Highway Model Research Articles

Mobile Ad-Hoc network, Mobile application, aircraft performance, software development

In the past decade, a steady increase has been noticed in the number of mobile users and the use of mobile communication. In wireless communication links are stable. Duration of the link is the most important design by which the performance of the mobile Ad Hoc network (MANET) can be determined. In this research article, a derivative metric of graph theory attained from mobility model technique in Ad Hoc network of casually moving object, link duration of probability density function (PDF)been examined by using three mobility models namely random waypoint, random direction, and freeway model. The performance is based on the node speed, transmission range, the density of node, area of simulation, and computational complexity with time. By using Monte Carlo simulations, the reliability and robustness of models have been analyzed. The main advantage of the proposed scheme is implemented is an easy hardware platform because of the concept in mobility models and the easiness of code.

Estimating the Impacts of AV and CAV and Technologies Transportation Systems for Medium, Long, and Buildout Transportation Planning Horizons

Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) are expected to have a significant impact on highways, but their planning horizon impacts have not been fully studied in the literature. This study seeks to address this gap by investigating the impact of AVs/CAVs at different stages of adoption on long-range transportation planning horizons in the United States. Planners use travel demand forecasts to make important and expensive transportation supply investment decisions, and this study uses oversaturated traffic data from the NGSIM database to estimate the parameters of the Wiedemann car-following model for a basic freeway. Using data from the European-funded Coexist Project, we construct AV/CAV scenarios that incorporate various mixes of AV/CAV technologies, including cautious driving behavior (AV-Cautious) and more aggressive driving behavior (AV All-Knowing), and span multiple planning horizon planning years. Our findings suggest that the capacity impact of AVs will change based on their penetration in the vehicle fleet. For medium-term planning horizons, AVs will reduce capacities, whereas for long-term planning horizons and the buildout, capacities will be positively impacted. However, the impact of AVs/CAVs on highway capacity is subject to two main limitations, including the assumptions made in this study about the behavior of AVs/CAVs and the lack of consideration for AVs/CAVs in oversaturated traffic in previous literature. Future studies could explore these limitations in more detail and consider other factors, such as the impact of AVs/CAVs on travel demand and the potential for AVs/CAVs to affect mode share. Overall, this research provides valuable information for transportation planners and decision-makers to consider as they develop medium and long-term transportation plans and make informed decisions about the impact of AVs/CAVs.

Vehicle Trajectory-Based Calibration Procedure for Microsimulation

In practice, traffic engineers and researchers calibrate microscopic driver behavior in microsimulation models using macroscopic inputs (e.g., aggregated traffic throughput) instead of microscopic inputs (e.g., inter-vehicle spacing and acceleration). This mismatch has naturally led to concerns that these models have not accurately captured the microscopic driver behaviors, despite the apparent goodness-of-fit in the macroscopic performance measures. There is renewed interest in trajectory-based calibration for microsimulation models given the recent improvements in data collection and data processing technologies. Toward this end, this project collected aerial vehicle trajectories using cameras attached to drones at three real-world sites (producing 800 ft trajectories) and a helicopter at one site (producing 1.2 mi trajectories). This study used these datasets to develop a new microsimulation model trajectory-based calibration method and calibrated four urban freeway models: I-270, I-15, I-75, and I-95. Next, this study calibrated models of the same four sites using macroscopic data (e.g., average segment speed and throughout). Finally, this study compared the two methods’ calibration accuracy to understand the benefits of the trajectory-based calibration method. The experimental results provided evidence that analysts should not trust traditional calibration methods to produce models with realistic vehicle trajectories. However, explicit integration of trajectories into the calibration process (i.e., hybrid calibration methods) can significantly improve the modeled trajectories’ realism. Calibrated model results were most impressive at I-75, which is the only site that collected data via helicopter, yielding significantly longer trajectories.

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles.

This study develops a computational model to predict drivers' response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs). Previous research has modeled drivers' takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking. A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists. Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting. The current model can successfully predict drivers' response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs. This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance.

