Left-turn Movements Research Articles

A Dictionary-Based Bayesian Approach to Optimizing Left-Turn Restriction Locations in Grid Networks

Left-turn movements at signalized intersections pose significant safety risks for the drivers and efficiency concerns for the traffic operations in urban networks. Restricting left-turn movements at selected locations has been shown to be effective at improving operational efficiency and mitigating safety concerns. However, determining optimal locations to restrict left-turns is a complex combinatorial optimization problem that is compounded by the lack of analytical forms for the objective function and constraints, as well as potential interdependencies between the decision variables. Following the common solution paradigm for this type of optimization problems, this paper presents a novel Bayesian approach that utilizes dictionary-based embeddings and is tailored for high-dimensional combinatorial (or even mixed) spaces. Simulation studies conducted using the Aimsun software under perfect or imperfect grid networks demonstrate that the presented method can consistently find promising left-turn restriction configurations that outperform the all-or-nothing strategies (to restrict all or none left-turn movements at all intersections), as well as the population based incremental learning algorithm. In addition, the presented method often does so with less simulation cost, thus showcasing its potential for efficient solution of more general traffic optimization problems.

Integrated Optimization Model of Lane Function and Signal Control for Tandem Intersections

This paper presents an integrated optimal controller for the tandem intersection with lane function design and signal control, aiming to improve intersection efficiency and service reliability with a multi-objective formulation. The tandem intersection is a type of unconventional intersection that can re-organize the vehicles at entrance lanes with sorting areas, and improve intersection capacity through the coordination of pre-signals and main signals. However, most existing studies related to tandem intersection control assume that the lane functions in the sorting areas for both the through and left-turn movements are the same, and the traffic demand remains static. To fill these gaps, this paper first identifies six different tandem control modes based on the different lane functions and phase sequence schemes in the sorting area, and the corresponding delay models for each mode are derived. Furthermore, an integrated optimization model is developed to minimize the mean and semistandard deviation of the intersection delay, and the Non-dominated Sorting Genetic Algorithm-II is used to obtain the optimal solution. A case study is conducted in a real-world intersection in Melbourne, Australia, under various traffic conditions. The results show that the proposed method can decrease average delay and queue length by 19.61% and 20.94%, respectively, compared with conventional intersection design.

Quantifying the impacts of right-turn-on-red, exclusive turn lanes and pedestrian movements on the efficiency of urban transportation networks

Previous studies demonstrated that restricting left turning movements can enhance transportation network efficiency. However, this strategy can lead to significant increases in the volume of right-turn movements. While these right-turn movements do not conflict with opposing through traffic, they still must interact with pedestrians in adjacent crosswalks. Further, their movement is influenced by the presence of right-turn-on-red, which is commonly applied at signalized intersections to improve intersection capacity, and the presence of exclusive right-turn lanes. This paper examines the influence of these three factors (pedestrian activity, right-turn-on-red, and exclusive right-turn lanes) on vehicular operational performance at a network-wide level. Simple grid network structures are considered due to their generalizability and the performance of three network types are tested: two-way streets that accommodate left turns, two-way streets that prohibit left turns, and one-way streets. The results reveal that when there are no pedestrians, right-turn-on-red can improve the operational performance regardless of the existence of exclusive lanes, especially for the networks restricting left-turn movements, and the presence of exclusive turn lanes increases the benefits obtained by allowing right-turn-on-red. The results also suggest that allowing right-turn-on-red is more important than providing exclusive lanes when the traffic load is light; however, under heavier traffic, exclusive turn lanes become more important. The presence of pedestrians reduces overall network performance and the benefits provided by right-turn-on-red for most scenarios, as expected. This decrease in performance is larger for networks made up of two-way streets compared to those made up of one-way streets. Exclusive lanes are also found to be critical for two-way streets with left turns protected to maintain network efficiency. Overall, prohibiting left turns on two-way streets still provides the largest operational performance of all networks with these features considered.

