Urban Road Research Articles

Impact of urban road characteristics on vehicle speed: Insights from Brescia, Italy

In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V85). While V85 can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V85 and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V85 in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented.This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V85 on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V85. Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained.The results revealed that V85 increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V85 decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model.These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.

A comparative study on contamination profiles of liquid crystal monomers (LCMs) between outdoor and indoor dusts, and the assessment of health risk of human exposure

Liquid crystal monomers (LCMs) are ubiquitous in various environmental samples, which has led to increasing concerns regarding their potential health risks to humans and wildlife. However, the comparison of the contamination patterns of LCMs between indoor and outdoor environments has rarely been studied. In this study, 35 LCMs were investigated in n = 55 dust samples collected from indoor (n = 20) and outdoor (n = 35) spaces in Yulin, Northwest China. The LCMs were widely detected in indoor and outdoor dusts; the total concentrations of LCMs ranged from 48.6 to 396 ng/g (median: 153 ng/g), and from not detectable to 388 ng/g (median: 56.4 ng/g) in indoor and outdoor dusts, respectively. The concentration levels of ΣLCMs in indoor dusts were significantly higher than those in outdoor dusts (p < 0.05). For each microenvironment, the ranking order of LCM concentrations was dormitory (mean: 202 ng/g) > teaching building (182 ng/g) > campus road (150 ng/g) > urban road (107 ng/g) > laboratory building (91.0 ng/g) > pedestrian street (20.1 ng/g). The mean estimated daily intake values of Σ35LCMs for adults were 2.48 × 10−2 and 1.37 × 10−3 ng/g BW/day in indoor and outdoor dusts, respectively. The hazard quotients of individual LCMs and hazard indices of all analytes were considerably less than one, indicating little health risk for humans via dust ingestion.

Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

Urban green streets and water management safety hubs in public schools lots

Abstract Green Stormwater Infrastructure (GSI) represents an effective approach to enhance urban resilience to climate change, contributing to runoff regulation and localized flood risk reduction. This study examines the potential for transforming open spaces in public schools into safety hubs for stormwater management in Mediterranean urban contexts, drawing on best practices from North America. Through a methodological analysis combining Research Through Design and an international literary scoping review, case studies of schools in the Metropolitan City of Naples are presented and compared with U.S. projects implementing GSI in school areas. The results highlight the importance of strategic coordination guidelines for local administrators, designers, and school principals, aimed at planning, designing, and managing highly performant green infrastructure networks. An integrated and shared metropolitan-level approach can maximize environmental benefits, transforming schools into community centers for urban resilience education and participatory management of risks related to extreme weather events.

Tire and road wear particles in infiltration pond sediments: Occurrence, spatial distribution, size fractionation and correlation with metals

Stormwater systems, such as infiltration ponds or basins, play a critical role in managing runoff water and reducing particulate pollution loads in downstream environments through decantation. Road runoff carries several pollutants, including trace metals and tire and road wear particles (TRWP). To improve our understanding of infiltration ponds as regards TRWP and their capacity to reduce TRWP loads, we have studied the occurrence, spatial distribution and size distribution of TRWP, as well as their relationship with metals, in considering the input of metals as tire additives, in the sediments of an infiltration pond located along the Nantes urban ring road (Western France), which happens to be a high-traffic roadway site. The sediment was analyzed using pyrolysis coupled with gas chromatography–mass spectrometry to determine the polymeric content of tires, specifically in quantifying the styrene-butadiene rubber (SBR) and butadiene rubber (BR) pyrolytic markers. By applying an SBR + BR-to-TRWP conversion factor, the results showed significant TRWP contamination, up to 65 mg/g, with a spatial enrichment from the entrance to the overflow section of the pond. Size fractionation revealed a bimodal distribution, indicating two distinct types of TRWP. The first type is characterized by small diameters (63–160 μm), suggesting the presence of TRWP less integrated with mineral and organic particles. The second type, characterized by larger diameters (200–500 μm), suggests a more pronounced integration with these same mineral and organic particles. A significant positive correlation between TRWP and metals (As, Cd, Cr, Cu, Li, Mo, Ni, Sb, V, Zn) was found (r > 0.739, p < 0.05). This correlation implies that TRWP and/or their associated phases may act as an indicator of metal contamination in the pond sediments. Lastly, a mass balance between TRWP inputs and the amount retained in the sediments underscores the role of infiltration ponds as “sinks” for TRWP.

