Multi-lane Traffic Flow Research Articles

Learning-based hierarchical cooperative eco-driving with traffic flow prediction for hybrid electric vehicles

The integration of autonomous driving and hybrid electric vehicle technologies presents a promising solution for achieving environmental sustainability. This paper introduces an innovative energy-efficient driving strategy for hybrid electric vehicles that incorporates real-time traffic flow prediction. The study delves into the impact of both lateral and longitudinal vehicle maneuvers on energy consumption within dynamic traffic environments, offering novel insights into optimizing energy utilization. Firstly, a multi-lane traffic flow state rolling predictor is constructed based on the Hankel dynamic mode decomposition algorithm. Subsequently, a vehicle longitudinal and lateral coordinated control strategy is established by integrating the prioritized experience replay double deep Q-network algorithm. Finally, a novel energy management strategy is proposed that leverages Simulink dynamic model and the deep deterministic policy gradient algorithm to address the vehicle dynamic decision-making planning results. Within a hierarchical cooperative optimization framework, this research comprehensively considers safety, comfort, traffic efficiency, and fuel economy. By introducing a novel hierarchical collaborative ecological driving framework, we have achieved a substantial improvement in environmental sustainability, with traffic efficiency increasing by 10.27%-14.41% and fuel economy rising by 9.44%-10.47%. Hardware-in-the-loop validation has confirmed the proposed approach’s real-time capabilities and promising practical applications.

Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior.

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.

On the singular limit problem in nonlocal balance laws: Applications to nonlocal lane-changing traffic flow models

We present a convergence result from nonlocal to local behavior for a system of nonlocal balance laws. The velocity field of the underlying conservation laws is diagonal. In contrast, the coupling to the remaining balance laws involves a nonlinear right-hand side that depends on the solution, nonlocal term, and other factors. The nonlocal operator integrates the density around a specific spatial point, which introduces nonlocality into the problem. Inspired by multi-lane traffic flow modeling and lane-changing, the nonlocal kernel is discontinuous and only looks downstream. In this paper, we prove the convergence of the system to the local entropy solutions when the nonlocal operator (chosen to be of an exponential type for simplicity) converges to a Dirac distribution. Numerical illustrations that support the main results are also presented.

Mean-field limit of a hybrid system for multi-lane multi-class traffic

This article aims to study coupled mean-field equation and ODEs with discrete events motivated by vehicular traffic flow. Precisely, multi-lane traffic flow in presence of human-driven and autonomous vehicles is considered, with autonomous vehicles possibly influenced by external policymakers. First, a finite-dimensional hybrid system is developed based on the continuous Bando-Follow-the-Leader dynamics coupled with discrete events due to lane-change maneuvers. Then the mean-field limit of the finite-dimensional hybrid system is rigorously derived for the dynamics of the human-driven vehicles. The microscopic lane-change maneuvers of the human-driven vehicles generate a source term for the mean-field PDE. This leads to an infinite-dimensional hybrid system described by coupled Vlasov-type PDE, ODEs, and discrete events.

Mean-field limit of a hybrid system for multi-lane car-truck traffic

<abstract><p>In the present work we model multi-lane traffic flow in presence of two population of vehicles: cars and trucks. We first develop a finite-dimensional hybrid system which rely on continuous Bando-Follow-the-Leader dynamics coupled with discrete events motivated by the lane-change maneuvers. Then we rigorously prove that the mean-field limit is given by a system of Vlasov-type PDE with source terms generated by the lane-change maneuvers of the human-driven vehicles.</p></abstract>

A Data Fusion Powered Bi-Directional Long Short Term Memory Model for Predicting Multi-Lane Short Term Traffic Flow

In intelligent transportation system (ITS), accurate and real-time prediction of short-term multi-lane traffic flow with existing traffic data is an important part of urban traffic planning, traffic management and control. The data generated in the process of vehicle driving has the cooperative characteristics of multi-source, space-time and dynamic. Combining the data with high-performance computing or cloud computing, a new space-time information framework is designed, which is of great significance for the analysis and prediction of traffic flow, as well as intelligent traffic management, service and decision-making. This paper analyzes the statistical characteristics of urban road traffic flow from the two dimensions of time and space through the spatial-temporal correlation between multi-lane short term traffic flow in single observation point and multi observation points. We constructed a data fusion powered bi-directional long short term memory (DFBD-LSTM) model for individual lane and aggregate traffic flow, then used this model to predict multi-lane short term traffic flow. By taking individual lane traffic flow and aggregate traffic flow as different variables, the model produces more accurate predictions, which can better guide people to travel, alleviate the congestion of urban road traffic network to a certain extent, and improve the utilization rate and transportation efficiency of traffic road.