Modeling and analysis of vehicle path dispersion at signalized intersections using explainable backpropagation neural networks

The dispersion of vehicular paths is a common phenomenon in the inner area of signalized intersections due to heterogeneous driver behavior and interactions. This study aims to develop an explainable neural network-based model to describe the vehicle path dispersion by exploring the relationship between the path dispersion and external factors. A backpropagation neural network model was established to analyze the effects of external factors on the dispersion of through and left-turn paths based on real trajectory data collected from 20 intersections in Shanghai, China. Twelve influencing factors in varying geometric, traffic, signalization, and traffic management conditions were considered. The predictive power and transferability of the model were verified by applying the trained model on the four new intersections. The contributions of the influencing factors on the path dispersion were explored based on the neural interpretation diagram, relative importance of influencing factors, and sensitivity analysis to offer explanatory insights for the proposed model. The results show that the mean absolute percentage errors of the path dispersion models for the through and left-turn movements are only 14.67% and 17.65%, respectively. The through path dispersion is primarily influenced by the number of exit lanes, the offset degree between the approach and exit lanes, and the traffic saturation degree on the through lane. In contrast, the path dispersion of the left turn is mainly affected by the number of exit lanes, the left-turn angle, and the setting of guide lines.

A traffic queueing model for exit lanes for left-turn intersections

The unconventional design of exit-lanes for left-turn (EFL) has been confirmed to be an effective way to increase intersection capacity when left-turn demand is high. However, because of the unique feature of EFL control, there would be a strong oscillation in the left-turn queues at signalized intersections when the EFL strategy is used, which makes the analytical evaluation of the EFL strategy very difficult. In particular, the random nature of traffic demand would make it even harder to estimate the queues. To this end, in this paper, we propose a signal state-dependent queueing model to quantify the dynamic behavior of left-turn queues at signalized intersections under EFL control. The obtained results will contribute to establishing an analytical numerical framework that gives us the possibility to study the effect of various factors on the left-turn queues at the EFL intersections and provide insight into the unique queueing behavior that is most likely to manifest this strategy. We validated the proposed model using field data, and it shows a good agreement between the estimated results and the field results. In addition, the numerical experimental results show that the obtained performance measures are practically beneficial to provide a numerical tool to quantify the uncertainty in the queues of left-turn movements with EFL control and thus enrich the guidelines for further improving the use of EFL design.

Impact of Restricted Crossing U-Turns on Vehicular Emissions

In recent years, several innovative intersections have been designed and implemented as an alternative to typical two-way intersections. The Restricted Crossing U-Turn (RCUT) is a type of innovative intersection design that limits left-turns from the minor road by directing cross-street traffic to one of two auxiliary U-turns. This design seeks to improve intersection safety by reducing conflict points, as well as increase the capacity and decrease travel time on certain movements, such as the mainline through traffic. However, the change in the cross-street vehicle paths through the intersection can affect acceleration profiles and emissions. This research develops a methodology to test emission differences through simulation. Models of an RCUT and a traditional intersection were constructed and tested under varying turn volumes and mainline speeds conditions. CO2 and NOx emissions were calculated using Vissim simulation vehicle trajectories and the Motor Vehicle Emissions Simulator (MOVES) model. The modeled emissions values were used to compare the overall emissions performance of the RCUT versus a traditional intersection layout. The RCUT intersection layout was associated with an increase in average cross-street through and left-turn movement emissions because of the longer travel distances. However, a reduction in emissions was seen on the main line, primarily as a result of the additional green-time afforded by the RCUT design. Where the mainline volume to side-street ratio becomes sufficiently large, the RCUT performed better in overall intersection emissions levels.

