Lane Choice Research Articles

Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.

Assessing the impact of ride-sourcing vehicles on HOV-lane efficacy and management strategies

High-occupancy vehicle (HOV) lanes, which exclusively allow vehicles with two or more travelers on board to enter, has been recognized as a promising and practical way to motivate travelers to carpool. However, in the presence of ride-sourcing services, ride-sourcing vehicles with one driver and one (or more) passenger on board also meet the vehicle requirements of HOV lanes. So instead of encouraging travelers to carpool, HOV lanes may benefit ride-sourcing users and stimulate ride-sourcing demand. In this paper, in a single-corridor network with both HOV lanes and general purpose (GP) lanes, we model travelers’ mode choices among transit, driving alone, ride-sourcing, and carpool, and delineate travelers’ lane choices at user equilibrium, with the travel time of GP/HOV lanes and the carpool price being endogenously determined. With numerical examples, we show that the occupation of HOV lanes by ride-sourcing vehicles will greatly reduce the time saving on HOV lanes, thus lower the attractiveness of carpool, and increase the total travel time cost on the corridor. To solve this problem, we propose to implement an additional tolling scheme on HOV lanes, so that carpool users can split the toll evenly, while ride-sourcing riders have to pay the toll alone. As shown by numerical examples, the optimal tolling scheme can effectively reduce the occupancy of ride-sourcing vehicles on the HOV lane and encourage users to carpool. And the impacts of the ride-sourcing price, driving car cost and the HOV lane capacity ratio on the effectiveness of the proposed hybrid toll and HOV strategies are examined through sensitivity analysis.

Psychological Factors Affecting Drive-by-Lane Behavior at Lane Offset Intersections: Analysis Based on Extended Theory of Planned Behavior in a Chinese Sample

Traffic conflict commonly occurs in intersections with offset lane lines because of mismatched choices of entrance and exit lanes. This conflict drives down overall traffic efficiency and can cause crashes. At such intersections, the driver’s lane choice behavior seriously affects the traffic system. The theory of planned behavior (TPB) was used in this study to excavate the impact mechanism of drive-by-lane behavior at offset intersections. A scale incorporating TPB constructs and additional variables (risk perception [RP], traffic environment [TE], optimization measures [OM], and driving style [DS]) was developed to collect empirical data with 557 valid samples in China. A structural equation model of drivers’ drive-by-lane behavior was established to explore the causal relationship between behavior and influencing factors, as well as the interrelationships between TPB variables. TE, behavior intention (BI), subjective norms (SN), and RP show the most significant effects on drive-by-lane behavior. DS, perceived behavior control, attitude (ATT), and OM have little impact on drive-by-lane behavior. BI, TE, RP, and DS have significant, direct effects on drive-by-lane behavior. The TE, OM, and DS variables significantly affect drive-by-lane behavior through mediators. RP, BI, and ATT mediate the influence of other variables as well as affecting drive-by-lane behavior. We put forward suggestions to improve driving behavior at offset intersections including changing external factors, education, and training. The direct and indirect relationships between the influencing factors and drive-by-lane behavior are discussed in this paper to provide technical and reference support for future research on managing irregular urban traffic intersections.

Application of machine learning models to predict driver left turn destination lane choice behavior at urban intersections

When there are multiple lanes to choose from downstream of a turning movement, drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in the outermost lane(s). However, human drivers do not always choose the innermost lane, which could lead to crashes with other vehicles. Therefore, predicting human driver behaviors is vital in reducing crashes, as the need to share the roadways with automated vehicles (AVs) continues to grow. In this research, various machine learning models have been used to predict the left turn destination lane choice of human-driven vehicles (HDVs) at urban intersections based on several quantifiable parameters. A total of 174 subject vehicles were extracted and analyzed in Los Angeles, California, and Atlanta, Georgia, using HDV trajectory data from the Next Generation SIMulation (NGSIM) database. Five machine learning techniques, namely binary logistic regression, k nearest neighbors, support vector machines, random forest, and adaptive neuro-fuzzy inference system, were applied to the extracted data to predict the lane choice behavior of drivers. The k nearest neighbors model showed the most promising results for the evaluated data with a correct decision score of over 93% for the unseen test data. This model may be programmed into: (i) AVs, in conjunction with sensors, to predict if an HDV is about to turn into the incorrect destination lane; and (ii) microscopic traffic simulation tools so that modelers can identify potential conflicts when HDVs do not select the appropriate destination lane.

