Left-turning Vehicles Research Articles

Potential risk and efficiency analysis of decision-making dilemmas in connected dual-vehicle interactions at uncontrolled intersections

In dual-vehicle interactive scenarios, drivers often encounter decision-making dilemmas, even with connected information, thus posing significant risk challenges to traffic operations. This study explored the impact of such dilemmas on decision-making and traffic operations for drivers involved in straight-going (SG) and left-turning (LT) vehicles at uncontrolled intersections. Employing driving simulation technology, a decision-making experiment incorporating preemptive (P) and yielding (Y) strategies was conducted with 72 S G and 72 L T drivers under connected information provision. By analyzing joint decisions (P-P, Y-P, and Y-Y) made by drivers on both sides, we investigated implications for traffic operational safety and efficiency. Furthermore, recognizing the diversity in individual characteristics and time pressure, we developed a random parameters multinomial logit model to examine the relationship between potential traffic operational performance (both safety and efficiency) and various contributing factors. Moreover, we calculated the marginal effects of these contributing factors. By assessing the probability change of Y-P joint decisions balancing safety and efficiency, we identified reference values for each contributing factor that promote effective interactive operations. The findings suggest that: (1) Regarding interactive dilemmas conditions, Conditions where the SG vehicle had higher speed and farther distance than the LT vehicle were more likely to result in heterogeneous Y-P joint decisions, in which one driver chooses to yield and the other party passes quickly, thereby improving traffic performance. (2) Time pressure increased the likelihood of heterogeneous Y-P joint decisions but also increased P-P joint decisions. (3) heterogeneous individual attributes did not always lead to higher heterogeneous decisions. Overall, this study provides insights into factors influencing joint decisions and traffic operational performance under connected information provision, considering individual heterogeneity and time pressure, aiming to the advancement of more efficient and safer vehicle-connected transportation systems.

Model predictive control for unprotected left-turn based on sequential convex programming

In autonomous driving, an unprotected left turn is a highly challenging scenario. It refers to the situation where there is no dedicated traffic signal controlling the left turns; instead, left-turning vehicles rely on the same traffic signal as the through traffic. This presents a significant challenge, as left-turning vehicles may encounter oncoming traffic with high speeds and pedestrians crossing against red lights. To address this issue, we propose a Model Predictive Control (MPC) framework to predict high-quality future trajectories. In particular, we have adopted the infinity norm to describe the obstacle avoidance for rectangular vehicles. The high degree of non-convexity due to coupling terms in our model makes its optimization challenging. Our way to solve it is to employ Sequential Convex Optimization (SCP) to approximate the original non-convex problem near certain initial solutions. Our method performs well in the comparison with the widely used sampling-based planning methods.

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections

Objectives The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes. Methods Video-derived conflicts in different severity levels between left-turning vehicles and opposing through vehicles, a well-recognized severe injury crash typology at signalized intersections, were identified by jointly integrating the indicators of frequency and severity, using an autoencoder neural network integration method to develop anomaly scores. Regression models were then developed to relate crashes at the same intersections to the classified conflicts based on the value of their safety indexes. Cumulative Residual plots were investigated. Finally, equations defining the boundary between consecutive anomaly score levels were developed to facilitate application in practice. Results Regression models for total and fatal plus severe (FSI) crashes utilizing classified extreme conflicts based on anomaly scores were found to outperform the models using total conflicts. The improvement is more pronounced for FSI crashes. The results also suggest that the machine learning integration method can efficiently classify conflicts accurately according to crash severity levels since the higher anomaly score is associated with a higher crash severity level (i.e., FSI). Conclusions The proposed framework represents a methodological advancement in traffic conflict-based estimation of crashes using a machine learning model to classify conflicts by their anomaly scores. For jurisdictions without the resources to develop such a model to classify conflicts for their own datasets, the simple equations defining the boundary between consecutive anomaly score levels could be used as an approximation.