Capacity at All-Way Stop Control Intersections: Case Study

This paper presents an empirical investigation into the capacity of all-way stop-controlled (AWSC) intersections. Video data was collected over four days at an AWSC intersection site in Bozeman, Montana. The site is characterized by single-lane approaches and high level of vehicular and pedestrian traffic. Using strict protocols, video records were processed at the individual vehicle level and several information metrics were extracted for each vehicle in the data set on all approaches. Study results indicate that the total intersection capacity at the study site varied between 400 and 1,400 vehicles per hour. The study suggests that the wide range of capacity observations is largely associated with the pedestrian crossing activity at the study site. Statistical tests confirmed that both pedestrian crossing activity and the level of conflict have significant effects on intersection capacity at the 95% confidence level. For movement type, the right-turn movement was not found to have a significant effect on intersection capacity while left-turn movement was found to negatively affect the intersection capacity. The results presented in this paper offer valuable information on AWSC intersection capacity, given the limited amount of information in the literature and the dated nature of those empirical observations.

Longitudinal Driving Behavior before, during, and after a Left-Turn Movement at Signalized Intersections: A Naturalistic Driving Study in China

A human-like driving model can help to improve the acceptance and safety of automated driving systems (ADS). To improve the performance of human-like driving and interaction with conventional vehicles of ADS, the speed behavior of left-turn vehicles at the signalized intersection was studied using natural driving data. In this study, 374 valid data points of left-turn snippets at signalized intersections were extracted and three phases were introduced based on the reaction behavior of braking, stopping, and accelerating in the left-turn process. Firstly, a one-way ANOVA was used to study the influence of traffic density, traffic light state, intersection type, and left-turn waiting area on the reaction position of each phase and the spatial distribution of the speed. The traffic light state and traffic density were the main significant effects. Furthermore, to analyze the spatial distribution of acceleration, a method of frequency contour was conducted. The butterfly-shaped frequency contour suggested that “the closer to the stop line, the higher the variation of acceleration”. Finally, the driving parameters at each phase were further analyzed. The main results indicate the following: (1) The red traffic light will lead to a larger variation of acceleration, a larger maximum deceleration, a larger starting acceleration, and a larger maximum acceleration. (2) On the condition of dense traffic density, more stops and the duration of the stop–go phase may cause the time pressure, and the driver tends to choose a greater maximum acceleration. (3) The red traffic light leads to a further reaction distance of all three phases, whilst increased traffic density only increases the reaction distance of the stop. (4) Both the dense traffic density and red traffic light lead to an earlier reaction time. The findings can provide a basis for the design of human-like driving of left-turn driving assistance systems and improve the interaction with left-turn conventional vehicles.

Development of Planning-Level Guidelines for Deploying Transit Signal Priority

Transit Signal Priority (TSP) has been proposed and implemented to reduce transit travel time, provide better schedule adherence, and improve transit efficiency. However, concerns have been raised about causing delays to cross street and left-turn movements. The cost-effective implementation of TSP requires methods to support the decisions associated with the implementation. The objective of this research was to examine the existing TSP guidance and propose enhanced planning-level guidelines for TSP deployment that can be applied to identify intersections where implementing TSP is beneficial. The proposed guidelines take consideration of bus delay, bus frequency, geometric and traffic feasibility, as well as the impacts on other movements. The effectiveness of the proposed guidelines was evaluated in comparison with existing guidelines using a microscopic simulation approach. The simulation results showed that the proposed TSP guidelines produce a better-balanced solution for minimizing both main street bus travel times and delays for other affected movements. The proposed guidelines also help to cut the TSP implementation costs by reducing the number of intersections identified for TSP implementation.

Roundabout with a strong U-Turn traffic

Abstract The traffic capacity of a roundabout depends very much on the existing traffic volumes on each approach, each direction of travel having an impact on the other movements in the intersection, especially on those with which it conflicts. Roundabouts where the turning maneuver has significant values, such as roads / streets where the left turn is allowed at long distances, substantially influence the capacity of the approach with which it intersects. This maneuver includes calculating delays and vehicle queues at all other arms, resulting in a reduction in their capacity and, ultimately, in the entire intersection. As can be seen from the results of the analyzed roundabout, U-turn (which replaces the left turn movement) produces a significant increase in delays on the rest of the approaches, thus preventing further development of the community.