An Alchemist of Super‐Cooled Liquid: The Art and Craft of Danny Lane

AbstractSculpting with glass is often the choice of artist and designer Danny Lane. He can combine it with steel, timber, acid etching and numerous other techniques and materials, yet at its root Lane's life's work is glass. He teases it to its limits for artistic effect – extruding, moulding, dripping, colouring, striating, cracking and nibbling at its edges. The other partner in his arsenal is light, the way it flickers, beams, glitters and projects. In an interview with AD Editor Neil Spiller, he reveals his thoughts on his creative history, his beloved material and his influences. Danny Lane, Etruscan Chair, 2016 This design for a glass and steel chair was originally conceived in 1986, and has been through various modifications since then; 1990s glass became 25mm thick and the metal was refined, with Jimmy Choo high‐heeled shoe tips.

Performance impact of autonomous trucks on flexible pavements: an evaluation framework and case studies

ABSTRACT The emerging technology of Autonomous Trucks (ATs) will significantly impact the transportation sector. Unique traffic characteristics of ATs, such as lane choice and lateral positioning, could intensify the potential risk of aggravated distress development on flexible pavements. In response, the present work proposes a framework to evaluate this impact, focusing on critical factors like wheel wander, load spectra, and AT road-sharing percentages. We utilized this framework to assess AT's effect on Texas' flexible pavements and conducted a parametric analysis, exploring their response to AT implementation and identifying the significance of some key parameters in different pavement designs. The key findings offer insights into how AT implementation could potentially reduce the lifespan of flexible pavements based on existing data. These insights will prove beneficial in designing future pavements that are better suited to accommodate the rising influx of autonomous trucks. Though some AT attributes are yet undetermined due to their emergent nature, our robust framework will accommodate new data when it is available, serving as a continuously improving evaluation tool.

Recreational Alpine Skiing An Introduction

Alpine skiing is the most common type of skiing performed on groomed and marked ski slopes. This review aims to help recreational skier improve their skiing ability by identifying conducive conditions for alpine skiing. Physically, strengthening balance, agility, and static leg strength via conditional training (ie. inline skating, roller blading, biking, running, sports stimulator) better prepares skiers for alpine skiing and reduces the chances of injury. Injury in knee and lower leg is the most common alpine skiing injury and the most severe are head injuries, highlighting the importance of wearing helmet, well-adjusted skis and suitable lane choice. Mentally, for beginners, increasing self-efficacy and decreasing fear and worry improves skiing performance. Whereas, for intermediate skiers, increasing worry or caution of the environment, increases performance. Technically, recreational skiers should attempt to perform symmetrical turns with greater range and speed of body movement. Lastly, the review presents recent technical and equipment advances in professional alpine skiing, reinforcing that the learning journey of alpine skiing is never ending. Hence, recreational skiers have a long and bright way to go, and researchers should try to expand their sample beyond adolescents and individuals in their early twenties, especially when investigating topics besides skiing injuries, to better generalize their impactful findings.

Assessing the Effects of Modalities of Takeover Request, Lead Time of Takeover Request, and Traffic Conditions on Takeover Performance in Conditionally Automated Driving