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Investigation of Analyzable Solutions for Left-Turn-Centered Congestion Problems in Urban Grid Networks

Traffic congestion caused by left-turning vehicles in a coordinated corridor is a multifaceted problem requiring tailored solutions. This study explores the impact of shared left-turn lanes within one-way couplets, particularly during peak hours, where high left-turn volumes, limited side street storage, and the overlapped green time between pedestrians and left-turners contribute to queue spillbacks, coordination interruption, and network congestion. The focus of this paper is on the solutions that can be easily analyzed by practitioners, here called “analyzable solutions”. This approach stands in contrast to solutions derived from “non-transparent” optimization tools, which do not allow for a clear assessment of the solution’s adequacy or the ability to predict its impact in real-world applications. This paper investigates the effects of employing two analyzable signal timing strategies: Lagging Pedestrian (LagPed) phasing and Left-Turn Progression (LTP) offsets. Using high-fidelity microsimulation, the authors evaluated different scenarios, assessing pedestrian delays, queue lengths, travel time index, area average travel time index, and environmental impacts such as Fuel Consumption (FC) and CO2 emissions. The effectiveness of the proposed strategies was comprehensively evaluated against the base case scenario, demonstrating considerable improvements in various performance measures, including approximately a 5% reduction in FC and CO2 emissions. Implementation of the LTP strategy alone yields substantial reductions in delays, the number of stops, the queue length for left-turning vehicles, travel times for all road users, and ultimately FC and CO2 emissions. This study offers innovative approach to addressing the complex and multifaceted problem of left-turn-centered congestion in urban grid networks using efficient and down-to-earth analyzable solutions.

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.

Multi-Type Traffic Conflict Identification at Signalized Intersections Based on LiDAR Point Cloud

Traffic conflicts have been widely used for proactive road safety evaluation, and this study develops methods to automatically identify different types of traffic conflict based on LiDAR point cloud data. With 10 h of data collected from a signalized intersection in Harbin, China, trajectories of motorized vehicles, bicycles, and pedestrians were extracted, and methods to handle the issues of trajectory discontinuity, type identification error, and same object with different trajectories were developed. Traffic conflicts between right-turn vehicles and through vehicles, between right-turn vehicles and left-turn vehicles, and between right-turn vehicles and pedestrians were considered, and detailed procedures for calculating the conflict indicators (i.e., time to collision [TTC] and post encroachment time [PET]) of different types were proposed. The identified traffic conflicts were also compared with the ones that were identified manually. A total of 5,352 and 1,366 traffic conflicts were identified by PET ≤ 4 s and TTC ≤ 4 s, respectively. The majority were during the through phase, and traffic conflicts between right-turn vehicles and through vehicles were the most common, followed by conflicts between right-turn vehicles and vulnerable road users (i.e., cyclists and pedestrians).The proposed automatic method considers the actual sizes of involved road users and thus leads to more accurate conflict indicator calculation, with an average accuracy over 90%.

Effects of road width, radii and speeds on collisions at three-arm priority intersections

Simulation and observational studies have identified the importance of intersection geometries and vehicle speeds in collisions. However, the causal mechanisms of such collisions in low-speed areas and for different collision types remain unclear. This observational study investigates the complex relationships between geometries, speeds, visibilities, and road traffic collisions in the context of low-speed urban areas.Data were collected from 120 three-arm priority intersections in Portsmouth, UK. In 2007, Portsmouth became the first city in the UK to adopt a 20mph speed limit on all residential streets. The city has also adopted the UK’s Manual for Streets (MfS) as the design standard for all new priority intersections in low-speed residential areas.Piecewise structural equation models (pSEM) were developed to represent the causal mechanisms that relate to geometries, speeds, speed limits and collisions. Findings indicate the role of combinations of approach lane width, corner radii, speed limit, and type of collision. The interaction of wider approach lanes on the minor arm and larger radii of turns for left-turning vehicles (left-hand driving perspective) was associated with higher numbers of road traffic collisions for right-turning vehicles. It is posited here that this is due to the orientation of the left-turning vehicle blocking the left visibility of the right-turning vehicle. These results give weight to the introduction of the 20mph speed limit zone in Portsmouth and some of the changes brought about by MfS. However, the combined effect of approach width and radii on collisions is novel and could form the basis of further guidance on reducing specific types of collisions at three-arm priority intersections.