Evaluation of Driving Behavior and Traffic Safety at a Shifting Movements Intersection

Unconventional intersection designs have been proposed for their theoretical potential to enhance traffic safety and operation simultaneously as a result of reducing the number of conflict points and signal phases. However, this has only been achieved with a very limited range of intersection designs which have a very low number of conflict points and under certain traffic conditions. For example, restricted crossing U-turn (RCUT) intersection, which has the lowest number of conflict points among other proposed intersection designs, has operational advantages at extremely unbalanced traffic volumes. Shifting movements (SM) intersection design, which has the same number of conflict points as the RCUT intersection, has been proposed to replace the RCUT implementation at intersections with medium to high minor traffic volumes. It was proven that SM outperforms an RCUT intersection which has medium to high minor traffic volumes in average delay and throughputs. This study aimed to investigate the safety aspects of this intersection design by utilizing the driving simulator. The effectiveness of using infrastructure-to-vehicle (I2V) communication for mitigating confusion at unconventional intersections was also investigated in the study. The results indicated that RCUT and SM intersections have similar safety performance and crossing them is completed with less risk than crossing the conventional intersection. However, there is a need to improve drivers’ knowledge about the SM intersection, especially with regard to the major left-turn movement. Most participants found that using I2V communication was helpful in understanding unconventional movement patterns.

Network-level turning movement counts estimation using traffic controller event-based data

Accurate turning movement counts (TMCs) data collected from regional-wide signalized intersections is critical to regional transportation planning and simulation modeling. A variety of existing traffic sensors, configured at intersections for traffic detection and signal control, can generate a large amount of real-time high-resolution event-based data from traffic controllers but few of these sensors are configured to collect TMC. This paper proposes a methodology for estimating network-level TMC using existing traffic controller event-based data without installing additional sensors. First, relevant features that can indicate traffic arrival are extracted from existing event-based data, including detector occupancy time, detector-triggered count, and green time duration. With these features, a multi-output multilayer neural network model is developed to estimate TMC. To further improve network-level TMC estimation accuracy, intersection infrastructure data and point-of-interest (POI) data are included as exogenous variables for the proposed model. Ninety-three signalized intersections are chosen from the Pima County region, Arizona, to calibrate and verify the developed model. The validation results show that the proposed model can accurately estimate TMC, as indicated by a median Root Mean Square Error (RMSE) of 41 veh/15 min, 11 veh/15 min, and 12 veh/15 min for through movement, left-turn movement, and right-turn movement volume estimation, respectively. This research provides a new possibility of utilizing existing data sources to obtain network-level TMC data without additional infrastructure and labor costs for transportation agencies.

Safety Performance of Unsignalized Median U-Turn Intersections

Alternative intersection designs can offer safety and operational benefits with potentially lower costs than conventional intersections when implemented in the proper setting. The Federal Highway Administration has previously identified a subset of alternative designs called reduced left-turn conflict intersections as a proven safety countermeasure. Median U-turn intersections (also known as “Michigan lefts” or “boulevard turnarounds”) are one such design that accommodates all left-turn movements via directional U-turn crossovers within the median. Prior work has consistently shown that median U-turn intersections can provide superior safety performance when used in the appropriate conditions. However, research that is specific to unsignalized reduced left-turn conflict intersections continues to be limited to work conducted before the Highway Safety Manual, or which includes restricted crossing U-turn intersections. This study included the evaluation of historical traffic crashes and volume data at 95 unsignalized intersections in the state of Michigan. This included the collection of data for 39 median U-turn sites and 56 reference group sites to estimate safety performance functions and crash modification factors that can be used when considering future conversions. Ultimately, crash modification factors for fatal and injury crashes of 0.438 and 0.686 are recommended when converting intersections with undivided two-lane two-way major approaches and four-lane divided boulevard major approaches, respectively. Although there was no significant difference in property damage only crashes associated with converting intersections with undivided, two-lane, two-way major approaches, a crash modification factor of 1.325 is recommended for property damage only crashes specific to conversions with four-lane, divided boulevard major approaches.

