Transportation Engineering Research Articles

Electron transport engineering of carbon hybrid network towards physiological signal monitoring and efficient heat management

E-skin that can mimic the complex functions of human skin has attracted considerable interest in health monitoring and medical diagnosis. However, the accurate and reliable detection of multiple external stimuli using a sole sensing component remains challenging. Herein, we describe a flexible strain-temperature sensor fabricated by employing conductive 1D multi-walled carbon nanotubes (MWCNTs), 2D reduced graphene oxide (RGO) and a flexible polydimethylsiloxane (PDMS) substrate to construct a sandwich structure. Consequently, the flexible MWCNTs@RGO hybrid film with optimized conductivity exhibits a high gauge factor (GF = 1888) at a strain up to 40 %, prominent long-term durability (8000 cycles at 25 % strain), low strain detection (0.05 %) and rapid response (57 ms). The significantly enhanced performance is attributed to the synergy of tubular MWCNTs and layered RGO, as revealed by finite element analysis. Additionally, the hybrid film sensor exhibits temperature-sensing ability with a typical negative temperature coefficient of resistance and delivers an improved sensitivity upon thermal stimulus via prestrain-induced microcracks and a thermal expansion strategy. Owing to its excellent sensing properties, it can not only detect human motions in various situations but also be adopted as a temperature warning/cooling system, supporting its feasibility for use by patients lacking temperature perception and bringing convenience to daily life.

Erratum to: Experimental Study on the Wavelengths of Two-Dimensional and Three-Dimensional Freak Waves

In the original publication of the article (Cui and Pan, 2023), the organization “National Engineering Research Center for Port Hydraulic Construction Technology, M.O.T., Tianjin Research Institute of Water Transport Engineering, Tianjin 300456, China” should be corrected as: “National Engineering Research Center of Port Hydraulic Construction Technology, Tianjin Research Institute for Water Transport Engineering, M.O.T., Tianjin 300456, China”.

METHOD FOR INCREASING THE UNIFORMITY OF THE GAS-AIR MIXTURE IN INTERNAL COMBUSTION TRANSPORT ENGINES

Increasing the uniformity of the gas-air mixture in gas transport engines makes it possible to ensure more complete combustion of fuel, reduce its specific fuel consumption and exhaust gas toxicity. There are known ways to increase uniformity, but there is no generalization of their principles and applicability for various engines. The article analyzes the existing methods and identifies their shortcomings. A method has been developed to increase the uniformity of the gas-air mixture in gas transport engines by controlling the position of the gas fuel jet in the incoming air stream. The dependence for controlling the position of the gas jet in the air flow is described. The results of experiments performed by means of computer modeling with central and distributed gas supply with different gas and air pressures are analyzed. During the experiments, the coefficient of uniformity of the gas-air mixture was determined. Separately, the depth of penetration of the gas fuel jet into the incoming air stream is estimated. Based on the simulation results, the effectiveness of the method was justified.
 Purpose. Development of a method for increasing the uniformity of the gas-air mixture in internal combustion transport engines.
 Methodology. Modeling of the movement of a methane jet in an air stream by the finite element method was used in the work.
 Results. A method for increasing the uniformity of the gas-air mixture using the inlet pressure of the gas as the main variable parameter is formulated and justified.
 Practical implications. The obtained results should be used to improve the efficiency and environmental friendliness of gas transport engines.

Improved RUL Predictions of Aero- Engines by Hyper-Parameter Optimization of LSTM Neural Network Models.

Abstract: In the industry domains such as transportation, power generation and heavy engineering industries utilize complex machinery, as their working principles are very complex and it is very difficult to predict their exact life and operational availability due to difficulties involved in their operations. PHM- Prognostics and health management is one such domain which supports in the management of this machinery also known as assets for continuous monitoring for their best utilization and maximum efficiency. For any machine, the current health of the system can be determined by operational and raw sensor data. This data can also be utilized for the prediction of remaining useful life (RUL) of the system provided a reliable prediction model is available. It is always be challenging to determine the remaining useful life of complex machines such as turbofan jet engines from the knowledge of the data available from operating conditions and sensor data. An HPT LSTM- advanced hyper parameter tuned LSTM- Long Short Term Memory neural network model is developed to accurately determine the remaining useful life of the turbojet fan engines. To test this predictive model, a NASA developed database CMAPSS- Commercial Modular Aero Propulsion System Simulation is used. The performance of this HPT LSTM is found to be much improved as compared to reference machine learning algorithms such as simple linear regression and Lagged Multi- Layer Perceptron Models

Wide-area vehicle trajectory data based on advanced tracking and trajectory splicing technologies: Potentials in transportation research

Trajectory data of road users play a crucial role in transportation engineering and traffic safety research. Previous vehicle trajectory data, including naturalistic driving data, trajectory data captured by drones and traffic cameras, have greatly promoted the research of traffic safety, traffic simulation, and other transportation engineering fields. However, existing technologies in collecting trajectory data are limited their detection range. With the development of the sensing and tracking technologies, it is possible to track the trajectory information of road users at the wide-area level, i.e., the real-time trajectory data of all road users on the entire road. Recently, we published our data sharing platform, Tongji Road Trajectory Sharing (TJRD TS, available online at https://tjrdts.com) Platform, providing our open wide-area vehicle trajectory data for researchers, academics, and road traffic professionals. Advanced vehicle tracking and trajectory splicing technologies, such as millimeter wave radar and edge computing technology, were employed to obtain such wide-area vehicle trajectory data. In this short communication article, we brainstorm ideas of using wide-area vehicle trajectory data in traffic safety research, traffic efficiency research, and connected and autonomous vehicle (CAV) application. We hope this short communication and our published data sharing platform can serve as the steppingstone for those who are interested in road transportation engineering and road safety research using such wide-area vehicle trajectory data.

Analysis on Effectiveness of Testing and Evaluation of Bridge Repair and Reinforcement

Bridge engineering is an important part of basic engineering in today’s transportation field, and its quality and performance have a vital impact on the improvement and development of modern transportation engineering. With the continuous development of transportation engineering, the maintenance and reinforcement of existing bridges are also being given more emphasis. In order to scientifically evaluate the effectiveness of bridge maintenance and reinforcement, this paper analyzes its detection and evaluation, including the significance, key points, and main methods of detection and evaluation. Therefore, this analysis aim to provide some reference for the maintenance and reinforcement and the quality improvement of bridge engineering.

Applications of Text Mining in the Transportation Infrastructure Sector: A Review

Transportation infrastructure is vital to the well-functioning of economic activities in a region. Due to the digitalization of data storage, ease of access to large databases, and advancement of social media, large volumes of text data that relate to different aspects of transportation infrastructure are generated. Text mining techniques can explore any large amount of textual data within a limited time and with limited resource allocation for generating easy-to-understand knowledge. This study aims to provide a comprehensive review of the various applications of text mining techniques in transportation infrastructure research. The scope of this research ranges across all forms of transportation infrastructure-related problems or issues that were investigated by different text mining techniques. These transportation infrastructure-related problems or issues may involve issues such as crashes or accidents investigation, driving behavior analysis, and construction activities. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)-based structured methodology was used to identify relevant studies that implemented different text mining techniques across different transportation infrastructure-related problems or issues. A total of 59 studies from both the U.S. and other parts of the world (e.g., China, and Bangladesh) were ultimately selected for review after a rigorous quality check. The results show that apart from simple text mining techniques for data pre-processing, the majority of the studies used topic modeling techniques for a detailed evaluation of the text data. Other techniques such as classification algorithms were also later used to predict and/or project future scenarios/states based on the identified topics. The findings from this study will hopefully provide researchers and practitioners with a better understanding of the potential of text mining techniques under different circumstances to solve different types of transportation infrastructure-related problems. They will also provide a blueprint to better understand the ever-evolving area of transportation engineering and infrastructure-focused studies.

Estimation of VMT using heteroskedastic log-linear regression models

ABSTRACT Vehicle miles traveled (VMT) is an essential input for many aspects of transportation engineering, and an accurate estimation of VMT is critical for practicing engineers. Linear regression models are a popular method to estimate VMT as they provide insight into the relationships between VMT and other external factors. In linear regression models the prediction of the response variable has a non-zero probability of resulting in a negative value. For this reason, the natural logarithm of VMT is often used as the response variable to force a positive outcome. However, these log-linear regression (LLR) models provide median VMT estimate instead of the mean estimate. To overcome this limitation of LLR models, this study proposes using heteroskedastic LLR and count data methods to estimate VMT. These methods are found to have better performance than LLR models in terms of data fit and prediction accuracy.

Fault Diagnosis of Mine Hoist Based on MFCC-SVDD

In the field of coal mine production, mine hoist plays a very important role in the whole mine transportation engineering. Its safety and stability directly affect the production efficiency of coal mine and the life safety of staff. In view of this, a fault diagnosis method of mine hoist based on MFCC-SVDD is proposed. By collecting the audio signal of the elevator, MFCC algorithm was used to extract the sound signal of multiple channels and the MEL frequency cepstrum coefficient was used to extract the fault characteristic parameters. Based on the one-class classifier SVDD, the hypersphere of the elevator was constructed to test and recognize the sound signals in the training, and the classification and recognition of the fault types of the elevator were completed. The MFCC characteristic parameters of 600 training samples were randomly selected as input to train the model, and 200 test samples were identified. The accuracy of fault identification reached 85%-96%, which provided a guarantee for mine production safety.

A revise on using surrogate safety measures for rear-end crashes

Traffic safety is among the most serious topics of transportation engineering studies. Different methods have been proposed for safety evaluations. Applying surrogate safety measures (SSM) to predict the crash likelihood and severity is a relatively new approach, which needs microscopic traffic data instead of crash data. Numerous SSMs have been introduced in the literature. Now, it is somehow hard to decide about the selection of suitable SSMs for safety assessments based on microscopic traffic data. In this paper, first a review on SSMs relating to rear-end conflicts introduced in the high-quality papers is done. Then, with the help of microscopic traffic data of NGSIM in I-80 freeway, SSMs are calculated for each car-following scenario. Because of the diversity of SSMs and contradicting results it was tried to propose a method for combining the results of these indicators to report one number as the rear-end collision risk. The efficiency of the new indicator is evaluated in comparison to individual SSMs. The proposed methodology can be beneficial to improve the efficiency of In-vehicle collision avoidance warning system (IVCAWS) and enhancing highway safety evaluations based on SSMs.

A hybrid approach of ANN and improved PSO for estimating soaked CBR of subgrade soils of heavy-haul railway corridor

ABSTRACT The determination of subgrade/subsoil strength is one of the most important pavement design factors in transportation engineering, particularly for railways, roadways, and airport runways. The California bearing ratio (CBR) is often used to measure the strength and stiffness modulus of subgrade materials. This study presents a novel machine learning solution as an alternate approach for estimating soil CBR in soaked conditions. The present approach is an integration of an artificial neural network (ANN) and improved particle swarm optimisation (IPSO). According to experimental results during the testing phase, the proposed hybrid model, ANN-IPSO has achieved the highest predictive precision with root mean square error, RMSE = 0.0711 and mean absolute error, MAE = 0.0546. The findings of the proposed model are far superior to those of employed models including the conventional ANN, support vector machine, and group method of data handling. Six additional hybrid models of ANN and standard PSO (SPSO), PSO with time-varying accelerator coefficients, modified PSO, Harris hawks optimisation, slime mould algorithm, and colony predation algorithm were also constructed for a detailed comparison. Based on the outcomes, the newly created ANN-IPSO has the potential to be a new tool to estimate soaked CBR of fine-grained soils in civil engineering projects.

A Space Fractional Uphill Dispersion in Traffic Flow Model with Solutions by the Trial Equation Method

This paper has two main objectives. First, we modify the traffic flow model by introducing the uphill dispersion that derives from the fact that, in peak hours, drivers tend to travel from low to high density regions. This means that the proposed model recovers wrong-way travel and is free from advected discontinuity. Second, in order to describe the anomalous transport behavior, we fractalize the proposed model to include dynamics with the fractional in space. As a result of adopting the fractional Fick’s law, several moving jam waves are presented which elucidate the non-homogeneity of driving styles. Then, the GFFD fractional derivative and the trail equation method are applied and for some special cases solutions are simulated which could help transportation engineers to understand traffic behavior and thus make appropriate decisions when constructing a traffic signals network.

Multi-objective optimization and heuristic based solutions for evacuation modeling

This paper presents efficient approaches to evacuation modeling through solving the underlying transit network design problem. Given the road network of an area, our goal here is to compute a set of routes to evacuate all residents (i.e., evacuees) within the earliest possible time. The paper presents two different approaches, namely, a multi-objective optimization based approach and a heuristics-based approach. The work has also implemented a bus-based simulator to simulate the solutions (i.e., generated routeset). Finally, the work has performed a case study on the city of Halifax to show the efficacy of our framework where our multi-objective optimization based solution (Heuristics-based solution) can evacuate all evacuees within six (nine) hrs. Our proposed framework and the case study show the application of an algorithmic approach to the transit network design problem to real world problem. The proposed framework has the flexibility to improve/customize based on the choices of decision-makers and transportation engineers.