Research of the left-turn vehicles lane-changing behaviors at signalized intersections with contraflow lane

The design of contraflow left-turn (CL) lane has effectively improved the capacity of left-turn vehicles at signalized intersections and has been widely used. Existing studies have focused more on the coordination and optimization of the main and pre-signal as well as capacity, delay time and queue dissipation. However, the vehicles lane-changing behaviors for CL lane design have not been adequately studied. Therefore, it is necessary to study of left-turn vehicles lane-changing behaviors at signalized intersections with contraflow lane and the traffic evolution law of the road. In this study, a cellular automaton (CA) model that simulates left-turn traffic flow at signalized intersections with CL lane was developed and verified by field data. Various factors are considered, including the length of CL lane and lane-changing area, as well as the location of the vehicles. The simulation results show that the density distribution of traditional left-turn (TL) lane is more influenced by the length of the lane-changing area when the probability of vehicle injection is small (α≤0.1). Furthermore, when the probability of vehicle injection is larger (α>0.1), the density distribution of CL lanes is more influenced by length of the lane-changing area. In addition, the departure ratios of CL and TL lanes is close to 50% when the probability of vehicles injection and length of the lane-changing area are larger (α>0.1and LB≥20cells), which is almost consistent with the peak hour vehicle departure ratios in the real scenario. Finally, this study can provide a reference for the actual application of CL lane design.

Analysis of left-turn behaviors of non-motorized vehicles and vehicle-bicycle conflicts.

In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.

Optimization of Signal Timing for the Contraflow Left-Turn Lane at Signalized Intersections Based on Delay Analysis

The exit-lanes for a left-turn (EFL) is an unconventional method of organizing traffic for left-turns at signalized intersections. In this paper, we propose a nonlinear optimization model to minimize delay by establishing a delay-time diagram for the left-turn traffic when the left-turn traffic is non-oversaturated, considering the relationship between the pre-signal start node of the exit-lanes for a left-turn and the queuing dissipation time of left-turn vehicles. Model validation is performed with example calculations to compare and analyze each operating index and fleet type after signal timing optimization of intersection lane borrowing left-turn lanes. The results show that compared with the conventional left-turn lane, the average delay of vehicles operating with the model decreased by 15.7%, exhaust emission decreased by 11.4%, and capacity increased by 65.7%. The model can support EFL pre-signal design, where signal control is related to queue dissipation time. In practice, a well-designed pre-signal control scheme based on traffic volume can improve capacity and reduce emissions while minimizing average vehicle delays.

Developing a Tracking-Based Dynamic Flash Yellow Arrow Strategy for Permissive Left-Turn Vehicles to Improve Pedestrian Safety at Intersections

Developing a Tracking-Based Dynamic Flash Yellow Arrow Strategy for Permissive Left-Turn Vehicles to Improve Pedestrian Safety at Intersections

Exploring operational characteristics of stop-controlled T-intersections on rural two-lane highways with passing lanes

Left turn traffic at unsignalized T-intersection on undivided rural two-lane high-speed highways poses both operational and safety challenges. More complexities are faced by through drivers in the same direction as the stopped or slowed down left-turn vehicle must choose to either slow down and wait or bypass the left-turn vehicle. Therefore, this study intends to explore the operational characteristics of these facilities. The focus is on the reaction of the drivers behind the left-turn vehicle in terms of the types of maneuvers taken to avoid collision and the distance upstream for the evasive maneuvers using field observations. Further, the impact of the drivers’ reaction on the intersection delay is assessed using a simulation analysis of 17 generic 10.5-mile two-lane corridors with varying configurations of passing lanes at or near the intersection with and without a left-turn lane. The field observation findings from five sites reveal that drivers will move to the shoulder to avoid slowing and stopping or colliding with the left-turn vehicle. The distance at which drivers move to the shoulder differs for the sites studied. The simulation results show that a relatively similar magnitude of reduction in intersection delay could be achieved by addition of either passing lane or left-turn lane, such addition is beneficial for at least 17 000 vpd intersection volume where the passing lane does not end within 1 500 ft is downstream of the intersection. The findings are expected to improve traffic operations at T-intersections on rural two-lane highways.

