Highway System Research Articles

Techno-economic evaluation of developing a zero-carbon highway service area energy system

ABSTRACT To further reduce CO2 emissions from highway service areas, this study proposes a novel zero-carbon highway service area energy system (S3). This system not only addresses energy supply challenges in remote locations but also expedites the reduction of CO2 emissions. A mixed-integer linear programming problem is formulated to optimize the configuration and operation of the proposed system. The system is compared with the conventional (S1) and low-carbon (S2) highway service area systems. Focused on northwest China, the results indicate that S3 can achieve zero CO2 emissions. However, the energy efficiency ratio (EER) of S1 is the highest at 84%, surpassing S3 by 2.4% (82%). In terms of economic analysis, the total cost and levelized cost of electricity (LCOE) of S1 are the highest at 1.62 MUSD and 0.14 USD/kWh, respectively. These values are 100% and 180% higher than those of S3, which are 0.81 MUSD and 0.05 USD/kWh.

Embedded Highway Health Maintenance System Based on Digital Twin Superposition Model

INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development.
 OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management.
 METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal.
 RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution.
 CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.

Technical Feasibility of a Hydrail Tram–Train in NA: Okanagan Valley Electric Regional Passenger Rail (OVER PR)

Booming population and tourism have increased congestion, collisions, climate-harming emissions, and transport inequities in The Okanagan Valley, Canada. Surveys suggest that over 30% of residents would shift from cars back to public transit and intercity tram–trains if regional service and connections were improved. Intercity streetcars (aka light-rail tram–trains) have not run in Canada since their replacement in the 1950′s by the national highway system. UBC researchers analyzed a tram–train service fashioned after the current Karlsruhe model but powered by zero-emission hydrogen fuel cell/battery hybrid rail power (hydrail) technology, along a 342 km route between Osoyoos, B.C. at the US Border and Kamloops, B.C., the Canadian VIA rail hub. Hydrail trains have operated successfully since 2018 in Germany and were demonstrated in Quebec, Canada in 2023. However, hydrail combined with tram–train technology has never been tried in Canada. Single-train simulations (STSs) confirmed its technical feasibility, showing a roughly 8 h roundtrip travel time, at an average train velocity of 86 km/h. Each hydrail tram–train consumed 2400 kWh of energy, translating to 144 kg of hydrogen fuel per roundtrip. In total, five tons of H2/day would be consumed over 16 h daily by the 16-tram–train-vehicle fleet. The results provide valuable insights into technical aspects and energy requirements, serving as a foundation for future studies and decision-making processes in developing zero-emission passenger tram–train services not just for Okanagan Valley communities but all of Canada and NA.

The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City

A large body of literature studies how infrastructure facilitates trade. We ask whether infrastructure also facilitates migration. Using a general equilibrium trade model and rich spatial data, we study the impact of a large, plausibly exogenous shock to highways in Brazil on both goods and labor markets. We find the highway system increased welfare by 2.8 percent, of which 76 percent was due to reduced trade costs and 24 percent to reduced migration costs. An implication of costly migration is spatial heterogeneity in benefits: the range of welfare improvement is 1 to 15 percent, as opposed to uniform gains with perfect mobility. (JEL H54, H76, O18, R23, R42, R53)

Highway Microgrid Project Evaluation under Energy Transportation Integration

The construction of highway microgrids is evolving into a new highway energy system that integrates “Source-Network-Load-Storage”. This paper provides a comprehensive evaluation of expressway microgrids from the perspective of transportation and energy integration. An index model is set up that considers the economy, technology, and environment. The grey evaluation method, on the strength of analytic hierarchy process–entropy weight method, shows that the integrated microgrid of “source-network-load-storage” promotes energy sustainability, supply reliability, and sustainable environmental development. When compared to different evaluation methods for microgrid planning schemes, our method yields the highest score of 0.9127, indicating superior results. This approach is suitable for the multicomponent evaluation system of expressway microgrids and allows for scientifically evaluating microgrid planning schemes.

The Application of the Typeface in the Signage System

Due to the expansion of the highway system and the continuous growth of traffic, the safety issues associated with the dissemination of traffic information have become more important. This study reviews the literature on the legibility of typeface used in highway signage systems and the types of fonts used in highway systems, focusing on the most significant findings on the characteristics of typefaces and the features that most affect legibility. The findings of this research identified the humanity typeface can help to satisfy the need for people to navigate from one place to another, then to create a clear and concise wayfinding system.

Development of an evaluation indicator for highway climate change adaptation projects based on analytical hierarchy process in South Korea

AbstractOwing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects.

