Transportation Engineering Research Articles

Analysis of factors influencing anti-slip pile support in tunnel landslide systems for tunnels with different burial depths

Research on the tunnel landslide system poses significant challenges in the field of transportation engineering. Investigating the factors that influence the stress between tunnels at different depths and the effectiveness of anti-sliding pile support holds practical importance for real-world engineering applications. This study utilized indoor experiments and value engineering theory to examine the stress characteristics of four tunnel lining depths and anti-sliding piles under the influence of factors such as pile spacing, sliding zone angle, and pile type. The following conclusions were drawn: for shallow tunnel depths (10 m), the tunnel lining experiences relatively low stress, but the left wall of the lining structure undergoes greater stress due to the push of the landslide in the tunnel landslide system. With increasing tunnel depth, the bias stress ratio approaches 1, and the discrepancies between various factors gradually diminish. By comprehensively analyzing the factors impacting the support of tunnels at different depths in the tunnel landslide system, it was observed that when the tunnel depth is beyond the sliding zone (≤20 m), the H-shaped composite anti-sliding pile with 2D and 6D pile spacing exhibits a higher cost-effectiveness ratio. Conversely, when the tunnel depth is within the sliding zone (≥20 m), the combination of 6D pile spacing and round piles demonstrates greater cost-effectiveness. Utilizing the principles of value engineering theory, a comprehensive analysis was conducted to determine the optimal ratio that offers better performance at a lower cost. The conclusions derived from this study serve as a valuable reference for future construction decision-making.

Combinatorial protein engineering and transporter engineering for efficient synthesis of L-Carnosine in Escherichia coli

L-Carnosine has various physiological functions and is widely used in cosmetics, medicine, food additives, and other fields. However, the yield of L-Carnosine obtained by biological methods is far from the level of industrial production. Herein, a cell factory for efficient synthesis of L-Carnosine was constructed based on transporter engineering and protein engineering. Firstly, a dipeptidase (SmpepD) was screened from Serratia marcescens through genome mining to construct a cell factory for synthesizing L-Carnosine. Subsequently, through rationally designed SmPepD, a double mutant T168S/G148D increased the L-Carnosine yield by 41.6% was obtained. Then, yeaS, a gene encoding the exporter of L-histidine, was deleted to further increase the production of L-Carnosine. Finally, L-Carnosine was produced by one-pot biotransformation in a 5 L bioreactor under optimized conditions with a yield of 133.2 mM. This study represented the highest yield of L-Carnosine synthesized in microorganisms and provided a biosynthetic pathway for the industrial production of L-Carnosine.

Eliminating the impacts of traffic volume variation on before and after studies: a causal inference approach

A before and after study framework measures the outcomes in a group of participants before introducing an intervention, and then again afterward. In this study, a before and after study framework is adopted to evaluate the effectiveness of transportation policies and emerging technologies. Generally, the outcome of every before and after study will help decision-makers to monitor and understand the effects of interventions and to make sound decisions. However, many factors such as seasonal factors, holidays, and lane closures might interfere with the evaluation process by inducing variation in traffic volume during the before and after periods. In practice, limited effort has been made to eliminate the effects of these factors. In this study, an extreme gradient boosting (XGBoost)-based propensity score matching (PSM) method is proposed to reduce the biases caused by traffic volume variation during the before and after periods. In order to evaluate the effectiveness of the proposed method, a corridor in the City of Chandler, Arizona where an advanced traffic signal control system has been recently implemented was selected. The results indicated that the proposed method can effectively eliminate the variation in traffic volume caused by the COVID-19 during the evaluation process. In addition, the results of the t-test and Kolmogorov-Smirnov (KS) test demonstrated that the proposed method outperforms other state-of-the-art PSM methods. The application of the proposed method is also transferrable to other before and after evaluation studies and can significantly assist transportation engineers to eliminate the impacts of traffic volume variation on the evaluation process.

