Intersection Control Type Research Articles

Comparative Sensitivity Analysis on Intersection Crash Prediction Models by Control Type: Highway Safety Manual Approach

A comprehensive, in-depth investigation into the comparative safety of different intersection control types is presented, specifically signalized and minor road stop-controlled intersections across facility types. Employing a rigorous sensitivity analysis, the study scrutinizes intersection crash prediction models (CPMs) rooted in the Highway Safety Manual (HSM) methodology. Through meticulous data analyses and thorough comparisons of predictive models across a range of major and minor road traffic volumes, the paper critically examines the portrayal of signalized and stop-controlled intersections within the default HSM Part C CPMs. Additionally, the study delves into the effects of certain crash modification factors on CPMs, further exploring the impacts of calibration factors and locally developed models on crash prediction results. CPMs for signalized intersections do not uniformly lead to fewer crashes compared with stop-controlled intersections, especially under certain traffic volumes and when considering fatal and injury crashes. Therefore, the choice of intersection control should be based on more nuanced criteria than traffic volume alone. Additionally, the research emphasizes the role of intersection geometry and operations, for example, the benefits of adding turn lanes and varying left-turn phasing, in influencing safety outcomes. This study significantly contributes to the understanding of intersection safety and HSM modeling approaches, providing a valuable resource for transportation professionals dedicated to advancing intersection safety. Examining strengths and limitations of existing models provides valuable input for researchers developing future intersection CPMs, and offers essential insights for transportation planners and engineers, informing decision-making for optimizing intersection control strategies and enhancing overall intersection safety and traffic management.

Examining Factors Contributing to Motorcycle Collisions with Left-Turning Vehicles at Urban Intersection Locations

Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note – in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists’ susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.

Recognition of Intersection Traffic Regulations from Crowdsourced Data

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

Effect of Intersection Control Type on Vehicle Emissions: A Case Study in Egypt

Effect of Intersection Control Type on Vehicle Emissions: A Case Study in Egypt

Identifying the Adaptability of Different Control Types Based on Delay and Capacity for Isolated Intersection

In urban road networks, intersections are the main bottlenecks. Selecting an appropriate intersection control type can significantly improve the performance of an isolated intersection. Therefore, this paper offers recommendations for selecting the most efficient control type among two-way stop control, signalized intersection (SIG), roundabout (RB), and signalized roundabout (SIGRB) based on capacity and delay. The procedure to calculate delay and capacity is taken from the Highway Capacity Manual 6th edition (2016), or developed separately if needed. Two flow patterns are assumed: fixed and time-varying demand. For fixed demand, the results show that SIGRB outperforms other control types both in capacity and delay at higher demand levels. It was also observed that increase in left-turn ratio increases the delay and decreases the capacity of all control types while its impact on SIGRB was the least. Considering time-varying demand, traffic volume fluctuates over the 5-h period of the analysis. It was found that using both RB and SIGRB together creates significantly less delay compared with the other options. Additionally, using RB provides less variability in delay when there is fluctuation in demand. The major finding of this research is that RB and SIGRB have potential benefits for delay in conditions of (i) high traffic volume, (ii) high left-turn ratio, and (iii) demand fluctuation. Furthermore, it is suggested that SIG should be used if the left-turn ratio is relatively low. The results of this study could help decision-makers to choose the best control type for an isolated intersection under various traffic conditions.

Selection of Intersection Control Type in Urban Areas

Abstract In Egypt, urban planning projects usually do not consider the intersection type. This study developed charts for practitioners that could be used to determine the optimal intersection. The paper studied intersections including; signalized, roundabout, and two-way stop control. It provides a study for the intersection at the targeted level of service D. The traffic patterns were used to calculate the intersection’s capacity and the average control delay. The combinations of turning distribution, percent of heavy vehicles, and type of control were considered. The relationship among capacity, delay and traffic volume has been used to deduce the minimum required lane configuration for each intersection. The results indicated that the roundabout is the best alternative for intersection control with maximum capacity and minimum delays for medium traffic. However, signalized intersection is the best alternative at heavy traffic volumes and a high proportion of left-turning volume.

A Scalable Deep Learning Framework for Extracting Model Inventory of Roadway Element Intersection Control Types From Panoramic Images

In the United States, the Model Inventory of Roadway Element (MIRE) provides a comprehensive list of data that are needed to support states’ data-driven safety programs. The intersection control is part of the MIRE Fundamental Data Elements (FDE) for which state Departments of Transportation are required to complete the collection by September 30, 2026. It is essential roadway data that have been used widely in traffic safety studies. This study proposes a scalable and automated deep learning framework for detecting and classifying stop and yield intersection controls using panoramic street view images. The Faster Region-based Convolutional Neural Networks (Faster R-CNN) model architecture was used to detect and classify stop and yield signs from the images. A transfer learning process was deployed using the Inception-ResNet-v2 generic feature extractor to accelerate the training and performance of the deep learning model with less data collection effort. The effectiveness and scalability of the proposed framework were tested on a sample of road intersections in the state of Michigan. The proposed deep learning model achieved a recall value of 97.7% and 98.2% for detecting and classifying stop and yield signs respectively. The evaluation of the model performance at a county level suggests that the model can be scaled to a statewide level without a substantial increase in the demand for computational resources. As demonstrated in this study, state DOTs can leverage the advancement of deep learning techniques and the availability of imagery data to expedite the process of collecting the MIRE data.

Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data

This paper proposes an automated framework that utilizes Light Detection and Ranging (LiDAR) point cloud data to map and detect road obstacles that impact drivers’ field of view at urban intersections. The framework facilitates the simulation of a driver’s field of vision to estimate the blockage percentage as they approach an intersection. Furthermore, a collision analysis is conducted to examine the relationship between poor visibility and safety. The visibility assessment was used to determine the blockage percentage as a function of intersection control type. The safety assessment indicated that intersections with limited available sight distances (ASD) exhibited an increased risk of collisions. The research also conducted a sensitivity analysis to understand the impact of the voxel size on the extraction of intersection obstacles from LiDAR datasets. The findings from this research can be used to assess the intersection without the burden of manual intervention. This would effectively support transportation agencies in identifying hazardous intersections with poor visibility and adopt policies to enhance urban intersections’ operation and safety.

Human-like speed modeling for autonomous vehicles during car-following at intersections

This study aims to model drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for autonomous vehicles (AV). Several car-following behavioral measures were extracted at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) was recorded to train the model using three machine-learning techniques. The results showed that the XGBoost model is superior to other techniques with R2 values of 0.99 and 0.97 for training and testing datasets, respectively. The results also indicated that the control type impacts driver speed at the minimum following distance. The modeled speed will provide a more comfortable experience for AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and their control type.

Identifying Roadway Physical Characteristics That Contribute to Emissions Differences between Hybrid and Conventional Vehicles

Conventional vehicles and hybrid electric vehicles (HEVs) will be the dominant vehicle types in the U.S. private passenger vehicle fleet for the foreseeable future. This study used a real-time data set obtained from monitoring vehicle performance and emissions from a series of test runs from a conventional vehicle and a HEV over an 18-month period. An innovative new method of analysis allowed the assessment of different emissions rates between the two propulsion systems and the attribution of these differences to physical roadway/infrastructure characteristics. Regression and cross-classification results identify specific roadway characteristics that contribute to emissions differences between the vehicle types. Overall, the models that included maximum grade and intersection control type performed best, however, speed limit and horizontal curvature were also shown to be important. The performance differences identified in this project confirm that engine controls that are responsive to roadway characteristics are necessary.

Integration of Microsimulation and Optimized Autonomous Intersection Management

AbstractAutonomous intersection management (AIM) is a type of intersection control for autonomous vehicles which eliminates the need for a traffic signal by using vehicle-to-infrastructure communic...

Factors influencing pedestrians’ decision to cross the road by risky rolling gap crossing strategy at intersections in Dhaka, Bangladesh

Pedestrian road-crossing strategy is one of the most important pedestrian road-crossing behaviors. The safety of the pedestrians often depends on it. Among the road-crossing strategies, rolling gap crossing strategy is the riskiest one. The objective of this research was to explore the factors that influenced pedestrians’ decision to cross the road by rolling gap crossing at intersection. Data regarding road-crossing strategy of the pedestrians, their characteristics, their road-crossing behavior, intersection geometry, and traffic environmental condition were collected through videography survey method, on-site observation, and secondary source from six intersections of Dhaka, Bangladesh. A binary logistic regression model was developed in this study by using the collected data. Results of the developed model showed that seven statistically significant factors strongly influenced pedestrians’ decision to cross the road by rolling gap crossing at intersections. These factors were intersection control type, median width, vehicle flow, available gap on the road, age group of the pedestrians, their crossing group size, and their behavior of crosswalk usage. The results of this study would help the policymakers to take proper interventions to alleviate pedestrian safety problems.

