Variable Speed Limit Research Articles

Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges

A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a mobility digital twin (MDT) framework is developed, which is defined as an artificial intelligence (AI)-based data-driven cloud–edge–device framework for mobility services. This MDT consists of three building blocks in the physical space (namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vehicle</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Traffic</i> ), and their associated Digital Twins in the digital space. An example cloud–edge architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. A case study of the personalized adaptive cruise control (P-ACC) system is conducted, which integrates the key microservices of all three digital building blocks of the MDT framework: 1) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human Digital Twin</i> with user management and driver type classification; 2) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vehicle Digital Twin</i> with cloud-based advanced driver-assistance systems (ADAS); and 3) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Traffic Digital Twin</i> with traffic flow monitoring and variable speed limit. Future challenges of the proposed MDT framework are discussed toward the end of the article, including standardization, AI for computing, public or private cloud service, and network heterogeneity.

A reliability-based weather-responsive variable speed limit system to improve the safety of rural highways

Weather-responsive Variable Speed Limit (WRVSL) systems treat speed limits as weather-dependent random variables, as opposed to the conventional static speed limits. This study (i) evaluates drivers’ response to a fixed speed limit in different road-weather conditions, and (ii) proposes an effective approach to set WRVSLs, for rural divided highways located in extremely cold regions. Study data: road-weather, and speed data, collected from a rural highway (fixed speed limit = 110 km/h), are used to (i) estimate the 85th percentile speeds of population-level speed distributions, and (ii) develop WRVSLs based on the reliability theory. More specifically, the WRVSLs are set based on reliability: the probability of a speed being (i) likely complied by drivers, and (ii) adequate to avoid a rear-end collision. The study results reveal that merely 73 % of the drivers at the study site comply with the existing posted speed limit under normal road-weather conditions i.e., no precipitation and dry pavements. The reliability of the current speed limit is revealed to be approximately-one under normal road-weather conditions; thus, the current speed limit is perceived credible under such road-weather conditions. Yet, reliability of the current speed limit is substantially reduced in the presence of slight snow, and ice warning pavement conditions. A set of reliability-based WRVSLs ranging from 80 to 110 km/h is proposed. Jurisdictions experiencing extreme road-weather conditions may adapt the proposed methodology to effectively manage speed, particularly in rural highways under adverse road-weather conditions to enhance the probability of speed limits being safe and complied by drivers and as a result reduce crash propensity.

Variable Speed Limit Strategy With Dynamic Control Cycle Based on Predictive Control

In recent years, variable speed limit (VSL) strategies have proven to be an efficient control measure to mitigate traffic congestion at freeway bottlenecks. In general, the VSL change time is a constant value. However, there are certain limitations in traffic situations handled by a constant cycle VSL. Therefore, in this paper, we develop a dynamic cycle strategy of VSL based on predictive control. We first analyze the applicable situation of the dynamic control cycle and establish a probability model to determine the range of cycle selection. Then, the cell transmission model predicts the parameters of macroscopic traffic flow. Finally, an optimization algorithm is designed that is suitable for this strategy, which optimizes the cycle and speed limit options. An objective optimization function is formulated to minimize the total travel time. A sensitivity analysis is applied to compare different control strategies under a variety of road bottleneck structures by both the numerical analysis and simulation experiments. The simulation results show that the strategies and algorithms proposed in this paper can effectively reduce traffic congestion duration and enhance the service level of a freeway network.