When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the takeover performance: (1) traffic conditions (complex and simple); (2) modality of takeover request (auditory and auditory + visual); (3) lead time of takeover request (TORlt, 5 s and 7 s). The scenario is the obstacle ahead. Indicators include: (1) Take Over Reaction Time (TOrt); (2) approximate entropy (ApEn), operating order of steering wheel Angle and pedal torque; (3) the choice of target lane and speed of lane-changing; (4) mean and standard deviation of acceleration and velocity; (5) quantifiable lateral cross-border risk and longitudinal collision risk. A driving simulation experiment is conducted to collect data for analysis. The effects of the three factors on takeover performance are analyzed by analysis of variance (ANOVA) and non-parametric tests. The results show that when the traffic conditions are complex, drivers have a larger ApEn of the steering wheel angle and brake pedal torque, and a smaller ApEn of acceleration pedal torque. In the 5 s TORlt case, drivers have a smaller ApEn of brake pedal torque the interaction between TORlt, traffic conditions, and modality of TOR affects ApEn of accelerator pedal torque. 5 s TORlt/complex traffic condition makes the scene more urgent, which is easy to cause driver to make sudden and simultaneous turning and sudden braking dangerous behavior meanwhile. Compared with other combinations of modality and TORlt, the combination of 7 s and auditory + visual significantly reduces the lateral cross-border risk and longitudinal collision risk.

Empirical investigation of lane usage, lane changing and lane choice phenomena in a multimodal urban arterial

The combination of multimodal interactions, dynamic congestion and lane changing phenomena in an urban environment can have a strong impact on traffic flow dynamics, active road capacity and road safety. However, while there is a vast understanding of microscopic traffic models for motorways with respect to lane changes and lane choices, there are not many empirical observations of the phenomena at the urban scale. Recently, new data collection techniques with unmanned aerial systems have emerged offering unique opportunities to study complex traffic phenomena. This paper investigates the interrelation between lane changing and lane choice using the pNEUMA dataset in one of the busiest arterials in Athens, Greece. The main concept relies on the definition of two layers regarding how the multiple lanes of congested arterials are actually being used. Specifically, we show that the marked lanes on the arterial (marked layer) are influenced by the frictions created by static and moving bottlenecks (bus and taxi stops, illegal parking, etc.). Using an existing lane detection algorithm, we show that there is an active layer which is different to the marked lanes on the road affecting capacity in the macroscopic level and driving behavior in the microscopic level. The analysis on lane changing highlights the conflicting areas where more lane changes can occur. Furthermore, the particularity of Powered-Two-Wheelers’ lane changing behavior is examined and evidence on the PTW filtering phenomenon is provided. Following, the analysis on driver lane choice shows how turning vehicles approach the target lane and the differences between left- and right-turning vehicles.

Can day-to-day dynamic model be solved analytically? New insights on portraying equilibrium and accommodating autonomous vehicles

This paper develops a new approach to portray the equilibrium and analyze the appropriate lane policy during different deployment stages of autonomous vehicles (AVs) by innovatively integrating Vickrey's bottleneck model into the day-to-day dynamic model. The travel cost function in the classical bottleneck model is described by considering the impact of AVs’ value of time (VOT) reduction and AV gain. The evolution of lane choice behaviors is described using the first-order and second-order day-to-day dynamic model. Furthermore, three lane policies are proposed to accommodate AVs’ development, and the properties for the endogenous market penetration of AVs and travel cost under three lane policies are examined to recognize which policy is more suitable at different development stages of AVs. The results demonstrate that such equilibrium solutions as traffic flow, total travel cost, the convergence time are all analytically solvable and thus can perfectly answer the problem of stability and convergence involved in the day-to-day dynamic model. Finally, the numerical analysis is conducted to verify the previous findings and get some intuitive insights on system performance. This work not only provides new insights on the evolutionary process of AVs’ and regular vehicles (RVs)’ lane choice behaviors, but also recognizes appropriate lane policy to accommodate AVs from a novel perspective.

Accounting for preference heterogeneity for high occupancy toll lanes in a Canadian city: a latent class approach

ABSTRACT We investigate traveler preference heterogeneity for high occupancy toll (HOT) roads in Calgary, Canada. We employ a set of latent class discrete choice models that allows for within-class heterogeneity to be captured. This is the first use of latent class models to understand lane choice along a freeway with managed lanes. The use of a latent class structure is justified based on both an improvement in overall fit relative to conventional models and the capacity of this model to provide behavioral insights into preference heterogeneity. The proposed latent class structure captures several interesting trends, including an inversion between travel time and travel cost elasticities across different classes. Using the approach of inferring lifestyles and mobility styles from class membership several interesting trends are established. The finding of high degrees of time sensitivity relative to cost sensitivity amongst certain segments of the population has implications for setting an optimal toll price.

