Left-turning Vehicles Research Articles

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.

Establishing patterns of change in the efficiency of regulated intersection operation considering the permitted movement directions

The object of this study is a regulated intersection of city streets. The main task being solved is the reduction of queue lengths on approaches to such intersections by organizational measures. As a result of the research, patterns of change in queue lengths in front of the stop line were revealed, taking into consideration the specialization of traffic lanes and the distribution of traffic flows by directions. It was established that with an increase in the share of left-turning vehicles in the lane by 15 % and above, there is an increase in the length of the queue before the intersection from 45 to 80 m. The results indicate that when choosing a traffic pattern at the intersection, it is necessary to take into consideration not only the intensity of the flow itself but also the proportion of vehicles performing certain maneuvers (in this work, turning left is considered). A feature of the results is that they are obtained using simulation tools. This research method has made it possible to derive the value of the length of the queue in front of the intersection, changing the load on the approach from 200 to 1000 vehicle/h and the share of left-turning vehicles from 5 to 15 %. In addition, using a simulation model, it was checked how the intersection functions under various traffic management schemes on it when the input parameters of traffic flows change. The research results are recommended to be used in the development of traffic management schemes at regulated intersections. When determining the permitted directions of movement in the lanes, it is necessary to take into consideration the patterns of change in the length of queues before the intersection since under certain circumstances they may exceed critical values.

Influential Factors and Determination Method of Unconventional Outside Left-Turn Lanes Based on a BP Neural Network

To reduce the delay caused by the interweaving and parallel driving of multiple left-turn vehicles and through vehicles upstream of the intersection entrance, the influencing factors and determination methods of unconventional left-turn lanes are studied in right-hand traffic (RHT) countries. For countries driving right, left-turn lanes are usually on the inside of roads. However, when there are a large number of vehicles turning left in the outer lane of the upstream section of the intersection, these vehicles will be forced to pass many consecutive parallel lanes and then enter the left-turn lane. During this process, many traffic conflicts will occur between left-turning vehicles and going-straight vehicles, which will lead to longer traffic delays. To reduce traffic conflicts and delays caused by problems mentioned before, a scheme of setting left-turn lanes abroad is proposed, and major influencing factors and judgment methods of such a scheme are also studied. With the help of traffic simulation software VISSIM, the simulation model of intersection entrance with a different number of through lanes, length of weaving section and left turn inner and outer lanes is established. By inputting different numbers of entry through vehicles and left-turning vehicles in the outer lane, the delay data under different geometric and traffic conditions are obtained for simulation analysis. With the help of MATLAB software, this paper analyzes the influence of the length of the weaving area and the number of left-turning vehicles on the delay of inside and outside left-turning lanes under the condition of a different number of straight vehicles, as well as the variation law between them. By inputting parameters such as the length of the weaving area and the number of lanes, go-straight vehicles and left-turning vehicles into the system of VISSIM, a BP neural network model is constructed and trained. When investigating the entrances of four intersections, the BP neural network model is used to analyze and calculate the traffic delay and determine the setting scheme of the inside or outside of the left-turn lane. Through experiments and further studies, a phenomenon was found: When more vehicles chose to turn left or go straight in the outside lane, the length of the weaving area will become shorter, and the delay reduction effect of the unconventional left-turn lane will more obvious. The specific location of the left-turn lane should be determined by the constructed BP neural network model through the comparative analysis of delay, and the judgment results are in good agreement with the realistic scheme.

Determining Yellow Change and Clearance Intervals for Left-Turning Phases: Evaluation of the Current Guidelines with Connected Vehicle Data

