State Of Road Network Research Articles

Impact Of Road Connectivity On Daily Livelihood Sustainability Of Commuters In Sandeshkhali-I CD Block, North 24 Parganas, West Bengal, India

Background: The study area has different infrastructural deficiencies with compare to other regions. The natural constraints for communication, Poverty, Infrastructural deficiency, Rurality, and Transportation barriers are the main problems in this region. This study has tried to determine the different road connectivity and their impacts on daily sustainable livelihoods among the commuters of Sandeshkhali-I CD block in North 24 Parganas district of West Bengal, India. Materials and Methods: The study has used the Primary and Secondary data to fulfill the objectives. To execute the objectives, road networks are differentiated to measure the road connectivity indices (Alpha, Beta and Gamma Index) from different Gram Panchayats (GPs) to find out the road network condition in this rural deltaic region of the Indian Sundarban. Using the Random sampling to collect the primary data through questionnaire from commuters at different busiest places in the study area. Based on different selected livelihood components, the daily livelihood sustainability index has been evaluated. To find out the relationship the road connectivity Indices (R.C.I.) and daily livelihood sustainability index (D.L.S.I.) are correlated with the help of Karl Person ‘r’ value. Results: The highest values of Alpha, Beta and Gamma Indices are 0.34, 1.89 and 0.65 with respect to Metalled and Unmetalled roads. The daily livelihood sustainability index value has been found 54.33 and 17.54 as the highest and lowest respectively. The positive and negative values are present in the correlation between R.C.I. and D.L.S.I. Conclusion: From the outcome, most of the GPs are under the low and very low livelihood sustainability category whereas, the rest of the GPs have medium to high livelihood sustainability status as per selected class. The study concluded that the development needs to be accounted for because of intra-regional imbalance among the road connectivity as well as daily livelihood sustainability that has already existed in this area

A Comprehensive Analysis and Research on the Factors Affecting the Growth of Rural Trade in Cultural Communities

This article analyzes the elements that impact the stages of rural retailing development in the Hwange villages, namely in the areas of Jambezi, Mununa, Milonga, Ndlovu, Chikhanda, Mashake, and Monde. The study used a quantitative technique with a sample size of 400 people, recruited by stratified random sampling. The data analysis was performed using SPSS version 26.0. The survey's main findings reveal that rural residents have a preference for firms that value outstanding customer service and offer a broader range of recreational choices. Furthermore, the condition of road networks was recognized as a crucial element impacting the operational effectiveness of rural businesses. The presence of essential services such as water and electricity was discovered to have a substantial influence on the condition of the retail sector. The paper provides numerous recommendations based on the findings. The implementation of these recommendations is anticipated to stimulate economic expansion, boost living standards, and promote overall welfare in rural regions. By effectively dealing with the highlighted causes, the communities in Hwange have the opportunity to experience positive changes in their retailing industry, which will result in sustainable development and an enhanced quality of life.

A New Methodology for the Diagnosis and Monitoring of Bridges Under Slow Deformation Phenomena

Roadway bridges can be subjected to several pathologies affecting their structural performance during operational conditions. Among others, slow deformation phenomena due to landslides often represent the primary cause of the onset of damage and collapse mechanisms. The timely identification of slow deformation phenomena on bridges and viaducts, as well as the evaluation of their extent and evolution over time, are therefore of crucial importance for preserving the safety conditions of road networks. In common practice, the preliminary analysis of slow deformation phenomena could be carried out over large geographical areas, hence used by managing institutions to set intervention priorities among the assets under their responsibility. In this regard, this paper proposes a new methodology for the diagnosis and monitoring of bridges under the effects of slow deformation phenomena based on the combined use of Synthetic Aperture Radar Interferometry (InSAR) techniques, visual inspections, geometric surveys, destructive and non-destructive testing methods, and numerical analyses. Specifically, InSAR is adopted to remotely identify bridges affected by slow deformation phenomena within large geographical areas/road networks. Then, visual inspections are carried out on the identified structures to investigate their defectiveness and the detected deformation phenomenon. Depending on the evaluated damage state, a bridge may be subjected to continuous InSAR monitoring, thus enabling the systematic analysis of the evolution of the deformation phenomenon and relevant anomalies over time, or an accurate structural assessment via numerical analyses. In this last circumstance, information gathered from geometric surveys and field tests can be used for the fine-tuning of the numerical models of the structures. A practical application of the proposed methodology for the diagnosis and monitoring of a curved roadway bridge subjected to landslide phenomena is also presented in the paper for illustrative purposes. Overall, the results obtained in the case study provide a sound demonstration of the effectiveness of the proposed approach.

Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver’s intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management.

Activities of the militia in Ukraine to ensure road safety in the 1950s

In the 1950s, the daily functions of the militia continued to include combating violations of street traffic rules and controlling the decrease in motor vehicle accidents. The accident rate on the roads of the republic at that time remained high. An analysis of the current state of road safety shows that the level of accidents, injuries and deaths on the roads was influenced by such objective factors as the level of development of the regulatory framework, the technical condition of vehicles, the level of development of the road network, its quality, and weather and climatic conditions. Subjective factors included the level of legal culture of road users (drivers and pedestrians), the degree of their readiness to participate in traffic, the professional qualifications and material and technical equipment of the employees of the State Automobile Inspection. During this period, state bodies carried out significant, but ambiguous in quality, rule-making work, the purpose of which was to develop measures aimed at improving both objective and subjective conditions that affected the state of road safety and were of a mutually agreed nature. The established driver training mechanism was one of the important components of the effective operation of the road safety system. Therefore, the activities of state bodies, in particular the State Automobile Inspection of the Ministry of Internal Affairs of the Ukrainian SSR, were aimed at forming not only stable skills in driving vehicles among the drivers, but also behavioral stereotypes that require compliance with legal norms in the field of road safety. At the same time, it was necessary to develop and legally establish criteria for assessing the quality of their training in the relevant institutions. Militia bodies carried out quite active work to improve the situation with road accidents, which included measures of administrative influence, involvement of the public in monitoring violations of traffic rules, various propaganda activities, but its effectiveness was ultimately insignificant.

Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting

Accurately predicting traffic flow is a crucial upstream technique in intelligent transportation systems for future travel plans, the efficiency of urban transport, and the regulation of transport departments, etc. The mainstream spatiotemporal graph convolutional neural networks are usually based on prior knowledge to predefine adjacency matrix graphs for spatial dependencies of the road network. However, modeling spatial correlation statically limits these models to accurately predict traffic flow, since the spatial correlations of road segments change over time. To address these issues, we propose a spatiotemporal gated transformer network with a graph latent information learning structure, termed GL-STGTN, for spatiotemporal traffic flow forecasting. First, we propose a graph latent information learning structure to dynamically learn the spatial dependencies for road network conditions from global and local learning perspectives. Second, we design a spatiotemporal gated transformer network (STGTN) block, which consists of a localized geographically aware block to extract the local embedding of spatial correlations and a temporal-aware enlarged block to extract local temporal dependencies. The learned spatial and temporal feature embeddings are further aggregated in a spatial multi-head attention module and a temporal multi-head attention module, respectively. In the end, a spatiotemporal fusion layer fuses the spatial and temporal information from the stacked STGTN blocks. Experiments on two public real-world benchmark datasets show that our model outperforms six state-of-the-art models for multi-step traffic flow forecasting.

Measurement of Road Transport Emissions. Case Study: Centinela-La Rumorosa Road, Baja California, México

Air pollution is a global issue, and the transportation sector is recognized as the third-largest contributor to anthropogenic greenhouse gas emissions. Vehicles emit a range of chemical compounds because of the combustion process. The nature and quantity of these emissions depend on the vehicle’s characteristics, the road, and weather conditions. These emissions require special attention due to the adverse effects contributing to global warming and human health. In this regard, diagnosing and monitoring air quality is crucial for understanding the number of emissions generated by various sources. However, in developing countries, the necessary data for conducting such analyses are not always available. The purpose of this study is to estimate emissions specifically generated from road operations. To achieve this, the HDM-4 calculation tool is utilized to estimate emissions. This tool was applied in Baja California, Mexico, on the Centinela-La Rumorosa highway. The results obtained show that annually, 372.5 tons of pollutant emissions are generated, composed of HC, CO, CO2, NOx, PM, SO2, and PB, covering a mere 128 km of length within a state road network spanning 11,429 km. This highlights the necessity of implementing strategies to reduce the environmental impact generated by vehicular operations on roads in developing countries.