Lane Choice Behavior at Toll Plaza Under Mixed Traffic Conditions Using TODIM Method: A Case Study

The main objective of the present study is to prepare an algorithm for the choice of lane causing minimum delay based on drivers’ perception using a Multi-Criteria Decision-Making (MCDM) method. The TODIM (Portuguese acronym for Interactive Multi-Criteria Decision-Making) is based on the prospect theory. The effect of human behavior on lane choice at toll plaza is studied using the user perception survey. The data required is collected by pictorial survey at the toll plaza, nearby petrol pumps, and hotels. The pictorial survey includes the scenarios to understand the lane choice based on the user preferences about the vehicle composition, number of vehicles in queue, and lane changes. In the case of toll plazas, toll lanes are the alternatives, and vehicle composition of toll lanes, number of vehicles in the queue, and number of lane changes required to choose the target lane are all different criteria. A vehicle entering the toll area has to take care of all the available conditions and select an alternative to minimize the delay. The TODIM method requires the weightage matrix of the criteria, which is obtained using the Analytical Hierarchical Process (AHP). It is seen that the number of vehicles in the queue has the highest weightage followed by the proportion of heavy vehicles. These are the prime factors affecting the lane choice behavior at the toll plaza. The algorithm can be further used to predict the best alternative for the emerging vehicle, which will help to minimize its delay.

Estimates of Willingness to Pay and Value of Time for the Dynamic Toll Lanes in Freeway PR-22 in Puerto Rico

Willingness-to-pay measures were estimated for users of the PR-22 Freeway in the Commonwealth of Puerto Rico. The first dynamic toll lane (DTL) facility in Puerto Rico started operations in 2013 on PR-22. The PR-22 segment examined consists of two reversible express lanes along the median with a dynamic pricing scheme and six general traffic lanes with a fixed toll rate. Willingness-to-pay for the DTL was assessed using the Van Westendorp Price Sensitivity Meter (VWPSM) analysis. Moreover, discrete choice analysis was used to estimate the users’ willingness to pay for travel time savings. The data used in this analysis was obtained through an online survey that presented participants with direct willingness-to-pay questions, toll lane choice scenarios, and, for declared DTL users, questions about their reasons for using the facility. The aggregate analysis of the VWPSM estimated a willingness-to-pay range for the DTL of $1.00 to $2.79, which is lower than the maximum toll rate of $4.95 charged for the managed lane facility. The stated preference scenarios provided a value of time (VOT) estimate of $21.47 per hour. The VOT value is higher than the estimate for local personal travel of $10.00 per hour. There is evidence in support of inflated VOT values for high-occupancy toll lanes and variable toll facilities. Another reasoning could be that PR-22 users overestimated their time savings. Still, the estimated VOT for the PR-22 DTL is relatively low in comparison with other managed lane facilities in the United States.

Trajectory Planning for Connected and Autonomous Vehicles at Freeway Work Zones under Mixed Traffic Environment

Mixed traffic control with connected and autonomous vehicles (CAVs) and human driving vehicles (HVs) is becoming a hot topic. CAV trajectory planning at work zones under the mixed traffic environment is a big challenge. Existing studies only focus on longitudinal trajectories (e.g., acceleration profiles), ignoring lateral trajectories (lane changing). This study proposes a trajectory planning model for CAVs at work zones under mixed traffic environment. Both longitudinal and lateral trajectories are considered. On the basis of the states of CAVs and of HVs observed by CAVs, the number and initial states of unobservable HVs in the planning horizon are estimated considering the interactions between vehicle driving behaviors. A trajectory planning model is then formulated to optimize acceleration profiles and lane choices of CAVs in the planning horizon in a centralized way. The minimization of total vehicle delay and fuel consumption is adopted as the objective function. A car-following model and a lane-changing model are adopted to capture the driving behaviors of HVs. The proposed model is a mixed-integer linear program. Numerical studies validate the advantages of the proposed trajectory planning model over late merge control for vehicle delay and fuel consumption.

Evaluating Traffic Impacts of Permitting Trucks in Transit-Only Lanes

With ongoing population growth and rapid development in cities, the demand for goods and services has seen a drastic increase. Consequently, transportation planners are searching for new ways to better manage the flow of traffic on existing facilities, and more efficiently utilize available and unused capacity. In this research, a lane management strategy that allows freight vehicles to use bus-only lanes is empirically evaluated in an urban setting. This paper presents an analysis of data that was collected to evaluate the operational impacts of the implementation of a freight and transit (FAT) lane, and to guide the development of future FAT lane projects by learning from the case study in Seattle, U.S. The video data was converted to vehicle counts, which were analyzed to understand the traffic impacts and used to construct a discrete choice model. The analysis shows that transit buses used the FAT lane 96% of the time, and authorizing trucks to use the lane did not affect that lane choice. Trucks used the FAT lane, but their utilization decreased with increasing numbers of buses in the FAT lane. Instead of higher rates of trucks, unauthorized vehicles, such as passenger cars and work vans, increasingly used the FAT lane during congestion. As a result of their differing schedule patterns, trucks and buses used the FAT lane at complementary times and trucks showed relatively low volumes in the FAT lane. Overall, the results are promising for a lane management strategy that may improve freight system performance without reducing transit service quality.

Questionnaire based study of drivers’ error and violation at four-legged signalized intersection

ABSTRACT It is an enduring challenge to know the underlying determinants (factors and actors) for developing safe roads from the driving behavior perspective. Human (driver) errors and violations are generally referred as the most frequent cause of road accidents. This research analyzed a driver questionnaire survey of 584 drivers covering their behavior (error and deliberate violations) and safety-related attitudes while completing their driving task, at four-legged signalized intersections in India. We analyzed the driver behavior in terms of lane choice, signage and marking apprehension, tailgating, and vehicle speed consciousness at intersections, and their tendency to commit errors or violations. Driving frequency was found to have a significant effect on both Inattentive Error/Confused Error (IE/CE) and Risky Self-willed Violation (RSV). Gender was found to significantly affect only RSV and age influences only IE/CE. Highway agencies can consider the findings in developing user-friendly and self-explanatory road infrastructure with improved road users’ safety features.

Exploring Factors Impacting on the Lane Choice of Riders of Non-Motorized Vehicles at Exit Legs of Signalized At-Grade Intersections.

For most signalized at-grade intersections, exclusive lanes for non-motorized vehicles have been applied to improve the level of service, capacity and safety of both motorized vehicles and non-motorized vehicles. However, because of various factors, riders of non-motorized vehicles have been observed using lanes for motorized vehicles instead of lanes for non-motorized vehicles, which usually negatively influences the performance of signalized intersections and sometimes may cause serious problems such as traffic congestion and accidents. The objective of this paper is to explore factors influencing the lane choice of riders of non-motorized vehicles at exit legs of signalized at-grade intersections and develop a prediction model for riders’ lane choice. Data concerning the lane choice of riders of non-motorized vehicles and other impacting factors were collected at exit legs of four typical signalized at-grade intersections. Applying binary logistic regression, a probability prediction model was developed to explain how various factors influence the lane choice of riders of non-motorized vehicles. The prediction model indicates that female riders of non-motorized vehicles have a higher probability of choosing the lane for non-motorized vehicles than male riders. Compared with riders of non-motorized vehicles powered by electricity, riders of traditional man-powered bicycles are more likely to choose the lane for non-motorized vehicles. Right-turning riders of non-motorized vehicles are more likely to choose the lane for non-motorized vehicles than straight-going riders, who in turn, are more likely to choose the lane for non-motorized vehicles than left-turning riders. Decreasing the volume of non-motorized vehicles, increasing the volume of motorized vehicles, and widening the lane for non-motorized vehicles will increase the probability of the correct choice of lane for non-motorized vehicles. The predictions of the model are in good agreement with the observed facts. The model is meaningful for guidance on the design and management of signalized at-grade intersections.