The connectivity indices concept of neutrosophic graph and their application of computer network, highway system and transport network flow

To address information ambiguities, this study suggests using neutrosophic sets as a tactical tool. Three membership functions (called Tr,In,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_r, I_n, $$\\end{document} and Fi\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ F_i$$\\end{document}) that indicate an object’s degree of truth, indeterminacy, and false membership constitute the neutrosophic set. It becomes clear that the neutrosophic connectivity index (CIN) is an essential tool for solving practical problems, especially those involving traffic network flow. To capture uncertainties, neutrosophic graphs are used to represent knowledge at different membership levels. Two types of CINs,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$CIN_s,$$\\end{document} mean CIN and CIN, are investigated within the framework of neutrosophic graphs. In the context of neutrosophic diagrams, certain node types-such as neutrosophic neutral nodes, neutrosophic connectivity reducing nodes (NCRN) , and neutrosophic graph connectivity enhancing nodes (NCEN) , play important roles. We concentrate on two types of networks, specifically traffic network flow, to illustrate the real-world uses of CIN. By comparing results, one can see how junction removal affects network connectivity using metrics like Connectivity Indexes (CIN) and Average Connectivity Indexes (ACIN) . A few nodes in particular, designated by ACIN as Non-Critical Removal Nodes (NCRNs)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ (NCRN_s) $$\\end{document}, show promise for increases in average connectivity following removal. To fully comprehend traffic network dynamics and make the best decisions, it is crucial to take into account both ACIN and CIN insights. This is because different junctions have different effects on average and overall connectivity metrics.

Thickness ranges calculation method of double asphalt overlay on concrete pavement

Asphalt overlay is widely used in maintaining and rehabilitating highway system performance. However, explicit calculation methods for the asphalt overlay thickness range is lacking. Taking stone mastic asphalt (SMA) and asphalt concrete (AC) asphalt overlay on cement concrete pavement as examples, the paper proposed a design method for the asphalt overlay thickness range based on the shear performance of the interlayer. Firstly, the shear stress distribution regularities on the asphalt overlay and Portland cement concrete interlayer was calculated with a multilayer elastic theory. Meanwhile, the shear strength was obtained from a series of direct shear tests. The shear characters of the asphalt overlay met with the Mohr–Coulomb criterion, and the shear strength parameters cohesive force c\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c$$\\end{document} and interface friction angle φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi$$\\end{document} on the interlayer were acquired. Finally, a method for determining the thickness range of double layer asphalt overlay under different traffic conditions was given. The epoxy resin adhesive was recommended for the highway with severe local premature shear failure compared with the modified emulsion asphalt. Therefore, through the above research, the amount of asphalt used is controlled in a reasonable range, thus improving the pavement structure durability and reducing energy consumption.

Chemometric Analysis Combined with GC × GC-FID and ESI HR-MS to Evaluate Ultralow-Sulfur Diesel Stability.

Diesel has been the most employed fuel in highway and nonhighway transportation systems. Many studies over the past years have attempted to classify diesel as a stable or unstable composition since this fuel can still degrade during storage or thermal oxidative processes. Products generated because of such degradation are the reason for the formation of soluble gums and insoluble organic particulates, which in turn cause a negative influence on engine performance. This work reports a detailed composition of nonpolar and polar compounds in many ultralow-sulfur diesel (ULSD) samples by comprehensive two-dimensional gas chromatography with a flame ionization detector (GC × GC-FID) and electrospray ionization high-resolution mass spectrometry (ESI HR-MS). In addition, chemometric approaches were applied for ULSD storage stability investigation. GC × GC-FID experiments achieved the nonpolar chemical characterization for the ULSD samples, including all main hydrocarbon classes: paraffins, mono- and dinaphthenics and olefins, and aromatics. The GC × GC-FID data combined with principal component analysis (PCA) described that the separation of the samples' concerning storage stability was mainly due to the contents of mono- and diaromatic compounds in the unstable ULSD samples. Moreover, PCA was also applied to the ESI (±) data set, and the results highlight the presence of compounds belonging to O class (natural antioxidants), which decrease the rate of oxygen consumption in the fuel, characterizing it as stable composition. The basic nitrogen compounds are mostly present in the stable ULSD samples indicating that they did not affect the stability of the fuel. On the other hand, the HC classes presented pronounced abundance among unstable ULSD samples suggesting that the fuel degradation may go through the oxidation of hydrocarbons and the formation of Ox compounds as byproducts. Furthermore, MS/MS experiments point to the formation of CcHhNnOo-like precursor species, which can react with each other and lead to the formation of gums and insoluble sediments in the fuel. In summary, the results express the potential of using the GC × GC-FID and ESI (±) Orbitrap MS techniques as valuable tools for diesel stability evaluations.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve ‘unintended pedestrians’, drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not ‘typical pedestrians’, a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014–2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian’s FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35–44 years, and spring season. The interaction of ‘pedestrian impairment’ and ‘weekend’ was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the ‘unintended pedestrians’ about the crash scenarios on the interstate and reduce these unexpected incidents.