Enhancing flood-response commuting resilience via driving mechanism investigation: New evidence from Wuhan, China

In the context of climate change, transportation resilience to flood threats has received significant attention. However, measures to improve practical resilience have not been addressed and elucidated. Various studies from the domains of disaster and transport have been devoted to commuting vulnerability analysis and transport network robustness assessment, whereas a systematic perspective is rarely used to analyze the damage mechanism of floods to the commuting system. To fill this research gap, we constructed an index system from the dimensions of hazard, transport network condition, and commuting pattern as driving factors of commuting risk during floods. The contribution to commuting loss was first assessed to guide intervention prioritization based on flood modeling and commute simulation. We adopted gradient boosting decision trees to examine the relationship between driving factors from three dimensions and commuting. The results of this study conducted in Wuhan indicate that the contribution rate of commuting pattern is the highest, followed by hazard, and finally road network conditions. The effective thresholds of the driving factors were also identified to guide urban governance. This study is a novel exploration of disaster impact mechanism that can provide new insights for the improvement of commuting resilience practices under climate change.

Understanding user equilibrium states of road networks: Evidence from two Chinese mega-cities using taxi trajectory mining

User equilibrium (UE) has long been regarded as the cornerstone of transport planning studies. Despite its fundamental importance, our understanding of the actual UE state of road networks has remained surprisingly incomplete. Using big datasets of taxi trajectories, this study investigates the UE states of road networks in two Chinese mega-cities, i.e., Wuhan and Shenzhen. Effective indicators, namely relative gaps, are introduced to quantify how actual traffic states deviate from theoretical UE states. Advanced machine learning techniques, including XGBoost and SHAP values, are employed to analyze nonlinear relationships between network disequilibrium states and seven influencing factors extracted from trajectory data. The results in these two study areas reveal consistent and significant gaps between actual traffic states and the theoretical UE states at various times of the day during both weekdays and weekends. The XGBoost analysis shows that differences in travel distances, travel speeds, and signalized intersection numbers among alternative routes are the primary causes of road network disequilibrium. The results of this study could present several important methodological and policy implications for using the UE models in transport applications.

Applying Digital Images to Identify Pavement Damage in Support of The Road Infrastructure Development Program

Road damage can cause discomfort while driving and even lead to accidents. According to the National Road Network Condition Map Data in 2017, the level of severe and minor road damage in the East Java region had reached 288 kilometers. Based on this data, periodic road condition assessments and maintenance are essential to minimize damage. Road maintenance efforts are crucial to support road infrastructure development programs. The initial step in road maintenance is to identify road damage, determining the necessary actions to be taken. In this research, road pavement damage identification is carried out using the Yolov5, Yolov6, and Yolov7 methods. Test results indicate that the Yolov5 method performed the best with a validation mAP (mean Average Precision) score of 42%, a Precision value of 0.544, and a Recall value of 0.453. These scores indicate that the accuracy of road pavement damage detection using the YOLO algorithm for depression, corrugation, potholes, and alligator cracking is at its maximum level.

UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA

Abstract. User equilibrium (UE) has long been regarded as the cornerstone of transport planning studies. Despite its fundamental importance, our understanding of the actual UE state of road networks has remained surprisingly incomplete. Using big datasets of taxi trajectories, this study investigates the UE states of road networks in Wuhan. Effective indicators, namely relative gaps, are introduced to quantify how actual traffic states deviate from theoretical UE states. Advanced machine learning techniques, including XGBoost and SHAP values, are employed to analyze nonlinear relationships between network disequilibrium states and seven influencing factors extracted from trajectory data. The results reveal significant gaps between actual traffic states and the theoretical UE states at various times of the day during both weekdays and weekends. The XGBoost analysis shows that differences in travel distances, travel speeds, and signalized intersection numbers among alternative routes are the primary causes of road network disequilibrium. The results of this study could have several important methodological and policy implications for using the UE models in transport applications.

