Stop Line Research Articles

Dynamic time distribution system monitoring on traffic light using image processing and convolutional neural network method

The number of vehicles that continues to increase every time can cause congestion and increase the potential for accidents. One type of violation that often results in accidents is motorists who break through traffic lights when the red light is on. One factor that encourage motorists to not obey traffic light signs is due to the distribution of green time which tends to be fast on relatively dense tracks. This final project aims to get dynamic green light on traffic light according to the volume of the vehicle for each lane using convolutional neural network method and can detect traffic light violations in the form of breaking the stop line markings on zebra cross. The sensor used is camera. There are three cameras used in each line. The number of cars detected will be sent to microcontroller via laptop. The data on the number of cars is used as input for the convolutional neural network to produce output data that serves as reference for obtaining the length of time for the green light. If the car stops above or exceeds the stop line on zebra cross when the red light is on, the car will be detected as violation.

Advanced stop boxes and their effect on traffic conflict rates between cyclists and turning vehicles

Advanced stop boxes (ASBs) (i.e., marked areas for cyclists in front of the stop line) are mentioned as a potential means to reducing the risk of crashes between cyclists and right-turning vehicles. This study estimates the safety effect of ASBs at signalised intersections using the log-odds method. Seven signalised intersections were filmed for 3,627 hours before and after constructing ASBs. Traffic conflicts were used as a surrogate for crashes. In total, the study found 644 traffic conflicts. The overall safety effect was not statistically significant: the conflict rate for right-hook conflicts decreased by 6% (p = 0.72), and for left-hook conflicts it increased by 21% (p = 0.26). The results differed at the seven sites, and there were only a few statistically significant results. At one site, the conflict rate of cyclists vs. right-turning vehicles decreased significantly, while it significantly increased at another site. One of the seven sites showed a significant increase in the conflict rate of cyclists vs. left-turning vehicles. A likely explanation is that few conflicts occur during the early green phase. In addition, the use rate of the ASB was low (0–2.7%).

P-OT015. Monitoring prefrontal cortical activation while driving a vehicle: A functional near-infrared spectroscopy study

Introduction . Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure changes in oxygenated- and deoxygenated-hemoglobin concentrations in response to neural activation during specific tasks and stimuli. In this study, we monitored the brain activity of the prefrontal cortex in subjects while driving to investigate the effects of speed on brain activity. Methods . Twenty healthy subjects with driving experience drove their own vehicles about 100 meters at any speed (5 trials) and at highest acceleration speed (5 trials) and were told to stop at the stop line. Sixteen channels of fNIRS recording were placed to overlie the prefrontal cortex of the subjects during driving. The distance between the stop line and the vehicle when the vehicle comes to a stop and oxygenated hemoglobin concentration was compared between driving at any speed and driving at the highest acceleration. Results . Variation in the distance between the stop line and the vehicle was significantly greater when driving at the highest acceleration compared to driving at any speed. Oxygenated hemoglobin concentration recorded in the prefrontal cortex was significantly greater while driving a car to a stop at the highest acceleration than at free speed. Conclusion . Our findings suggest that driving at highest acceleration speeds requires more attention resources than driving at any speed. We have shown that monitoring brain activity using fNIRS has advantages and usefulness of in real driving.

EFFICIENCY UTILIZATION OF GREEN TIME AT COUNTDOWN SIGNALIZED INTERSECTION

The use of countdown traffic signals, although not common in North America, is increasing in Asia and European countries. Past studies conducted albeit outside the United States have addressed some operational benefits and issues associated with the use of countdown traffic signals, including but are not limited to effect on saturation flow rate, and red light running. Experience from the successful use of pedestrian countdown signals at urban traffic intersections has engendered some local interest in the use of countdown traffic intersections has engendered some local interest in the use of countdown traffic signal devices in the United States. The purpose of this research was to analyse using driver behaviour survey the efficiency of green-time utilization rate at countdown traffic signal devices in the United States. Specifically, this research performed a comparative analysis based on the postulation that countdown traffic signal is associated with underutilized effective green time when compared to the scenario at intersections controlled by traditional (non-countdown) traffic signals. Binary regression models were developed, using data obtained from the driver behaviour survey, to estimate decision probabilities for different combinations of vehicle position (i.e. distance to the stop line) and available green time (i.e. time left before the transition to yellow phase). A comparison between the estimated decision probabilities (determined from deductive reasoning) for traditional traffic supported a pattern of underutilization of green time for the countdown signal scenario. The underutilization of green time was associated with the likelihood of drivers slowing down prematurely to stop in reaction to the visual display of available green time before the change of signal phase. Underutilization of effective green time before the change of signal phase. Underutilization of effective green time translates into a lower saturation flow rate and hence reduced intersection capacity. The findings of this research could motivate additional studies pertaining to the determination of net operational benefits or plausibility of using countdown signal devices at urban traffic intersection in the United States.

The Saturated Flow Modeling on Motorcycle Behavior Based on Through Movements at Signalized Intersections

In developing countries such as Indonesia the proportion of motorcycles to other vehicles is reaching 76%, which is very high. The proportion of motorcycles in Bali Province exceeds 85% while in Denpasar City is 82%. Previous studies suggest that a high proportion of motorcycles will certainly affect the saturated flow. Therefore, the effect of motorcycles on saturation flows should not be neglected. With a high proportion of motorcycles that will affect the behavior of motorcyclists at irregular intersections such as in front of the stop line, besides other vehicles and behind other vehicles or in the flow, the behavior of the motorcycle affects the saturated flow. In calculating saturated flow, the Indonesian Highway Capacity Manual (IHCM) does not have specific measures that adopt the behavior of motorcycles. If the motorcycle is not properly regulated, it will interfere with the movement of other vehicles when the green light is on. The behavior to be examined in this study is the behavior of motorcycles in front of the stop line, outside the current (beside flow), or in the current (inside flow). In this study, a model was developed to determine saturation flow based on the behavior of motorcycles for through movements at signalized intersections with or without ESSM.

Hand Detection for Safety to Avoid Accidents

Detecting the hand when it crosses the safety level and in return it also raises an alert in the form of alarm. So that the threat can be identified and proper measures are taken to overcome that. The methodology of the project goes as follows, taking input from camera , Image processing to detect hand, Projecting a line using computer vision, Raising alarm when hand crosses this projected safety line. The real time data is taken from the camera as an input to the Image processing algorithm. Then this input is processed to find the hand in image in it and checks whether the hand is crossing that safety line. If that hand is crossing the safety line we can simply raise alarm. The applications of the project are to the Employees who are working at industry are pushing the material into shredder machine. But somehow while pushing these material into shredder machine the employees are pushing their hands itself in the flow of work and the hands of employees were cut in that cause. So from a certain distance from shredder machine input we project a imaginary line using computer vision, So that if any hand crossing that imaginary line which is for safety we will raise an alarm. In addition, we can also extend the applications, by just replacing hand with the Bike, we can detect the bike, which is crossing the staggered stop line, and we can punish or fine them. As a part of object detection we are using Single short multibox detector.

Examining the efficacy of improved traffic signs and markings at flashing-light-controlled grade crossings based on driving simulation and eye tracking systems

The majority of the collisions at grade crossings occurred at flashing-light-controlled grade crossings. Understanding drivers’ behaviors and visual performances in the process of approaching the crossings is the foundation of improving crossing safety. This study aims at utilizing driving simulation and eye tracking systems to investigate the efficacy of improved traffic signs and pavement markings (PSM) at flashing-light-controlled grade crossings. The improved signs and markings were modeled in a driving simulation system and tested with a series of flashing light trigger time (FLTT) ranging from 2 s to 6 s with 1 s interval increment. Foggy conditions and drivers’ genders and vocations were considered in experiment design. Thirty-six fully-licensed drivers between 30 and 48 years participated in the experiment. Several eye-movement and behavioral measures were adopted as reflections of the subjects’ performances, including the first fixation time on signs and signals and distance to stop line, total fixation duration, compliance rate, stop position, average speed at the stop line, maximum deceleration rate and brake response time. Results showed that compared with traditional grade crossings signs and pavement markings, drivers could perceive signs timelier and fixate on the flashing-light signal earlier in PSM, especially in the scenarios of earlier FLTTs. The improvement in fixation performance and sign design contributed to a higher stop compliance rate. Importantly, it was found that drivers would hesitate to decide whether to stop or cross facing with flashing red lights, which is similar to the dilemma zone of roadway intersections. Drivers were more likely to fall into the dilemma zones when FLTT was <4 s. When FLTT was 2 s, it was particularly difficult to stop in front of the stop line. Moreover, under a foggy condition, drivers had a difficulty in searching signs and had a longer brake response time compared with a clear condition. For the characteristics of drivers, male drivers had longer fixation duration on signs than females. Professional drivers had a higher maximum deceleration rate compared with non-professional drivers. Above findings implied that improved traffic signs and markings would have a potential to improve traffic safety and deserve a field implementation in the future.

Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection

ABSTRACT Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods’ feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case’s traffic emissions.

Cooperative ship formation system and control methods in the ship lock waterway

To improve the safety and efficiency when ships pass through a lock, a Cooperative Ship Formation System (CSFS) in the lock waterway is proposed. An improved ship formation structure with the combination of leader–follower and behaviour-based structure is designed, and the relevant control modes are described according to the process of ship formation passing through the lock. MilliMetre-Wave (MMW) radars are introduced for the perception of ship states. Moreover, speed control, stop control and distance keeping control methods are proposed, which are related to the distance keeping control mode, speed control mode, stop control mode, stop-finished mode and emergency stop mode. Finally, a real CSFS platform with three different engineering ships was implemented in the Gezhouba Lock waterway to verify the effectiveness of CSFS and proposed control methods. Several experimental tests were carried out with the CSFS platform. The experiment results show that: 1) the average tracking error of the distance keeping control is 2.43 m; 2) the average speed control error of the leading ship is 0.08 m/s; 3) the leading ship can stop at around 2.0 m to the stop line, and both the leading ship and the following ship can switch smoothly between the different control modes.

Association of Infrastructure and Route Environment Factors with Cycling Injury Risk at Intersection and Non-Intersection Locations: A Case-Crossover Study of Britain.

Objective: This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak. Methods: The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case–control groups was used to compare characteristics of control and injury sites. Results: High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a ‘safety in numbers’ effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line (‘bike box’), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads. Conclusions: Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.

Investigating dilemma zone boundaries for mixed traffic conditions using support vector machines

ABSTRACT Crashes take place at intersections even when signalized due to the presence of dilemma zone, where the driver feels uncertain whether to cross or stop at the trigger of yellow signal. Erroneous decisions taken by the driver leads to rear-end or right-angle crashes, causing safety implications. Accurate position of the dilemma zone can help in implementing various safety measures and enhance the safety at signalized intersections. This study evaluates the dilemma boundaries for mixed traffic conditions using Support Vector Machine (SVM), a supervised machine learning technique. The study results indicate that the position of the dilemma zone is influenced by traffic and geometric characteristics. Results show that trucks and motorized three-wheelers have dilemma zone closer to the intersection compared to other vehicle types. The dilemma zone for mixed traffic conditions is found to be in the range of 30 m to 80 m from the stop line. The outcome of this study contributes to better understand the dilemma boundaries in developing nations with mixed traffic conditions, like India.

DRIVER PERFORMANCE THROUGH THE YELLOW PHASE USING VIDEO CAMERAS AT URBAN SIGNALIZED INTERSECTIONS

The main objective of this research is to examine the influencing parameters of driver performance through the yellow phase at urban signalized intersections with and without red-light running (RLR) cameras. Data were collected to include the intersection type, vehicle type, turning movement type, whether the vehicle position is in a platoon or not, presence of RLR cameras, green light flash devices, pedestrians, and pavement markings. A total of 2168 driver observations were extracted. Only 33.3% of the drivers stopped before the stop line, 59% of the drivers passed the intersection through the yellow phase, and 7% of the drivers committed RLR violations. The results showed that drivers were more likely to stop before the stop line through the yellow phase at locations with RLR cameras, green light flash devices, pavement markings, where pedestrians were present, and at a four-leg intersection. Chi-square tests indicated that all parameters had a significant impact on driver performance, except for the type of turning movement.