Integrated Traffic Control for Freeway Recurrent Bottleneck Based on Deep Reinforcement Learning

Recent advances in deep reinforcement learning have shown promising results in solving sophisticated control problems with high dimensional states and action space. Inspired by this, we use the latest deep reinforcement learning (DRL) methods to improve freeway traffic mobility and alleviate recurring bottlenecks and congestion. More specifically, this paper proposes a centralized traffic control system that can coordinate multiple ramp metering (RM) and variable speed limit (VSL) traffic controllers on freeways to minimize the total travel time. The system uses a novel double-layer structure to synchronize different traffic controllers and introduces the actor-critic-based DRL methods to learn joint actions in a high-dimensional traffic environment. The reward function takes into account the waiting time of vehicles, the average speed of different road sections, and the on-ramp queuing limit to improve traffic mobility. We also proposed an integrated feedback controller as a benchmark. The simulation results show that the actor-critic-based methods are superior to other methods and can save more than 20% of the total travel time. We also analyzed the curse of dimensionality problem by comparing the performance of two scenarios in the simulation: one is a single-ramp interweaving area scenario; the other is a large freeway corridor with multiple on-ramps and off-ramps. The results show that our system can effectively handle these two situations without significant performance degradation, which means that the centralized control system can effectively control freeway corridors by directly guiding various traffic controllers. This also leads to the conclusion that we can use a centralized actor-critic-based control unit to manage medium-scale freeway traffic to save computing resources instead of using complex collaboration strategies.

Modeling System Dynamics of Mixed Traffic With Partial Connected and Automated Vehicles

This research aims to model system dynamics for mixed traffic flow consisting of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs). It quantifies the impact of CAVs’ speed change on the overall traffic state on a real-time basis. The model describes the impedance of CAVs’ speed reduction on traffic flow and considers the impact of potential additional lane change induced by the speed reduction. To validate the effectiveness of the proposed model, a VISSIM based microscopic simulation evaluation is performed. The results confirm that the accuracy of the proposed model is generally over 80% with the CAVs’ speed reduction constrained within 20 km/h. Sensitivity analysis is conducted in terms of various CAV penetration rates and congestion levels. The proposed model demonstrates consistently good performance across all CAV penetration rates and congestion levels. A showcase is presented to show the effect of the system dynamics in active traffic management. The proposed model could serve as the foundation of CAV based traffic management applications, such as variable speed limit and speed harmonization.

Real-time snowy weather detection based on machine vision and vehicle kinematics: A non-parametric data fusion analysis protocol

Introduction: This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips. Method: In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data. Results: Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data. Practical Applications: Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways.

Developing a Variable Speed Limit Control Strategy for Mixed Traffic Flow Based on Car-Following Collision Avoidance Theory

Variable speed limit (VSL) control is an effective technology to improve safety near freeway bottlenecks. This study aims to develop a control strategy for mixed traffic flow consisting of both human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) based on collision avoidance theory. A microscopic simulation platform is first established, and four vehicle longitudinal dynamic models including Cruising model, Intelligent Driver Model (IDM), Adaptive Cruise Control model (ACC), Cooperative Cruise Control model (CACC) and one vehicle lateral dynamic model Minimizing Overall Braking Induced by Lane Changes model (MOBIL) are incorporated into the simulation platform. Then, a new VSL control strategy derived from collision avoidance theory is proposed for mixed traffic flow at the initial stage of CAVs’ popularization. Extensive simulation experiments are conducted, and surrogate safety measures and total travel time indicators are utilized to evaluate the safety and efficiency performances of the proposed VSL control. Results indicate that the proposed VSL control strategy can effectively improve the safety performance near freeway bottlenecks with an acceptable efficiency level.

Fault-Tolerant Control of Variable Speed Limits for Freeway Work Zone With Recurrent Sensor Faults

Congestion associated with freeway work zones can adversely affect mobility, safety, and sustainability. Variable speed limits (VSL) control has been widely studied to mitigate the congestion caused by lane closures at work zones. However, most VSL controllers are designed without consideration of traffic sensor faults, especially recurrent sensor faults (RSFs) that commonly exist in freeway transportation systems. Therefore, this study proposes an interacting multiple model approach with a pseudo-model set (IMMP) to achieve VSL control with fault tolerance to different types of RSFs. With the design of a traffic flow model, an adaptive model set is developed using likelihood estimation to reduce the associated computational complexity. To ensure reliable RSF diagnosis, state covariance adaption is proposed to compensate for potential discrepancies caused by improper model parameters. A pseudo-mode set is designed to provide accurate traffic state estimations for VSL control without the prerequisite of a good match between the model parameters and the extent of corresponding sensor failures. The proposed system is evaluated under a realistic work zone environment using the traffic simulator SUMO. The results demonstrate that the system can achieve reliable RSF diagnosis and consistent improvements in mobility, safety, and sustainability near a freeway work zone area despite RSFs.