Characterizing the dynamics and uncertainty of queues at signalized intersections with left-turn bay

In most signalized intersections, in order to solve the problem of insufficient road space, left-turn bays are commonly set up at intersections with a shared through and left-turn lane. However, this design tends to result in the potential queue overflow and blockage problems, which further make the discharge process of either left-turn vehicles or through vehicles at the intersections to be interrupted unexpectedly during their respective green phase. As a result, the queueing processes of vehicles present high nonlinearity and uncertainty during a signal cycle. In this paper, a stochastic queueing model is proposed to characterize the dynamics and uncertainty of queues at signalized intersections with a left-turn bay. The proposed model takes full account of the random interaction between the through movement and the left-turn movement under the influence of overflow and blockage of queues. We obtain some average performance indicators as well as the signal-state-dependent dynamic performance measure of queues and its variance at any given time point within a signal cycle. The proposed analytical model is validated against the VISSIM simulation model under different scenarios; the results show a good degree of agreement. In addition, in order to systematically determine the queue dynamics and uncertainty, we also conduct a series of numerical simulations to quantitatively capture the queue characteristics of vehicles at signalized intersections with left-turn bay by considering possible factors.

A comprehensive analysis on the effects of signal strategies, intersection geometry, and traffic operation factors on right-turn crashes at signalised intersections: An application of hierarchical crash frequency model

Right-turn movements (equivalent to left turn movements for countries that drive on the right) at intersections are among the most complex driving maneuvers and require a high level of attention for turning across (potentially) oncoming traffic by accepting a safe gap. Not surprisingly, right-turn-involved crashes are one of the most frequent collision types at intersections (e.g., 42% of all signalised intersection crashes in Queensland, Australia). Unfortunately, the causes and contributing factors to right-turn crashes are not well understood, particularly the effect of right-turn signal strategies on the crash risk. In the safety literature, signal strategies are coarsely considered in two generic categories-protected right-turns and permitted right-turns. In reality, right-turn signal strategies could be of various types (usually 5) based on the level of intersection complexity and potential traffic conflicts. The effects of these signal strategies, along with the geometric and traffic factors, have not been well studied. To fill this gap, this study investigates the effects of right-turn signal strategies, intersection geometry and traffic operations factors on right-turn crashes at signalised intersections. To achieve this aim, crash frequency models were estimated using crash data from 221 signalised intersections in Queensland from the years spanning 2012 to 2018. Hierarchical Poisson Regression Models (random intercept models) were employed to capture the hierarchical structure of influences on crashes, with upper-level capturing intersection characteristics and lower-level capturing approach characteristics. The hierarchical model structure, disaggregate exposure variables, and signal strategies examined in this study give rise to an entirely unique study in the literature.

Continuous Flow Intersection Performance Measures Using Connected Vehicle Data

Continuous flow intersections (CFIs), also known as displaced left turns (DLTs), are a type of alternative intersection designed to improve operations at locations with heavy left-turn movements by reallocating these vehicles to the left side of opposing traffic. Currently, simulation is commonly used to evaluate operational performance of CFIs. However, this approach requires significant on-site data collection and is highly dependent on the analyst’s ability to correctly model the intersection and driver behavior. Recently, connected vehicle (CV) trajectory data has become widely available and presents opportunities for the direct measurement of traffic signal performance measures. This study utilizes CV trajectory data to analyze the performance of a CFI located in West Valley City, UT. Over 4500 trajectories and 105,000 GPS points are analyzed from August 2021 weekday data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) and extended PPDs to estimate split failures (SF), arrivals on green (AOG), traditional Highway Capacity Manual (HCM) level of service (LOS), and the distribution of stops. The estimated operational performance showed effective progression during the PM peak period at all the critical internal storage areas with AOG levels at exit traffic signals between 83% and 100%. In contrast, all external approaches with longer queue storage areas had AOG values ranging from 2% to 81% during the same time period. The presented analytical techniques and summary graphics provide practitioners with tools to evaluate the performance of any CFI where CV trajectories are available without the need for on-site data collection.