Impact of Climate Change on the Performance of Permafrost Highway Subgrade Reinforced by Concrete Piles

Climate change has a detrimental impact on permafrost soil in cold regions, resulting in the thawing of permafrost and causing instability and security issues in infrastructure, as well as settlement problems in pavement engineering. To address these challenges, concrete pipe pile foundations have emerged as a viable solution for reinforcing the subgrade and mitigating settlement in isolated permafrost areas. However, the effectiveness of these foundations depends greatly on the mechanical properties of the interface between the permafrost soil and the pipe, which are strongly influenced by varying thawing conditions. While previous studies have primarily focused on the interface under frozen conditions, this paper specifically investigates the interface under thawing conditions. In this study, direct shear tests were conducted to examine the damage characteristics and shear mechanical properties of the soil-pile interface with a water content of 26% at temperatures of −3 °C, −2 °C, −1 °C, −0.5 °C, and 8 °C. The influence of different degrees of melting on the stress–strain characteristics of the soil-pile interface was also analyzed. The findings reveal that as the temperature increases, the shear strength of the interface decreases. The shear stress-displacement curve of the soil-pile interface in the thawing state exhibits a strain-softening trend and can be divided into three stages: the pre-peak shear stress growth stage, the post-peak shear stress steep drop stage, and the post-peak shear stress reconstruction stage. In contrast, the stress curve in the thawed state demonstrates a strain-hardening trend. The study further highlights that violent phase changes in the ice crystal structure have a significant impact on the peak freezing strength and residual freezing strength at the soil-pile interface, with these strengths decreasing as the temperature rises. Additionally, the cohesion and internal friction angle at the soil-pile interface decrease with increasing temperature. It can be concluded that the mechanical strength of the soil-pile interface, crucial for subgrade reinforcement in permafrost areas within transportation engineering, is greatly influenced by temperature-induced changes in the ice crystal structure.

Preface

Proceedings of the 4th International Conference on Transportation Infrastructure and Sustainable Development TISDIC 2023 Editors Rafat Siddique, Phan H. Nam, Nguyen M. Hai, Nguyen P. Q. Duy, Pham N. Phuong Hoang P. Tung, Nguyen V. Te Ron The 4th International Conference on Transportation Infrastructure and Sustainable Development (TISDIC 2023) is scheduled to take place from 26th to 28th August 2023 at the Da Nang Administrative Centre in the captivating city of Da Nang, Vietnam.Renowned as one of the most distinguished international conferences in the field of transportation infrastructures within Vietnam, TISDIC has garnered widespread acclaim. It stands as a pivotal platform where leading academics, government entities, civil society representatives, and the private sector converge to exchange research insights and innovative solutions, propelling sustainable development goals in transportation infrastructures.TISDIC 2023 includes five primary topics, offering a comprehensive exploration of Advanced Structural and Bridge Engineering, Road and Transport Engineering, AI, IoT and Automation in Transportation Systems, Construction and Building Materials for Sustainable Development, and Geotechnical and Geoenvironmental Engineering. By focusing on these areas, the conference aims to highlight the latest advancements and address the pressing challenges facing the field.The overwhelming response to TISDIC 2023 is a testament to its significance, with a remarkable total of 142 papers submitted for consideration. Through a rigorous evaluation process, 106 exceptional papers were selected for publication in this proceeding, boasting an impressive acceptance rate of 74.6%.We extend our heartfelt appreciation to the authors for their invaluable contributions, which have enriched the conference proceeding. We also express our deepest gratitude to the dedicated reviewers, whose expertise and meticulous evaluation ensured the inclusion of high–quality papers. Their contributions are instrumental in fostering intellectual growth and pushing the boundaries of knowledge in transportation infrastructures.Finally, we would like to express our gratitude for the support provided by the Vingroup Innovation Foundation (VINIF – VINBIGDATA) towards the publication of this proceedings.The EditorsList of Reviewers, Scientific Committee and Organizing Committees are available in this pdf.

Predicting the Rheological Properties of Bitumen-Filler Mastic Using Machine Learning Techniques

This study uses the artificial neural network and response surface methodology to develop two models for predicting the rheological properties, complex modulus (G*) and phase angle (δ) of bitumen-filler mastic. It also analyses and evaluates the accuracy of both models by determining the coefficient of determination (R2), mean squared error (MSE), and root mean squared error (RMSE). The prediction models use the G* and δ data from a previous study by researchers at the Nottingham Transportation Engineering Centre to determine three types of bitumen-filler mastic (limestone, cement and grit stone) with varying filler concentrations of 15, 35, 40 and 65%. The analysis shows that both models perform well in predicting the rheological properties of bitumen-filler mastic. A comparison of the two models shows that the artificial neural network (ANN) has higher accuracy than the response surface methodology model, with an R2 value exceeding 0.92. The results of the ANN achieve a higher R2 value and lower MSE and RMSE values. In summary, the performance of the artificial neural network model is better than the response surface methodology model, which uses the full quadratic, pure quadratic, linear and interaction mathematical methods.