Developing dynamic speed limit strategies for mixed traffic flow to reduce collision risks at freeway bottlenecks

Connected and automated vehicles (CAVs) have the great potential to improve traffic flow because of their particular characteristics of connectivity and automation. This study aims to develop CAV control strategies based on car-following speed balance, which are defined as dynamic speed limit (DSL) strategies, and examine their performances on reducing freeway collision risks via microscopic simulations. The core idea of DSL strategy is to command the CAVs to slow down actively before reaching the bottlenecks, and form moving barriers to guide the following human driven vehicles to passively decelerate. Three DSL strategies are first developed for CAVs based on vehicle dynamics principles, and the influences of various position distribution patterns of CAVs on three strategies are compared in the one-lane scenario. Then, the DSL strategy with the best performance is selected based on simulation experiments, and a conventional variable speed limit control is used to compare the performance of our proposed methods. Finally, the DSL strategy based on Min control is tested in the two-lane, three-lane and four-lane scenarios to verify the effectiveness. Simulation results indicate that: (1) three DSL strategies based on CAVs can significantlyreduce collision risks when CAVs reach a certain proportion; (2) the uniform distribution of CAVs can maximize the effect of the moving barriers; (3) DSL strategy based on Min control is negatively affected by lane-changing behaviors, but still works well in the multi-lane scenario.

Influence of Spatial Placement of Variable Speed Limit Zones on Urban Motorway Traffic Control

Traffic control approaches, in particular Variable Speed Limit (VSL), are often studied as solutions to im-prove the level of service on urban motorways. How-ever, the efficiency of VSL strongly depends on the spa-tiotemporal arrangement of VSL zones. It is crucial to determine the length and location of VSL zones for best VSL efficiency before deployment in a real system, as the optimal length of the VSL zone and their distance from the bottleneck directly affects traffic dynamics and, thus, bottleneck control. Therefore, in this study, we perform the analysis of different VSL zones lengths and their po-sitions by using a closed-loop Simple Proportional Speed Controller for VSL (SPSC-VSL). We evaluate the different VSL zone configurations and their impact on traffic flow control and vehicle emissions in a SUMO microscopic simulation on a high traffic demand scenario. The results support the observations of previous researchers on the significant dependence of VSL zone placement on VSL efficiency. Additionally, new data-based (traffic parame-ters and vehicle emissions) evidence of the performance of the SPSC-VSL design are provided regarding the best placement of consecutive VSL zones for motorway bottle-neck control not analysed in previous research.

Developing Statewide Safety Performance Functions for Commercial Trucks Transporting Hazardous Materials on Interstate Rural Roads in Wyoming

Truck crashes in Wyoming are considered a major issue. Nearly 26% of crashes on rural interstate roads involve hazardous materials (HAZMAT) trucks. Wyoming has a high rate of activities related to the energy industry, as it is considered among the top energy producing states in the U.S.A., and mainly relies on the trucking industry for transportation. A crash involving a HAZMAT shipment might have a catastrophic impact because of the nature of the HAZMAT shipment. Therefore, it is crucial to identify the traffic safety performance of HAZMAT trucks, so suitable countermeasures can be identified to reduce the frequency and severity of these crashes. This study aims to develop safety performance functions (SPFs) for crashes involving HAZMAT utilizing traditional negative binomial (NB) models, as well as variations of the NB model, namely, NB-1 and NB-P. The results indicate that HAZMAT truck crashes are associated with vehicle miles traveled, truck percentage, horizontal and vertical characteristics of road geometry, pavement type, and speed limit. The findings from this study show that the NB-P models outperformed the traditional NB models based on likelihood ratio tests, information criteria, and prediction measures. Relevant insights are made on traditional countermeasures, such as road geometry, warning signs, slippery road surface warnings, and climbing lanes, as well as non-traditional countermeasures including updating variable speed limit (VSL) algorithms, adding variable message signs, and integrating roadway geometry information into connected vehicle applications in Wyoming. These could be considered to assist stakeholders and emergency management agencies in better decision making toward safer operations of HAZMAT trucks.