Transferability of the highway safety manual freeway model to EU countries

IntroductionIn roadway safety management processes, Accident Prediction Models (APMs) represent the best available tools to analyse potential safety issues, to identify safety improvements and to estimate the potential effect of these improvements in terms of crash reduction. The Highway Safety Manual (HSM) provides consistent predictive methods for estimating the predicted average crash frequency, but an appropriate calibration is necessary to use them in contexts different from the ones where they were developed. MethodThe present study provides a contribution in this field of research providing a European APM based on the one proposed by HSM and introducing a new methodology to transfer the HSM to different European rural freeways. Specifically, a new set of jurisdiction-specific (JS) base safety performance functions (SPFs) have been developed as a function of annual average daily traffic volume and roadway segment length, considering JS base conditions specific for each different national network, different from the HSM base conditions. These new SPFs were then used for the calibration of the full models, and the results compared with those obtained applying the HSM predictive model. This allows to evaluate the potential benefits of calibrating jurisdiction-specific base SPFs, with different base conditions. ResultsThe results showed that the local SPFs development approach allowed to obtain a better fit than the HSM predictive model for the analysed European countries. ConclusionsThe findings suggest that the development of jurisdiction-specific base SPFs will offer more suitable APMs for countries that differ from the USA, thus a more reliable prediction could be obtained by applying this procedure. Practical ApplicationsThe use of JS SPFs allowed developing European APMs based on those proposed by HSM but applicable to different conditions. This represents a useful starting point for further analysis and improvements in accident prediction modelling.

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

Simulating the effectiveness of wave dissipation by FollowerStopper autonomous vehicles

The traffic jam phenomenon known as stop-and-go waves is commonplace on many roadways and is unavoidable due to the imperfect nature of human driving behaviour. Previous research by Stern et al. (2018) has demonstrated experimentally that a single Autonomous Vehicle (AV) can be harnessed to dissipate stop-and-go waves produced by 20 other passenger vehicles driving in a cycle. However, the experiment was conducted in an idealistic situation of a single-lane ring road with one AV; meaning no lane changing was possible and multiple AV interaction was left untested. To address these limitations, the AV driving behaviour used to achieve this, known as FollowerStopper (FS), was modelled for use in the Aimsun traffic simulation software and validated before further numerical experiments were conducted. The microsimulation scenarios were designed to observe how multiple AVs driven by the FS controller (referred to as FS-AVs in this paper) affect their traffic wave dissipation capability. Both single and double-lane ring roads experimental designs were used with the latter capturing the untested effect of lane changes. FS-AV was found to be less effective than originally documented in both cases. Multiple FS-AVs included within one lane were still able to improve traffic flow and dissipate the stop-and-go waves, although it is sensitive to the combination of different factors and any deviation away from the ideal condition is likely to produce worse-off traffic operations in terms of traffic flow. For double-lanes, human-driven vehicles (HDVs) would change lanes at heightened rates to occupy the larger gap the FS-AV needs for its dissipating strategy, causing it to further pullback and set off a chain reaction to upstream traffic. Stop-and-go waves had not been dissipated and decreased traffic performance in terms of flow, speed and delay time resulted. Our results also showed that the wave dampening effect of the FS-AV does not translate between the ring road and its linear equivalent. On the other hand, our preliminary simulation results using a real-life freeway model did not suggest multiple FS-AVs worsen traffic conditions. The contradicting results might be due to different traffic conditions such as vehicle density. Further research is required to investigate what factors could adversely affect FS-AV’s ability to dampen shockwaves and explore possible improvement to its driving algorithm.

A simulation assessment of shockwave detection and damping algorithms based on magnetometers and probe vehicle data

Nowadays, connected cars are uncommon on our streets, but their percentage is expected to grow uninterruptedly. These devices would provide a lot of data in real-time that can be used for road operators to improve the traffic. This research is focused on one of these applications, which is shockwave damping on freeways. This work evaluates two shockwave detection methods that use probe vehicle data and fixed sensors data and one mitigation algorithm that uses variable speed limits to resolve shockwaves. This paper analyses through microscopic traffic simulation the performance of the selected algorithms and how their parameters affect them in several scenarios. Also, the effect of the penetration rate of probe vehicle data is evaluated. Finally, the best algorithm with the best parameters configuration is applied to a realistic model of the AP7 freeway in Girona (Spain). The obtained results show that the algorithms applied greatly reduce the total travel time in this network.

Integrated Traffic Control for Freeways using Variable Speed Limits and Lane Change Control Actions

The wide deployment of vehicle automation and communication systems (VACS) in the next decade is expected to influence traffic performance on freeways. Apart from safety and comfort, one of the goals is the alleviation of traffic congestion which is a major and challenging problem for modern societies. The paper investigates the combined use of two feedback control strategies utilizing VACS at different penetration rates, aiming to maximize throughput at bottleneck locations. The first control strategy employs mainstream traffic flow control using appropriate variable speed limits as an actuator. The second control strategy delivers appropriate lane-changing actions to selected connected vehicles using a feedback-feedforward control law. Investigations of the proposed integrated scheme have been conducted using a microscopic simulation model for a hypothetical freeway featuring a lane-drop bottleneck. The results demonstrate significant improvements even for low penetration rates of connected vehicles.