Low Cost Evolutionary Neural Architecture Search (LENAS) Applied to Traffic Forecasting

Traffic forecasting is an important task for transportation engineering as it helps authorities to plan and control traffic flow, detect congestion, and reduce environmental impact. Deep learning techniques have gained traction in handling such complex datasets, but require expertise in neural architecture engineering, often beyond the scope of traffic management decision-makers. Our study aims to address this challenge by using neural architecture search (NAS) methods. These methods, which simplify neural architecture engineering by discovering task-specific neural architectures, are only recently applied to traffic prediction. We specifically focus on the performance estimation of neural architectures, a computationally demanding sub-problem of NAS, that often hinders the real-world application of these methods. Extending prior work on evolutionary NAS (ENAS), our work evaluates the utility of zero-cost (ZC) proxies, recently emerged cost-effective evaluators of network architectures. These proxies operate without necessitating training, thereby circumventing the computational bottleneck, albeit at a slight cost to accuracy. Our findings indicate that, when integrated into the ENAS framework, ZC proxies can accelerate the search process by two orders of magnitude at a small cost of accuracy. These results establish the viability of ZC proxies as a practical solution to accelerate NAS methods while maintaining model accuracy. Our research contributes to the domain by showcasing how ZC proxies can enhance the accessibility and usability of NAS methods for traffic forecasting, despite potential limitations in neural architecture engineering expertise. This novel approach significantly aids in the efficient application of deep learning techniques in real-world traffic management scenarios.

Rational engineering of an elevator-type metal transporter ZIP8 reveals a conditional selectivity filter critically involved in determining substrate specificity

Engineering of transporters to alter substrate specificity as desired holds great potential for applications, including metabolic engineering. However, the lack of knowledge on molecular mechanisms of substrate specificity hinders designing effective strategies for transporter engineering. Here, we applied an integrated approach to rationally alter the substrate preference of ZIP8, a Zrt-/Irt-like protein (ZIP) metal transporter with multiple natural substrates, and uncovered the determinants of substrate specificity. By systematically replacing the differentially conserved residues with the counterparts in the zinc transporter ZIP4, we created a zinc-preferring quadruple variant (Q180H/E343H/C310A/N357H), which exhibited largely reduced transport activities towards Cd2+, Fe2+, and Mn2+ whereas increased activity toward Zn2+. Combined mutagenesis, modeling, covariance analysis, and computational studies revealed a conditional selectivity filter which functions only when the transporter adopts the outward-facing conformation. The demonstrated approach for transporter engineering and the gained knowledge about substrate specificity will facilitate engineering and mechanistic studies of other transporters.

Transport Week 2022

The journal publishes a selection of materials based on the news reports of the press centre of the Ministry of Transport of the Russian Federation and dedicated to the events held during 16th Transport of Russia International Forum and Exhibition.The Forum, which opened on November 15, 2022, during its first day hosted the Session of the Committee of the State Council of the Russian Federation, the panel discussion «Container Flow during the Era of Global Shocks. Current Trajectories for Market Development», the roundtable discussion «Life-Cycle-Long Maintenance of Motor Transport» and the industrial conference «Efficiency of State Support for Air Transport During Unprecedented Sanctions Pressure».On the second day of the Forum, the participants discussed the role of science in providing technological stability of transport, water transport and assessment of compliance in the new conditions, the formula for cooperation in transport engineering, new digital tools, information services and technologies, transport safety and security and digital management ecosystems. Besides, were held the Session of the Council of Heads of Transport Authorities of the Eurasian Economic Union Member Countries and the presentation of the RZD Transport Accelerator.The final day was devoted to international cooperation. It was opened by the plenary discussion «International Transport Cooperation in 2022. New Directions, Trends, Results», which focused on the actions of countries on the issues of building transport communications under sanctions, the law enforcement, and the coordination of work aimed at creating more competitive transport products within the EAEU. Then, the session «Sunrise on the East: Significance of Trade with Asian Countries for the Russian Economics and the Ability of Transport to Support the Trade» demonstrated the achieved results in rerouting of external trade communications to the East.The business program was concluded with the plenary discussion. In the opinion of a Deputy Minister of Transport of the Russian Federation, Valentin Ivanov, «activity of the Forum was organised in a very productive manner. Many issues were discussed and many meeting points and solutions were found».Winners of the «Formula of Movement» premium were awarded. The premium is designed to promote development of transport infrastructure, improve the quality of transport services, as well as stimulate government and commercial structures to solve significant problems in the transport sector. Only implemented projects or services that have received practical application in the field of transport were eligible to participate.The topic of «Transport Week» is also highlighted on the second cover and on the pages of the central insert of the issue.

Concomitant Production of Erythritol and β-Carotene by Engineered Yarrowia lipolytica.

While the expansion of the erythritol production industry has resulted in unprecedented production of yeast cells, it also suffers from a lack of effective utilization. β-Carotene is a value-added compound that can be synthesized by engineered Yarrowia lipolytica. Here, we first evaluated the production performance of erythritol-producing yeast strains under two different morphologies and then successfully constructed a chassis with yeast-like morphology by deleting Mhy1 and Cla4 genes. Subsequently, β-carotene synthesis pathway genes, CarRA and CarB from Blakeslea trispora, were introduced to construct the β-carotene and erythritol coproducing Y. lipolytica strain ylmcc. The rate-limiting genes GGS1 and tHMG1 were overexpressed to increase the β-carotene yield by 45.32-fold compared with the strain ylmcc. However, increased β-carotene accumulation led to prolonged fermentation time; therefore, transporter engineering through overexpression of YTH1 and YTH3 genes was used to alleviate fermentation delays. Using batch fermentation in a 3 L bioreactor, this engineered Y. lipolytica strain produced erythritol with production, yield, and productivity values of 171 g/L, 0.56 g/g glucose, and 2.38 g/(L·h), respectively, with a concomitant β-carotene yield of 47.36 ± 0.06 mg/g DCW. The approach presented here improves the value of erythritol-producing cells and offers a low-cost technique to obtain hydrophobic terpenoids.

Traffic Flow Prediction Based on Explainable Machine Learning

Traffic flow prediction is one of the important links to realize an urban intelligent transportation system. Thanks to the in-depth research of artificial intelligence theories, the machine learning method has been widely used in intelligent transportation engineering. However, due to the “black box” as its characteristics, its application and further development are limited. Exploring the explainability of machine learning models in traffic flow prediction is an important issue to make it more reliable in traffic engineering and other practical applications. Apart from selecting the RandomForest model and the CatBoost model as the objects to research the traffic flow prediction against temporal and spatial changes, this paper makes a comprehensive evaluation and comparison with LightGBM and the other two prediction models through different indicators. Meanwhile, aiming at the low explainability of the models, their feature importance is analyzed and compared with reality. The results show that the RandomForest model and CatBoost model make good predictions, whose feature importance is consistent with the actual situation, verifying their explainability.

Two-dimensional calculation model for hazardous substances formation in combustion products of a spark-ignition engine

Introduction (problem statement and relevance). Improvement of environmental parameters of internal combustion engines is one of the most relevant tasks of the domestic transport engineering. Calculation support allows reducing the time and raising the efficiency of works for refining of the bench work process in order to meet the required parameters. The paper is devoted to development of a two-dimensional calculation model for hazardous substances formation in combustion products of spark-ignition engines based on the non-equilibrium kinetic models of formation of nitrogen oxides and carbon monoxide.Methodology and research methods. Computational methods for differential equations (ordinary and partial ones) are applied. Non-equilibrium kinetics of hazardous substances formation in combustion products is considered. The work process is calculated based on a combination of the mesh control-volume method and determination of the observable flame rate based on the experimental data (taking into account the development stage of the flame source).Scientific novelty and results. The new calculation method for flame propagation in the spark-ignition engine is suggested. The developed model combines such advantages as good predictability and short time of calculation.Practical significance. The model can be applied, in particular, to preliminary planning of three-dimensional calculations of the work process or their check in the absence of experimental data (at the stage of preliminary/concept design). The calculation results can be used as initial data for modeling of the exhaust gas aftertreatment (additional purification) system.

Trends and Interdisciplinarity Integration in the Development of the Research in the Fields of Sustainable, Healthy and Digital Buildings and Cities

This article provides a thorough bibliometric analysis of significant research trends in sustainability from 1988 until now, focusing on sustainable, healthy and digital buildings and cities. It exemplifies how research emphasis has shifted from explicit ecological investigations to nature-based solutions and city greening programs, with a rising interest in the many responsibilities of urban stakeholders in attaining sustainability. Despite weak integration at the literature and author cooperation levels, the “healthy buildings and cities” topic indicates promise for multidisciplinary integration. The “digital buildings and cities” topic, on the other hand, presents a more particular concern with strong cross-cluster collaboration and significant integration possibilities. Global relevance has been demonstrated through research on “sustainable buildings and cities,” mainly in journal papers. This topic’s study clusters show remarkable synergy across management, transportation, ecology, remote sensing and environmental engineering domains. In comparison to “healthy buildings and cities” and “digital buildings and cities” topics, the study of “sustainable buildings and cities” demonstrates a deeper level of interdisciplinary integration, highlighting the significant potential for further exploration within sustainability science research. This study emphasizes the ongoing worldwide relevance of sustainability science research and identifies significant opportunities for multidisciplinary integration across the investigated subjects.

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

One of the primary objectives of transportation engineering is to increase the safety of road infrastructure. This study seeks to determine the relationship between geometric design parameters in relation to road accident criteria based on accident hotspots on Jordan’s Desert Highway. The road accident data (from 2016 to 2019) were collected from the Jordan Traffic Department. The spatial pattern of hotspots was identified using a GIS tool named Getis-Ord Gi* based on the severity index of road accidents. A topographic survey was conducted to investigate the road alignment and intersections at hotspot locations. The study utilized the Structural Equation Modeling (SEM) technique via SmartPLS to highlight the correlation between geometric designs in relation to road accidents. The hotspot analysis (Gits-Ord Gi) discovered 80 road accident hotspots along the highway. The study found that horizontal alignment and road intersections significantly impact road accidents in hotspot locations. Furthermore, vertical alignment has no effect on road accidents in hotspot areas. The study enhanced the comprehension of the factors associated with road geometrics and intersections that affect the occurrence of road accidents.

Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithms that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives.

Empowering revealed preference survey with a supplementary stated preference survey: demonstration of willingness-to-pay estimation within a mode choice case

Mode choice models play a pivotal role in transport demand modelling and help transport planners, engineers and researchers with policy and infrastructure investment evaluation. Recent mode choice studies primarily use revealed preference (RP) data to reflect individuals’ true behaviour. However, this may not be the best practice, given the lack of information in RP data. This study uses a nonlinear utility specification for a multinomial logit mode choice model development using high-quality travel data collected by a GPS-based smartphone application complemented by stated preference (SP) data. The model results highlight the impact of sociodemographic variables on mode choice behaviour and individuals’ willingness-to-pay (WTP) when the model is jointly developed compared to stand-alone SP and RP models. The main message of this study is that in addition to collecting RP, which is a reliable and unbiased source of data, collecting complementary SP data is beneficial as it provides information that is not otherwise available in RP data. This may include a proper variation in the public transport cost variable as demonstrated in this study. Moreover, to better understand the travellers' behaviour regarding the trade-off between time and cost a mixed multinomial logit (MMNL) model in the willingness to pay space is developed on the SP data. capturing the unobserved heterogeneity within the estimated WTPs, the MMNL model outputs reveal a higher variation in WTP of car in-vehicle travel time compared to bus in-vehicle travel time.

Adapting to the Emergence of Generation Z in Tertiary Education: Application of Blended Learning Initiatives in Transport Engineering

Adapting to the Emergence of Generation Z in Tertiary Education: Application of Blended Learning Initiatives in Transport Engineering

Lyapunov-Based Model Predictive Control for Shipboard Boom Cranes Under Input Saturation

