301 research outputs found

    The development and evaluation of a detection concept to extend the red clearance by predicting a red light running event

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    This study focuses on developing and evaluating a detection concept to extend the red clearance by predicting a RLR event. It will dynamically extend the red clearance several seconds when a RLR is predicted to happen otherwise zero. Therefore the time will be used more efficient. In order to evaluate the influence caused by alternative detector positions, a VISSIM network was built up, connecting Econolite ASC/3 controller and ATACID. Due to the lack of realistic data, all the data in this study are fictional, but close to real. Several parameters are artificially modified in order to gain larger RLR occurrence. The two of the most crucial changes are the changing of reaction to amber signal and decreasing yellow interval. In this study, a MATLAB program predicts the RLR violation based on the data received from VISSIM via COM Interface, and makes decisions And then, ASC/3 controller would execute every command received from the MATLAB program.Within every cycle, the MATLAB program would output data into a texture file, including the red extension type and red extension length. The result has four types: red clearance is extended while there is RLR violation (RERV); red clearance is extended no RLR violation (RENRV); red clearance not extended RLR violation (RNERV); red clearance not extended no RLR violation (RNENRV). RENRV and RNERV are two types of error that should be controlled in a reasonable range, especially RNERV. There are five scenarios while the position of the prediction detector is separately 100, 125, 150, 175 and 200ft away from the stopping bar. Each scenario has 5 runs with different simulation seed.By comparing the percentile of those four types of red extension among five scenarios, the system is more likely to extend all red as the distance increases; system accuracy will increase first and then decrease; Because detector located at 150ft has the least RNERV value, 2.1%, and least summation of RNERV and RENRV, 6.6%, it can be tentatively concluded that 150 ft is the appropriate position to locate the red extension detector, while the speed limit is 60 MPH in this study

    Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles

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    The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications

    Empirical evaluation of drivers’ start-up behavior at signalized intersection using driving simulator

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    Start-up behavior at signalized intersection mainly depends on perception reaction time of drivers to the green phase. This study investigated the start-up behavior at signalized intersections by considering reaction time, acceleration and jerk (the rate of change of acceleration) of drivers in the state of Qatar. Distributions for reaction time, acceleration and jerk were plotted and the mean and 50th percentile values were presented. Three demographic factors (i.e., gender, ethnicity and age) were analyzed using two-tailed/unpaired t-tests. The relationships between acceleration and reaction time, and jerk and reaction time were investigated by linear regression analyses. Descriptive analysis showed that drivers had a mean reaction time of 2.91 s. Furthermore, Arab drivers had significantly lower reaction time than non-Arab drivers. Regarding the jerk maneuvers, young drivers (below 30 years) displayed significantly higher jerk than drivers of 30 years or above. Results from linear regressions showed significant negative correlations in both models (i.e., reaction time on acceleration, reaction time on jerk). As this study targeted multi-cultural drivers’ population, the results of reaction time and jerk distributions could be used as inputs in simulation models which are developed for evaluating driver behavior and safety at signalized intersections in regions with multi-cultural driving population

    Intergreen Time Calculation Method of Signalized Intersections Based on Safety Reliability Theory: A Monte-Carlo Simulation Approach

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    In China, around ninety percent of the traffic accidents at signalized intersections occur within the signal change intervals, especially during signal change from green to red. Hence, intergreen time (IGT), that is, yellow change interval plus red clearance interval, is of great significance to the safety at signalized intersections. The conventional calculation method of IGT ignores the randomness of drivers’ behaviors, which we believe is an important factor in calculation of IGT. Therefore, the purpose of this research is to investigate a new approach to calculate the IGT based on safety reliability theory. Firstly, a comprehensive literature review concerning the conventional calculation methods of IGT is conducted. Secondly, a theoretical calculation method of IGT based on safety reliability theory is put forward; different from the conventional methods, this model accounts for the uncertainty of driving behavior parameters. Thirdly, a Monte-Carlo simulation is employed to simulate the interactive process of perception-reaction time (PRT) and vehicular deceleration and solve the proposed model. Finally, according to the Monte-Carlo simulation results, the curve clusters describing the relationship between IGT, safety reliability (50%-90%), and intersection width (15-35m) are drawn. Results show that the IGT of a signalized intersection, obeying the normal distribution, is influenced by multiple factors and most sensitive to the PRT and vehicular deceleration. Our method thus successfully incorporates the probabilistic nature of driving behavior. Taking the safety reliability into consideration can provide a more reasonable method to calculate the IGT of signalized intersections. Document type: Articl

    Dynamic Dilemma Zone Protection System: A Smart Machine Learning Based Approach to Countermeasure Drivers\u27s Yellow Light Dilemma