Route Planning Based on Parallel Optimization in the Air-Ground Integrated Network

Recent advancement in propulsion technologies to reduce the need for travel or increase the share of sustainable unmanned devices has accelerated the shift toward sustainable transport. To achieve the optimization of route planning in the air-ground integrated network (AGIN), we design an optimization strategy of accompanying graph navigation for unmanned devices, which aims to reduce the power consumption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CO_2$</tex-math> </inline-formula> gas emissions. The optimization of accompanying graph navigation is composed of three strategies, namely, the navigation based on the complete maps, the navigation based on the partitioned maps, and the navigation without maps. We propose a Two-tiered Grid (TG) index and Distributed AGIN Navigation (DAN) to navigate on partitioned maps. The top layer of the TG-index is composed of the border vertices of the global road network, which reflects the overall traffic conditions of the global road network and provides coarse-grained navigation routes. The bottom layer is a grid index composed of subgraphs, which reflects traffic conditions in local areas and provides fine-grained navigation routes. The navigation optimization is implemented in several segments, which can be run by multi-processors and realize rapid response to a large number of concurrent queries.

The scheduling methods with different demand priorities for shared autonomous vehicle system in hybrid demands mode considering dynamic travel time

The shared autonomous vehicle (SAV) is an emerging intelligent transportation mode driven by artificial intelligence, and the vehicle scheduling method is a crucial technology for SAVs. However, researches on the scheduling method of hybrid demand in SAVs lack consideration of dynamic travel time and an analysis of the impact of demand characteristics on system performances. According to the distinct submission and processing approaches of reservation and real-time demands, this paper proposes two hybrid demands scheduling methods for SAV system: the scheduling method with no demand priority (NDP) and the scheduling method that prioritizing short-term reservation demands (PSRD). The NDP method processes the short-term reservation and real-time demands together. The PSRD method processes the short-term reservation demands first, then inserts other requests into the arrangements of reservation demands. A method for calculating dynamic travel time using the ant colony algorithm is proposed. This method aims to identify the optimal path according to the future road network conditions. The results indicate that the PSRD method can give full play to the advantage of knowing the travel information of reservation demand in advance and has good system performance and running efficiency, especially when the number of reservation demands is large. This forms the foundation for advancing research in the SAV traffic systems.

Assessment of future parking systems with autonomous vehicles through agent-based simulation: A case study of Hangzhou, China

Autonomous vehicles (AVs) are expected to transform urban parking. AVs may not require dedicated parking spaces near their destination. Instead, AVs can drive to the optimal parking lot, cruise, or return to the origin after the passenger gets off. Previous research on AV parking strategies has largely overlooked actual trip patterns and road network conditions that can affect the effectiveness and feasibility of these strategies. In this paper, we construct an agent-based simulation model to assess the potential impacts of AVs on urban parking, focusing on new AV parking strategies. Using the real traffic network and trip data of Hangzhou, we assess the impacts on overall network efficiency, personal trip, and parking demand under different AV penetration rates. Results show that AVs could decrease the total parking cost from 23 to 6 RMB and increase parking convenience for both AVs and human-driven vehicles (HDVs). 21% of AVs will still choose to park, while 70% will return to the origin. On average, each trip with AV will generate an additional zero-occupant distance of 8.8 km, exacerbating the congestion. The findings indicate the benefits and challenges of new AV parking strategies, providing valuable insights for future urban parking planning and policy making.

Panic-Pressure Conversion Model From Microscopic Pedestrian Movement to Macroscopic Crowd Flow

Abstract Understanding the effects of panic on crowd dynamics in emergency situations has long been considered necessary for pedestrian evacuation control. In the case of disasters, stampedes caused by panic behaviors occur with high possibility, and pedestrians are crushed or trampled, leading to enormous casualties. To eliminate the computational errors accumulated in the traditional macromodel, a macro-microconversion model based on the SF (social force) model and the AR (Aw-Rascle) model is proposed in this paper. The purpose is to use the crowd parameters of the microscopic model as the input part of the macroscopic model and to combine the advantages of the two models to ensure accuracy and improve calculation performance. The concept of the “pressure term” is defined to measure the panic level of the crowd. In addition, a flowchart of the numerical simulation is designed based on the road network conditions at the trampling site. To validate the conversion model, a numerical simulation is conducted in a case study of the Mecca Hajj stampede in 2015. The simulation results display the whole process of crowd marching and meeting with the dynamic variations of the “pressure term.” The simulation results are compared with the traditional simulation results based on a Gaussian distribution, which verifies that the simulation results obtained by the proposed method are closer to the real situation. Moreover, in this study, a new micromacro transformation method for crowd evaluation dynamics, which can enhance computing speed and execution efficiency, is provided.