Impact of Turning Lane Storage Length and Turning Proportions on Throughput at Oversaturated Signalized Intersections

During oversaturated conditions, common objectives of signal timing are to maximize vehicle throughput and manage queues. A common response to increases in vehicle volumes is to increase the cycle length. Because the clearance intervals are displayed less frequently with longer cycle lengths and fewer cycles, more of the total time is used for green indications, which implies that the signal timing is more efficient. However, previous studies have shown that throughput reaches a peak at a moderate cycle length and extending the cycle length beyond this actually decreases the total throughput. Part of the reason for this is that spillback caused by the turning traffic may cause starvation of the through lanes resulting in a reduction of the saturation flow rate within each lane. Gaps created by the turning traffic after a lane change may also reduce the saturation flow rate. There is a relationship between the proportions of turning traffic, the storage length of turning lanes, and the total throughput that can be achieved on an approach for a given cycle length and green time. This study seeks to explore this relationship to yield better signal timing strategies for oversaturated operations. A microsimulation model of an oversaturated left-turn movement with varying storage lengths and turning proportions is used to determine these relationships and establish a mathematical model of throughput as a function of the duration of green, storage length, and turning proportion. The model outcomes are compared against real-world data.

The effect of elimination the left-turn movements on the intersections Capacity-A simulation study

Left-turn movement is an important element to determine the capacity of intersections due to high conflicts points. Therefore, finding the alternatives for such left turn movements may increase the intersection throughput and make it safer. This study discusses the effect of eliminating the left turn movements on the intersection capacity by adding U-turn sections after each approach to apply the left turning away of the intersection. This means that the left turn traffic will pass the intersection using through movements, use the added U- turns and use the intersection again to reach the destination direction using right turns. To apply such scenario, traffic simulation has been applied as a tool using Paramics traffic simulator which is one of the most confident tools over the world. Simulation models have been developed using Paramics considering both signalized and un-signalized intersections with different effective green times. The results suggested that applying such alternatives will significantly increase the intersection capacity. However, further research is needed to examine the geometric layout that would be useful for such alternatives and to confirm the validity of the findings by validation the developed simulation models.

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

Estimating cycle-level real-time traffic movements at signalized intersections

Real-time traffic movements at intersections is vital for transportation and traffic engineering. It helps in providing intersection traffic data and optimizing signal control plans. This study attempts to extend the data coverage by developing algorithms to estimate through and left-turn movements in real-time at signalized intersections. This study is the first attempt to estimate short-term traffic movement counts at signalized intersections at the cycle-level for signal control. Real-time data were collected from 19 intersections along two main corridors in Orange county, Florida. A framework was proposed to identify different variables considering the characteristics of the cycle-level traffic movement estimation. To develop generic algorithms, a proposed approach was utilized to generalize the traffic movement estimation at the corridor level. Signal timing and movement counts at the upstream and downstream intersections were utilized to estimate through and left-turn movements at target intersections. An extensive comparison study was carried out based on the processed data. The experimental results show that the proposed Gradient Boosting model outperformed the baseline models with Mean Absolute Percentage Error 9.53% and 4.6% for the through and left-turn movements, respectively. In addition, the transferability of the developed models for the abnormal traffic conditions was validated and the estimated model proved that it could instantaneously capture the traffic change due to an accident. It is expected that the proposed method could reduce the sensor cost and extend the movement data coverage, which could help in developing efficient signal control plans at signalized intersections.