Economic Ripple Effects Analysis of Cross-Laminated Timber Manufacturing in Japan

Japan has been actively promoting the various uses of timber to efficiently utilize its plantation forests and contribute to its climate commitments. Cross-laminated timber (CLT), owing to its unique structural characteristics and environmental advantages, has received widespread attention in Japan. Being a high-value-added timber, CLT’s value chain involves various industries, including forestry, timber processing, transportation, construction, and civil engineering. However, the economic impact associated with the increasing production and use of CLT is ambiguous. Targeting CLT manufacturers in Japan, this study evaluated the economic ripple effects of CLT through an input–output analysis, which is a method that is used to evaluate the independent association between different economic sectors and industries. An extended input–output table was established according to the survey of revenue and expenditure data of the largest CLT manufacturers in Japan in 2020. The result was compared with the glued-laminated timber (GLT) scenario at the same final demand value. The results showed that activities not elsewhere classified, timber, logs, road freight transport (except self-transport), and wholesale trade were the top five sectors that had the largest economic impact on CLT manufacturing. Our research has implications for policies and programs to promote the revival of forestry and sustainable development of the timber industry in Japan.

A Pilot Demonstration of Flaring Gas Recovery during Shale Gas Well Completion in Sichuan, China

Summary As one of the largest emitters in the world, the oil and gas industry needs to apply more effort to greenhouse gas (GHG) reduction. Methane, as a potent GHG, could largely determine whether natural gas could serve as a bridging energy toward a sustainable future. In the past decade, oil and gas companies in China have significantly enhanced casing gas recovery and reduced large volume flaring (>2×104 m3/d). However, the remaining low- to mid-volume flaring gas was left for further recovery. Shale gas production in China has met a surge in the number of drilling wells. Those new wells were characterized by a relatively low gas production rate (<1×106 m3/d) in comparison with shale gas wells in the US. As a result, flaring gas during well completion needs to be recycled or used to enhance the gas recovery rate. In this study, we carried out a pilot demonstration project of flaring gas recovery to reduce GHG emissions in the Weiyuan shale gas region in Sichuan Province, China. We adopted the technical route of dehydration and natural gas compression. The recycled natural gas was transformed into compressed natural gas (CNG) and transported to the nearest CNG station for further use. The inlet gas pressure was between 2.85 and 5.82 MPa, and the outlet pressure was kept stable at around 20 MPa to meet the standard of CNG. The manufactured device also showed sound flexibility with the recovery rate between 523.22 and 1224.38 m3/h, which was 28–157% of the designed capacity. The combination of the molecular sieve with high capacity, post low-pressure dehydration, and the application of hydraulic piston in the compression system have guaranteed the equipment to meet the designed performance. The equipment applied in the pilot demonstration has well matched with the local transportation, gas composition, and surface engineering of the well completion. It has the potential of popularization and application in the shale gas tight gas regions in China. Other technical routes, such as small-scale gas to chemicals or natural gas hydrate, should be considered for industrial application for gas flowing rate less than 2×104 m3/d to ensure a further drive down of methane emission along the value chain.

A Critical Review on Improving the Fatigue Life and Corrosion Properties of Magnesium Alloys via the Technique of Adding Different Elements

Magnesium is the eighth-most abundant element in the world and its alloys have a widespread application in various industries such as electronic and transport (i.e., air, land, and sea) engineering, due to their significant mechanical properties, excellent machinability, high strength to weight ratios, and low cost. Although monolithic Mg metal is known as the lightest industrial metal (magnesium density is 30% less than the density of the aluminum, and this unique property increases the attractiveness of its usage in the transportation industry), one of the significant limitations of magnesium, which affects on its applications in various industries, is very high reactivity of this metal (magnesium with an electronegativity of 31.1 can give electrons to almost all metals and corrodes quickly). To overcome this problem, scholars are trying to produce magnesium (Mg) alloys that are more resistant to a variety of loads and environmental conditions. In this regard, Mg alloys include well-known materials such as aluminum (Al), Zinc (Zn), Manganese (Mn), Silicon (Si), and Copper (Cu), etc., and their amount directly affects the properties of final products. In the present review paper, the authors attempted to present the latest achievements, methods, and influential factors (finish-rolling, pore defects, pH value, microstructure, and manufacturing processes, etc.) on the fatigue life and corrosion resistance of most significant Mg alloys, including AM50, AM60, AZ31, AZ61, AZ80, AZ91, ZK60, and WE43, under various conditions. The summarized results and practical hints presented in this paper can be very useful to enhance the reliability and quality of Mg-made structures.