Seasonal variation of particle number concentration in a busy urban street with exposure assessment and deposition in human respiratory tract

Ultrafine particles (UFP) associated with air quality and health impacts are a major concern in growing urban regions. Concentrations of UFP (particles of size between 10 and 100 nm) and accumulation mode (Nacc) (particles of size >100 and up to 1000 nm), are analyzed over a highly polluted megacity, Delhi, in conjunction with vehicular flow density, during peak (morning, and evening) and non-peak hours. UFP contributes ≥60% to total particle concentration during autumn and monsoon. UFP concentrations are about 50,000 particles per cm3 in winter which reduces to about 25,000 particles during monsoon. Nacc are about 20,000 (winter) and 10,000 (monsoon) particles per cm3. UFP concentration and Nacc during peak hours are at least twice higher than those obtained in non-peak hours, confirming the dominant influence of emissions from vehicular exhaust in the study region. Seasonal analysis of UFP size distribution reveals that direct emissions dominate the particle concentrations during winter and autumn, whereas new particle formation mechanism contributes the highest in spring and summer. Assessment of inhalable particle number concentration and particle deposition in the human respiratory tract using Multiple Path Particle Dosimetry (MPPD) model, performed for the first time, shows that the order in which these particles deposit in the human respiratory tract is alveoli > bronchiole > bronchus. The deposition ranges between 10 and 18 million nanoparticles during different hours of the day, whereas the estimated inhalable particle concentration (IPN) varies between 0.5 and 1 billion. Results on the IPN during activities classified from light (walking), medium, heavy, very heavy to severe (long-distance running) provide insights into health effects on vulnerable populations. These quantitative results obtained over a megacity on hourly and seasonal variations of nanoparticles along with IPN and deposition rates for different activities are important, and are invaluable inputs for developing mitigation policies aimed to improve air quality and public health, both of which are major concerns in South Asia.

GIS-based analysis of relationship between building density and capacity of urban streets

Governments adopted the policy of increasing accumulation in cities in order to maximum use of desired lands of city districts and to prevent from city buildings in the environment created subsequent to the horizontal city developments. One of the basic foundations that must be evaluated while the population accumulation of capability increase, is the traction ability of accountability communication network in both public and private transport system to new travels created by any new user introduction. In this paper, we discussed about the relation between accumulation and traffic capacity of city streets with regard to a case study of Imam Khomeini Street in Tabriz city, Iran. This relation has been modeled with Logistic and AHP models. After investigating the service level of the street for 14 consecutive hours it is evident that the traffic is saturated at the peak hours and based on this study the suggestion for density increase is eliminated. Because the density increase is possible when v/c

A numerical study on the issue of Reynolds independence of flow and dispersion within isolated street canyons

Achieving Reynolds number independence is a prerequisite for conducting scale model experiments. This paper presents a numerical model of the two-dimensional isolated street canyon, utilizing the validated reliable k-epsilon turbulence model based on wind tunnel experimental data, to investigate the influence of approaching wind profiles (α) and windward building width (WB) on achieving Reynolds number independence in the isolated street canyon and compare these characteristics with those observed in urban street canyon. quantitative criteria, including revised relative change ratios (RRCs) ≤ 20% and dimensionless concentration relative difference (K_RD) ≤ 5%, were employed in conjunction with velocity, concentration contours, and streamlines to determine the corresponding critical Reynolds numbers. The results suggest that variations in approaching wind profiles primarily influence the flow field structure within the isolated street canyon after achieving Reynolds number independence, particularly impacting the scale of the counterclockwise vortex. However, these variations do not significantly alter the critical Reynolds number (Recrit) required for achieving Re-independence, which is estimated to be Recrit = 2.2×10⁴ for the isolated street canyon. Varying WB affects the formation of counterclockwise vortex within the isolated street canyon. A reduction in WB facilitates its formation and accelerates reaching Re-independence. The Recrit is found to be linearly proportional to WB variation. The flow field structure in urban street canyons remains relatively stable, with a recommended Recrit = 3.1×10⁴. When WB is 18 cm, the Recrit for the isolated street canyon aligns with that of the urban street canyon.

Optimal Drive-By Sensing in Urban Road Networks With Large-Scale Ridesourcing Vehicles

Optimal Drive-By Sensing in Urban Road Networks With Large-Scale Ridesourcing Vehicles

A trajectory optimization method for CAVs at intersections considering energy-efficiency balance

With the development of vehicle-to-everything (V2X) technology, connected automated vehicles (CAVs) will be an important part of urban road transportation. However, frequent acceleration, deceleration, and rapid stopping within intersections lead to increased energy consumption, reduced driving efficiency and balanced benefits. The study proposes an optimal control method for vehicle trajectories that considers energy-efficiency balance (OCM-EEB) based on the characteristics of V2X technology. The method first develops a vehicle trajectory control model based on the Intelligent Driver Model (IDM) by accounting for the features of V2X technology, and further, introduces the idea of balanced optimization, considering energy-efficiency balance, a vehicle trajectory optimal control model (OCM) is proposed. The proposed model was solved by design algorithms based on the non-dominated sorting genetic algorithm-II (NSGA-II). The experimental results showed that the vehicle trajectory optimization control method could increase the balanced driving efficiency of the vehicle within intersection, which is conducive to improving the robustness of the vehicle’s trajectory state, and then prove the effectiveness of the method proposed in this study. The method also provides an important technical basis for the vehicle trajectory optimization experiments under a real V2X environment in the future.

Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning

Real-time conflict prediction at signalized intersections is crucial for urban road safety management. This study developed a real-time conflict prediction framework for signalized intersections using video data real-time recognition technology and deep learning techniques, incorporating lane-level information and feature interactions. The modeling framework consists of three stages: real-time video data extraction and processing, the development of a Deep and Cross Network (DCN)-based real-time traffic conflict prediction model, and conflict-driven factor interpretability analysis through SHapley Additive exPlanations (SHAP). In the first stage, an efficient automated trajectory extraction system is designed to obtain vehicle trajectories in real time for dynamic traffic parameters and conflict frequency identification. In the second stage, a DCN model is developed to construct the relationships between dynamic traffic parameters, including their interactions, and traffic conflicts. In the third stage, SHAP explores the impact mechanisms of different dynamic traffic parameters on traffic conflicts. The model’s predictive performance and interpretability are evaluated using intersection video data from Changsha City, China. The results show that: (1) In real-time traffic conflict prediction at signalized intersections across different modified time-to-conflict thresholds (1.5 s and 3.0 s), the DCN model consistently outperformed statistical and machine learning models. (2) High traffic flow on main and secondary roads at signalized intersections significantly increases the complexity and frequency of conflicts, with varying sensitivity depending on the interaction of traffic flow, speed, and platoon length. (3) The proposed framework provides a safety measurement standard for data-driven road safety management methods.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

Loofah-based self-powered triboelectric nanogenerator-supercapacitor for an integrated self-charging energy system

New urbanization is ushering in a surge in construction and amplifying a substantial demand for urban road development, simultaneously spurring innovation and development of environmentally sustainable power sources. Triboelectric nanogenerator (TENG), a revolutionary technology of energy conversion, efficiently capturing high-entropy energy from the environment and offering the advantages of self-powered and immediacy. Recognizing the critical need to store the energy harvested by TENGs, we present an integrated energy conversion and storage system that combines a loofah-based self-powered rotating TENG (LS-TENG) with a supercapacitor. The maximum instantaneous output power and the corresponding instantaneous power density of the LS-TENG are 0.75mW and 202.69mW/m2 at 30rpm. The supercapacitor, featuring carbon nanotube (CNT) interdigitated electrodes created through microelectronic printing technology, utilizes polyacrylamide (PAM)-lithium chloride (LiCl) as a gel electrolyte (adhesion strength of 16.69 kPa). The supercapacitor is seamlessly integrated onto the back of the LS-TENG’s stator, which effectively simplified the energy conversion and storage system. At a current density of 0.01mA/cm2, the supercapacitor achieves a high volumetric capacitance of 341.47 mF/cm3, an energy density of 17.07mWh/cm3, and a power density of 257.07 mW/cm3, with a remarkable capacitance retention rate of 99.12% after 30000 cycles at a current density of 0.02mA/cm2. Furthermore, a concept demonstration of an integrated self-charging energy system is implemented, in which the supercapacitor can be charged at wind speed rate of 3.0m/s. The proposed integrated energy storage and conversion system makes it ideal for converting and storing wind energy.

Koopman Modeling for Optimal Control of the Perimeter of Multi-region Urban Traffic Networks

The purpose of perimeter control is to regulate the transfer flow between the perimeters of the urban traffic network, so that the vehicle aggregation in each region is maintained at a desired level. It is regarded as one of the most important methods to solve traffic congestion in urban road networks. Accurate mathematical modeling of perimeter controlled traffic dynamics remains a challenge due to its high nonlinearity and uncertainty. Machine learning methods have been used to learn traffic dynamic models for perimeter control. However, these existing techniques lack generalization and interpretability. In this paper, we propose a Koopman modeling approach (i.e., a two-stage method) that uses a new eigenfunction of the Koopman operator based on a novel L0 norm approximation to construct an interpretable finite-dimensional approximation of the Koopman operator, which is a linear operator that describes how eigenfunctions evolve along the trajectory of a specific nonlinear dynamical system. The main advantage of utilizing the Koopman operator is that it can characterize the nonlinear system in a global linear lifted feature space. Furthermore, an algorithm based on the Koopman modeling method and model predictive control is proposed for real-time perimeter control of urban road networks. Some experiments are carried out to demonstrate the effectiveness of the proposed algorithm.

Impact of Median Separation Facilities on Drivers’ Shy-Away Effects: Why Are Median Separation Facilities Widely Used in China?

In recent years, China has begun introducing readily installable median separation facilities into its urban road network. Although some studies have recognized the benefits of using median separation, few have investigated the effects of different types of facilities on driver behavior. This driving simulator study aims to determine whether different types of medians on urban arterial roads in China affect driving behavior, and to provide new insights for designing more effective and safer forms of median separation. A driving simulator study was conducted, with 50 participants. The scenario exactly reproduced an existing six-lane urban arterial road in China, and four different median separation types were tested: (1) double yellow line; (2) raised pavement markers; (3) reboundable traffic cylinders; (4) traffic separator railing. Their effects were statistically analyzed using data on driver eye movements, trajectories, and speeds collected from simulation tests. The results showed that the median separation facilities significantly improved drivers’ access to road information, corrected drivers’ attention, and suppressed driving speeds. Although median separation facilities can be an effective solution to the current problem of chaotic vehicular traffic on China’s urban roads, their setup still needs to be optimized. This study demonstrates the calculation method for optimal median facility setup. The resulting 1.21-m-high median facility, paired with a 1.17-m-wide median strip and a certain lane-width compression, more effectively addresses driver factors, the driving task, and the spatial right-of-way. The study’s calculations can also be used to improve other types of roads that can take a median separation facility.

Improving safe pedestrian behavior through virtual reality: an empirical study

INTRODUCTION. Over the last few years, the number of pedestrian fatalities on urban roads has increased, largely due to infractions associated with their behaviors (e.g., crossing when traffic lights are red). It is argued that these behaviors reflect a lack of risk perception. Road safety programs have tried to raise awareness through various methods, using quite often emotionally powerful experiences (e.g. testimonies of people who have experienced an accident themselves). Recently, Virtual Reality (VR) has been deployed with the aim of increasing the efficacy of these safety programs. Previous studies have demonstrated the potential of VR to improve pedestrian safety, especially when it is accompanied bydebriefing and critical reflection. METHOD. A total of 43 participants (M = 24.5 years old; SD = 5.14; 65.12% female) were involved in an experimental study with a 2x2 factorial design and pre-post measures. They were randomly assigned to one of four groups (Experience a VR accident /Experience VR without an accident; having a debriefing after the VR experience/not having a debriefing after the VR experience). Pre-post measures were of two kinds, (a) self-report measures and (b)VR behavioral measures. Multivariate Analysis of Variance (MANOVA) and general linear mixed models (GLMM) were used to analyze the data. RESULTS AND DISCUSSION. The main results revealed that (a) participants reported a general reduction in the number of rules violations, regardless of condition, and (b) there was a significant reduction in the number of violations committed in VR (i.e., crossing when the traffic light is red) in the condition where participants had previously experienced an accident. These results support the potential of using VR environments to improve pedestrian safety-related behavior. Implications for future research are delineated.

Short term road network Macroscopic Fundamental Diagram parameters and traffic state prediction based on LSTM

Macroscopic Fundamental Diagram (MFD) is widely used in traffic state evaluation due to its description of the macro level of urban road network. This study focuses on the discrimination and short-term prediction of macro traffic states in urban road networks, using MFD combined with FCM clustering for state partitioning to characterize different macro states of the road network. To predict the MFD state, this paper builds two LSTMs to perform short-term predictions on two important parameters in MFD: road network weighted flow qw and road network weighted density kw. The parameters of the first three statistical intervals and the predicted time period are used as inputs to output the MFD parameters for the predicted time period. To ensure that the two LSTM structures and hyper-parameters settings can achieve the best prediction performance for MFD, the parameter optimization process of both should be included in the same search framework for hyper-parameters search. Therefore, this paper uses GA algorithm combined with multi-objective particle swarm optimization algorithm as the solving algorithm, with the accuracy of solving two MFD parameters and the accuracy of MFD point positioning as the hyper-parameters solving objectives. The study was validated using actual road network data from Hong Kong, and the results showed that the method proposed in this paper has an MRE prediction error of less than 7.8% for the two parameters of MFD, and can predict the future temporal trend of the two parameters, demonstrating the feasibility of MFD related predictions. The model's prediction of the overall shape and change trajectory trend of MFD is consistent with reality, and some test sets in MFD state prediction show high accuracy, the overall accuracy is 81.45%. To verify the effectiveness of the multi-objective search algorithm, typical LSTM models and RNN models were used for comparison. The experiment proved that the model used in this study performed better in error control and state prediction. This study explores and practices a short-term prediction method for road network MFD parameters, MFD status, and their changing trends. Provided path reference for urban road network prediction, traffic control status, and MFD related research.

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

A COMPARATIVE STUDY OF HEADWAY-BASED AND TRANSPORT SYSTEM-BASED ASSIGNMENTS OF PUBLIC TRANSPORT IN VISUM: THE CITY OF KRYVYI RIH CASE

Many researchers have explored public transport assignment methodologies employing transport modelling software. Nevertheless, there remains a gap in evaluating real-world public transit networks utilizing diverse assignment procedures within Visum software. This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. The three scenarios of the model were developed depending on the public transport assignment procedure: headway-based, transport system-based utilized to all links, turns, and major turns in the network graph, and transport system-based applied only to the links, turns, and major turns traversed by the active public transport lines. The model of the network comprises 13 transport systems, 7 transport modes, 27598 links, 10097 nodes, 83270 turns, 238 zones, 1748 connections for private transport, 3013 connections for public transport, 534 stops, 1165 stop areas, 1190 stop points, 130 lines and 218 line routes. The transport demand model encompassed 14 demand segments. Compared to the outputs of the model calculation using the headway-based procedure, in the scenarios with transport system-based assignment, passenger flows on rail tram lines significantly decreased. Also, the results of scenarios with transport system-based assignment showed that the passengers extremely use parallel streets for travel from an origin to a destination. The modelling outputs for an actual urban network illustrate that the choice of the shortest route in the transport system-based assignment is closely linked to the main urban roads. This circumstance could potentially result in a rise in the number of transfers needed to sustain accessibility to districts residing far from the central highway.

METHODOLOGY OF BUILDING INFORMATION MODELING OF URBAN STREETS AND ROADS

Problem statement. New challenges in road construction require the application of modern and globally recognized best practices in the design and management of road infrastructure construction processes, involving the use of the latest Building Information Modeling (BIM) technologies. The implementation of BIM technologies necessitates conducting research to systematize information, forecast and evaluate outcomes, and develop a modeling methodology. The purpose of the article is to develop a methodology for Building Information Modeling of urban streets and roads. Research Results. The proposed methodology for Building Information Modeling is demonstrated using sections of the street and road network in the city of Dnipro. Digital terrain and site models were created in the InfraWorks software based on geospatial data from remote sensing. Conceptual models of urban streets and roads were developed. The conceptual models were exported to Civil 3D, where the terrain model was refined using ground survey data, analyzed, and used to develop route plans, longitudinal and cross profiles of streets and roads. The detailed and coordinated road design solutions in Civil 3D were exported to InfraWorks to build an integrated road infrastructure. An analysis of the spatial smoothness of routes and sight distances on roads was conducted using the building information model. Models of an overpass and bridge developed in Revit were exported to the road infrastructure model. Traffic management measures were modeled. Using the “Traffic Simulation” tools in InfraWorks, vehicle traffic flows on roads and interchanges of the studied street and road network were simulated, taking into account the actual traffic intensity. The building information models were checked for structural deficiencies using Navisworks software. Conclusion. The proposed methodology for designing urban streets and roads using BIM technology involves the step-by-step development of a digital model utilizing software for the automated design of road infrastructure with the required level of detail at each stage of the design process.