In March 2020, the Institute of Transportation Engineers (ITE) published new guidelines for determining traffic signal change and clearance intervals that included using an extended kinematic equation for left turns. Whereas previous guidelines assumed constant speed for all vehicles approaching an intersection, this new equation accounted for left-turning vehicles needing more yellow time because they decelerate (assuming a maximum safe and comfortable rate) before making turning maneuvers. This paper evaluates these guidelines using real-world vehicle trajectories from the Ann Arbor Connected Vehicle Test Environment (AACVTE). These trajectories confirm that free-flowing left-turning vehicles decelerate, but deceleration usually starts at a moderate rate before reaching the critical distance and continues to the middle of the intersection. Vehicles then accelerate to a departure speed at the clearance point. Since the goal of the yellow change interval is to eliminate the dilemma zone such that a free-flowing vehicle can safely traverse the critical distance, these observations imply that the extended kinematic equation will overestimate the required duration for the following two reasons: (1) the critical distance is shortened by the speed reduction before the braking point; (2) the average traversing speed is higher as vehicles usually decelerate at a moderate rate instead of the maximum rate. The equation will underestimate the clearance interval as the average traversing speed through the intersection is slower than the intersection entry speed. We propose a new left-turn kinematic equation for determining yellow change and clearance intervals, and the results are validated from the observed vehicle trajectories.

Machine Learning Based Automated Left-Turn Vehicle Counts With Conventional Presence Mode Long-Loop Detectors: Alabama Case Studies

Inductive-loop detectors are still used in many intersections because highway agencies have insufficient budget to replace them with new technologies or when accurate traffic monitoring is not necessary. Particularly, long-loop stop-bar detectors have been widely used for left-turning vehicles and are operated under “Presence” mode, which focuses only on detecting the presence of vehicles for signal operations, not for counting vehicles. As a result, a significant discrepancy exists between the number of vehicle detections and the actual vehicle observations. The present study developed machine learning (ML)-based methods to overcome this discrepancy so that conventional long-loop detectors can also be used for estimating hourly left-turn volume. Several ML classifiers were adopted to predict the difference (i.e., detection versus observation) for every vehicle detection event. Three predictors (detector occupancy time, left-turn phases when the detector is on and off) from high-resolution data were used as input of the ML models. Results showed that all the models are statistically significant with p-value <0.05. The developed models were then applied to estimate hourly left-turn volume, by adding its prediction result to left-turn detection counts by the detector. The hourly left-turn volume estimated with the proposed method accounts for 96% of the actual left turns for low left-turn traffic conditions, and 93% for heavy left-turn traffic conditions. This indicates that the proposed method can provide a good estimation of hourly left-turn volume for signalized intersections operated with long-loop detectors. The method is simple and based on data from existing vehicle detection technologies already in typical signalized intersections.

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.

Safety and Operational Effectiveness of Protected Only Versus Protected/Permitted Left-Turn Signal Phase

Roadway intersection fatalities account for around 24% of total traffic fatalities each year, with left-turning vehicles associated with most of these crashes. To manage left-turning movements, various left-turn signal phases like protected only (PO), protected/permitted left-turn (PPLT), and flashing yellow arrow (FYA) are currently in use. However, their overall effectiveness has not been well established. The paper evaluates the safety and operational effectiveness of PO over PPLT left-turn signal phase using sample intersections from Louisiana. A total of 151 signalized intersections with uniform left-turn signal phases at all of their approaches were selected. Crash data recorded over five years at those intersections comprising a total of 13,278 crashes, including 1,275 left-turn crashes, were collected. A crash modification factor (CMF) was developed using the negative binomial model (NBM) to assess the safety benefits of PO over PPLT. The Highway Capacity Manual 6th Edition (HCM 6) control delay field measurement technique was used for the operational analysis. The study first developed CMFs for total crashes and only left-turn crashes at various severity levels. CMF of PO over PPLT revealed that PO significantly reduced all severity levels of left-turn crashes by more than 50%, however, it was only able to reduce injury and fatal total crashes. Second, the study found average control delay at PO (50.69 s/vehicle) significantly higher than at PPLT (46.04 s/vehicle). The difference in delay was only significant during the morning peak hour. Overall, from both safety and operation perspectives, the results indicate that PO performs better than PPLT.