Machine and Deep Learning Techniques for Daytime Fog Detection in Real Time with In-Vehicle Vision Systems Using the SHRP 2 Naturalistic Driving Study Data

The main focus of this study is to develop a system that can accurately detect the presence of fog in real time at a trajectory level. This study leveraged video data from the SHRP 2 Naturalistic Driving Study (NDS). Extensive data reduction steps were taken to classify various levels of foggy weather conditions from the video data to form two unique image data sets. Afterward, features based on the gray level co-occurrence matrix (GLCM) were extracted from the images and used as classification parameters for training support vector machine (SVM) and K-nearest neighbor (K-NN) algorithms. In addition, a convolutional neural network (CNN) was also examined to improve the detection performance. Although the analysis was done initially on a data set consisting of two weather conditions, clear and fog, it has been extended to include different levels of fog, that is, near fog and distant fog. While the accuracy of the first analysis with two categories was approximately 92% and 91% for SVM and K-NN classifiers, respectively, the CNN produced much greater accuracy of 99%. As expected, the accuracy of the second analysis, with more refined weather categories, was relatively lower than the first analysis where CNN, SVM, and K-NN models produced an accuracy of about 98%, 89%, and 88%, respectively. With the rapid advances in connectivity and affordable cameras, the proposed detection models could be integrated into the smartphones of regular road users, creating an effective way to collect real-time road weather information that could be used to improve weather-based variable speed limit (VSL) systems.

Optimal control method of freeway based on tollbooths lane configuration and variable speed limit control

There are tollbooths on some freeway ramps, and toll lanes can be divided into Electronic Toll Collection (ETC), Manual Toll Collection system (MTC), ETC/MTC hybrid toll lanes and closed. The traffic capacity of different lanes is different. Different lane combinations are set to adjust the traffic flow of the ramp into the main line. Therefore, this strategy can be regarded as a kind of ramp metering (RM). This paper presents an optimal control method for freeways based on tollbooths lanes configuration and variable speed limit (VSL) control. Firstly, cellular automata model and cell transmission model are used to describe the operation of toll plazas and main line traffic flow respectively. Furthermore, the freeway control methods are constructed by using tollbooths RM (TB-RM) and TB-RM combined with main line VSL control respectively to improve the freeway traffic operation efficiency. Finally, the proposed method is simulated experimentally. Results from simulation experiments demonstrate that the proposed method (i) significantly improves the traffic performance in terms of total time spent savings; (ii) observably enhances driving safety.

SWSCAV: Real-time traffic management using connected autonomous vehicles

Traffic management methods aim to increase the infrastructure’s capacity to lower congestion levels. Using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connectivity technologies, connected autonomous vehicles (CAVs) have the potential to operate as actuators for traffic control. In this study, a CAV-based alternative approach for traffic management is proposed (SWSCAV), and its performance is compared to that of lane control signals (LCS) and variable speed limits (VSL), which are also traffic management systems. When a shockwave is detected due to an incident, the CAVs on the road slow until they reach the speed of the observed shockwave (SWS), according to this proposed procedure. Thus, the incoming traffic flow towards the incident is slowed, preventing the queue behind from extending. In a simulation of the urban mobility (SUMO) environment, the suggested method is evaluated for 4800 scenarios on a three-lane highway by varying the market penetration rate of CAVs in traffic flow, the control distances, the incident lane, and the duration. The proposed method reduces the incidence of density values of over 38 veh/km/lane and 28 veh/km/lane in the vicinity of the incident region by 12.68 and 8.15 percent, respectively. Even at low CAV market penetration rates, the suggested method reduces traffic density throughout the network and in the location of the incident site by twice as much as the LCS system application.