A Lyapunov-based model predictive control (MPC) scheme is firstly developed for shipboard boom cranes with ship rolling, while considering optimization and input saturation. By integrating the ship rolling into original state variables, the shipboard boom crane model is transformed to facilitate the successive controller design. A Lyapunov-based MPC framework is then constructed for the new dynamical model, while respecting actuator capability. To ensure the recursive feasibility and stability of the established framework, a contractive constraint with a bounded energy-based controller is further introduced, for which the bounds are intrinsical with the utilization of the arctan function. Stability analysis for the bounded controller is also discussed based on Lyapunov theory and LaSalle&#x2019;s invariance principle. Compared with the bounded controller, optimization is taken into account by the Lyapunov-based MPC. Suboptimal solutions are also accepted to ease the computational burden. Moreover, the conservation existing in the bounded controller can be entirely circumvented by the Lyapunov-based MPC, so the capacity of actuators can be fully exploited while obeying the input saturation. Finally, numerical simulations are elaborately devised and implemented to show the effectiveness of the bounded controller and superiority of the Lyapunov-based MPC controller. <i>Note to Practitioners</i>&#x2014;Shipboard crane is an important industrial equipment in ocean transportation and offshore engineering. Cranes are installed on ship deck, so the movements of ship, such as rolling, and pitching, may introduce difficulties, inefficiency, and unsafety to the manual operation. The conditions mentioned above motivate the study of automation control for shipboard cranes. A Lyapunov-based model predictive control (MPC) framework is constructed in this paper. Based on this framework, the payload can be conveyed to the desired location accurately and eliminate the swing quickly. Simultaneously, optimization is considered, thus, better control performance can be achieved by consuming less energy. Moreover, the physical limits of the actuators can be satisfied all the time. The present research only considers 3 degrees of freedom (DOF) shipboard boom cranes, for which the slew movement of boom and payload tangential swing are not involved. In the future research, the 5-DOF shipboard boom cranes will be studied to promote the application of the method proposed in this paper.

Financial and credit support of market-oriented management of transport engineering enterprises

Purpose. To determine the priorities of the financial and credit support of transport machine building taking into consideration the needs of the national economy, state of marketing environment, and requirements of corporate management. Methodology. The information basis of the study is represented by the statistic data formed according to the classification of economic activity types KVED-2010 in terms of section C “Processing industry”, divisions 29 “Production of motor vehicles, trailers, and semitrailers” and 30 “Production of other transport means”. The research methodology involves analysis of dynamics and structure of production volumes, net profit of the transport engineering enterprises, formalization of trends, evaluation of credit capacity of the industry, and marketing and financial diagnostics. Findings. Key trends in the development of the transport machine-building market within the period of 2012–2020 are generalized: reduction of total production volumes and identification of subindustries-drivers of its growth. Structural shifts in the national economy owing to the growing specific weight of the production of motor vehicles, trailers, semitrailers in terms of prevalence of other transport means (first of all, railway and air ones) are identified. The priorities of financial and credit support of the transport machine building are specified taking into account the needs of civil national economy (railway locomotives and rolling stock, motor vehicles, air- and spacecraft, accompanying facilities) and military economy (military vehicles). To evaluate the objects of credit support, it is proposed to use a concept of credit capacity of an enterprise as its ability of accumulating banking credits on its balances in terms of certain currencies, terms, designated purpose, interest rates etc. as well as their timely servicing and payment. Originality. The scientific and methodological foundations of determining the priorities of financial and credit programme of the transport engineering enterprises are substantiated in the context of market-oriented corporate management, whose advantages are as follows (compared with the current ones): highlighting the time lags of production crisis and after-crisis renovation in the context of industries; determining structural shifts in the field of transport machine building, and identifying the economic activity types being leaders or outsiders in the sphere of financial and credit support of the expanded reproduction on this industry; defining polarization of business entities, being different in their size, according to the financial state and solvency with the focus on competitive advantages or crisis. Practical value. Proposals as for restructuring the activities of the transport engineering enterprises are substantiated basing on marketing diagnostics of the state of their internal and external environment, threats and possibilities in terms of war time, Ukraine’s getting a status of the member for the EU accession, and post-war renovation of the economy. In the framework of market-oriented management of the transport engineering enterprises, certain tendencies in the financial and credit support of their complex innovative development are substantiated.

Generating and Calibrating a Microscopic Traffic Flow Simulation Network of Kyoto

Microscopic traffic flow simulations as tools for enabling detailed insights on traffic efficiency and safety gained numerous popularity among transportation researchers, planners and engineers in the first to decades of the 21st century. By implementing a test bed for simulation scenarios of complex urban transportation infrastructure it is possible to inspect specific effects of introducing small infrastructural changes related to the built environment and to the introduction of advanced traffic control strategies. The possibility of reproducing present problems or the transportation services, such as the ones of public bus services is a key motivation of this work. In this research, we reproduce the road network of the city of Kyoto for observing specific travel patterns of public buses such as the bus bunching phenomena. Therefore, a selection of currently available data sets is used for calibrating a cutout of the Kyoto road network of a relatively large extent. After introducing a method for geodata extraction and conversion, we approach the calibration by introducing virtual detectors representing present inductive loops and make use of historical traffic count records. Additionally, we introduce bus routes partially contributed by volunteer mappers (OSM project). First simulation outcomes show numerous familiar (local knowledge) flow patterns.