The dynamics of traffic congestion: Data from a freeway Electronic Toll Collection system

Electronic Toll Collection systems installed at regular intervals along freeways provide a tremendous amount of individualized vehicle data useful for traffic analyses and predictions. We demonstrate how real data from the Taiwan freeway system enables us to monitor the evolution of traffic congestions on the fundamental diagram, and find that congestion is characterized by a loopy trajectory such that the area enclosed by the loop increases with the severity of congestion. There is a power law relating the area of the loop to the drop of influx during the congestion period. Data extracted from the time delays of individual vehicles show that the area enclosed by the loop in the fundamental diagram is a measure of the economic loss due to congestion. The use of the loss area to understand various highway characteristics is also explored.

Application solutions of highway freight information systems based on quantum communication.

To improve the security of data transmission in the highway freight information system, this study is an application plan for the highway freight information system based on quantum communication. This solution is based on quantum communication technology to encrypt and transmit key sensitive data[1]; it realizes unified management of quantum keys through the quantum key cloud terminal and provides key services for the highway freight information system; it realizes access to the system through the quantum key cloud service platform. The secure use of mobile terminal quantum keys improves the overall security of the road freight information system. This scheme uses the quantum encryption key generated only once, effectively protecting the entire system's security. The quantum key management server and quantum key cloud platform defined in this plan manage terminals and quantum keys respectively, and jointly produce and distribute quantum keys with the help of other hardware facilities and software to provide secure transmission of key information.

Designing a dual-camera highway monitoring system based on high spatiotemporal resolution using neural networks

The criticality of infrastructure to societal development has seen highways evolve into an essential component of this ecosystem. Within this, the camera system has assumed significant importance due to the necessity for monitoring, evidence collection, and danger detection. However, the current standard of using high frame rate and high-resolution (HSR-HFR) cameras presents substantial costs associated with installation and data storage. This project, therefore, proposes a solution in the form of a High Spatiotemporal Resolution process applied to dual-camera videos. After evaluating state-of-the-art methodologies, this project develops a dual-camera system designed to merge frames from a high-resolution, low frame rate (HSR-LFR) camera with a high frame rate, low-resolution (LSR-HFR) camera. The result is a high-resolution, high frame rate video that effectively optimizes costs. The system pre-processes data using frame extraction and a histogram equalization method, followed by video processing with a neural network. Further refinement of the footage is performed via color adjustment and sharpening prior to a specific application, which in this case is license plate recognition. The system employs YOLOv5 in conjunction with LPRNet for license plate recognition. The resulting outputs demonstrate significant improvement in both clarity and accuracy, providing a more cost-effective solution for highway monitoring systems.

Comprehensive analysis of renewable hybrid energy systems in highway tunnels

In order to explore the feasibility of a renewable hybrid energy system in highway tunnels, a scenario-coupled construction method for a highway tunnel renewable hybrid energy system is proposed. Based on this method, a tunnel on a highway in southern China serves as an example, and a renewable hybrid energy system for the highway tunnel is constructed. Using HOMER, the hybrid energy system is simulated, optimized, and analyzed, studying the characteristics of the operation of the highway tunnel hybrid renewable energy system in an off-grid mode. The optimal configuration for the hybrid energy system is proposed. The research results indicate the feasibility of constructing a highway tunnel renewable hybrid energy system by utilizing natural resources within the road area (solar energy, wind energy). The hybrid renewable energy system for this tunnel can provide 1112392 kWh of electrical energy annually, with the lowest energy cost being $0.17/kW. Furthermore, the hybrid energy system can reduce greenhouse gas emissions, contributing to the sustainable development of the environment.

Feasibility of Modernizing the Acceptable International Roughness Index Value

The U.S. National Highway System has set a standard for acceptable pavement roughness, measured by the international roughness index (IRI) at less than 2.68 m/km (170 in./mi). This value corresponds to a present serviceability rating (PSR) of 2.5, where half of the highway drivers perceive the pavement ride quality as unacceptable with a repair need. For over 3 decades, public transportation agencies in the U.S. have used this IRI value to determine when pavement surface rehabilitation is needed. However, with advancements in vehicle suspension systems improving ride quality, the current acceptable IRI value corresponding to 2.5 PSR has been questioned. To our knowledge, studies in the literature have yet to explore this issue. This study aims to evaluate the feasibility of modernizing the acceptable IRI value through nationwide, large-scale field testing, which is typically expensive and time-consuming. The IRI-present serviceability index (PSI) equation that is capable of converting the acceptable IRI value into the estimated 2.5 PSR (i.e., the PSI) was selected and calibrated by applying test quarter-car parameters synthesized through a literature review. Then, the calibrated IRI-PSI equation was used to find a new acceptable IRI value that was evaluated for the statistical significance and the financial benefits of the new value. The evaluation results suggest a need for a nationwide field test to modernize the current acceptable IRI value. Public transportation agencies can easily adapt the technical approach we used to calibrate the IRI-PSI equation without altering their current IRI measurement systems.

