Road Network Management Research Articles

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes.

Numerically simulating the interconnected nature of the road-soil-pipe infrastructure

Successful operation of infrastructure, such as the road, rail, and utility networks, are fundamental to modern living, and failure of these can have significant consequences. Yet they are likely, when in proximity, to interact, and failure of one can cause cascade failure of others. Equally, all are supported by the ground, which can also experience changes in properties as the infrastructure deteriorates. Research into the interconnected nature of these infrastructure is not new, but much focuses on the structural behaviour of the human-made components (road structures, pipes, etc). This paper presents a numerical model developed to systematically evaluate the impacts of simulated pipe leakage on the surrounding ground and pavement layer above. The model outputs indicate that the road surface experiences increased strains due to weakening ground conditions around the leaking pipe and these are exacerbated by asymmetrical traffic loading. Findings indicate that leakage can cause differential settlements (observed both at the surface and pipe levels), which could cause localised deterioration of the pavement material (due to tensile cracking, etc.) precipitating surface anomalies like cracks or potholes. This suggests that understanding the root-cause of road surface deterioration is critical to efficient long-term management of road networks so that the symptoms are not remediated whilst the root-cause remains. This research provides a crucial step towards enhancing predictive maintenance and calls for further investigation into the long-term geotechnical impacts of leakage, to develop robust repair and maintenance frameworks that address underlying causes of road surface anomalies.

Traffic planning in modern large cities Paris and Istanbul

The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes a widely acknowledged trend in the development of urban infrastructure. The proliferation of various types of transportation vehicles exacerbates the complexity of traffic regulation. Intelligent transportation systems, leveraging real-time traffic status prediction technologies, such as velocity estimation, emerge as viable solutions for the efficacious management and control of urban road networks. The objective of this project is to address the complex task of increasing accuracy in predicting traffic conditions on a big scale using deep learning techniques. To accomplish the objective of the study, the historical traffic data of Paris and Istanbul within a certain timeframe were used, considering the impact of variables such as speed, traffic volume, and direction. Specifically, traffic movie clips based on 2 years of real-world data for the two cities were utilized. The movies were generated with HERE data derived from over 100 billion GPS (Global Positioning System) probe points collected from a substantial fleet of automobiles. The model presented by us, unlike the majority of previous ones, takes into account the cumulative impact of speed, flow, and direction. The developed model showed better results compared to the well-known models, in particular, in comparison with the SR-ResNet model. The pixel-wise MAE (mean absolute error) values for Paris and Istanbul were 4.299 and 3.884 respectively, compared to 4.551 and 3.993 for SR-ResNET. Thus, the created model demonstrated the possibilities for further enhancing the accuracy and efficacy of intelligent transportation systems, particularly in large urban centres, thereby facilitating heightened safety, energy efficiency, and convenience for road users. The obtained results will be useful for local policymakers responsible for infrastructure development planning, as well as for specialists and researchers in the field. Future research should investigate how to incorporate more sources of information, in particular previous information from physical traffic flow models, information about weather conditions, etc. into the deep learning framework, as well as further increasing of the throughput capacity and reducing processing time.

Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology

Abstract The importance of site selection for road transportation management and emergency services has been underscored, particularly in the wake of the global Covid-19 pandemic in addition to road accident injuries, which emphasized the need for swift patient, injured and wounded peoples transfer to hospitals. As a result, comprehensive road transportation management is essential during crises to ensure the safety and convenience of road users. To achieve effective road network management, a robust database is imperative. The aim of this study is to employ multi-criteria decision analysis with the aid of Geographical Information System (GIS) technology and remote sensing for the optimization of site selection for traffic signals, fire stations, and emergency services in Kafr El-Sheikh city, Egypt. The methodology incorporates two different methods which are Fuzzy Analytical Hierarchy Process (F-AHP) and the spline transformation method. The integration of Fuzzy set theory with AHP principles has given rise to a variant of AHP known as F-AHP. F-AHP combines qualitative and quantitative approaches. Fuzzy logic handles situations that are vague or poorly defined by assigning numerical values. Through the F-AHP approach, criteria that decision-makers unanimously deem “absolutely not important” in comparison to others are eliminated, allowing them to focus on the most significant criteria. As a case study, an Egyptian city was selected to apply these techniques. The results revealed multiple sites that were identified and selected through the applied analytical methods. These selected sites were also marked on the city map and verified in reality.