Efficient Freeway MPC by Parameterization of ALINEA and a Speed-Limited Area

Freeway congestion can reduce the freeway throughput due to the capacity drop or due to blocking caused by spillback to upstream ramps. Research has shown that congestion can be reduced by the application of ramp metering and variable speed limits. Model predictive control is a promising strategy for the optimization of the ramp metering rates and variable speed limits to improve the freeway throughput. However, several challenges have to be addressed before it can be applied for the control of freeway traffic. This paper focuses on the challenge of reducing the computation time of MPC strategies for the integration of variable speed limits and ramp metering. This is realized via a parameterized control strategy that optimizes the upstream and downstream boundaries of a speed-limited area and the parameters of the ALINEA ramp metering strategy. Due to the parameterization, the solution space reduces substantially, leading to an improved computation time. More specifically, the number of optimization variables for the variable speed limit strategy becomes independent of the number of variable message signs, and the number of optimization variables for the ramp metering strategy becomes independent of the prediction horizon. The control strategy is evaluated with a macroscopic model of a two-lane freeway with two ON-ramps and OFF-ramps. It is shown that parameterization realizes improved throughput when compared with a non-parameterized strategy when using the same amount of computation time.

Mobility-Aware GPCR-MA for Vehicular Ad hoc Routing Protocol for Highways Scenario

Vehicular ad hoc network systems are highly important experiments because of enthusiastically exchanging information and symmetric relations of systems. The best transmitting device supports a successful arrangement of networks. This article presents GPCR-MA mobility aware routing protocol concepts based on the Gauss Markov Mobility and Freeway Model. This concept is applied on highway networks because highways have a number of road segment and junctions. The performance of this experiment is verified based on the QoS parameter performance between of the mobility model, which is presented based on their mathematical modeling and simulation structure. After comparing the mobility model based on the parameters: network delay, energy consumption, average throughput and packet delivery ratio, a Freeway mobility model is better in terms of network delay and packet delivery ratio and Gauss Markov model, is better in terms of throughput because it works on broken links in the network, based on rapidly changes in simulation time and increased network traffic length.

The impact of self-stabilization on traffic stability considering the current lattice’s historic flux for two-lane freeway

Self-stabilization effect reflects the adaptive change of the current lattice in traffic flow. To improve traffic flow, a new self-stabilization term in this paper is inserted into lattice hydrodynamic model for two-lane freeway. It is shown that the self-stabilization effect can increase traffic stability on two lanes whether lane changing occurs or not according to linear stability analysis. In view of numerical simulation, the self-stabilization effect enhances the stability of the traffic flow in the modified lattice hydrodynamic model for two-lane freeway.

Nonlinear analysis of a new lattice hydrodynamic model with the consideration of honk effect on flux for two-lane highway

Influence of the honk on traffic flux is taken to construct a new lattice model for two-lane freeway. The linear stability analysis is applied to obtain the stability condition involving the honk effect on traffic flux in two-lane system. Nonlinear analysis is implemented to get the mKdV equation for the phase transition of two-lane lattice traffic flow. Numerical simulation is worked out to authenticate that the impact of honk on traffic flux strengthens the stability of traffic flow and suppresses the traffic congestion effectively under lane changing in two-lane lattice traffic flow, which is accordance with the analytic results.

The flux difference memory integral effect on two-lane stability in the lattice hydrodynamic model

In this paper, the flux difference memory integral (FDMI) effect is introduced into the lattice hydrodynamic model for a two-lane freeway. The FDMI effect plays an important role on the linear stability condition, from theoretic analysis, in a two-lane system. The FDMI effect including the intensity reaction coefficient and the integral historical time are investigated on two lanes via simulation. From numerical simulation, both lane changing rate and FDMI effect strengthening the stability of traffic flow on two lanes is determined.