Time for a Left-Turner to Block a Shared Lane of a Signalized Intersection

Frequently, signalized intersections have a single lane for both left-turners and through traffic. If the vehicles leaving this lane are opposed by through traffic, then no vehicles can depart after a left-turner reaches the head of the queue. This paper evaluates a single lane of left-turning and through vehicles in the subject stream and a single lane of through vehicles in the opposing stream. The first left-turning vehicle is delayed if the opposing flow is high and this further delays other vehicles in the subject stream. The time for the first left-turning vehicle to arrive at the stop line ( gf) and block the through vehicle is important when evaluating the queue accumulation polygon (QAP). If the process to determine the gf term is not well established and accurate, then all other aspects of QAP will not be accurate and the prediction of both capacity and delay will also be inaccurate. gf was determined through a theoretical evaluation to establish the form of the equation and simulation to establish empirical values for the parameters. The resultant equation had a standard error of 0.83 s. The proposed equation was compared with other expressions for gf, including the equation currently in use in the Highway Capacity Manual, and found to have a much lower standard error than those of other methods.

Safety Evaluation for Conversion From Protected-Only Left-Turn Phasing to Time-of-Day Protected-Permissive Left-Turn Phasing Using Flashing Yellow Arrows

This study investigated the safety effects of the conversion from a protected-only left turn to protected-permissive left turn with flashing yellow arrow (FYA−PPLT) with time-of-day operation. The observational before–after study with the comparison group method was used to develop crash modification factors (CMFs) for the total crashes and two types of target crashes that involve left-turning vehicles on treated approaches (left-turn−same roadway and rear-end crashes). For all potential crashes, crash reports, crash diagrams, and narratives were manually inspected to identify the actual target crashes. The CMFs were separately developed for a full 24-h day and specific time-of-day based on FYA-PPLT operation, and for two severity categories (all severities and fatal & injury). The results showed that the total crashes for 24 h had a slight increase, whereas the target crashes had substantially larger changes (increase in left-turn−same roadway crashes and decrease in rear-end crashes). When looking at the specific time-of-day use of FYA-PPLT, the increase in left-turn−same roadway crashes was significantly higher than the full 24-h period. This implies that engineers could extend protected-only use more on the fringes of the peak periods to mitigate the increase in left-turn-related crashes. The analysis results also showed a trade-off between left-turn–same roadway and rear-end crashes after the signal conversion. The findings reveal how important it is for engineers to properly understand the safety effects on the different categories of target crashes and time periods when considering the signal conversion from a protected-only left turn to FYA-PPLT.

Optimization of Safety Facilities for Collision Analysis between Motor and Non-Motor Vehicles at Intersections

With the increasing number of the motor and non-motor vehicles, conflicts and safety risks between motor vehicles and non-motor vehicles are increasing. Among them, the collision between right-turning vehicles and non-motor vehicles going straight leads to traffic accidents accounting for up to 50% of the non-accident cases at intersections. The intersection of Erdos East Street and Xing an’ South Road in Hohhot City is selected as the research object. Analyze the conflict between right-turning motor vehicles and non-motor vehicles. The optimized design was carried out from three perspectives: right-turn channelization island design, road deceleration facilities layout, and signal control, and the road network model was built with VISSIM survey data to demonstrate and evaluate the scheme. The results show that when the traffic volume is unchanged, the number of non-collision decreases and the passage time of non-motor vehicles decreases by 4%. The travel time for left-turn vehicles also dropped 16 percent. It shows that the collision between motor vehicles and non-motor vehicles at right-turn intersections is significantly improved through the above optimal design scheme.

