Real Highway Research Articles

Applying reflections in the FHWA Traffic Noise Model

The latest version of the Federal Highway Administration (FHWA) Traffic Noise Model (TNM), version 3.2, was released early 2024 and is encouraged for use to conduct highway noise impact and noise abatement analyses. TNM 3.2 has the advantage of allowing single order reflections in addition to a parallel barrier/wall analysis. This paper presents a process to apply single order reflections considering both their sound contribution and their efficiency in modeling and calculations. The process was applied to a real highway project to evaluate sound level increases due to single reflections and to compare predicted sound levels with and without reflections to measured sound levels. Reflecting surfaces evaluated include noise walls on one side of a highway, building facades facing the highway, and sides of buildings.

Towards efficient traffic crash detection based on macro and micro data fusion on expressways: A digital twin framework

AbstractEfficient detection of traffic crashes has been a significant matter of concern with regards to expressway safety management. The current challenge is that, despite collecting vast amounts of data, expressway detection equipment is plagued by low data utilization rates, unreliable crash detection models, and inadequate real‐time updating capabilities. This study is to develop an effective digital twin framework for the detection of traffic crashes on expressways. Firstly, the digital twin technology is used to create a virtual entity of the real expressway. A fusion method for macro and micro traffic data is proposed based on the location of multi‐source detectors on a digital twin platform. Then, a traffic crash detection model is developed using the ThunderGBM algorithm and interpreted by the SHAP method. Furthermore, a distributed strategy for detecting traffic crashes is suggested, where various models are employed concurrently on the digital twin platform to enhance the general detection ability and reliability of the models. Finally, the efficacy of the digital twin framework is confirmed through a case study of certain sections of the Nanjing Ring expressway. This study is expected to lay the groundwork for expressway digital twin studies and offer technical assistance for expressway traffic management.

Modeling the Deployment and Management of Large-Scale Autonomous Vehicle Circulation in Mixed Road Traffic Conditions Considering Virtual Track Theory

This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the “virtual track” theory and creates a model for virtual track autonomous vehicle management and control based on the ideas and methods of railway train operation. The developed model and adopted algorithm allow for large-scale autonomous driving vehicle control on the highway while considering the temporal-spatial distribution of vehicles, temporal-spatial trajectory diagram optimization, and the management and control model and algorithm for autonomous vehicles, as design goals. The ultimate objective is to increase the safety of the road traffic environment when autonomous vehicles are operating in it together with human-driven vehicles and achieve more integrated and precise organization and scheduling of these vehicles in such mixed traffic conditions. The developed model adopted a “particle swarm” optimization algorithm that is tested in a hypothetical network pending a full-scale test on a real highway. The paper concludes that the proposed management and control model and algorithm based on the “virtual track” theory is promising and demonstrates feasibility and effectiveness for further development and future application.

Integrated Variable Speed Limits and User Information Strategy

This paper deals with the study of variable speed limits (VSLs) for traffic control and their integration with user information strategies. As few studies have addressed the integrated VSL and user information strategy, we focus on comparing the adoption of the latter with the VSL alone strategy application and the no-control case, highlighting the benefits the integration brings. The integrated strategy is able to smooth the severity of congestion, shifting its occurrence in a section of the mainstream mostly suited to vehicle accumulation. An application on a real network is carried out. The traffic congestion conditions along the real highway are simulated by means of Dynameq simulation software and the METANET macroscopic model. The VSLs are applied in a control area aiming to evaluate the potential and the limitations of the strategy on a real network as well as the integration of variable speed limits and user information strategies. Two different cases of road congestion caused by the presence of on-ramps are studied. Results show that the integration of the two strategies leads to a redistribution of flows, achieving a reduction in the total travel time spent in the network and an increase in the traveled distances, i.e., reducing the overall network time despite the increase in assigned flows. However, an integrated strategy requires adequate transportation supply and mainly crossing demand.

Comparing the Effects of Visual Distraction in a High-Fidelity Driving Simulator and on a Real Highway

Driving simulators have been widely used in driving behavior analysis and intelligent driving algorithm development. However, the validity of driving behavior data derived from driving simulators remains unclear. In this study, 30 Chinese drivers were recruited to participate in two experiments: on-road and simulator experiments. An instrumented vehicle and a high-fidelity simulator were used in the on-road and simulator experiments, respectively, to investigate the effects of high speed (60, 80, and 100 km/h) and a visual distraction task on the lateral driving performance, including lane deviation (LD), standard deviation of the lane position (SDLP) rate, standard deviation of the steering wheel angle (SDSWA) rate, and steering wheel reversal rates (SRRs) (at levels of 1.3° and 2.5°). It was found that the visual distraction task impaired the drivers’ lane-keeping ability. Furthermore, the driving task had similar effects on the LD, SDLP rate, and SRRs (2.5°) in the on-road and simulator experiments. The effects of the driving speed on the LD, SDLP rate, and SDSWA rate were comparable in both driving environments. However, the results confirmed that even a high-fidelity driving simulator could not achieve perfect absolute validity. The results provided preliminary evidence that the high-fidelity driving simulator used in this study might be an effective tool for investigating the effects of visual distractions task on lateral driving behavior.