Simultaneous location and vehicle fleet sizing of relief goods distribution centers and vehicle routing for post-disaster logistics

The occurrence of a sudden-onset disaster such as a major earthquake brings about a great deal of uncertainty in, for example, the severity of damage to the road network and other infrastructure across the disaster region, the traffic flow conditions in the post-disaster network, and the demand for relief goods at relief centers. Despite these uncertainties, crucial decisions must still be made both before and rapidly after the occurrence of the disaster such that the logistical operation of relief goods distribution can have the greatest possible chance of success. In this research, we propose a two-stage stochastic programming model in which the first stage optimizes the location of relief goods distribution centers as well as the number of vehicles allocated to the logistical operation, while the second stage determines the best vehicle and inventory routing decisions for the logistics operation in the critical first time window after the random factors associated with the disaster are revealed. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to effectively model post-disaster road network conditions and the speed of vehicle traffic as a function of earthquake parameters (e.g., magnitude, time of strike). An efficient simulation optimization algorithm with feedback is proposed to tackle the two-stage stochastic programming model. We carry out an empirical study that consists of a set of experiments based on real data from NCDR to investigate the impact of critical factors in the proposed model under a variety of different conditions. Managerial insights are derived for decision makers pertinent to both the preparation and response phases of a sudden-onset disaster.

Replacement of cast iron piers on an 1886 wrought iron truss bridge in New South Wales, Australia – the challenge of preserving heritage significance during major rehabilitation work

The Namoi River Road Bridge is a colonial era high level wrought iron lattice truss bridge crossing the Namoi River (Manilla) built in 1886 to replace a dangerous river crossing. The bridge was part of the main road between Sydney (New South Wales – NSW), and Brisbane (Queensland), with the opening of the bridge allowing wool trade from the northern extents of the colony of NSW to be exported via the Port of Sydney. The six approach spans crossing the southern flood plain have history of differential settlement due to ground conditions which has resulted in damage to five bridge piers and variations to the vertical alignment of the bridge. This paper describes the method employed to restore the vertical alignment of the bridge and replace the damaged piers whilst ensuring retention of the structure’s cultural heritage significance, enabling the bridge to continue to operate as part of the NSW State Road network.

Combined extreme learning machine and max pressure algorithms for traffic signal control

Nowadays, rush-hour traffic congestion problems persist in most major cities around the world, resulting in increased pollution, noise, and stress for citizens. Therefore, an optimal traffic light strategy is needed. For this purpose, several models have been proposed. However, these models often overlook the non-stationarity of traffic, which occurs due to changing traffic conditions over time. Additionally, these models are steady-state process models, leading to a decrease in their predictive power over time. To address these issues, this paper proposes the combination of two algorithms: a passive Extreme Learning Machine with periodic mini-batch learning (PB-ELM) for predicting traffic flow and the Max Pressure control algorithm (MPA) for signal control. In the first step, the passive periodic Extreme Learning Machine (PB-ELM) adjusts quickly and regularly based on new data, overcoming traffic non-stationarity and improving long-term performance. In the second step, the MPA is preferred for signal control due to its simplicity and speed. The PB-ELM-MPA model is a combination of predictive algorithms that takes the current road network conditions as input and predicts the flow of vehicles at intersections. The model utilizes learned characteristics of the source and destination roads to estimate the number of vehicles in each movement. The PB-ELM outputs serve as the starting point for the max-pressure algorithm, which reduces congestion by considering only the vehicles on road segments closest to the intersection and selecting the highest pressure at each time interval. The proposed PB-ELM-MPA model is evaluated on an isolated intersection simulated with the SUMO micro-simulator, demonstrating a significant improvement in avoiding traffic jams. The total staying time of all vehicles present at the intersection is reduced by 65% compared to the fixed configuration of traffic lights. Additionally, CO2 emissions and fuel consumption are reduced by approximately 34% compared to the classic MPA and Deep Q-Network approaches.