Lane Changing Models: A Short Review

Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles.

A new two-lane changing model with inter-vehicle communication based on intelligent transportation system for traffic flow is proposed. By taking the information of several front vehicles in both self-lane and objective lane into consideration, drivers can predict the velocity tendency of next time steps on both lanes, thus decide whether to change lanes or not. Simulations with both periodic boundary and open boundary under the new lane changing model and Rickert's lane changing model [17] are conducted. Compared with previous lane changing models, the lane changing model in this paper can reduce ping pang lane changing frequency effectively. When there is a bottleneck on the one lane, the new lane changing mechanism performs better than the traditional model.

Microscopic traffic simulation tools are becoming increasingly popular in evaluating transport options. Driving behaviour models (e.g. route choice models, lane-changing models, etc.) are essential components of these tools. The state-of-the-art driving behaviour models assume that drivers make instantaneous decisions. However, in reality, many of the driving decisions are based on a specific plan. The plan is however unobserved or latent and only the manifestations of the plan through actions are observed. Examples include selection of a target lane before execution of the lane change, choice of a merging tactic before execution of the merge. Ignoring the effect of plans in the decision framework can lead to incorrect representation of congestion in traffic simulation tools. In this article, we present a modelling methodology to address the effects of unobserved plans in the decisions of the drivers. The actions of the driver are conditional on the current plan and can be influenced by anticipation of downstream traffic conditions. The heterogeneity in decision making and planning capabilities of drivers are explicitly addressed. The methodology has been applied in developing lane-changing behaviour models with disaggregate trajectory data extracted from video recordings of an urban road using the maximum likelihood technique. Estimation results show that the latent plan models have a significantly better goodness-of-fit compared to the ‘reduced form’ models where the latent plans are ignored. The latent plan models were also found to outperform the reduced form models in validation case studies within the microscopic traffic simulator MITSIMLab.

Lane change detection is crucial for intelligent transportation systems, as it affects traffic flow on both macroscopic and microscopic levels. Lane change models are widely used in traffic and transportation studies, making it important to understand the factors that affect drivers’ lane changing behavior. In this context, we proposed a novel model for detecting lane changes by applying wavelet transform to high-resolution data from unmanned aerial vehicles. The model was trained and tested using empirical lane changing data from pNEUMA. Firstly, the azimuth angle was calculated on WGS-84 coordinates of each vehicle found in the specified road segment. Next, a multi-level wavelet transform was applied to the azimuth series using mother wavelets such as Haar, Daubechies, and Symlet for each vehicle. Machine learning method was applied to extracted features to detect lane changing. Additionally, the lane changing style of drivers was classified as sudden or normal using the same model. The results indicate that the proposed data-driven model is able to accurately detect lane changes and the type of lane change.

This paper presents a driver lane change model that adapts to individuals with different driving behavior. For lane change assistance systems, its acceptance by customers greatly relies on the assistant algorithm which can accurately reflect drivers' individual characteristics. By analyzing the real world vehicle data and drivers' characteristic parameters, such as time to lane change (TTLC), the steering angle and lateral offset were obtained, and consequently a lane change model was proposed. The inputs of the model are the vehicles' current state (e.g. velocity) and drivers' characteristic parameters (e.g. TTLC, lateral offset), and the output is the steering angle. Drivers' individual differences can be demonstrated by the variation of model parameters. Comparing the simulation results to the real world vehicle data indicates that the developed model is able to imitate driver operations during lane changes, and it can be integrated into the control algorithm of the lane change assistance system embodying drivers' characteristics.

The expressway traffic flow characteristics of off-ramp have an important impact on traffic congestion. By analyzing the vehicle car-following and lane-changing behavior, the cellular automata model of off-ramp area was built. When analyzing lane-changing behavior, the behavior of vehicle changing lane from main lane to off-ramp buffer lane was considered, and the information interaction process between the vehicle ready to changing lane and the rear vehicle on the target lane was considered, the new lane-changing rules were built. In the new lane-changing rules, free lane-changing and compulsory lane-changing were included. By analyzing the simulation results, when traffic flow density is lower, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane has less effect on traffic capacity of the road section, but when traffic flow density rising to a certain extent, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane will bring an adverse effect on traffic capacity of the road section.

Understanding and replicating driver lane-changing behavior is essential for developing advanced driver assistance systems that align with driver expectations and for constructing dynamic, realistic traffic flow scenarios in autonomous driving tests. This study proposes a two-dimensional, force-based lane-changing dynamics model that represents vehicle motion as acceleration changes under the influence of (surrounding) social forces. The model captures longitudinal and lateral interactions with neighboring vehicles and incorporates personalized parameters to reflect trajectory-level heterogeneity. A temporal attention mechanism is introduced to model the dynamic shift of driver focus during lane change, ensuring continuity in interaction forces. For parameter calibration, AdaP+ is developed, a gradient-based optimizer that integrates Nesterov momentum, decoupled weight decay, and gradient belief within the Adam framework, and improved with a pruning strategy for better initialization and faster convergence. Experimental findings on the HighD dataset demonstrate that the social force lane change model outperforms baselines in both trajectory fitting and behavioral simulation. Gradient-based optimizers, particularly AdaP+, achieve higher precision than swarm intelligence algorithms. Overall, the proposed model accurately replicates complex lane-change dynamics, while the integration of personalized parameters and attention mechanism effectively captures trajectory-level heterogeneity and interaction continuity, with potential to support naturalistic, personalized lane-change trajectory generation in future connected-vehicle settings.