Road Network Traffic Flow Prediction Method Based on Graph Attention Networks

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities.

Integrating Tensometer Measurements, Elastic Half-Space Modeling, and Long-Term Pavement Performance Data into a Mechanistic–Empirical Pavement Performance Model

Pavement performance models (PPMs) are utilized to predict pavement network conditions which is an essential part of any sustainable pavement management system (PMS). The reliability of a PMS and its outputs is proportional to the reliability of the PPM used. This article describes a mechanistic–empirical pavement performance model based on pavement response parameters—strains calculated in the pavement layers measured by tensometers embedded in the pavement surface and verified by calculations in the elastic half-space model and supplemented by empirical data from long-term pavement performance monitoring and accelerated pavement testing. Hence, the herein described PPM combines pavement serviceability evaluation, pavement bearing capacity, and the physico-mechanistic properties of paving materials. The analytical methods which were used to ascertain the physico-mechanistic characteristics, the material fatigue degradation model, and the surface degradation, unevenness in particular, are described. A comparison of the empirical PPM created in the last century used by the national road administrator to this day and the newly created PPM is presented. The comparison shows the difference in the calculated socio-economic benefits and subsequent cost–benefit analysis results. The comparison shows that the use of the old PPM may have produced false economic evaluation results that have led to poor decision making, partially explaining the unsustainable trend of road network management in our country.

Foresight plus: serverless spatio-temporal traffic forecasting

Building a real-time spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on achieving (often marginal) improvements in evaluation metrics such as MAE/MAPE on static benchmark datasets, with less attention paid to building practical pipelines which achieve timely and accurate forecasts when the network is under heavy load. Transport authorities also need to leverage dynamic data sources such as roadworks and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. Our cloud-based forecasting solution Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, enhanced with dynamic vehicle-level flow and urban event information, to produce regularly scheduled forecasts with high accuracy. In this work, we extend Foresight with several novel enhancements, into a new system which we term Foresight Plus. New features include an efficient method for extending the forecasting scale, enabling predictions further into the future. We also augment the inference architecture with a new, fully serverless design which offers a more cost-effective solution and which seamlessly handles sporadic inference workloads over multiple forecasting scales. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent performance up to 48 hours ahead. This is in contrast to the 1 hour forecasting periods popularly considered in this context. Further, our serverless inference solution is shown to be more cost-effective than provisioned alternatives in corresponding use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.

Mathematics for optimal design of sustainable infrastructures

AbstractThe central aim of this paper is to show that mathematical sciences—in particular a smart combination of modeling, numerical simulation and optimization—can be seen as a useful tool in the design of sustainable infrastructures. Thus, in the first part of this work, we clarify what we mean by systems optimization and suggest how to apply these techniques to improve sustainability. Then, in the second part, we present three interesting environmental problems—all of them involving sustainable infrastructures—that we have studied in the last decade and fit well within this framework: (a) the design of a river fishway to help migratory fish to climb a dam, (b) the design of an irrigation channel to minimize sedimentation and erosion, and (c) the management of an urban road network from an environmental perspective. Based on our experiences, we believe that this innovative use of non-trivial mathematical tools is destined to play a leading role in achieving the United Nations Sustainable Development Goals by helping stakeholders develop the best scientifically informed strategies.

Retracted: Collaboration and Management of Heterogeneous Robotic Systems for Road Network Construction, Management, and Maintenance under the Vision of “BIM + GIS” Technology

Retracted: Collaboration and Management of Heterogeneous Robotic Systems for Road Network Construction, Management, and Maintenance under the Vision of “BIM + GIS” Technology

Speed Data Reconstruction: Analyzing and Improving the Accuracy of MFD Estimation Using Loop Detector and Floating Car Data

Accurate MFD estimation is essential for the efficient management of urban road networks. Loop detectors and probe vehicles collect traffic data required for empirical MFD estimation. Different road network coverages of loop detectors and probe vehicles and penetration rates of probe vehicles affect the accuracy of the MFD estimation. The literature calculated the error in the MFD at different road coverage of loop detectors and penetration rates of probe vehicles. However, the error in MFD at different road coverage of probe vehicle was missing and explained the quality of FCD data as the product of probe vehicle road coverage and the square root of their penetration rate. The linear behavior leads to conflicting results and affects the accuracy of the MFD estimation. In this study, the impact of probe vehicle road coverage was initially determined, and a methodology for improving the quality of speed data was developed. Later, the study analyzed the combined effect of road coverage and penetration rate on the MFD estimation and proposed a performance factor for probe vehicle data.The results showed that the error in the MFD is a negative power function of the probe vehicle road coverage, and the intensity of the error is negatively correlated with the penetration rate. The methodology of enhancing the speed data quality improved the quality significantly, notably at lower penetration rates and road coverages of probe vehicles. Further, the proposed performance factor is consistent with the error of the MFD compared to the former approach.

Intelligent Road Network Management Supported by 6G and Deep Reinforcement Learning

Intelligent Road Network Management Supported by 6G and Deep Reinforcement Learning

Multi-level Graph Memory Network Cluster Convolutional Recurrent Network for traffic forecasting

Traffic forecasting plays a vital role in the management of urban road networks and the development of intelligent transportation systems. To effectively capture spatial and temporal dependencies within traffic data, Graph Neural Networks (GNNs) emerge by combining recurrent neural networks and dynamic graph learning. However, traditional GNNs face challenges in efficiently memorizing patterns at different levels of these dependencies, including long-term shared patterns and short-term specific patterns. We propose a traffic forecasting model, the Multi-level Graph Memory Network Cluster Convolutional Recurrent Network (MMNCCRN). The MMNCCRN consists of four modules: Encoder Module (EM), Attention Module (AM), Memory Network Cluster Module (MNCM), and Decoder Module (DM). To enhance the model’s performance, AM incorporates a concise yet efficient attention mechanism that augments important information in the output of EM. This alleviates the pressure on MNCM to identify patterns at different levels. Meanwhile, in MNCM, we extensively leverage memory networks and introduce the concept of clustering. By storing and memorizing patterns implicit in spatial and temporal dependencies, MNCM assists the model in learning underlying graph structures and discovering new hidden patterns. Experimental results show that our model outperforms others in most metrics on four datasets.

SIGeR-RV: A Web-Geographic Information System-Based System for Risk Management of Road Networks Exposed to Natural Hazards

The consequences of natural hazards are frequently estimated by the direct cost of recovering damaged infrastructure and the indirect costs to road users, economic activities, and impacts on society. Road networks are crucial in economic systems, logistic chain continuity, accessibility, mobility, and the evacuation of population during and after extreme events. Risk management systems (RMS) are used to estimate the potential consequences of natural events and to assess strategies for risk reduction. These commonly assess hazards, assets exposure, economic losses, and risk mitigation actions, among others. The general framework of RMS can be adapted to different scenarios. Still, local characteristics, such as the types of hazards and physical assets, cannot always be directly adopted from these systems. This study discusses the development of SIGeR-RV, an RMS developed in Chile for road networks exposed to multiple natural hazards. The RMS was implemented in a web-based geographic information system platform able to display hazard maps, quantify risk levels, prioritize mitigation strategies, and estimate direct and indirect costs and social vulnerability. The content and various capacities of the system are detailed, following the steps marked in its framework. This first version of SIGeR-RV considers seismic hazard, volcanic lahars, and hydro-meteorological hazards that affect road platforms, bridges, tunnels, and cut slopes. The system currently serves as a tool for the Ministry of Public Works of Chile and other decision makers to estimate budget requirements for increasing resilience of the road network, identifying vulnerable road segments, and assessing the socioeconomical impacts of risk reduction.