A COMPOSITIONAL APPROACH FOR TRAFFIC DISTRIBUTION EVALUATION OF TRIPLE LEFT-TURN LANES FROM AN INDIVIDUAL PERSPECTIVE

This study analysed unbalanced traffic distribution on Triple Left-Turn Lanes (TLTLs) at signalized intersections that is caused by left-turn drivers’ unequal lane preferences. To develop statistical bonding between the multilane traffic flow and individual lane choices, the lane volumes are formatted as compositional data to subject the sum-constant constraint. One-way and two-way Compositional ANalysis Of VAriance (CANOVA) models were formulated respectively to estimate the independent effect of one factor and its joint effects with other factors on the multilane traffic distribution. TLTL volume composition was the dependent variable of the models, while the factors of geometric design and traffic control that could affect left-turn drivers’ lane choice were the independent variables. Results indicate that variance of vehicle turning curve, length of the upstream segment, the location of triple left-turn sign, signal phase / cycle length, could affect the traffic distribution, and its balance could be achieved at specific levels of a factor. The joint effects of some factor couples could improve the unbalanced traffic distribution while others could not work.

Characteristics of Drivers’ Lane Choice at Large Multi-lane Roundabout

The use of modern roundabout as an intersection treatment is a popular choice. To increase the capacity, large multi-lane roundabout has been implemented to solve traffic congestions. Comparatively to small single-lane roundabouts, where many studies have been devoted to, the maneuver at multi-lane roundabout is a highly complex situation due to drivers’ behavior in lane choosing. Little is known on the drivers’ behavior at large multilane roundabouts. This paper aims to explore the drivers’ lane choice behavior and its impact on the performance of large multi-lane roundabout. Traffic data were collected at three-lane roundabout on a normal day at peak traffic hours by using voice recorders and video cameras. Drivers’ behavior had been characterized by assessing their lane choices from entry lanes to circulating lanes and from circulating lanes to exit lanes. Two models of drivers’ lane choices were generated, the first representing reality and the second depicting standard roundabout lane discipline. Both models had been analyzed by using SIDRA Intersection software. Results indicated that the large number of lanes provides high freedom for drivers on their behavior which cannot be fully controlled. Also, drivers tend to follow the natural movement path to go through the roundabout correctly. Lane discipline marking is effective for multi-lane roundabout especially for roundabout with equal number of entry and circulating lanes in improving the traffic performance.

A game theoretic macroscopic model of lane choices at traffic diverges with applications to mixed–autonomy networks

Vehicle bypassing is known to increase delays at traffic diverges. However, due to the complexities of this phenomenon, accurate and yet simple models of such lane change maneuvers are hard to develop. In this work, we present a macroscopic model for predicting the number of vehicles that perform a bypass at a traffic diverge when taking an exit link. We interpret the bypassing maneuver of vehicles at a traffic diverge as drivers acting selfishly; every vehicle selects lanes such that its own cost of travel is minimized. We discuss how we model the costs that are incurred by the vehicles. Then, taking into account the selfish behavior of vehicles, we model the lane choice of vehicles at a traffic diverge as a Wardrop equilibrium. We state and prove the properties of the equilibrium in our model. We show that there always exists an equilibrium for our model. Moreover, although our model is an instance of nonlinear asymmetrical routing games which in general have multiple equilibria, we prove that the equilibrium of our model is unique under certain assumptions that we observed to hold in all our case studies. We discuss how our model can be calibrated by running a simple optimization problem. Then, using our calibrated model, we validate it through simulation studies and demonstrate that our model successfully predicts the aggregate lane change maneuvers that are performed by vehicles at a traffic diverge. Having shown the predictive power of our model, we discuss how our model can be employed to obtain the optimal lane choice behavior of vehicles, where the social or the overall cost of all vehicles is minimized. Finally, we demonstrate how our model can be utilized in scenarios where a central authority can dictate the lane choice and trajectory of certain vehicles, for example autonomous vehicles directed by a central authority, so as to increase the overall vehicle mobility at a traffic diverge. Examples of such scenarios include the case when both human driven and autonomous vehicles coexist in the network.