Macroscopic Analysis Of The Viscous-Diffusive Traffic Flow Model

Second-order macroscopic traffic models are characterized by a continuity equation and an acceleration equation. Convection, anticipation, relaxation, diffusion, and viscosity are the predominant features of the different classes of the acceleration equation. As a unique approach, this paper presents a new macro-model that accounts for all these dynamic speed quantities. This is done to determine the collective role of these traffic quantities in macroscopic modeling. The proposed model is solved numerically to explain some phenomena of a multilane traffic flow. It also includes a linear stability analysis. Furthermore, the evolution of speed and density wave profiles are presented under the perturbation of some parameters.

Lightweight Deeplearning Method for Multi-vehicle Object Recognition

The recognition method based on deep learning has a large amount of calculation for the changes of different traffic densities in the actual traffic environment. In this paper, an integrated recognition method YOLOv4-L is proposed for reducing computational complexity based on the YOLOv4. The characteristics of multi-lane traffic flow with different flow densities were analyzed for statistical data sets, and k-means++ clustering algorithm was used to optimize the prior frame parameters to improve the matching degree between the prior frame. GhostNet was used to replace CSPDarknet53 of original network structure of YOLOv4 as the feature extraction network. The depthwise separable convolution module was introduced to replace the original 3×3 common convolution in feature extraction network, reduce model parameters and improve detection speed. The network model is further improved both with accuracy and robustness with the help of comprehensive method of Mosaic data enhancement, learning rate cosine annealing and label smoothing. Experimental results show that, Recognition speed is greatly improved at the expense of minimal recognition accuracy reduction: the recognition speed improvement value is 47.81%, 49.15% , 56.06% in detection speed (FPS), respectively in free flow, synchronous flow and blocked flow, the reduction value of accuracy is 2.21%, 0.67%,, 0.05% mAP, respectively.

Estimating Freeway Lane-Level Traffic State with Intelligent Connected Vehicles

This paper proposes a methodology for estimating lane-level traffic state for freeways by fusing data from intelligent connected vehicles (ICVs) with fixed detector data (FDD) and probe vehicle data (PVD). With microscopic vehicle trajectories of ICVs and their surrounding vehicles, the proposed methodology integrates a multilane traffic flow model into the data assimilation framework based on extended Kalman filter (EKF), in which traffic measurement models are formulated for ICV data, PVD, and FDD, respectively, to fit their different characteristics. Simulation experiments are conducted to test the performance of the proposed methodology with various penetration rates of ICVs, using a set of simulated ICV data based on the Next Generation SIMulation (NGSIM) data sets. The results demonstrate that by utilizing only 3% to 5% ICVs in the mixed traffic, the proposed methodology could produce an accurate estimate of lane-level traffic speed and a reasonable estimate of lane-level traffic density.

Research on Differences and Effects of Lateral Distribution Characteristics of Multi-lane Traffic Flow on Freeway

In order to obtain the probability distribution characteristics and change rules of the vehicle flow in different lanes of the expressway, based on the WIM data, the probability statistics analysis of the distribution characteristics of the vehicle type, the distance between the heads, the vehicle weight, and the speed on the different lanes of the expressway was carried out, and based on the extreme value theory. The extreme vehicle weights of different lanes were extrapolated, and the load effect was further analyzed. The results show that the probability distribution of vehicle types in each lane is different. Vehicles on one-way two-lane expressways have obvious two-tier distribution probabilities. For slow lanes, the selection probability of vehicles with 3 axles and above exceeds 0.85;The selection probability of cars with 3 axles and above on the slow lane of a one-way 3-lane expressway is higher than 0.4, while the selection probability of cars with 2 axles is about 0.15; The lateral distribution of vehicle weight meets the law of increasing from the inner lane to the outer side. The average vehicle weight of the same model on the slow lane is about 1.3-2.0 times that of the fast lane, and the average total weight of the middle lane of the three lane high-speed is the largest; The probability density distribution of vehicle weight in slow lane shows the characteristics of thick tail and long tail, while that in fast lane shows single peak distribution, and the vehicle weight is mainly concentrated in 3000kg; Each vehicle has a specific speed range, and the vehicle mixing ratio has a certain impact on the vehicle speed. The average speed of 2-axle vehicles in the same lane is 20km · h-1 higher than that of 3-axle and above vehicles. The extrapolation results of the extreme value of the load effect show that the bending moment effect of the slow lane is about 1.3-1.4 times that of the fast lane and 1.1-1.2 times that of the middle lane. The middle-span bending moment effect value increases from the inner lane to the outer lane. The transverse reduction coefficients of vehicle load on multi-lane expressways are all higher than the normative values.