A New Cellular Vehicle-to-Everything Application: Daytime Visibility Detection and Prewarning on Expressways

Driving on an expressway is dangerous on a foggy day due to drivers’ inability to obtain visibility information in time. In this research, a new daytime visibility-detection method based on pixel contrast implemented in a prewarning system is proposed. The new visibility-detection method considers various interference factors on low-visibility foggy days and is easy to implement. In addition, a new convenient and safe camera-calibration method is proposed. The prewarning system is based on the low-latency cellular vehicle-to-everything (C-V2X) communication technology; it enables drivers to obtain low-visibility information in real time. Our experiment results prove that the output visibility-distance information obtained from the proposed method is more stable, smooth, and robust than that of other methods. Currently, the proposed C-V2X application has been implemented on a real expressway, and its actual operation shows a high detection accuracy and stable performance.

Integrated Life Cycle Analysis of Cost and CO2 Emissions from Vehicles and Construction Work Activities in Highway Pavement Service Life

In this study, we aimed to provide a practical method to estimate the economic and environmental impact of vehicle and work activities throughout the entire service life of a pavement area to support pavement management strategies and decisions. To achieve this, we integrated two key life cycle analysis methods, life cycle assessment (LCA)) and life cycle cost analysis (LCCA). The integrated model not only considers CO2 emissions associated with the four main modules—the materials module, the work activities module, the work zone module and the usage module—mentioned in LCA, it also considers agency costs and user costs related to highway usage, work activities, work zone traffic delays and detours in the LCCA process. We used detailed and integrated methods to compute CO2 emissions and costs based on the four modules and two components of agency and users mentioned above. A case study based on a real freeway project in China was used to verify the applicability of the integrated model. The results of the application of the integrated LCA-LCCA model indicate that maintaining the typical activity profile could be beneficial in terms of both CO2 emissions and cost, with savings of 36.8 ton/lane/km of CO2 emissions and 10,530 USD/lane/km (in 2007 dollars) representing the total benefits during the pavement’s service life. This means that timely maintenance could help to achieve savings in terms of financial costs and CO2 emissions simultaneously.

A possible implementation of non-destructive data surveys in the definition of BIM models for the analysis of road assets

Recently, a strong push towards the use of BIM-based procedures and practices in the field of linear transport infrastructures has been addressed. In Italy, a series of ministerial decrees have been approved to encourage the use of this methodology in the design and management operations of civil works. In such a framework, a novel approach to the management phases of civil works is required, based on different types of data and analyses within an integrated process, making use of digital models of the assets. This study aims at defining a procedure able to integrate non-destructive surveys data, such as those from Ground Penetrating Radar (GPR) and Mobile Laser Scanner (MLS), into a BIM model of a road infrastructure. The different types of surveys allow to implement useful and multiple pieces of information regarding the assets of the infrastructure in the corresponding BIM model. The main goal of the research is to optimize the management phase of a road by combining different observations, made by separate operators, in a unified BIM environment, to define a methodology that can be applied to real infrastructures. In order to validate the proposed methodology, a digital model of a real highway has been generated by making use of non-destructive survey data obtained from inspections that have been carried out by means of GPR and MLS. The different datasets have been individually processed so that they could be unified in a single BIM model. As a result, the use of specifically designed parametric sections, along with the processed datasets, allowed to define a BIM model of the road that can also be analyzed in areas that usually cannot be reached by operators. This allows a more efficient management of the asset, showing that the proposed methodology could be a viable tool in the monitoring operations of linear transport infrastructures.

Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment

This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encountering a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and then defined using vehicle kinematic parameters (velocities and distances). The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicles; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle. Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers. A multi-dimensional distribution analytical approach was used to derive a correlation between the following vehicle and the preceding vehicle considering the initial longitudinal velocities. Results suggest that there should be no risk of a collision between the following vehicle and the preceding vehicle when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s. These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly.