A Study of the Impact of the Transport Queue Structure on the Traffic Capacity of a Signalized Intersection Using Neural Networks

The article deals with the development of a computer system, which allows us to recognize vehicles, track them, and measure the time needed to cross an intersection by each car in the lane. The main area of research is the analysis of the dependence of the intersection crossing time on the position of vehicles in the queue formed in the traffic lane. To count the vehicles in the queue and determine their category, we used the Yolo v3 neural network and the SORT tracker modified to return the object class. The article describes in detail the proposed algorithm for collecting the data on the queue of vehicles: the number of vehicles in the queue and their classes, the time of passing the stop line and crossing the intersection, as well as determining the driving direction. All vehicles are divided into three categories depending on their acceleration. We analyzed the collected data on the queue structure and the time of its unloading and demonstrated their direct interconnection.

Optimization of adaptive traffic light control modes based on machine vision

Urbanization leads to a significant increase in traffic density in large cities. The growing transport concentration is accompanied by an increase in traffic congestion and emissions of harmful substances. The policies and decisions developed by the authorities, such as the expansion of existing roads and construction of new roads, as well as increase in transport taxes, no longer make it possible to maintain the adequate mobility of the population. One of the solutions is to increase the efficiency of road infrastructure utilization by forecasting the traffic situation and using the adaptive adjustment of traffic lights operation. With dynamic collection and interpretation of traffic flow data from traffic monitoring cameras, it will be possible to use the dynamic parameters of vehicles as indicators for the adaptive adjustment of traffic light regulation. The first part of the paper describes the use of machine vision and a neural network (YOLOv4) in tracking the parameters of traffic flows on road sections in front of intersections. The second part of the paper presents a methodology based on the dynamic regulation of traffic lights cycles and their duration, taking into account the current and forecast parameters of the traffic flow. The algorithm for the adaptive adjustment of traffic lights regulation considers the following parameters: the number and dynamic dimensions of vehicles moving towards the intersection; the number of vehicles in the queue in front of the stop line and their acceleration at the start of the movement. We determined the relationship of the intersection capacity when driving straight ahead with the dynamic dimensions of vehicles and the formation of a queue in front of the stop line, waiting for the green light. The study resulted in the development of an algorithm for setting the duration of both a particular phase and the entire traffic lights cycle in the tasks of eliminating or minimizing the possibility of congestion.

Methods to evaluate the effectiveness of advanced stop lines for motorcycles in improving road safety

In Barcelona, Advanced Stop Lines (ASL) for motorcycles, were implemented since 2009. This paper aims to describe the process followed in determining the best statistical model to analyse the effectiveness of ASL in preventing road traffic injury collisions. A quasi-experimental design of an evaluation study of an intervention with comparison group was performed.•The starting model is the Poisson regression model, including type of area (ASL, comparison zone), period (pre and post-intervention), linear trend of the number of collisions, period and type of area interaction, as explanatory variables.•Various models are tested to correct existing overdispersion in the starting model: Poisson -Lognormal, Poisson- Gamma, Zero-Inflated Poisson, Zero-Inflated Negative Binomial, Hurdle model with Poisson distribution and with Negative Binomial distribution. To select the best model the Akaike Information Criterion is used. The final model is the Poisson-lognormal, adding the area as random factor (for each area repeated measures for different years are available).•The coefficients of the model parameters are interpreted in terms of relatives risks (RR), and the percentage change in the number of collisions is estimated in the post regards the pre-intervention period, from the RRs (- (1-RR)), to quantify the impact of ASL. The interaction allows assessment of whether the effect of the intervention differed between ASL and comparison zones.