Study on Pedestrian Compliance Behavior at Vehicular Traffic Signals and Traffic-Police-Controlled Intersections

The compliance behavior of pedestrians at controlled intersections is an important determinant of the number of crashes involving pedestrians at those intersections. The objective of this study was to explore compliance behavior of the pedestrians at vehicular traffic signals and traffic-police-controlled intersections in Dhaka, Bangladesh. Two types of compliance behavior were examined: compliance with vehicular traffic signals and traffic police direction, and compliance with crosswalk. First, factors influencing each compliance behavior of pedestrians were identified from the existing literature and correlation test results. With those identified factors, two discrete choice models were developed: a multinomial logistic (MNL) model for explaining the compliance behavior with vehicular traffic signals and traffic police direction, and a binary (BLR) model for exploring the compliance behavior with crosswalk. The results of the MNL model showed that compliance behavior was significantly associated with intersection control type, gender, crossing group, baggage handling by pedestrian, and vehicle flow. Whereas, the BLR model showed that compliance with crosswalk was significantly influenced by age of the pedestrians, compliance with intersection control direction by pedestrians, and vehicle flow. These findings would help the policy-makers to take countermeasures to alleviate traffic safety related problems.

Analysis of Pedestrian Crossing Speed and Waiting Time at Intersections in Dhaka

Pedestrian crossing speed and waiting time are critical parameters for designing traffic signals and ensuring pedestrian safety. This study aimed to carry out microscopic level research on pedestrian crossing speed and waiting time at intersections in Dhaka. To fulfill this aim, crossing-related data of 560 pedestrians were collected from three intersections in Dhaka using a videography survey method. Descriptive and statistical analyses were carried out, and then two multiple linear regression (MLR) models were developed for these two parameters by using the collected data. From the results, 1.15 m/s was found to be the design pedestrian crossing speed. Results also show that the crossing speed of pedestrians was associated with intersection control type, gender, age, crossing type, crossing group size, compliance behavior with control direction, and crossing location. In case of waiting time, findings show that pedestrians did not want to wait more than 20–30 s to cross the road. Furthermore, the waiting time of the pedestrians varied with intersection control type, gender, age, minimum gap, waiting location, and vehicle flow. Findings of this study will help to alleviate traffic safety problems by designing an effective intersection control system.

Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics – driver error, crash type, vehicle damage and injury severity – by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety.The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver’s age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.

Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling

Pedestrians are considered the most vulnerable road users who are directly exposed to traffic crashes. With a view to addressing the growing concern of pedestrian safety, Federal and local governments aim at reducing pedestrian-involved crashes. Nevertheless, pedestrian volume data are rarely available even though they among the most important factors to identify pedestrian safety. Thus, this study aims at identifying surrogate measures for pedestrian exposure at intersections. A two-step process is implemented: the first step is the development of Tobit and generalized linear models for predicting pedestrian trips (i.e., exposure models). In the second step, negative binomial and zero inflated negative binomial models were developed for pedestrian crashes using the predicted pedestrian trips. The results indicate that among various exposure models the Tobit model performs the best in describing pedestrian exposure. The identified exposure-relevant factors are the presence of schools, car-ownership, pavement condition, sidewalk width, bus ridership, intersection control type and presence of sidewalk barrier. It was also found that the negative binomial model with the predicted pedestrian trips and that with the observed pedestrian trips perform equally well for estimating pedestrian crashes. Also, the difference between the observed and the predicted pedestrian trips does not appear as statistically significant, according to the results of the t-test and Wilcoxon signed-rank test. It is expected that the methodologies using predicted pedestrian trips or directly including pedestrian surrogate exposure variables can estimate safety performance functions for pedestrian crashes even though when pedestrian trip data is not available.