Collaboration and Management of Heterogeneous Robotic Systems for Road Network Construction, Management, and Maintenance under the Vision of “BIM + GIS” Technology

In modern society, intelligent spatial analysis services have been playing an important role in building smart cities. Especially, people’s spatial information needs have changed with the technological progress of times, from traditional two-dimensional appearance to three-dimensional interior. In this case, the construction of street networks is a key part of spatial information analysis research. Establishing an integrated indoor-outdoor street network data model with accurate information and complete attributes can provide strong technical support for indoor-outdoor navigation, emergency evacuation, and other application services. However, most of the current studies on internal and external road networks are independent of each other. This means that indoor road networks are mainly based on floor plans, which contain little information and do not represent the spatial relationships between floors. Indoor and outdoor links often suffer from poor connectivity and data loss. Heterogeneous robotics is a high degree of integration of intelligent control and platform control technologies. In a sense, it is a technology platform for connecting heterogeneous robotic populations. By developing effective collaboration and control strategies, tasks can be performed with the highest success rate and the lowest risk while respecting spatial and temporal constraints. As a result, the study of the coordination and management of heterogeneous robotic systems for road network construction, management, and maintenance is a topic of a great theoretical value and significance. Therefore, this paper proposes an intelligent road network construction method that incorporates BIM and GIS technologies. This study uses the building information model to enrich the information of indoor building components and combines the strong spatial analysis capability of outdoor areas with GIS road network technology to build an indoor road network and design a connection scheme for indoor and outdoor areas. After that, the construction of an integrated indoor-outdoor street network is completed, and the interaction between indoor as well as outdoor spatial data is very smooth.

Challenges and opportunities regarding the COVID-19 pandemic on urban mobility in Constanta, Romania

The global COVID-19 pandemic has had a significant impact on urban mobility in Constanta, Romania and other cities around the world. This has resulted in reduced travel, increased bicycle and pedestrian trips, fewer taxicabs, and shorter public transport hours. It has also generated increased challenges for local authorities regarding safety, hygiene, air quality, road network management and public trust in public transit systems. On the other hand, the pandemic presents many opportunities for cities to rethink their urban mobility plans and adopt more sustainable transport solutions. This paper will look at the challenges and opportunities posed by the pandemic on urban mobility in Constanta and the measures that can be taken to mitigate the negative impacts while taking advantage of the opportunities it provides. This paper aims to provide an overview of the perception of urban mobility in the city of Constanța, Romania. It draws upon the results of a survey conducted among citizens in the city, which asked respondents to assess their perception regarding the quality, availability, and efficiency of urban mobility services in their area. The results show that most respondents feel that the availability of urban mobility services is limited and that the quality and efficiency of these services are poor. Additionally, majority of respondents feel that the lack of public transportation options and the lack of bicycle lanes impede urban mobility in Constanța. The paper concludes by suggesting that the city should consider investing in improved public transportation options and bike lanes in order to increase its urban mobility. Additionally, public education surrounding urban mobility and transportation safety is recommended.

A GIS-supported methodology for the functional classification of road networks

The management and monitoring of road networks require constant effort from road owners and managers. This study proposes a decision support method to assist decision makers in planning interventions on road networks. The proposed method supports the functional classification of road networks and their hierarchy using well-defined rules and criteria. GIS tools are used to visualize the resulting classification. The analysis, which does not focus on single roads but on routes composed of one or more roads, proposes a hierarchization based on a set of indicators. These latter describe accessibility, functionality, geometrical and safety characteristics based on which routes can be classified according to different levels of importance. The indicators used are attributable to three main groups. The first group relates to infrastructure connectivity and accessibility; the second to functionality and performance (design speed, homogeneity of traffic flows, etc.) while the third describes the type of road according to variables related to width, tortuosity, accidents, etc. The proposed methodology can support the effective management of road networks and provide road practitioners with useful knowledge elements, also for monitoring road safety.

Complex disaster response framework to reduce urban disaster vulnerability.

Cities are vulnerable to a range of disasters that can occur simultaneously due to their complexity. Therefore, an effective disaster response plan is needed to reduce the disaster vulnerability of cities. In particular, evacuation route management is important for reducing the losses from a disaster. Efficient disaster response can be realized by searching for suitable evacuation routes and effective road network management. In this paper, we propose a disaster response framework based on a multilayered road network structure and evacuation routes based on our road network. The suggested road structure consists of three layers for the effective management of the network. An A* algorithm-based search for multiple evacuation routes under different conditions in response to an individual disaster on the configured road map provides a safe route for evacuees. As a result, the damage caused by disasters in urban areas can be ameliorated.

An Integrated Risk Management System for Road Infrastructures: Focus on Seismic Risk and Network Performance

As part of an integrated risk management process of road infrastructures, the Asset Management Systems of existing bridges play an increasingly significant role, in order to define the programmatic strategies of road owners and managers.In this perspective, the introduction of the new Italian “Guidelines for risk classification and management, the safety assessment and the monitoring of existing bridges” represents a strong innovation. These Guidelines, based on a modern risk-based approach, call road managers to activate their procedures for the knowledge and the classification of the state of their structural assets, identifying, at the same time, the needs in terms of maintenance, works and interventions. The results of the application of these Guidelines also represent the basis for defining, through the tools of the Integrated Management Systems, an optimal operational and financial planification of interventions, to be carried out to maximize the safety of the existing bridges.In this paper, after focusing on the importance of the application of the Guidelines procedures to the Safety Management Systems of Road network managers, the importance of the completeness of the cognitive process, provided by the Guidelines - in terms of correct definition of the priorities and of the most suitable intervention aimed to mitigate the seismic risk - is illustrated. In particular, the activities for the detection of the aspects related to the seismic vulnerability are identified as the key process in the context of the Safety Management Systems of Road managers.In the second part of the paper, the aspects related to the adoption, by all the road managers operating in a certain territorial area, of the approach defined by the Guidelines have been analyzed. This aspect plays a key role in order to improve the network safety performance for the areas characterized by a greater seismic hazard and for the most strategic and/or heavily trafficked road infrastructures.

Hierarchical control for stochastic network traffic with reinforcement learning

This study proposes a hierarchical control framework to maximize the throughput of a road network driven by travel demand with uncertainties. In the upper level, a perimeter controller regulates the traffic influx into the core road network. The upper level uses a reinforcement learning algorithm that learns and responds to the traffic dynamics in the core road network without the need for an underlying system model and macroscopic fundamental diagram. The lower level is a local signal control system that regulates the spatial distribution of traffic flow within the core network. The results show that the hierarchical control framework can improve road network throughput by coordinating control actions conducted at the two levels. The improvement in system-wide performance is validated by a range of performance metrics and macroscopic flow-accumulation patterns realized under different control settings. The study contributes to the management of urban road networks with advanced computing technologies.

Use of corridors to select bridges to retrofit in road networks in seismic regions

ABSTRACT This study proposes the use of bridge clusters, defined as Corridors, to support optimal bridge retrofitting strategies for seismic risk management of road networks. A Corridor is defined as a set of bridges that works jointly to ensure connectivity and traffic flow between different areas of a region. To detect Corridors, a Markov Clustering Algorithm is proposed. Using the San Francisco Bay Area road network as a testbed, this clustering technique selects sets of bridges that correspond to main traffic arteries such as highways and high-capacity road segments. After Corridors have been detected, a two-stage stochastic optimization is implemented to detect which bridges should be retrofitted to ensure an acceptable network performance. This optimization couples retrofitting actions in a Corridor with the repair actions to damaged bridges after an earthquake. The Corridors-Supported Optimization decreases road network disruption more than other approaches based on ranking bridges according to their traffic capacity or location in the network.