Lane changes significantly affect the characteristics of traffic flow. Lane-changing models are therefore important in microscopic traffic simulation. Existing lane-changing models emphasize the decision-making aspects of the task but generally neglect the detailed modeling of the lane-changing action itself and model it only as an instantaneous event. However, research indicates that lane-changing durations are on average in the range of 5 to 6 s. The omission of lane-changing durations from simulation models may have a significant impact on simulation outputs. Models of the duration of lane changes are presented. These models are estimated by using detailed vehicle trajectory data that were collected in naturalistic driving with high-mounted video cameras. Separate models are presented for passenger cars and for heavy vehicles and statistical tests are conducted for the similarity between the lane-change durations of the two vehicle types.

Stop and go traffic that leads to oscillate traffic flow frequently is observed on congestion flow. Unexpected reasons such as lane - changing maneuvers, lower speeds of leader vehicle and moving bottleneck cause stop and go traffic and amplifying delay and environment impacts. Stop and go traffic exactly can't be modeled by traffic models, and also car following models based on kinematic flow theory can't be implied correct perception of stop and go traffic. Based on asymmetric microscopic theory and trajectory data of NGSIM, traffic flow can be classified into five phases according to speed and movement of the vehicle: Free flow, acceleration and deceleration, stationary and coasting phases. Analyzing stop and go traffic based on asymmetric theory of acceleration and deceleration phase will result to classify them into three cases: generation, growth and dissipation of traffic waves. Analyzing of traffic oscillation implies that stop-and-go traffic is relatively small and can't be propagated upstream unless the following traffic is also near D-curve; while the effect on lane changes are greater, and can propagate even the following traffic is not near the D-curve. In this paper, using time window in trajectory data clarify relation between the total number of lane changes and stop-and-go waves for congestion traffic. Analyzing net lane changes inside the searching window for incoming and outgoing lane changes about growth and dissipation of traffic waves indicate how characteristics of stop-and-go waves are intimately related to driver's asymmetric behavior of acceleration and deceleration. The comparison result on the growth and dissipation indicated that under the same net lane changes, growth wave case occupy the regions in fundamental diagram, flow - density diagram, deceleration curve, and dissipation wave case occupy the regions of flow - density, acceleration curve. Language: en

<div class="section abstract"><div class="htmlview paragraph">Segment with lane drops are very important in freeway systems since they are major constrains to traffic flow and safety. The frequency of capacity reductions and higher safety risks is proportional to an increase in lane-changing actions, which worsen traffic congestion, decrease road capacity, and increase the risk of an accident. Traditional traffic management strategies that rely on physical structures and driver’s decision making often fail under such conditions. This paper provides a detailed lane change control strategy specific to freeway segments with lane reduction in the connected and autonomous vehicle (CAV) environment. The strategy combines both centralized and decentralized techniques to improve the vehicle’s lane-changing behavior and density. A cellular transmission model of lane-level is proposed for the centralized control of the linked vehicles based on the ratio of the driver compliance. The model derives the density equation and transforms the lane-changing problem before the work zone into merging traffic flow problem. The optimization model is developed based on the total trip time, density deviation, and total lane changes, with constraints on the cell reception capacity and lane changing ratios. Control parameters for lane change distribution are identified using genetic algorithms to solve the problem. For the decentralized control, a reinforcement learning solution is introduced which uses deep Q-networks (DQN) to improve lane-changing actions. The reward function takes into account the traffic efficiency and the impact of lane changing, and the continuous action space is discretized for application. The control mechanism is evaluated by the simulation of a work zone scenario that includes two restricted lanes on the Shanghai-Nanjing Expressway. It also shows that there is an improvement of 3% to 6% in traffic flow and velocity as compared to single-strategy approaches. The collaborative control strategy significantly enhances traffic flow and reduces congestion at bottlenecks and offers valuable information for future traffic control in CAV environments.</div></div>

Investigating model and impacts of lane-changing execution process based on CA model

This chapter summarizes a series of advances in lane changing models aiming at providing a more complete and integrated representation of drivers' behaviors. These advances include the integration of mandatory and discretionary lane changes in a single framework, the inclusion of an explicit target lane choice in the decision process and the incorporation of various types of lane-changing mechanisms, such as cooperative lane changing and forced merging. In the specifications of these models, heterogeneity in the driver population and correlations among the various decisions a single driver makes across choice dimensions and time are addressed. These model enhancements were implemented in the open source microscopic traffic simulator of MITSIMLab, and their impact was demonstrated in validation case studies where their performance was compared to that of existing models. In all cases, a substantial improvement in simulation capability was observed.

Lane change (LC) behavior has critical effects on traffic flows and safety due to its complex interactions with surrounding vehicles. To ensure safe lane changes and prevent potential crashes, it is important to recognize the potential crash risk of lane change in real time. This study proposes an improved risk estimation (IRE) model to evaluate the potential collision risk of lane change (LCR) vehicle groups. The safety margin is introduced to consider the deceleration capability of vehicles to measure the reaction time of drivers during the LC. Then the IRE model is established, incorporating the collision probability and collision severity measured based on the safety margin. The trajectory data, extracted from the highD dataset, are used and 1536 LC samples are investigated. We compare the LCR under different contextual factors, including vehicle types (cars and trucks), two lane change directions (left and right lane change, LLC and RLC), and traffic flows (low and high traffic). It was found that truck drivers keep higher LCR compared with car drivers due to limited brake capacity, and the left lane change results in higher LCR compared with the right lane change. Additionally, lane change is associated with higher crash risk in high traffic flow, as compared to low traffic flow. The understanding of the crash risk of lane change behavior under different contextual factors, can be useful for real-time crash prediction and devising traffic management strategies.

Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior