Lane-changing Frequency Research Articles

Multi-lane traffic flow model based on cellular automaton fine-scale under cooperative vehicle infrastructure system

Microscopic traffic flow modeling is crucial for predicting future traffic demand and optimizing the design of transportation systems. At the microscopic level, a well-crafted model is capable of describing the interactions and dynamic variations among vehicles with greater accuracy. The cell scale in the classical cellular automaton (CA) multi-lane traffic flow model is a critical parameter to accurately express the location relationship of vehicles. In this paper, a new method based on Symmetric Two-lane Cellular Automaton (STCA), entitled STCA-X, is proposed for the cell scale selection. Firstly, the position, velocity, acceleration, and interaction of vehicle operation in the urban multi-lane environment are analyzed, and a feature model is built based on CA. One important drawback of the existing models is that they do not match the vehicle movement in the actual lane. To handle this problem, a fine-scale CA multi-lane model is developed. Secondly, a new traffic flow model is created by redefining several key behaviors such as road following, lane changing, and blocking in the STCA model. The experimental results are analyzed and compared with several of the state-of-the-art methods. Analysis of the simulation results proves that STCA-X improves vehicle lane change frequency and lane utilization efficiency. However, the enhancements in the model have resulted in increased computational complexity. Consequently, future research will delve into novel approaches to boost computational efficiency, thereby propelling the progress of traffic flow modeling technology.

User Opinion on Factors Affecting Drivers Frequent Lane-Changing Behavior: A National Sample of Drivers in Pakistan

Abstract: In recent years massive traffic volume increase has demanded the implementation of traffic improvement plans in Pakistan. The urban Highway transportation networks are becoming congested in developed as well as in most of the developing countries. The main causes of unmanageable heterogeneous traffic congestion on the highway are an increase in population, change in lifestyle, and economic development of a country. To recognize the characteristics of a driver who frequently changes lanes and analyze the techniques of frequent lane-changing behavior. The researchers collected data via a questionnaire survey and analyzed the influence of traffic environment and driver characteristics violation on frequent lane-changing behavior on travel speed in detail by manually Excel statistics. Results showed that the impact of a heterogeneous traffic environment of two lanes and three lanes cars, and traffic flow density was sharply associated with frequent lane-changing behavior. The drivers were the main factors affecting frequent lane-changing behavior in heterogeneous traffic conditions, which was most popular during aggressive driving. Only 8.33% of drivers were never involved in this behavior. Most liable to frequent lane-changing behavior were males as compared to females, 31–40-year-old drivers, and those with low academic qualifications and private vehicle personalities. Among various traffic factors, changes in required speed had the most significant impact on how often lane changes occurred. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency. This research recommends strict enforcement of lane discipline to counter traffic congestion and the remedial measures that are to be taken for the improvement of better and sustainable road infrastructure conditions. Overall the research study will have practical implications for improving traffic management and safety on highways, and it provides a foundation for further research in the field of traffic-related technologies.

Traffic behavior analysis of the urban expressway ramp based on continuous cellular automata

Urban expressway is an essential part of the urban transportation network, and its role is to speed up traffic operation and relieve traffic congestion. To investigate the complex traffic flow phenomena at urban expressway ramps, we use the Continuous Cellular Automata (CCA) model to model and analyze the urban expressway with entrance and exit ramps in a unified manner. Considering the vehicle's merging, diverging and following behaviors on urban expressway with an entrance and exit ramp, we design the vehicle's main road merging rules, ramp inflow and outflow rules, Cooperative Car-Following for Lane Change process analysis (LC-CCF) model, and improved Nagatani lane change rules to control the overall road vehicle operation. Based on this, we propose a CCA model for urban expressway ramp traffic flow. To clarify the model, The Chengdu Second Ring Elevated Road with entrance and exit ramps as a scenario is used for experiments. The results show that: (1) The spatio-temporal diagrams show a significant metastable congestion phenomenon and distinct congestion boundaries primarily located near the merge and divergence area. Congestion tends to start at the merge and divergence area and then spread outward. (2) The ramp disturbance has an increasingly pronounced effect on average speed and density as Pin−main and Pin−ramp increases. (3) The density-lane change frequency relationship tends to increase and then decrease, with an increase in the frequency of lane changes increasing the interference between vehicles in different lanes and affecting the average traffic speed. The lane change frequency is the main reason for the differences in traffic flow between lanes. (4) The CCA model can effectively characterize congested traffic flows on urban expressway ramps.

Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making

Deep learning occupies a prominent position in the field of machine learning, where researchers have introduced autoencoder into extreme learning machine (ELM) to establish deep extreme learning machine (DELM), with the aim of enabling autonomous feature learning from data. This approach has found widespread applications in various domains, including finance and electrochemistry. However, the network accuracy of DELM is sensitive to the initial weighting and bias configurations, which can be negatively impacted by random initialization. To mitigate the adverse effects of such random initialization on network performance, this study employs two swarm intelligence optimization algorithms, particle swarm optimization (PSO) and harris hawks optimization (HHO), to optimize the input weights and biases of DELM. In addition, we apply the variational mode decomposition (VMD) technique to decompose the original data, enabling a deeper exploration of latent information within the data. Subsequently, two integrated models are constructed: VMD-HHO-DELM and VMD-PSO-DELM. We apply these models to address road traffic condition prediction and autonomous lane change decision-making problems, with a specific focus on the transportation domain. Through extensive experimentation on two real-world datasets, we observe that deep learning models based on swarm intelligence optimization algorithms exhibit excellent performance across various scenarios. In the context of road traffic condition prediction, our experiments reveal superior results under different time intervals and traffic volumes compared to traditional models, all while maintaining higher operational efficiency. For the autonomous lane change decision experiment, we establish a driver dissatisfaction model that aids in formulating more reasonable lane change decisions, thus reducing the frequency of abrupt lane changes.

Heterogeneous Traffic Flow Signal Control and CAV Trajectory Optimization Based on Pre-Signal Lights and Dedicated CAV Lanes

This paper proposes a control system to address the efficiency and pollutant emissions of heterogeneous traffic flow composed of human-operated vehicles (HVs) and connected and automated vehicles (CAVs). Based on the comprehensive collection of information on the flow of heterogeneous traffic, the control system uses a two-layer optimization model for signal duration calculation and CAV trajectory planning. The upper model optimizes the phase duration in real time based on the actual total number and type of vehicles entering the control adjustment zone, while the lower model optimizes CAV lane-changing strategies and vehicle acceleration optimization curves based on the phase duration optimized by the upper model. The target function accounts for reducing fuel usage, carbon emission lane-changing costs, and vehicle travel delays. Based on the Webster optimal cycle formula, an improved cuckoo algorithm with strong search performance is created to solve the model. The numerical data confirmed the benefits of the suggested signal control and CAV trajectory optimization method based on pre-signal lights and dedicated CAV lanes for heterogeneous traffic flow. Intersection capacity was significantly enhanced, CAV average fuel consumption, carbon emission and lane-changing frequency were significantly reduced, and traffic flow speed and delay were significantly improved.

Heterogeneous traffic flow cellular automata model mixed with intelligent controlled vehicles

Autonomous driving technology is advancing rapidly, and heterogeneous traffic flow consisting of intelligent controlled vehicles (ICV) and human driving vehicles (HDV) is expected to become the dominant mode on roads. In this study, three driving styles of HDV were analyzed using actual data sets, and the Gipps rule was introduced to establish safety conditions for following and lane-changing rules. A two-lane heterogeneous traffic flow cellular automata model was proposed and simulated using Matlab. According to the simulation results, the impact of ICV penetration rate on traffic capacity is significant, exhibiting a 23.4% to 62.5% increase compared to homogeneous HDV flow within the range of 0.2 to 0.8 ICV penetration rate. Homogeneous ICV flow is about 1.5 times higher than homogeneous HDV flow. Moreover, as the ICV penetration rate increases, there is a gradual decrease in both the lane-changing frequency and congestion level. These findings provide valuable insights into the influence of ICV mixed on traffic flow performance and offer a theoretical basis for managing and controlling heterogeneous traffic flow.

Lane changing and congestion are mutually reinforcing?

This study presents a comprehensive analysis of the relationship between congestion and lane changing, using vehicle trajectory data from the M1 motorway in Sydney. We establish a connection between the distribution of travel time and lane changing frequency and employ a Poisson process to describe the intensity of lane changing occurrences in different travel time ranges. From an individual perspective, lane changing does not bring significant speed benefits in most cases, except when the speed range is between 45 and 50 ​km/h. From a system perspective, the relationship between lane change rate and speed depends on the purpose of the lane changes. In merging, diverging, and lane restriction areas, for instance, mandatory lane changes dominate. In most sections of the motorway, discretionary lane changes are motivated by the expectation of improving speed and/or safety. Additionally, we demonstrate a mutual causality relationship between lane changing and congestion through the Granger causality test. This relationship is more pronounced in general areas during peak periods and contributes to the deterioration of the driving environment.

Tactical Decisions for Lane Changes or Lane Following: Assessment of Automated Driving Styles Under Real-World Conditions

Automated vehicles offer various benefits for private transport. Besides an increase in road safety and driving comfort, higher levels of automated driving provide the opportunity to perform non-driving related tasks and to save time. Fundamental requirements for the success of automated driving are an appropriate tactical decision-making of the automation system and accepted automated driving styles. For this reason, this article focuses on the assessment of automated driving styles with different lane change frequencies and the potential influence of non-driving related tasks. A driving study ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 60) was conducted on a German highway under real-world conditions using a vehicle with a prototype automation system. The evaluation of automated driving styles and driving experience of participants was based on well-being, driving comfort, trust in automation, and acceptance of the automation system. The study did not reveal any statistically significant interactions between three non-driving related tasks and two automated driving styles with a low and a high lane change frequency. However, the driving style with a low lane change frequency resulted in significantly more discomfort than the driving style with a high lane change frequency. Furthermore, the authors confirmed the assumption that intentions of an automated drive and external circumstances, such as time pressure or specific driving scenarios, affect the preferred lane change frequency.

Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction

Connected and autonomous vehicles (CAVs) are developing rapidly nowadays. In the near future, we may see human-driving vehicles (HVs) and CAVs running on the same road. The characteristics of human drivers and the interaction between HVs and CAVs, therefore, need to be further studied. All these factors will have a great impact on traffic operation. This paper aims to explore a modeling method to analyze the operation mechanism of mixed traffic flows of HVs and CAVs. Considering HV drivers’ cognitive behavioral characteristics and HV–CAV interaction effects, the forward and lateral movement rules of HVs and CAVs are proposed based on the Cellular Automata model. Then the fundamental diagram, congestion degree, lane-changing frequency, and time-spatial diagram are obtained by numerical simulation. The results show that the presence of CAVs is positively related to macroscopic traffic parameters, including velocity, flow and critical density. CAVs help to relieve traffic congestion and instabilities. The congestion degree of pure CAV traffic flow is about 1/3 that of pure HV traffic flow. When the CAV penetration rate is relatively low, the interaction between HV and CAV has a significant impact on traffic operation. Specifically, when the CAV penetration rate is below 0.5∼0.6, increasing the CAV penetration rate will increase the lane-changing frequency of the whole traffic flow.

Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach

Ramps provide entrances and exits for residents to conveniently use the freeway service. Due to the high construction cost and geometric design requirements, the decision of ramp locations involves a trade-off between multiple influencing factors, such as accessibility, safety, efficiency, construction costs, etc. This study proposed a methodology for optimizing freeway ramp placement in an effort to improve freeway accessibility. The freeway ramp locating problem was formulated as a bi-objective optimization model. Two objectives were pertinent to the reduction of total social costs: the minimization of total travel cost and minimization of total construction cost. To reflect the safety concern of ramp locations, the frequency of lane changes around the ramps and the minimum spacing between ramps were constrained. We developed an exact solution method based upon dynamic programming to solve the proposed model. Finally, a case study of the Beijing–Hong Kong–Macau Expressway within Henan Province, China, was conducted to verify the effectiveness of the proposed model and solution method.

Developing the Dynamic Bus Lane Using a Moving Block Concept

Existing dynamic bus lanes (DBLs) typically use a fixed and long blocking space to guarantee bus priority, leading to a conflict between bus priority and traffic capacity. This study proposes an innovative approach in a connected-vehicle (CV) environment, called dynamic bus lane with moving block (DBLMB). The length of the moving block can be adjusted with the bus speed in real time, aiming to ensure bus priority with fewer road resources, thus improving traffic capacity. A three-lane cellular automata model is established to evaluate DBLMB in comparison with two other strategies, bus lane without priority and bus lane with intermittent priority (BLIP). First, the benefits of DBLMB over the established DBLs are analyzed qualitatively by the macroscopic fundamental diagram. Next, simulation experiments are conducted to compare the coordination between bus priority and capacity under different strategies. Then, the impact of DBLMB on microscopic traffic flow is investigated through evaluation indicators including lane density, lane speed, lane-changing frequency, and travel speed. Finally, the sensitivity of bus delay and traffic capacity to CV penetration is discussed. The results show that: (1) the capacity of a three-lane road adopting DBLMB strategy can be stabilized above 5200 passenger car units per hour (pcu/h) when the bus departure interval is higher than 60 s and the expected level of service is below B. (2) With 100%, 80%, 60%, 40%, and 20% of CV penetration, DBLMB improves the average capacity of the road by 523 pcu/h compared with BLIP, with only an incremental bus delay of 6.43 s per vehicle.

Characterizing lane changing behavior and identifying extreme lane changing traits

ABSTRACT This study characterizes lane changing behavior of drivers under differing congestion levels and identifies extreme lane changing traits using high-resolution trajectory data. Total lane change frequency exhibited a reciprocal relationship with congestion level, but the distribution of lane change per vehicle remained unchanged as congestion increased. On average, the speed of trajectories increased by 5.4 ft/s after changing a lane. However, this gain significantly diminished as congestion worsened. Further, the average speed of lane changing vehicles was 3.9 ft/s higher than those that executed no lane changes. Two metrics were employed to identify extreme lane changing behavior: critical time-to-line-crossing (TLCc) and lane changes per unit distance. The lowest 1% TLCc varied between 0.71–1.57 seconds. The highest 1% of lane change rates for all lane changing vehicles was 2.5 lane changes per 1,000 ft traveled. Interestingly, no drivers in thisdataset had both excessive lane changes and lane changes with low TLCc.

Improved STCA Model for Multi-Lane Using Driving Guidance under CVIS

In a multi-lane area, the increasing randomness of lane changes contributes to traffic insecurity and local traffic flow instability. A study on safe lane shifting activity that focuses on threat assessment under real-time knowledge is necessary to enhance smooth vehicle flow. This paper proposed a more comprehensive lane changing guidance rule to investigate the status of surrounding vehicles to accommodate future vehicle-on-road collaborative environments based on these parameters 1) lane change demand and 2) treat assessment function. The collaborative relationships between vehicles are analyzed using a cellular automata model based on their location, velocity, and acceleration. We analyze and examine the relationship between the number of lanes and traffic flow when the road capacity is heavily mined via intelligent lane changing. Our analysis can further provide theoretical guidance for the selection of road expansion mode. Our proposed STCA-L is compared based on the average speed, average flow, lane changing frequency, spatial and temporal pattern of STCA, STCA-I, and STCA-S, and STCA-M under different vehicle densities. The numerical simulation results show that our proposed STCA-L provides the most flexible lane changing guidance in the multi-lanes road. Moreover, the simulated results show that the exponential growth of physical space cannot provide the corresponding increase in the average flow of vehicles.

Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour

Recently, electric vehicles (EVs) have become an important traffic tool in connected and automated environments due to their own advantages. However, the optimal driving control strategies of EVs and charging modes of wireless charging lanes are complex since wireless charging technology is limited when EVs are moving and traffic conditions are random. This paper constructs a scenario in which wireless charging facilities are deployed in part of a single lane and proposes a cooperative control method of mixed fleets including EVs and fuel vehicles based on the four objectives of driving safety, traffic efficiency, passenger comfort and energy management. Combined with the particle swarm optimization algorithm, the average driving velocity, lane-changing frequency and location of vehicles under different deployment lengths of charging facilities and mixing ratios are compared to discuss the influence of deployment length on the control effect of energy consumption and traffic efficiency. The numerical results indicate that when the lane length is 1500 m, with the increasing proportion of EVs, the optimal deployment lengths are approximately 700 m, 550 m and 400 m in the case of low traffic density and approximately 550 m, 500 m and 450 m in the case of high traffic density.

A data-driven two-lane traffic flow model based on cellular automata

In this paper, a data-driven two-lane traffic flow model based on cellular automata is proposed. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are used to learn the characteristics of car following behavior and lane changing behavior, respectively, from real operation data of vehicles. Under optimal network parameters, the mean absolute errors of the LSTM network for training and testing data are only 0.001 and 0.006, respectively; while the prediction accuracy of the SVM classifier for both data reaches higher than 0.99. Moreover, forward rules and lane changing rules which are more consistent with actual situation are designed. The simulation results show that: (1) the new model can reflect the first-order phase transition from free flow to synchronized flow; (2) the frequency of unsuccessful lane changing is near zero in low-density traffic areas, but increases sharply in high-density regions; and (3) the lane changing duration and unsuccessful lane changing frequency display similar trends as traffic densities increase.