Modeling route choice criteria from home to major streets: A discrete choice approach

Abstract A discrete choice model that consists of three sub-models was developed to investigates the route choice criteria of drivers who travel from their homes in the morning to the access point along the major streets that bound the Traffic Analysis Zones (TAZs). The first sub-model is a Nested Logit Model (NLM) that estimates the probability of a driver has or has no multiple routes, and if the driver has multiple routes, the route selection criteria are based on the access point’s intersection control type or other factors. The second sub-model is a Mixed Logit (MXL) model. It estimates the probabilities of the type of intersection control preferred by a driver. The third sub-model is a NLM that estimates the probabilities of a driver selecting his/her route for its shortest travel time or to avoid pedestrian, and if the aim is to take the fastest route, the decision criteria is based on the shortest distance or minimum stops and turns. Data gathered in a questionnaire survey were used to estimate the sub-models. The attributes of the utility functions of the sub-models are the driver’s demographic and trip characteristics. The model provides a means for transportation planners to distribute the total number of home-based trips generated within a TAZ to the access points along the major streets that bound the TAZ.

Investigating the role of exiting vehicles and turn indicator usage in gap acceptance at single-lane roundabouts

Roundabouts are becoming a preferred form of intersection control type due to their safety and capacity benefits. Yield control at roundabout entries inherently reduces fuel consumption and emissions when compared to other conventional intersection types. However, these benefits can be limited by poor driver behavior and judgment when entering the roundabout. This research addresses the nature of gap distributions and use of turn indicators by exiting vehicles at three single-lane roundabouts in Vermont, New York, and Alaska. Presented here is a comparison of vehicle headways measured at two different locations in each roundabout. Rejected headways are analyzed in the context of priority abstaining events when entering drivers yield to vehicles exiting on the same leg of the roundabout suggesting that “true” critical gaps are being overestimated. Results indicate that exiting vehicles, particularly those that do not use their turn indicators when departing from the major-stream of traffic have an influence on the entry decision of drivers on the same approach. This behavior is of particular concern for intersection efficiency (i.e., delay) and sustainability (i.e., excess fuel consumption and emissions). Results prompt the consideration of more consistent guidance on and enforcement of turn indicator use during roundabout negotiations.

Evaluation of Roundabout-Related Single-Vehicle Crashes

Roundabouts reduce fatal and injury crashes at intersections when converted from other intersection control types. In Wisconsin, roundabouts have been linked to a 38% decrease in fatal and injury crashes. Part of this reduction can be attributed to crash types that result in the mitigation of more serious injuries. However, the reduction comes at a cost because other crash types, such as single-vehicle collisions, may increase. Six years of crash data on 53 roundabouts in Wisconsin were examined for crash causes and geometric characteristics that affected single-vehicle crashes. Weather and impaired driving, particularly by younger drivers, were primary causes for more than half of all single-vehicle crashes at the study roundabouts. Younger drivers (18 to 24 years of age) were involved in a significantly higher proportion of single-vehicle crashes than the total proportion of licensed drivers in that age group. Younger drivers were involved in approximately one-third of all crashes that involved impaired driving and in two-thirds of all speed-related single-vehicle crashes. A negative binomial model was constructed to estimate run-off-road crashes at approaches. It was found that roundabouts with higher approach speeds and higher traffic volumes experienced more run-off-road crashes. Landscaped central islands experienced significantly lower frequencies of run-off-road crashes.

Comparison of exhaust emissions at intersections under traffic signal versus roundabout control using an instrumented vehicle

Abstract The traditional approach to the comparison of alternative types of road intersection control has focused mainly on efficiency and safety. In recent years, the increasing importance of air pollution produced by vehicular traffic has suggested that environmental considerations should be added to the above aspects as a criterion for intersection design. This study describes a before-and-after analysis conducted on a road intersection where a roundabout has replaced a traffic signal. Using a Portable Emission Measurement Systems (PEMS) installed on a test car, the instantaneous emissions of CO2, NOX and CO have been measured over repeated trips along a designated route. A total of 396 trips have been carried out in different traffic conditions and in opposite directions along the chosen route. Using statistical methods the existence of significant differences in emissions attributable to the type of intersection control has been investigated based on the experimental data. The results indicate that replacing the traffic signal with the roundabout tends to reduce CO2 emissions, even if the differences are not always statistically significant; on the contrary, the signalized intersection performs better in terms of NOX emissions. Finally, results are less clear for CO emissions, and differences are statistically non significant in most cases.