Identifying subdominant collective effects in a large motorway network

In a motorway network, correlations between parts or, more precisely, between the sections of (different) motorways, are of considerable interest. Knowledge of flows and velocities on individual motorways is not sufficient, rather, their correlations determine or reflect, respectively, the functionality of and the dynamics on the network. These correlations are time-dependent as the dynamics on the network is highly non-stationary. Apart from the conceptual importance, correlations are also indispensable to detect risks of failure in a traffic network. Here, we proceed with revealing a certain hierarchy of correlations in traffic networks that is due to the presence and to the extent of collectivity. In a previous study, we focused on the collectivity motion present in the entire traffic network, i.e. the collectivity of the system as a whole. Here, we manage to subtract this dominant effect from the data and identify the subdominant collectivities which affect different, large parts of the traffic network. To this end, we employ a spectral analysis of the correlation matrix for the whole system. We thereby extract information from the virtual network induced by the correlations and map it on the true topology, i.e. on the real motorway network. The uncovered subdominant collectivities provide a new characterization of the traffic network. We carry out our study for the large motorway network of North Rhine-Westphalia, Germany.

Combined Control of Freeway Traffic Involving Cooperative Adaptive Cruise Controlled and Human Driven Vehicles Using Feedback Control Through SUMO

In this study, we propose and test through micro-simulation a novel controller for the combined control of freeway traffic by adopting coordinated Ramp Metering (RM) and Variable Speed Limiting (VSL) strategies. In order to figure out the performance of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> State Feedback Controller we have designed, field observations on a real freeway segment with four on-ramps and an off-ramp in the city of Istanbul are used to calibrate the Intelligent Driver Model at SUMO (Simulation of Urban MObility). Scenarios with varying penetration rates of vehicles with Cooperative Adaptive Cruise Control (CACC) in mixed traffic are simulated through SUMO specific to the cases of no control, only coordinated RM control, and the combined coordinated RM + VSL control. Performance of the controller we have proposed has been analyzed considering a number of measures on traffic flow dynamics and emissions exhausted. We define three critical levels for penetration rates of vehicles with CACC in freeway traffic.

Network traffic instability with automated driving and cooperative merging

Connected and/or automated vehicles (CAVs or AVs) have been shown to dampen stop-and-go waves in mixed autonomy traffic, thus improving string stability. However, their effects on network traffic instability due to turning and merging maneuvers are less known. In this paper, we characterize such effects using the macroscopic or network fundamental diagram (MFD or NFD). We first revisit and extend the theoretical two-ring network, and then develop an integrated modeling and simulation framework that explicitly accounts for different microscopic traffic models of human-driven vehicles (HVs), AVs, and CAVs. Results suggest that network traffic instability resulting from turning and merging maneuvers persists even if vehicles become automated and cooperative. When the turning probability is low, the presence of CAVs does not induce a significant change in the bifurcation point of the NFD. Scatter in both link fundamental diagrams (FDs) and NFDs, however, reduces resulting in higher and more stable network flows. When the turning probability is high, AVs without cooperation turn out to worsen network traffic stability, giving rise to an NFD that undergoes bifurcation long before the theoretical critical network density is reached. This is in contrast to the case with CAVs that perform cooperative merging. Results also suggest that, whenever the penetration rate of CAVs is too low or too high, making HVs connected is not as effective in delaying the bifurcation of the NFD as when the penetration rate is moderate. We further compare cooperative merging with adaptive signal control and adaptive driver routing to demonstrate its benefits in improving network flows. Simulation experiments on a real motorway segment in Sydney, Australia are also performed to confirm our findings.

Influence of Permanent Deflections on The Vibrations of Bridge Spans in Operating Conditions

Abstract The paper presents the method and results of numerical simulations of the mutual dynamic interactions between vehicles and the bridge structure, which has defect in the form of excessive permanent deformations. The parametric analyses were carried out taking into account the following parameters: permanent deflections of spans, with a maximum value ranging from 0 to 150 mm, vehicle speed from 10 m/s (36 km/h) to 30 m/s (108 km/h), and two types of heavy vehicle suspension systems, each with very different properties. The presented analyses are based on the characteristics of real motorway bridge structure with permanent deflections of the multi-span main girders, constructed of steel beams and reinforced concrete slab. The proposed procedure of dynamic numerical analysis can also be useful in the assessment of the influence of permanent deflections on the interactions between vehicles and other types of bridge structures.

Determination of minimum horizontal curve radius for safe stopping sight distance of vehicles overpassing truck platoons

AbstractIn the last few years, the importance of trucks on inland cargo transportation has not stopped increasing. Meanwhile, truck platooning is emerging, along with automated driving, to reduce costs using new technologies. In this context, this research aims to provide a first study on the effects of truck platoons on freeways’ road safety, focusing on the reduction of visibility caused by truck platoons with shorter gaps on horizontal curves. This safety issue will also affect motorways and multilane roads. A geometric model has been developed and computed, which provides the available sight distances and the stopping sight distance (SSD) for a vehicle overpassing a platoon in a circular curve without transition curves. There are many variables, such as radius, lane width, vehicle and truck platoon parameters, and relative position. The overpassing vehicle has been included in the model for both human‐driven and automated, considering the adaptive cruise control radar cone of visibility. The main result of this study is the minimum curve radius in order to allow a safe SSD, considering different design criteria. Moreover, depending on the level of automation of the vehicle, this minimum radius will be different, being higher for automated vehicles. Results prove the importance of the studied phenomenon and the necessity to implement further countermeasures. Additionally, a case study where the effects of truck platooning on the visibility of a real motorway stretch are evaluated.