Impact of Delay and Queue on the Length of Left-Turn Storage at Palestine Intersections in Baghdad city, Iraq

At intersections, Left-turning vehicles seek to occupy the same physical space as close to the stop line as possible. These result in high conflicts, delays, and blockage of vehicles by turning vehicles and vice versa. The impact of the lengths of Left-turn lanes on intersection delays is considered to optimize the lengths of the Left-turn lanes. Data for traffic counts, queue lengths, and signal timing are collected from three intersections in Baghdad city in Iraq. The methodology involves the development of estimation models using traffic Simulation SIDRA INTERSECTION 8.0 and simulating various scenarios by varying traffic signal conditions to evaluate delays and queues caused by varying lengths of the Left-turn Lane. Optimal lengths are computed and compared to existing lengths in intersections. No differences in delay, queue, and lengths of the Left turn lane are found using t-test analysis for significance. Outputs from the three models were compared to the maximum observed in the field from the selected intersections. Data analysis involved determining the R2 and the standard error mean between the model output and the observed data. In general, SIDRA INTERSECTION 8.0 overestimated queue vehicles and length of storage for approaches with a high degree of saturation ratios and underestimated it for those with a high degree of saturation ratios.

Traffic Queue Length Estimation at Permissive Left-Turn Signalized Intersections with Probabilistic Priority

Probabilistic yielding behaviors are often observed at permissive left-turn signalized intersections in some countries, which tend to affect the operational performance of intersections such as queue length of left-turn traffic. Based on queuing theory, this research developed an analytical queue length estimation model that took into account the probabilistic priority phenomenon at permitted left-turn signals. The service time distributions for both queued and nonqueued left-turn vehicles were derived, and the M/G2/1 queuing model was applied to determine the queue length of left-turn traffic. To validate the developed queue length model, stochastic simulations with different combinations of left-turn yielding rates, left-turn traffic saturation ratios, and through traffic flow rates were performed. It was found that the left-turn traffic saturation ratio is the most critical factor for the estimation of left-turn traffic queue length. In addition, for permitted intersections with a single through lane and a single left-turn lane, when the opposite through traffic flow rate is greater than 0.3 veh/s, the impact of left-turn yielding rate on left-turn queue length tends to be more significant.

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

This study used a pedestrian-involved near-crash database and adopted an interpretable machine learning framework using SHapley Additive exPlanations (SHAP) to understand the factors associated with critical pedestrian-involved near-crash events. The results indicate that pedestrians with a relatively higher walking speed are more likely to be involved in critical near-crash events. Furthermore, critical pedestrian-involved near-crash events are highly associated with vehicles with driving speeds of less than 10 mph. A higher pedestrian volume is highly associated with critical near-crash events with left-turn vehicles. It is possible that a higher pedestrian volume increases the occurrence of jaywalking behavior or encourages more pedestrians to step into the crosswalk when they should not. By contrast, a higher pedestrian volume is highly associated with non-critical near-crash events with right-turn vehicles. Right-turn vehicles often expect that there will be pedestrians crossing, and a higher volume of pedestrian traffic increases a driver’s awareness and caution while turning. The study also found that a longer signal cycle is highly associated with critical near-crash events when the pedestrian volume is low, while a relatively short signal cycle length is highly associated with critical near-crash events when the pedestrian volume is high. During non-peak hours, pedestrians have less tolerance for a relatively longer signal cycle. Moreover, a relatively shorter signal cycle length at peak hours will limit the number of pedestrians that can cross during a cycle and encourage the possibility of pedestrians jaywalking or stepping onto the crosswalk when they should not.

An Origin-Destination Demands-Based Multipath-Band Approach to Time-Varying Arterial Coordination

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination.