characteristics Analysis." MATEC Web of Conferences 81 (2016): 02021. http://dx.doi.org/10.1051/matecconf/20168102021.

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49

Lee, Yongdoo, Zongzhi Li, Shengrui Zhang, Arash M. Roshandeh, Harshingar Patel, and Yi Liu. "Safety impacts of red light running photo enforcement at urban signalized intersections." Journal of Traffic and Transportation Engineering (English Edition) 1, no. 5 (October 2014): 309–24. http://dx.doi.org/10.1016/s2095-7564(15)30278-6.

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50

Retting, Richard A., Robert G. Ulmer, and Allan F. Williams. "Prevalence and characteristics of red light running crashes in the United States." Accident Analysis & Prevention 31, no. 6 (November 1999): 687–94. http://dx.doi.org/10.1016/s0001-4575(99)00029-9.

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