Road Network Model Research Articles

An Indoor-Outdoor Layered HD Map Construction for Unmanned Ground Vehicles

Abstract. High-Definition Maps (HD Maps) are lane-level 3-dimensional road network models that ensure the safe operation of autonomous vehicles and drive the continuous advancement of autonomous driving technology to higher levels. However, HD Maps currently face challenges regarding limited coverage and description of indoor environments (e.g. underground parking), as well as the lack of uniformity between indoor and outdoor map formats. Therefore, this paper proposes a Layered HD Map model for both indoor and outdoor environments. The map model consists of 6 layers, allowing effective representation of indoor and outdoor environments, as well as transitional areas. The paper also establishes an Unmanned Ground Vehicle (UGV) to collect and update map data using LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) and autonomous exploration technology, aiming to enhance the efficiency and automation of HD Map acquisition. In order to verify the validity of the proposed map model, this paper conducted a navigation experiment in a double decker parking lot containing entrances and exits. The results of the experiments demonstrate that the Layered HD Map generated through autonomous LiDAR SLAM can effectively describe the indoor and outdoor environments, and enable successful navigation of UGVs.

Resilience enhancement strategies for power distribution network based on hydrogen storage and hydrogen vehicle

AbstractIn light of the increasing hydrogen permeability in distribution networks as a means to cope with extreme events and improve network resilience, this paper introduces a novel strategy for enhancing power distribution network resilience. It outlines a comprehensive approach that focuses on dispatching hydrogen storage (HS) and hydrogen vehicle (HV) within hydrogen penetrated distribution systems (HPDS), segmenting the strategy into pre‐disaster and post‐disaster stages. Firstly, in the pre‐disaster stage, models for HS and HVs are established to gather operational data and facilitate rapid post‐disaster response, alongside a coupled electric grid and road network model for optimising HV routing and dispatch. Subsequently, the post‐disaster stage focuses on a scheduling model that aims to minimise load power losses and economic costs, balancing immediate power support with cost‐effectiveness through detailed analysis of HS and HV dispatch strategies. Finally, this paper demonstrates the effectiveness of this strategy via a case study, highlighting significant improvements in network resilience and recovery and underscoring the potential of hydrogen technologies in enhancing infrastructure resilience.

Expressway Network Model Construction and Road Section Impedance Estimation Based on Mixed Data

The acquisition of high-speed road network information and traffic environment information is of great significance for traffic management and planning, and at the same time, an accurate and efficient road network model is also the basis for the acquisition and application of traffic parameters. In order to quickly obtain high-speed road network information and road section traffic state information, this paper takes Chongqing Highway as an example, analyzes the traffic condition and characteristics of the highway network, and establishes the topology of the high-speed road network using network data and actual high-speed toll data from the graph theory and complex network theory. At the same time, based on the topology theory, fully exploiting the high-speed toll data, a more accurate estimate of different time periods, vehicles in the high-speed road section of the travel time, for highway traffic analysis to provide data reference, to improve the traffic planning program to enhance the efficiency of traffic management, to promote traffic planning and decision-making, as well as to enrich the theoretical basis of transportation research is of great significance.

Research on the Effects of Spatial Forms in Residential Blocks on Road Traffic Noise Distribution in Typical City of China

Integrating the block system into construction is the current trend in the development of residential areas in China. Road traffic noise is the major noise source in residential blocks, and its relationship with spatial forms of blocks remains unclear. In this study, 852 block models (258 block road network models and 594 models with buildings inside) were established and simulated with SoundPLAN 8.2 software, in order to reveal the impact of spatial forms (road network morphology, neighborhood scale, and architectural texture) on the road traffic noise distribution in residential blocks. Meantime, a prediction model based on spatial morphology parameters is proposed. It was found that (1) Without considering the impact of buildings, both the road network morphology and neighborhood scale parameters have significant effects on the distribution of road traffic noise, but road network morphology has a larger effect than neighborhood scale. (2) In the presence of buildings within the block, architectural texture parameters have effects on the distribution of road traffic noise, but to a lesser extent than road network morphology and neighborhood scale parameters. (3) This research employs principal component analysis to reduce the dimensionality of urban spatial form parameters. Subsequently, a model was developed to predict overall noise exposure levels in residential areas, which was validated by example. This model can be used as a tool for rapid prediction and diagnosis of the block acoustic environment. These findings offer insights for the planning and design of residential blocks from the perspective of optimizing the acoustic environment.