Cellular automata-based modeling and simulation of the mixed traffic flow of vehicle platoon and normal vehicles

The development of autonomous driving technology and communication technology has made it possible for the realization of vehicle platoon. In the future, for a long time, there will be a mixture of platoons and normal vehicles on the road. The platoon will have a greater impact on traffic flow. Therefore, it is necessary to study the characteristics of the traffic mixed by platoons and normal vehicles. In this paper, we propose a traffic flow model for the traffic mixed by platoons and normal vehicles based on cellular automata and analyze the influence of platoon market penetration and platoon size on the road capacity, traffic congestion, and lane change frequency. The paper draws the following main conclusions. Under a given traffic environment, there is an optimal platoon penetration and platoon size that can maximize the capacity. Platoons can effectively alleviate traffic congestion, while the congestion ratio approaches zero when the proportion and size of the platoon reach a certain threshold. When the platoon market penetration is below 40%, the lane change frequency of the platoon gradually increases with the increment of the platoon penetration and size, while the platoon penetration is above 60%, the lane change frequency of the platoon decreases with that. When the platoon size is large enough, the lane change frequency will reduce to zero.

Three-Player Cooperative Game With Side-Payments for Discretionary Lane Changes of Connected Vehicles

The rapid development of new technologies, e.g., connected vehicles and e-wallet, offers us opportunities to rethink cooperative lane-change (LC) models. Most of existing cooperative LC models require a selfless assumption by default, which is usually too stringent for travelers. A cooperative LC game framework with appropriate side-payments can make up for this deficiency. In such games, when travelers maximize their own benefits, they will naturally cooperate to improve the system performance. This paper designs a four-step transferable utility based three-player game framework, which can cover all LC scenarios on multi-lane roads combing with the two-player model in the previous work. Our four-step framework is also suitable for other cooperative games with side-payments. Furthermore, the simulation results show that, cooperative games with side-payments can reduce vehicles’ LC frequency and meanwhile benefit all transaction vehicles in most scenarios in expectation. Moreover, the urgent vehicles with very high values of time can save up to 42% travel time on congested roads under the simulation settings.

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

In this paper, a two-lane cellular automata traffic flow model based on regular hexagonal cells is proposed. The realistic lane changing maneuvers with the lateral movement process, the lane changing duration, and the unsuccessful lane changing are considered by the new model. The vehicle trajectories from NGSIM dataset are used to extract the realistic lane changing maneuvers and calibrate the lane changing angle. The numerical results showed that the flow in the intermediate density range of the new model is lower than that of the traditional model. While the lane changing frequency in the same density range of the new model is higher than that of the traditional model. The lane changing duration will increase as the density grows. And the unsuccessful lane changing rate grows to large values in high density range. As to the heterogeneous traffic flow, the lane changing frequency is obviously greater than that in homogeneous condition. The trucks are more likely to make unsuccessful lane changing. And the lane changing duration of trucks is longer than that of cars in free flow region, but this is not the case in congested flow.

Impact of Cruising for Parking on Travel Time of Traffic Flow

Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed in with normal cars that are actually going somewhere. In order to mitigate the influence of cruising for parking on the normal cars, the park-and-visit cruising tests with GPS and cameras was applied to collect the behavior of the cruisers, and the videotapes of traffic flows were used to measure the volume of cruising cars and the traffic status of normal cars, simultaneously. On this basis, a parking time model based on proportional hazard-based duration model was proposed, and the factors affecting cruise for parking were analyzed, including the volume, search time, speed, acceleration, lane-change frequency, and distracted time of the cruising car. The multiple linear regression model was also established to compare with proportional hazard-based duration model results. The results indicated that between 9 and 56 percent of the traffic was cruising for parking, and the average search time was about 6.03 min. The low-speed, volume, high acceleration frequency, and lane-change times of cruising cars have a negative effect on shortening travel time of the normal traffic flow. Conversely, high-speed of cruising cars has a positive effect on shortening travel time of traffic flow. Moreover, travel time changes in varying degrees due to various factors. Under postulated conditions, the model can be used to estimate the travel time. It is hoped that this study will contribute to improve the planning and management of cruising for parking.

Lane-change behavior in low illumination: Research based on a questionnaire investigation

Illumination is an environmental factor affecting lane-change behavior. To identify the factors associated with lane-change behaviors in low illumination, the Lane-change Behavior Questionnaire in Low Illumination was developed based on the Chinese traffic environment and low-illumination environmental conditions. The appropriate dimensional structure of the questionnaire was identified. In a sample of 214 drivers in Hefei, China, the study found that male and younger drivers were more likely to change lanes while driving in a low-illumination environment. Drivers with more experience driving in low illumination were more likely to change lanes. Risky driving behaviors and lane-change frequency were significantly positively correlated. Lane-change intention was significantly higher when respondents drove on roads with street lighting than on roads without street lighting. The regression model predicted the frequency of lane-change behavior well based on gender, weekly driving time, and lane-change and risky driving behavior factors.