A variable speed limit model for freeway bridges considering the influence of both traffic service level and visibility in fog

Fog often occurs on freeway bridges crossing rivers, reducing the drivers’ visibility and increasing the vehicles’ braking distance. Setting a reasonable speed limit under foggy conditions is required to ensure safe driving on freeway bridges. The traffic status also affects the speed limit in foggy conditions. Different from most existing variable speed limit (VSL) models that only considered the traffic condition or visibility in fog, this study modifies an existing VSL model by considering both traffic service level and visibility in fog on a river-crossing freeway bridge. The relationship between visibility and car-following distance is innovatively analyzed in our newly developed VSL model to meet the safe driving conditions more accurately. The E’dong Yangtze River Bridge is used as an example to evaluate the performance of our developed VSL model. The VISSIM simulation is applied to obtain the average and standard deviation of the speed difference of the VSL model. The standard deviation of following distance is included as the supplement to evaluate the safety benefits of the VSL model. The results of a multi-way ANOVA show that the new VSL strategy results in a lower average and standard deviation of the speed difference at a low traffic service level. The standard deviation of following distance indicates that the driver’s longitudinal maneuvering ability is improved under the new VSL model. Therefore, using the new VSL model can increase road traffic safety in fog. In addition, we preliminarily investigate the role of the model in reducing energy consumption and improving the operating efficiency using fuel consumption (FC) and traffic flow as indicators, respectively. The results show that the choice of speed limit strategy depends on the optimization objective. If environmental benefits are optimized, it is recommended to choose the new VSL strategy. If traffic efficiency is optimized, the new VSL strategy is suggested when the driving conditions are favorable (i.e., the visibility and traffic service level are high). In contrast, the strategy without VSL is a better choice for poor driving conditions (i.e., the visibility and traffic service level are low). This research provides a more scientific and reasonable VSL strategy in specific visibility and traffic service level conditions, thus is helpful for driving safety on freeway bridges.

Investigation on variable speed limit control strategy of expressway under adverse weather conditions

In order to effectively improve the capacity of expressways under adverse weather conditions (rainy, foggy and snowy) and reduce the potential safety hazards of expressways, a variable speed limit control strategy is proposed. First, the impact of road alignment on traffic capacity is considered, and road traffic capacity reduction coefficients under different bends and slopes are added so that the cell transmission model and a dynamic traffic flow model can be established; Secondly, the maximum speed limit value under adverse weather conditions is determined according to the influence of rainfall intensity under rainy weather conditions, visibility under foggy weather conditions, and road adhesion coefficient under snowy weather conditions on traffic capacity; Finally, an adaptive genetic algorithm is employed to solve the function that satisfies the safety and efficiency of traffic operation. The results show that the variable speed limit control strategy under adverse weather conditions effectively improves the road capacity, reduces the driving risk of vehicles, and alleviates traffic congestion.

Operational and safety impacts of integrated variable speed limit with dynamic hard shoulder running

The combination of various Intelligent Transportation System (ITS) strategies generally has manifold effects, much greater than when strategies are implemented individually. This research introduces a novel dynamic control strategy, which includes the dynamic use of a Model Predictive Control (MPC) based Variable Speed Limit (VSL) integrated with the dynamic use of Hard Shoulder Running (HSR). Both VSL and HSR are proactively triggered to allow for necessary control measures to be taken to delay and possibly avoid the formation of a bottleneck. For traffic prediction, a modified METANET model is developed which takes into consideration the complex nature of driver’s behavior along with driver’s compliance, capacity drop and posted speed limits. The modified METANET model is shown to be more efficient than conventional macroscopic prediction models in detecting traffic congestions. This MPC based strategy was tested on a section of Deerfoot Trail, Calgary, Alberta using an exclusively developed integrated VISSIM-COM-MATLAB interface. The results from this study suggested that the integrated VSL and HSR control strategy results in a 21.09% increase in average speed and 33.44%. in vehicle-throughput. Furthermore, there was a noticeable reduction in the average travel time by 39.98% and in the total number of stops, by 32.43%. Importantly, the safety analysis performed using Surrogate Safety Assessment Model (SSAM) revealed a notable reduction in collisions, by 29.73%.