Intelligent planning of road pavement rehabilitation processes through optimization systems

Pavement rehabilitation comprises a very time sensitive and complex set of operations. Not only does this type of construction typically rely on very expensive heavy mechanical equipment, it also usually involves closing lanes that will inevitably cause delays to users. Although there is an increasing interest in completing these rehabilitation interventions with the lowest costs and durations, the planning of this process is currently mostly based on experience. Thus, this paper focuses on developing an optimization system based on an evolutionary multi-objective approach, capable of supporting decision making in road pavement rehabilitation planning. The main contributions of this paper are two-fold. On the one hand, it tackles an identified research gap regarding the study of the viability and the gain associated with the implementation of multi-objective optimization in planning of pavement rehabilitation projects. On the other hand, a more practical contribution is related to the development of a system capable of supporting decision making throughout design phases. The capabilities of the system are validated in a real motorway pavement rehabilitation project. Results demonstrate the ability of the system to quickly output optimal solutions, that not only support decision making, but also enhance flexibility and efficiency from the decision-maker viewpoint. The optimization system is an important contribution to more intelligent and sustainable pavement engineering, by promoting the global optimization of technical, economic and environmental objectives.

Modeling the vehicle-bridge interaction in the conditions of road level deformation

Abstract: The paper presents a method and results of numerical simulations of dynamic interactions between vehicles and the bridge structure, which has defects in the form of excessive permanent deflections of their spans. The simulation analyzes were carried out on the example of a real motorway bridge, on which the presence of such defects was found. A typical 5-axle truck with a total weight of 40 tons was modeled as a moving load. During the analyzes, the main attention was paid to the values of vertical displacements and accelerations of vibrations of the bridge spans generated by heavy vehicles as a function of the intensity of permanent deflections of the structure and the vehicles speed. Keywords: Dynamic analysis of bridges; Bridge vibrations; Finite Element Method; Structure deformations

Electrical Infrastructure Design Methodology of Dynamic and Static Charging for Heavy and Light Duty Electric Vehicles

Full electrification of the transport sector is a necessity to combat climate change and a pressing societal issue: climate agreements require a fuel shift of all the modes of transport, but while uptake of passenger electric vehicles is increasing, long haul trucks rely almost completely on fossil fuels. Providing highways with proper charging infrastructure for future electric mobility demand is a problem that is not fully investigated in literature: in fact, previous work has not addressed grid planning and infrastructure design for both passenger vehicles and trucks on highways. In this work, the authors develop a methodology to design the electrical infrastructure that supplies static and dynamic charging for both modes of transport. An algorithm is developed that selects substations for the partial electrification of a highway and, finally, the design of the electrical infrastructure to be implemented is produced and described, assessing conductors and substations sizing, in order to respect voltage regulations. The system topology of a real highway (E18 in Norway) and its traffic demand is analyzed, together with medium-voltage substations present in the area.

Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations

Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning models show promising predictive performance, but inherently suffer from a lack of interpretability. This difficulty largely originates from the inconsistency between the static input–output mappings encoded in deep neural networks and the dynamic nature of traffic phenomena. Under different traffic conditions, such as freely-flowing versus heavily congested traffic, different mappings are needed to predict the propagation of congestion and the resulting speeds over the network more accurately. In this study, we design a novel variant of the graph attention mechanism. The major innovation of this so-called dynamic graph convolution (DGC) module is that local area-wide graph convolutional kernels are dynamically generated from evolving traffic states to capture real-time spatial dependencies. When traffic conditions change, the spatial correlation encoded by DGC module changes as well. Using the DGC, we propose a multistep traffic forecasting model, the Dynamic Graph Convolutional Network (DGCN). Experiments using real freeway data show that the DGCN has a competitive predictive performance compared to other state-of-the-art models. Equally importantly, the prediction process in the DGCN and the trained parameters are indeed explainable. It turns out that the DGCN learns to mimic the upstream–downstream asymmetric information flow of typical road traffic operations. Specifically, there exists a speed-dependent optimal receptive field – which governs what information the DGC kernels assimilate – that is consistent with the back-propagation speed of stop-and-go waves in traffic streams. This implies that the learnt parameters are consistent with traffic flow theory. We believe that this research paves a path to more transparent deep learning models applied for short-term traffic forecasting.

Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach

This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.

PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

We propose a mobile system, called PotholeEye+, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye+ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye+ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye+ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of 110 km/h on a real highway.