Using Wrong-Way Driving Detection Data to Identify Factors That Affect Wrong-Way Driving Behavior and Compare Detection Technologies

Previous research has studied wrong-way driving (WWD) crashes, citations, and 911 calls to understand WWD characteristics, but no research has utilized WWD detection data. In this paper, WWD detection data from 48 toll road exit ramps were modeled to identify the factors that affect WWD frequency. The developed negative binomial model showed that exit ramps with more lanes, a toll booth, and higher crossing street volumes were predicted to have more WWD, whereas having more lanes on the opposing approach to the exit ramp was predicted to reduce WWD. Examination of 726 WWD detections showed that 23% were initiated by left-turning vehicles and 18% were initiated by right-turning vehicles, with significantly more left-turn entries occurring at night than right-turn entries (at α = 0.05). A case study of five ramps showed that exits with an extended median on the crossing road had fewer left-turn entries and exits with right-turn lanes on the crossing road had fewer right-turn entries. Lastly, radar, laser, and thermal WWD detection technologies were compared based on their WWD detections and false alarms. The laser sites had significantly more detections and false alarms than the radar sites (at α = 0.05), with the radar sites having a significantly higher WWD detection rate than false alarm rate. Radar false alarms were mainly caused by large trucks, whereas the laser sites had issues with equipment damage and traffic queues. The results of this paper can help agencies better understand WWD behavior and identify the most appropriate WWD detection technologies.

Analysis of left-turn lane capacity under autonomous vehicle mixed flow considering the influence of pedestrian volume and crosswalk length

Conflicts between pedestrians and left-turning vehicles cause pedestrian safety hazards and decrease vehicle turning capacity at signalized intersections. This commonly occurs in Japan where they share the same traffic light phase. Road capacity is influenced by the interaction between them. Autonomous vehicles (AVs) promise to improve the safety and efficiency of road networks. However, they will face the same problems as human-driven vehicles with pedestrian avoidance. Thus, this study estimated the capacity of the left-turn lane, in Japan, under AV mixed flows taking into account AV penetration rates, pedestrian flow rates, and crosswalk length. The impact of AVs on capacity was analyzed with different settings for the critical gap between vehicles and pedestrians. It was found that if AVs comply perfectly with traffic law, the capacity of the left-turn lane sharply decreases, especially in the case of long crosswalks.

An Analysis on Bicycle and Left-Turn Vehicle Conflict with Trajectories Extracted from Faster Regions with Convolutional Neural Networks Method at Signalized Intersections

An Analysis on Bicycle and Left-Turn Vehicle Conflict with Trajectories Extracted from Faster Regions with Convolutional Neural Networks Method at Signalized Intersections

Signal Control Method for Through and Left-Turn Shared Lane by Setting Left-Turn Waiting Area at Signalized Intersections

This paper proposes a signal control method for the through and left-turn shared lanes at signalized intersections to solve traffic conflicts between left-turn vehicles and opposing through vehicles by setting left-turn waiting area (LWA). Delays and stops are weighted to form an integrated performance index (PI) in a vehicle-to-infrastructure cooperation system. The PI models pertaining to all vehicles are established based on the LWA intersection. In addition, an optimized method of signal timing parameters is constructed by minimizing the average PI. VISSIM simulation shows that the average PI decreases by 6.51% compared with the original layout and signal timing plan of the intersection, since the increased delay of the side-road left-turn vehicles is insufficient to offset the reduced delay of the side-road through vehicles after the improvement. The sensitivity analysis shows that the greater the traffic volume of the phase including the through and left-turn shared lanes, the higher the operation efficiency of the LWA intersection compared with the typical permitted phase intersection. When the left-turn vehicles of the shared lanes in each cycle are less than the stop spaces, the LWA intersection can effectively reduce the average PI of the shared lanes. Furthermore, the more the stop spaces in the LWA, the lower the average PI in the same traffic conditions.