Evaluating Driver Behavior in Yellow Interval at Signalized Intersections

Signalized intersection is one of the most popular type of control at intersections in urban areas. Study of the elements of this type of control is vital to reduce accident rate, improve performance, and decrease delay. Yellow period is one of the parameters which has influence in performance of these type ofintersections specially accident rate. At the indication of yellow interval, when drivers approach a signalized intersection, they need either to proceed and cross the intersection or stop safely before the stop line. The objective of this study is to identify significant factors that impact driver’s decision at yellow interval in dilemma zone. Data was collected using questionnaire survey and video recording technique at two signalized intersections in Duhok city for the period of peak and off-peak hours. Assessing the effects of influential factors including vehicle speed, distance to the stop line and vehicle type on driver behavior has been achieved through observations of trends from graphs and statistical analyses using Logistic regression. Results of questionnaire survey indicate that the younger drivers of (18-30) years are more aggressive and end up running a red light. Moreover, the results of trend analysis showed that number of drivers who run the yellow decreases as the distances to stop line increases and dilemma zone at peak hours are located nearer to the stop line as compared to off peak hours. The lower green time to cycle time ratio in four leg intersection increased the probability of crossing the intersection instead to wait for another green period. Results of all three parts show that both the vehicle speed and distance to the stop line are significant factors affecting driver decision.

Assessing Drivers’ Compliance with Restrictive Yellow Traffic Lights in a Developing Country

Driving rules adopt permissive or restrictive policies concerning yellow light running (YLR). In a restrictive policy, vehicles behind the stop line are not allowed to enter the intersection on yellow no matter how close they are to the stop line. YLR policy affects driving risks, safety, and operation. There is limited knowledge about the restrictive policy and drivers’ compliance with this rule. Previous studies on YLR are limited in scope since they tended to use binary stop/go decision models without considering red light running decisions. This potentially results in the loss of information about drivers’ conformity to red signals. This paper examines whether drivers are only non-compliant with yellow lights or whether non-conformity to any prohibitive yellow/red signal emerges as a wider behavioral issue. This study develops regression choice models to predict drivers’ illegal yellow-light passing decisions in a developing country with a poor safety record and explores reasons for drivers’ non-compliance. The results obtained show that the restrictive policy is ineffective in relation to driver compliance, especially in cases where drivers’ non-conformity to any restrictive rule emerges as a behavioral issue of concern. Drivers make their stop/go decisions according to the time needed to cross the intersection, and they consider the yellow light as an opportunity for crossing. Yellow (red) light running rates were 101 (31) per 1,000 vehicles per hour (vph) for the restrictive policy, whereas these rates for the U.S.A., with a permissive policy, were at most 29 (6) per 1,000 vph.

Special Issue on Real World Robot Challenge in Tsukuba and Osaka

The Tsukuba Challenge is an open experiment of autonomous mobile robots in the real world. In its third stage since 2018, it is now to be held on a new course that starts at the Tsukuba City Hall. New tasks that require functions expected for autonomous travel in the real world have now been added, including passing checkpoints announced a day before the event, starting two vehicles simultaneously, traveling in an unmeasured environment, and strictly observing stop lines in the course. Also, in the spirit of the Tsukuba Challenge, the Nakanoshima Challenge, an open demonstration experiment project, has been held in the city of Osaka since 2018. As the only event in which autonomous mobile robots travel in the urban area of Osaka, the Nakanoshima Challenge is expected to identify new issues peculiar to autonomous navigation in real urban environments and to find solutions to them. This special issue includes a review paper on the Tsukuba Challenge, four research papers on the results of experiments done in the Tsukuba Challenge, four research papers related to the Nakanoshima Challenge, and three development reports. This special issue provides its readers with the frontline issues and the current status of development of autonomous mobile robots in real-world environments. We hope that the innovative efforts presented in this special issue will contribute to the development of science and industry.

Signal Parameters Estimation and Optimization Using Mobile Navigation Data

In the management and evaluation of traffic network, signal parameters are important for monitoring and evaluating the operation state and the traffic capacity of intersection. However, a wide range of real-time signal timing schemes lacks a clear and effective method. In this paper, we propose the signal parameter calculation method based on mobile navigation data. Then, the possibility of crossing intersection passing time of the stop line is studied. The time differences between passing times of different cycles are distributed periodically that several peaks appear cycle by cycle. The relationship between sampling rate and relative error is discussed. Combined with the distribution peak normality test, the appropriate distribution peak is selected through the actual case. The cycle lengths and effective red time parameters are calculated and compared with the known signal parameters. The result demonstrates the proposed method has high accuracy and provides data support for the research of the traffic management.