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    Drivers’ indecisions within the dilemma zone (DZ) during the yellow interval is a major safety concern of a roadway network. The present study develops a systematic framework of a machine learning (ML) based dynamic dilemma zone protection (DZP) system to protect drivers from potential intersection crashes due to such indecisions. For this, the present study first develops effective methods of quantifying DZ using important site-specific characteristics of signalized intersections. By this method, high-risk intersections in terms of DZ crashes could be identified using readily available intersection site-specific characteristics. Afterward, the present study develops an innovative framework for predicting driver behavior under varying DZ conditions using ML methods. The framework utilizes multiple ML techniques to process vehicle attribute data (e.g., speed, location, and time-of-arrival) collected at the onset of the yellow indication, and eventually predict drivers’ stop-or-go decisions based on the data. The DZP system discussed in the present study has two major components that work with synergy to ensure the total safety of a DZ affected vehicle: dynamic green extension (DGE), and dynamic green protection (DRP) system. Based on the continuous vehicle tracking data, the DGE system uninterruptedly monitors vehicle within the DZ and xiv predict vehicles that may face the decision dilemma if there is a sudden transition from green signal to yellow. After detecting such vehicles, the DGE system provides an exact amount of extended green time so that the detected vehicles could safely clear the intersection without any hesitation. There could be some vehicles that may end up running the red light due to various limitations. In this case, the DRP system provides an extended amount of all-red extensions after predicting potential red light running vehicles to nullify the likelihood of any intersection crashes. After the development, the DZP system is then implemented in several selected intersections in Alabama. Performance assessments are accomplished for the to see the safety and operation impact of the DZP system in implemented sites. The comprehensive assessment of the DGE system is accomplished with ten performance measures, which include percent green arrivals, percent yellow arrivals, percent red arrivals, dilemma zone length, and red-light running vehicles before and after the system implementation. Results show that the DGE system could significantly improve the overall intersection safety and efficiency. A short-term study on performance assessment of DRP systems shows that such a driver behavior prediction method could effectively predict 100% red-light-runners as well as efficiently provide the required amount of clearance time without hampering overall intersection efficiency. Based on the outcomes from the performance assessments of the DGE and DRP systems, it is safe to say the machine learning based DZP system would be able to promote intersection safety by protecting the dilemma zone impacted vehicles from potential intersection crashes as well as enhance the operational performance of intersections by intelligently allocate exact right-of-way to the vehicles and reducing the overall delays

    Hum Factors

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    Objective:This study reports current status of knowledge and challenges associated with the emergency vehicle (police car, fire truck, and ambulance) crashes, with respect to the major contributing risk factors.Background:Emergency vehicle crashes are a serious nationwide problem, causing injury and death to emergency responders and citizens. Understanding the underlying causes of these crashes is critical for establishing effective strategies for reducing the occurrence of similar incidents.Method:We reviewed the broader literature associated with the contributing factors for emergency vehicle crashes: peer-reviewed journal papers; and reports, policies, and manuals published by government agencies, universities, and research institutes.Results:Major risk factors for emergency vehicle crashes identified in this study were organized into four categories: driver, task, vehicle, and environmental factors. Also, current countermeasures and interventions to mitigate the hazards of emergency vehicle crashes were discussed, and new ideas for future studies were suggested.Conclusion:Risk factors, control measures, and knowledge gaps relevant to emergency vehicle crashes were presented. Six research concepts are offered for the human factors community to address. Among the topics are emergency vehicle driver risky behavior carryover between emergency response and return from a call, distraction in emergency vehicle driving, in-vehicle driver assistance technologies, vehicle red light running, and pedestrian crash control.Application:This information is helpful for emergency vehicle drivers, safety practitioners, public safety agencies, and research communities to mitigate crash risks. It also offers ideas for researchers to advance technologies and strategies to further emergency vehicle safety on the road.CC999999/ImCDC/Intramural CDC HHS/United States2020-11-24T00:00:00Z29965790PMC76855298713vault:3620

    Can i Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

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    Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. Providing remarkable perspectives on understanding e-bikers’ YLR behaviours, the predicting probability of e-bikers’ YLR violation could improve traffic safety under mixed traffic and fully autonomous driving condition in the future

    Hardware-in-the-Loop Simulation to Evaluate the Performance and Constraints of the Red-light Violation Warning Application on Arterial Roads

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    Understanding the safety and mobility impacts of Connected Vehicle (CV) applications is critical for ensuring effective implementations of these applications. This dissertation provides an assessment of the safety and mobility impacts of the Red-Light Violation Warning (RLVW), a CV-based application at signalized intersections, under pre-timed signal control and semi-actuated signal control utilizing Emulator-in-the-loop (EILS), Software-in-the-loop (SILS), and Hardware-in-the-loop simulation (HILS) environments. Modern actuated traffic signal controllers contain several features with which controllers can provide varying green intervals for actuated phases, skip phases, and terminate phases depending on the traffic demand fluctuation from cycle to cycle. With actuated traffic signal operations, there is uncertainty in the end-of-green information provided to the vehicles using CV messages. The RLVW application lacks accurate input information about when exactly a phase is going to be terminated since this termination occurs when a gap of a particular length is encountered at the detector. This study compares the results obtained with the use of these three aforementioned simulation platforms and how the use of the platforms impacts the assessed performance of the modeled CV application. In addition, the study investigates using HILS and a method to provide an Assured Green Period (AGP) which is a definitive time when the green interval will end to mitigate the uncertainties associated with the green termination and to improve the performance of the CV application. The study results showed that in the case of pre-timed signal control, there are small differences in the assessed performance when using the three simulated platforms. However, in the case of the actuated control, the utilization of EILS showed significantly different results compared to the utilization of the SILS and the HILS platforms. The use of the SILS and the HILS platforms produced similar results. The differences can be attributed to the variations in the time lag between vehicle detection and the use of this information between the EILS and the other two platforms. In addition, the results showed that the reduction in red-light running due to RLVW was significantly higher with pre-timed control compared to the reduction with semi-actuated control. The reason is the uncertainty in the end-of-green intervals provided in the messages communicated to the vehicles, as stated above. In the case of semi-actuated control, the results showed that the safety benefits of the RLVW without the use of AGP were limited. On the other hand, the study results showed that by introducing the AGP, the RLVW can reduce the number of red-light running events at signalized intersections by approximately 92% with RLVW utilization of 100%. However, the results show that the application of the AGP, as applied and assessed in this dissertation, can have increased stopped delay and approach delay under congested traffic conditions. This issue will need to be further investigated to determine the optimal setting of the AGP considering both mobility and safety impacts
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