Enhancing highway transportation safety resilience during emergencies: A network-based analysis and assessment.

In order to evaluate the impact of emergencies on the resilience of highway transportation, a resilience network hierarchical model of the highway transportation system was constructed by analyzing the formation and emergence process of safety resilience in the highway transportation system. Four layers of networks were divided, including highway network, transport network, traffic network, and emergency network. Combined with the network hierarchical model, a resilience evaluation index system was designed, and an assessment method for highway transportation systems based on the fuzzy analytic hierarchy process(FAHP) was proposed. Finally, a case study of a public health emergency in a region of Hunan was carried out. The results show that the proposed method for evaluating the safety resilience of highway transportation systems can better reflect the overall resilience under public health emergencies, which is consistent with the quantitative analysis results through the system resilience curve. It helps to accurately evaluate the safety resilience of the system. At the same time, this method has the advantages of flexibility and simplicity in solving unstructured decision-making problems of the system, which helps to improve the safety production management and safety resilience level of highway transportation systems. In the future, the scope of research scenarios and regions can be expanded, and further analysis of the evolution of safety resilience and the ability of resilience development in different stages under external disturbances can be conducted in order to further explore and optimize the resilience of the system.

Value of Resilience for Self-Sustained Highway Transportation Systems

Nowadays, self-sustained highway transportation systems (HTSs) are calling for the inter-operation of road networks, distribution networks (DNs), and electric vehicles (EVs) towards the unexpected impacts inducted by extreme weather events (EWEs). A novel joint resilience assessment scheme is proposed to quantify the resilience of self-sustained HTSs under tropical cyclone (TC) events. The impacts of TCs on transmission lines and highways are formulated using a two-step ambiguity set. In the first step, a multitask learning (MTL) based model with a novel training strategy is proposed to simulate the future TC tracks; the impacts of TCs are then calculated in the second step based on the simulation results. Using the ambiguity set, the self-sustained HTSs resilience assessment problem is formulated based on the dynamic optimal traffic assignment problem, where EVs are integrated as third-party emergency resources before the advent of TCs. This scheme is then formulated as a two-stage distributionally robust optimization (DRO) problem and reformulated as a mixed-integer linear programming (MILP) problem. Case studies have been conducted on a self-sustained HTS consisting of a modified IEEE-14 bus test system with 2 wind farms (WFs) and a 14-node transportation network with 4 charging stations (CSs). Numerical results indicate that the proposed scheme can quantify the resilience of self-sustained highway transportation systems under the joint operation of EVs regarding the unmet travel demand and load-shedding costs. In comparison to the statistical TC forecasting models, the proposed MTL-based model is more accurate and can reduce the system cost in most cases.

Highway digital twin-enabled Autonomous Maintenance Plant: a perspective

Abstract The importance of automating pavement maintenance tasks for highway systems has garnered interest from both industry and academia. Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site maintenance work still requires manual processes using special vehicles and equipment, reflecting a clear gap to transition to fully autonomous maintenance. This paper reviews the current progress in pavement maintenance automation in terms of inspection and repair operations, followed by a discussion of three key technical challenges related to robotic sensing, control, and actuation. To address these challenges, we propose a conceptual solution we term Autonomous Maintenance Plant (AMP), mainly consisting of five modules for sensing, actuation, control, power supply, and mobility. This AMP concept is part of the “Digital Roads” project’s cyber-physical platform where a road digital twin (DT) is created based on its physical counterpart to enable real-time condition monitoring, sensory data processing, maintenance decision making, and repair operation execution. In this platform, the AMP conducts high-resolution survey and autonomous repair operations enabled (instructed) by the road DT. This process is unmanned and completely autonomous with an expectation to create a fully robotized highway pavement maintenance system.

Deep Reinforcement Learning-Based Spatiotemporal Decision of Utility-Scale Highway Portable Energy Storage Systems

Mobile charging is an efficient solution to meet peak charging demand on highways. In this paper we propose a deep reinforcement learning (DRL)-based approach to maximize the revenue of a utility-scale highway portable energy storage system (PESS) for on-demand electric vehicle charging. We consider a PESS that consists of an electric truck, battery, and charging stations on highways. Actions include the selection of stations and routes, the selection of charging and discharging, and the corresponding charging/discharging power selection. The first two are discrete, while the last is continuous. To deal with the hybrid action space and time-varying state space of PESS, we design an action space, observation space, and reward function according to its characteristics; in addition, we develop a state-of-the-art DRL model for online decision-making considering the uncertainty in the real-time market price of electricity. Through numerical simulations with real-world electricity price data in California, we demonstrate the effectiveness of the proposed method.