Using multinomial regression to explore the spatial factors affecting left-turn oncoming accidents involving motorcycles

Objective In mixed traffic with a high proportion of motorcycles, accidents involving motorcycles usually account for the highest percentage of total accidents and are thus important. Because the motorcycle can easily weave around within the traffic flow, leading to traffic conflicts and accidents, this research analyzed left-turn oncoming collisions at intersections and explored the impacts of crossing positions of the motorcycles and opposite left-turn cars on accident risk. Furthermore, based on the analysis results, possible ways to prevent crashes are proposed. Methods We collected accident videos to develop an image dataset to explore the spatial factors affecting left-turn accidents by multinomial regression model (MNL). In addition, to collect samples without incidents as the reference category of the MNL, this research used post encroachment time analysis to screen the samples of general traffic flow. Finally, elasticity analysis was employed to explore the influences of the spatial factors on incident risk probability. Results The results showed that the accident risk was significantly affected by the cross-section locations of the left-turn vehicles and motorcycles during the passing process. intersection. Conclusions When a straight motorcycle travels in the curb lane or an opposite left-turn car does not turn with an appropriate turning trajectory, the risk of a left-turn oncoming collision will increase. Therefore, even if a straight motorcycle has the right-of-way, the operator should choose an appropriate cross-section position and avoid riding to the right of a car when passing through an intersection.

ANALISIS KINERJA BUNDARAN TUGU PANCASILA JL. DIPONEGORO KABUPATEN JEPARA

The increase in population is always accompanied by an increase in the process of moving from one place to another. One of the engineering measures that can be done as an effort to regulate the displacement process is the existence of a roundabout. Pancasila Monument roundabout Jl. Diponegoro, Jepara Regency is one of the objects for conducting a performance review using vehicle survey methods, road inventory, and analysis. The survey results obtained that VLHR at the Tugu Pancasila Roundabout is quite dense with a capacity of up to 1,503.09 smp / hour from West-North, 1,890.40 from North-South, and 1,557.12 South-West. The level of service value of 0.88 is included in the service level category E, namely the traffic flow indicator is higher than the service level D with traffic volume approaching road capacity and very low speed. The driver began to feel the congestion with a short duration. The ratio of non-motorized vehicles to motorized vehicles was 0.015%, the ratio of left-turning vehicles was 19.790%, the ratio of right-turning vehicles was 29.867%, the ratio of PMI minor road vehicles was 50.342%.

A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections

With heterogeneous traffic agents moving at unprotected phase, severe crossing conflicts are witnessed at mixed-flow intersections, especially when left-turning vehicles are confronted with motorcycles. However, for modelling vehicle turning behaviour, potential conflicts involving diagonal-crossing motorcycles are seldom investigated in existing studies. To explore these scenes, we present a novel interaction-aware deep-learning framework. Firstly, a Long Short-Term Memory (LSTM) based network is employed to encode vehicle historical motion features. Secondly, each vehicle’s potential target lanes are identified with a probabilistic method, followed by a pooling module that extracts and summarizes intention features. Thirdly, Graph Attention Network (GAT) and a synthesized network are introduced to model vehicle-vehicle interaction and vehicle-motorcycle interaction respectively. Finally, multiple kinds of obtained features are sent to a LSTM based decoder module, where both future displacement and body orientation of vehicles are predicted. In short-time simulation experiments, average displacement error is reduced by 47.7% and 20.0% compared to baseline and state-of-the-art methods, with ablation studies conducted to quantify the efficacy of each kind of feature. Moreover, regarding recursive simulation, our model shows availability of reproducing lane-selecting and motorcycle-evasive behaviours. Distributions of post-encroachment time further indicate that the proposed framework can serve as a promising method to realize reliable motion planning for autonomous vehicles.

Optimal Trajectory Control for Left-Turn Vehicles at Exit Lane for Left-Turn Intersections

The exit lane for left-turn (EFL) intersection is a newly proposed unconventional intersection that can alleviate left-turn traffic congestion at intersections. The EFL design characteristic lies in that a part of exit lanes can be provisionally used as left-turn lanes to provide additional capacity for left-turn traffic. The inappropriate lane selection of human drivers decreases the effectiveness of such design. This paper establishes an optimal trajectory planning model for left-turn vehicles at EFL intersections under the automated driving environment. The longitudinal acceleration and lane changing variables are optimized simultaneously. The optimization model is formulated as a mixed-integer linear model, which ensures the computational efficiency. The effectiveness of the proposed model is explored under various traffic, geometric, and signal control conditions. The results show that the model can provide a suitable control strategy for left-turn vehicles. On average, traffic delay can be reduced by 47.3%. The proposed model has a promising control effect under high volume conditions. Moreover, the proposed model performs stably under the various length of the mixed-usage area.

Efficiency and Safety Evaluation of Left-turn Vehicles and Crossing Pedestrians in Signalized Intersections under the Autonomous Vehicle Mixed Flow Condition

Efficiency and Safety Evaluation of Left-turn Vehicles and Crossing Pedestrians in Signalized Intersections under the Autonomous Vehicle Mixed Flow Condition

Tandem Design of Bus Priority Based on a Pre-Signal System

Giving buses priority is an important measure to improve the attractiveness of public transport and to reduce urban traffic congestion. Reducing bus service delays as much as possible will have a positive impact on urban traffic. Based on the pre-signal system, a bus at an intersection with a left-turn special phase is optimized by “tandem design”. The design model is applied to the entrance of an intersection to study the process of vehicle arrival and departure at the main signal and pre-signal, and to calculate and analyze the delay changes of buses, straight social vehicles (meaning vehicles other than those required to be open to traffic) and left-turn vehicles before and after the adoption of “tandem design”. The results show that when the vehicle capacity at the intersection is saturated, the delays to buses and the delays of left-turn vehicles will be significantly reduced once the “tandem design” is adopted at the entrance of a cross intersection with a special left-turn phase. However, it has little effect on the delay of straight-on vehicles; with this system, the total delay experienced by straight vehicles will be reduced to one cycle.

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%).

Multi-type Bayesian hierarchical modeling of traffic conflict extremes for crash estimation

Most existing Extreme Value Theory (EVT) models were developed based on the total number of conflicts or a single type of traffic conflict to estimate the corresponding frequency of crashes. Using the total number of conflicts to estimate the total number of crashes may make it difficult to diagnose safety problems as countermeasures are usually related to specific conflict/crash types. Single-type EVT models may help to better explain the mechanism of crash occurrence of a certain type, but they only reflect the partial safety of a road entity. Therefore, developing EVT models for multiple types of traffic conflicts would be more representative. However, one important issue in modeling various types of traffic conflicts is that there will be considerable correlation among various conflict types. The modeled crash prediction results would be biased if the conflict type correlation is not accounted for. This study proposes a multi-type Bayesian hierarchical extreme value modeling approach, which has four advantages: 1) integrates multiple types of traffic conflicts; 2) incorporates the influence of several covariates; 3) combines traffic conflicts from different sites; 4) accounts for the unobserved heterogeneity in conflict extremes. The proposed multi-type approach was applied to estimate rear-end crashes and side-impact crashes of left-turning vehicles based on their corresponding traffic conflicts observed from two signalized intersections in the city of Surrey, British Columbia. Both conflict types of left-turning vehicles were characterized by the same indicator time-to-collision (TTC). Overall, the results show that the standard errors of the multi-type model parameters are smaller than those of single-type models. Moreover, the multi-type model produces more accurate crash estimates than its corresponding single-type models. The more accurate crash estimates are probably attributed to the two-type model accounting for the conflict type correlation.

An Innovative Signal Timing Strategy for Implementing Contraflow Left-Turn Lanes at Signalized Intersections with Split Phasing

Contraflow Left-Turn Lanes (CLLs) have the potential of being a solution for mitigating congestions at signalized intersections where split phasing is recommended or required. However, the current signal timing strategy for the intersections with CLLs cannot be directly applied at the signalized intersections with split phasing (SIWSP). To address this problem, this study proposed an innovative signal timing strategy, which is referred to as Counterclockwise Split Phasing (CSP) signal timing, for implementing the CLLs at the SIWSPs. A traffic simulation-based case study was conducted and the results indicate that, by using the proposed CSP signal timing plan, CLLs can be implemented at the SIWSP and can significantly reduce the traffic congestions caused by the high left-turn demand at this type of intersection. In addition, since the proposed CSP signal timing design procedure has fully considered the clearance time requirements for the left-turn vehicles on the CLLs, the risk associated with the use of CLLs can be controlled which makes it safe to use this innovative intersection design at SIWSPs.