Robust Global Adaptive Exponential Stabilization of Discrete-Time Systems With Application to Freeway Traffic Control

This paper is devoted to the development of adaptive control schemes for uncertain discrete-time systems, which guarantee robust global exponential convergence to the desired equilibrium point of the system. The proposed control scheme consists of a nominal feedback law, which achieves robust global exponential stability properties when the vector of the parameters is known, in conjunction with a nonlinear dead-beat observer. The proposed adaptive control scheme depends on certain parameter observability assumptions. The obtained results are applicable to highly nonlinear uncertain discrete-time systems with unknown constant parameters. The successful applicability of the obtained results to real control problems is demonstrated by the rigorous application of the proposed adaptive control scheme to uncertain freeway models. A provided example demonstrates the efficiency of the approach.

A multivariate-based conflict prediction model for a Brazilian freeway

Real-time collision risk prediction models relying on traffic data can be useful in dynamic management systems seeking at improving traffic safety. Models have been proposed to predict crash occurrence and collision risk in order to proactively improve safety. This paper presents a multivariate-based framework for selecting variables for a conflict prediction model on the Brazilian BR-290/RS freeway. The Bhattacharyya Distance (BD) and Principal Component Analysis (PCA) are applied to a dataset comprised of variables that potentially help to explain occurrence of traffic conflicts; the parameters yielded by such multivariate techniques give rise to a variable importance index that guides variables removal for later selection. Next, the selected variables are inserted into a Linear Discriminant Analysis (LDA) model to estimate conflict occurrence. A matched control-case technique is applied using traffic data processed from surveillance cameras at a segment of a Brazilian freeway. Results indicate that the variables that significantly impacted on the model are associated to total flow, difference between standard deviation of lanes’ occupancy, and the speed’s coefficient of variation. The model allowed to asses a characteristic behavior of major Brazilian’s freeways, by identifying the Brazilian typical heterogeneity of traffic pattern among lanes, which leads to aggressive maneuvers. Results also indicate that the developed LDA-PCA model outperforms the LDA-BD model. The LDA-PCA model yields average 76% classification accuracy, and average 87% sensitivity (which measures the rate of conflicts correctly predicted).

Traffic Control Method on Efficiency of Urban Expressway Accompanied Frequent Aggressive Driving Behavior

In order to ensure that traffic capacity is stable, and avoid incident congestion is anabatic, a double-layer ramp-metering model is proposed in this paper to control the traffic flow at each on-ramp, nearby incident congestion. The function of the lower model is to recognize where the incident congestion exists, based on an adaptive neural network, whose inputs are traffic flow, velocity and density. The outputs of the lower model are the section number where the congestion occurs and the ramp number which should be controlled. If there is congestion, the upper model would be activated. The function of the upper model is to calculate the control strategy. The required control ramp number from the lower model would be transmitted to the upper model together with the real-time state of controlled section. The upper model would calculate the ramp-metering ratio for each required ramp by minimizing the main time cost, total average queue length and equity index. In order to describe the traffic state more accurately, a tailored traffic model for urban freeway in China is established in the upper model to get the prediction, which considers the speed-limited value on main roadway, and corrects the calculation method of traffic flow state on origin point and exit ramp. The simulation results show the double model is effective to control the incident congestion induced by aggressive behavior.

The driver’s anticipation effect with passing in lattice model for two-lane freeway

In this paper, a new lattice model is proposed with the consideration of the driver’s anticipation effect with passing for two-lane traffic system. The linear stability condition and the mKdV equation which are correlative to the driver’s anticipation effect with passing are derived from linear stability analysis and nonlinear analysis, respectively. Numerical simulation shows that the driver’s anticipation effects with passing can efficiently enhance the stability of traffic flow under lane changing on two-lane highway.

Global exponential stabilization of freeway models

SummaryThis work is devoted to the construction of feedback laws, which guarantee the robust global exponential stability of the uncongested equilibrium point for general discrete‐time freeway models. The feedback construction is based on a control Lyapunov function approach and exploits certain important properties of freeway models. The developed feedback laws are tested in simulation, and a detailed comparison is made with an existing feedback law reported in the literature and employed in practice. The robustness properties of the corresponding closed‐loop system with respect to measurement errors are also studied. Copyright © 2015 John Wiley & Sons, Ltd.

Influence of the traffic interruption probability on traffic stability in lattice model for two-lane freeway

In this paper, a new lattice model is proposed with the traffic interruption probability term in two-lane traffic system. The linear stability condition and the mKdV equation are derived from linear stability analysis and nonlinear analysis by introducing the traffic interruption probability of optimal current for two-lane traffic freeway, respectively. Numerical simulation shows that the traffic interruption probability corresponding to high reaction coefficient can efficiently improve the stability of two-lane traffic flow as traffic interruption occurs with lane changing.