Coupling of electronic transport and defect engineering substantially enhances the thermoelectric performance of p-type TiCoSb HH alloy

Further advancements in thermoelectric technology rely on the capacity to control both electrical and thermal transport properties simultaneously. Although TiCoSb-based half-Heusler compounds are promising for mid-range-temperature thermoelectric applications owing to their high Seebeck coefficient and good electrical conductivity, their high thermal conductivity has been so far the main issue to overcome. Here, we show that a combined approach of tuning the electronic properties and defect engineering enhances the thermoelectric performance of p-type TiCoSb-based compounds. By alloying on the Co and Ti sites with Fe and Zr, respectively, an overall increase in the peak ZT value of up to ∼90% at 823 K is achieved in Ti0.8Zr0.2Co0.85Fe0.15Sb. This enhancement is directly tied to the more pronounced metallic nature of transport upon Fe alloying combined with a significant reduction in thermal conductivity due to mass and strain field fluctuations driven by the substitution of Zr for Ti, as evidenced by the Debye-Callaway model. Further adjusting the hole concentration with aliovalent Sn doping leads to an additional increase in ZT, eventually leading to a peak value of ∼0.54 at 823 K in Ti0.8Zr0.2Co0.85Fe0.15Sb0.96Sn0.04, which is 224% higher than in TiCo0.85Fe0.15Sb, and the highest value reported so far in Hf-free p-type TiCoSb based HH alloys.

Technology of Obtaining Mixed Alternative Fuel Based on Organic Substances for Mining Technological Transport Engines

Abstract In connection with the increase in the price of fuel based on hydrocarbons, the search and use of alternative types of fuel for machines in the mining and oil production industry is quite relevant today. Most of the existing alternative fuels by themselves cannot be considered as ready-to-use motor fuels. One of the ways to solve this problem is to use their mixtures with commercial fuels in certain ratios. This work deals with the development and research of the main characteristics of the mixer for obtaining mixed fuels based on diesel fuel and soybean oil, as well as the results of the study of the physical and operational properties of the obtained mixtures of different volume ratios of diesel fuel and soybean oil. The process of obtaining a mixed alternative fuel based on diesel fuel and soybean oil was implemented on a developed installation consisting of a mixer, the design of which is protected by a patent of Ukraine, and a gear pump. The study of the characteristics of the mixer as part of the installation showed that at a temperature of the components of the alternative fuel of 20°С, with a loss of full pressure on the mixer Δр ≈ (60-65) kPa, the volume consumption of soybean oil reached 10.7.10-6 m3/s. The time of preparation of the fuel mixture in the amount of 195 liters with a content of soybean oil of 10% ranged from 30 to 33 minutes. As research has shown, mixing soybean oil with diesel fuel in the amount of 5-50% vol. provides an improvement in viscosity-temperature properties and makes it possible to use such mixtures in diesel engines without changes in the design of the power supply system and regulation of fuel equipment, which characterizes soybean oil as a promising additive to diesel fuels to improve their technical and operational performance.

Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.

Energy Consumption Reduction and Sustainable Development for Oil & Gas Transport and Storage Engineering

The oil & gas transport and storage (OGTS) engineering, from the upstream of gathering and processing in the oil & gas fields, to the midstream long-distance pipelines, and the downstream tanks and LNG terminals, while using supply chains to connect each part, is exploring its way to reduce energy consumption and carbon footprints. This work provides an overview of current methods and technological improvements and the latest trends in OGTS to show how this industry strives to achieve sustainable development goals. The critical analyses are from increasing flexibility, energy saving, emission reduction, and changing energy structure. The study shows the need to focus on improving energy efficiency further, reducing energy/water/material consumption and emissions, and maintaining safety for such an extensive oil & gas network.