Boundary Feedback Stabilization of Freeway Traffic Networks: ISS Control and Experiments

The boundary feedback control of networks of freeway traffic is considered in this article by means of the partial differential equation (PDE)-based techniques. The control and measurements are all located at the boundaries of each link. We have established the boundary control model for the system, a linear hyperbolic system of balance laws, which includes not only the traffic flow dynamics of the network described by combining the linearized Aw&#x2013;Rascle&#x2013;Zhang (ARZ) traffic flow model of each link but also the integrated on-ramping metering and variable speed limit control modeled as the boundary condition. As the traffic demand of the network is fluctuated, the boundary input-to-state stability (ISS) controller is designed to suppress the disturbance and regulate the traffic flow into the boundedness regions of the desired states. Based on a novel Lyapunov function, some sufficient conditions in terms of the matrix inequalities are derived for the ISS boundary stabilization in the <inline-formula> <tex-math notation="LaTeX">${L}^{2}$ </tex-math></inline-formula>-norm. The numerical simulation is given to illustrate the effectiveness of the developed boundary feedback control. Moreover, an interesting traffic experiment is carried out by using the traffic simulation software, AIMSUN, and some specific traffic data are collected and analyzed to verify the feasibility and effectiveness of the boundary control.

Impact of Deep Reinforcement Learning on Variable Speed Limit strategies in connected vehicles environments

The Variable Speed Limit (VSL) control is considered in the context of connected vehicles acting as moving sensors, while their obedience to speed limit is enforced by a mandatory Intelligent Speed Adaptation (ISA) system. The objective of this study is to extend spatially static differential VSL control by introducing novel VSL strategies that are based on spatially dynamic speed limit zones. The spatial configuration of speed limit zones requires a novel traffic state representation based on the set of consecutive matrices which encode each vehicle position and speed within the controlled motorway during the control time step. The actions for all proposed VSL strategies are computed by the same Deep Reinforcement Learning (DRL) approach based on the Deep Deterministic Policy Gradient (DDPG) architecture. The DDPG learning models contain integration of Convolution and Long Short-Term Memory (LSTM) known as ConvLSTM layers, along with the Convolution and Fully Connected layers. Thus, those models can learn complex spatio-temporal traffic dynamics based on proposed traffic state representation. The results show that proposed VSL strategies have achieved higher overall motorway throughput compared to one which is based on the static speed limit zones and baseline cases (no-control and Simple Proportional Speed Controller algorithm). Simultaneously, they achieved a minimal increase in the number of aggressive braking, while the average headway is increased. All proposed VSL control approaches are simulated and analyzed by using a synthetic microscopic motorway model and characteristic traffic scenario for urban motorways.

Variable Speed Limit Control with Dynamic Location of Message Signs for the Internet-of-Vehicles Environment

The control position for traditional variable speed limits (VSL) is generally fixed. However, when congested traffic flow reaches the variable message sign, the effect of VSL on easing congestion will be reduced. It is, therefore, necessary to dynamically set the location of the variable message sign while implementing dynamic traffic control. Combined with the emerging Internet of Vehicles environment, this paper designs a variable-length cell transmission model to describe the uneven uniformity of traffic and to calculate the speed limits position of dynamic VSL. Then combined with the VSL control, an objective optimization function is established, with the travel time being the object. By numerical analysis and simulation, the sensitivity of the dynamic transmission model is verified under different traffic conditions. The results show that the proposed model can change the length of the cell and better describe the uneven distribution of the crowded tuple. The location of variable message signs can be set dynamically, and it can significantly alleviate the congested area. In addition, the new dynamic variable message sign control method can shorten travel times and improve traffic efficiency.