A rigorous multi-population multi-lane hybrid traffic model for dissipation of waves via autonomous vehicles

In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5\% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on three components that we treat as parameters in the model: safety, incentive and cool-down time. The choice of these parameters in the lane-change mechanism is critical to modeling traffic accurately, because different parameter values can lead to drastically different traffic behaviors. In particular, the number of lane-changes and the speed variance are highly affected by the choice of parameters. Despite this modeling issue, when using sufficiently simple and robust controllers for AVs, the stabilization of uniform flow steady-state is effective for any realistic value of the parameters, and ultimately bypasses the observed modeling issue. Our approach is based on accurate and rigorous mathematical models, which allows a limit procedure that is termed, in gas dynamic terminology, mean-field. In simple words, from increasing the human-driven population to infinity, a system of coupled ordinary and partial differential equations are obtained. Moreover, control problems also pass to the limit, allowing the design to be tackled at different scales.

Improved multi-lane traffic flow simulation based on weigh-in-motion data

Improved multi-lane traffic flow simulation based on weigh-in-motion data

Improved multi-lane traffic flow simulation based on weigh-in-motion data

Vehicle load is the main variable load of highway bridges, which significantly affects the security and duration of bridges. Highly precise vehicle load models are the basis for studying vehicular load responses and providing reliability assessments of highway bridges, whose core is the spatio-temporal distribution of vehicular load on a bridge deck. Vehicle type, axle weight, axle distance and position are the main parameters of spatio-temporal simulation of traffic flow. Weigh-in-motion (WIM) systems can obtain these original vehicle parameters efficiently. However, there are two main problems about how to accurately simulate the above traffic flow parameters based on WIM data: (1) A large number of WIM systems have low accuracy for vehicle classification and an efficient classification method for vehicles from massive WIM data has not been proposed; (2) The most popular MC simulation method ignored the statistical dependence of parameters (vehicle sequence, axle weight, axle distance, etc.), which results in the deviation between traffic flow simulation samples and measured data. In this study, to obtain traffic flow simulation results closer to measured data, a multi-lane traffic flow simulation method via fusion of efficient vehicle classification and the statistical dependence of parameter was proposed. First, the overall framework of the traffic simulation was introduced. Subsequently, the vehicle classification process and traffic flow simulation were introduced respectively. For the vehicle classification, the abnormal vehicles were removed based on the box diagram method, and then the vehicle classification was completed based on the improved K-means++ clustering algorithm. The vehicle sequence and the axle weight/distance samples were obtained by MCMC simulation method and Copula function theory respectively. Finally, a case study was presented using WIM measured data of an 8-lane expressways. The results showed that the optimal number of clusters determined based on the K-means++ clustering algorithm was reasonable. The vehicle simulation samples considering the statistical dependence of parameter were closer to the measured data than previous methods and the normal copula function was slightly better than the t-copula function.

A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate

A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate

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.

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Real-time and accurate multi-lane traffic condition forecasting is of great importance to the connected and automated vehicle highway system. However, the majority of existing deep learning based traffic prediction methods focus on pursuing the precision of the methods while neglect to improve the efficiency of the methods. To achieve the high accuracy and high efficiency of multi-lane traffic flow prediction simultaneously, this study proposes a novel combination method via the integration of the clockwork recurrent neural network (CWRNN) and random forest (RF) method, which is RF-CWRNN. To the best of the authors’ knowledge, this is the first time that the CWRNN is introduced to capture the temporal feature of lane-level traffic flow and make traffic speed prediction. Meanwhile, the RF method is employed to measure the temporal relevance of the traffic flow and determine the optimal input time window. To verify the performance of the RF-CWRNN method, the ground-truth data of the expressways in Beijing were utilised to carry out experiments. The results indicate that the RF-CWRNN method is superior to the baseline models in terms of accuracy and robustness. Besides, the proposed method can save plenty of training time compared with the classical long short-term memory neural network.

A Hybrid Submicroscopic-Microscopic Traffic Flow Simulation Framework

Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.

Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

Multi-lane traffic flow monitoring and detection system based on video detection

Multi-lane traffic flow monitoring and detection system based on video detection