Integration of two different road network models for emergency rescue pathfinding in indoor and outdoor environments

Abstract The environment of buildings and outdoor transportation in urban areas is becoming increasingly complex. This poses a challenge for rescue pathfinding once an emergency occurs, which relies on the generation of road network models. To better balance efficiency and accuracy in pathfinding, a three-dimensional integrated road network model (3D-IRNM) derived from grid and topological road network models was developed in this study. Firstly, a cross-platform data loading method from building information model to geographic information systems (GIS) is proposed, to serve as a data source for road network model generation, visual enhancement, and breaking the barriers between different spatial data format models. Then, the topological and grid road network models are generated in indoor spaces of different functions, with IRNM obtained by the integration strategy. The 3D-IRNM can be formed by extracting indoor vertical paths from the stairs and connecting them with the IRNM of each floor. To further combine the 3D-IRNM with outdoor environments, a CrossNode model is then proposed. Besides, an adaptive pathfinding algorithm is also proposed. Finally, the construction of an emergency pathfinding system based on GIS technology is achieved. The pathfinding algorithm and the structure of 3D-IRNM help the pathfinding both efficiency and accuracy. The practicability of the designed 3D-IRNM together with its pathfinding algorithm is well verified.

Spatial matching of emergency shelters to the distribution of residents in the light of transport behaviours of evacuees during war: the case of Suwałki

The aim of the article was the assessment of the spatial matching of existing shelters (supply) to the distribution of residents in Suwałki (demand), considering their declared transport behaviours while evacuating during war. The analysis was conducted based on the locations of existing emergency shelters using data on population distribution (registration data with building accuracy). Spatial alignment was determined using the P-Median problem and E2SFCA. In terms of establishing vehicular or pedestrian travel time, the Manhattan metric based on the urban road network model was utilised. A model of vehicle movement speed was then constructed, while a constant speed was assumed for pedestrian movement. Additionally, survey data on the transport behaviour of inhabitants of Suwałki in the case of war were conducted in 2023. The study concluded that the population residing within the city limits should evacuate on foot, and that prior training on the evacuation process is especially necessary for those who reside in less populated areas of the city. The analyses also showed that existing emergency shelters are overly dispersed, making management difficult for emergency services. Since the current capacity of emergency shelters is not sufficient for the number and distribution of inhabitants of Suwałki, the most practical significance of this article in this respect is to indicate to the authorities the optimal number and location for emergency shelters (to improve the evacuation process).

Combination of H∞ perimeter control and route guidance for heterogeneous urban road networks

Due to the effects of capacity differences and other factors, congestion may occur at local nodes of a network. This situation reduces the reliability of the network and leads to regional deadlock. To improve system stability and reduce the influence of local road network heterogeneity on traffic efficiency, a macroscopic fundamental diagram (MFD) model of a nonuniform density road network is proposed. Second, based on the MFD model, a two-level strategy for H∞ perimeter control and traffic flow regulation is proposed. The upper layer uses an H∞ controller to adjust the inflow at the boundary of congested region. The lower layer uses a traffic density regulator (TDR) inside the region. Finally,a simulation is conducted to compare with traditional H∞ control, bang-bang-TDR, PI-TDR and MPC-TDR strategy. The results show that the H∞-TDR strategy proposed in this paper can improve operation efficiency and reduce delay in network.

Urban road waterlogging multi-level assessment integrated flood models and road network models

Road centerline and maximum inundation depth methods have been used in most previous studies to assess road waterlogging, but accurately expressing the spatiotemporal characteristics of road waterlogging is difficult. To address this issue, a new multilevel road waterlogging assessment model based on four spatial scales and four assessment indices was developed. Then, a road waterlogging information acquisition method was designed based on flood models (FMs) and spatial analysis methods, and existing multihierarchical road network models (RNMs) were extended to integrate road waterlogging information. Next, with a typical region selected as the study area, road waterlogging information under specific rainstorm scenarios was acquired and analyzed with the FMs and RNMs. Then, a multilevel assessment of road waterlogging and various analyses were conducted. The case study showed that compared to traditional models, the new model improved the prediction of the spatiotemporal characteristics of road waterlogging.

Road network model in Thailand for highly efficient transportation

This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.

Analyzing urban scaling laws in the United States over 115 years

The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data.

Spatiotemporal gated traffic trajectory simulation with semantic-aware graph learning

Traffic trajectories of various vehicles, bicycles and pedestrians can help understand the traffic dynamics in a fine-grained manner like traffic flow, traffic congestion and ride-hailing demand. The comprehensive usage of traffic trajectory data has not been fully investigated due to the prevalent privacy concerns and commercial limitations. The traffic trajectory simulation task has emerged to generate high-fidelity trajectories in demand for downstream tasks to fill the gap between the scarce trajectory data and the widespread applications. Previous state-of-the-art methods build the spatiotemporal dependencies of trajectories with Graph Neural Networks (GNNs) under generative adversarial training, yielding better yet unstable trajectory quality. We observe that the unsatisfied synthetic trajectories are caused by the insufficient spatiotemporal modeling of road networks and trajectory semantics. In this paper, we propose a novel SpatioTEmporal GAted (STEGA) framework equipped with semantic-aware graph learning for traffic trajectory simulation to enable the explicit modeling of spatiotemporal dependencies throughout the learning pipeline. On the one hand, STEGA employs a graph encoder with the semantics of road networks for the spatial points of a trajectory, together with a time encoder for the time points. On the other hand, STEGA devises two spatiotemporal gates with the semantic graphs for the predictions of the future trajectory. Boosted by the semantic-aware graph learning, the proposed STEGA outperforms the state-of-the-art counterparts consistently at both macro- and micro-level metrics on two datasets. Elaborate ablation studies and downstream tasks of the synthetic trajectories further demonstrate the superiority of STEGA. Our code is available at https://github.com/Star607/STEGA.

EXAMINING THE SAFETY IMPACTS OF TRANSIT PRIORITY SIGNAL SYSTEMS USING SIMULATION TECHNIQUES

Transit Priority Signal (TPS) systems are increasingly used to improve traffic efficiency and reduce passenger waiting times. However, such systems may carry potential safety risks. This study aims to investigate the safety effects of TPS at intersections. Our study utilized the SUMO traffic simulation program to create a road network model containing nine signalized intersections. Subsequently, the TPS system was applied to selected bus routes within the road network, and the cases with and without TPS implementation were compared in terms of safety and performance. In safety-oriented comparisons, surrogate safety measures were employed, including number of conflict and Time to Collision (TTC). Signalized intersection performances were measured and compared in terms of the number and duration of stops. The analysis results indicate that TPS enhances safety and transportation performance for buses, but adversely impacts safety and transportation performance for passenger cars. This study underscores the importance of considering safety aspects in the implementation of TPS aimed at improving passenger transportation efficiency. These findings may contribute to the enhancement of public transportation infrastructure and the implementation of appropriate safety measures.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

Simulation modeling as a means of integrating infrastructure for individual mobility in urban environments

The article presents the concept of sustainable development of the territory of the Moscow city district by introducing paths for individual mobility into the transport network. A simulation model of the street road network is created to substantiate the efficiency of implementation, traffic safety, and connectivity of street routes. The accuracy and validity of the obtained model is determined by the use of research methods, which provide a detailed description of the problem area, structured processes and objects, and also establish features of their interaction. To visualize the obtained results, a graph of conflicting and connecting flows in the transport network of the district and graphical models of their behavior are developed.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Towards Topological Geospatial Conflation: An Optimized Node-Arc Conflation Model for Road Networks

Geospatial data conflation is the process of identifying and merging the corresponding features in two datasets that represent the same objects in reality. Conflation is needed in a wide range of geospatial analyses, yet it is a difficult task, often considered too unreliable and costly due to various discrepancies between GIS data sources. This study addresses the reliability issue of computerized conflation by developing stronger optimization-based conflation models for matching two network datasets with minimum discrepancy. Conventional models match roads on a feature-by-feature basis. By comparison, we propose a new node-arc conflation model that simultaneously matches road-center lines and junctions in a topologically consistent manner. Enforcing this topological consistency increases the reliability of conflation and reduces false matches. Similar to the well-known rubber-sheeting method, our model allows for the use of network junctions as “control” points for matching network edges. Unlike rubber sheeting, the new model is automatic and matches all junctions (and edges) in one pass. To the best of our knowledge, this is the first optimized conflation model that can match nodes and edges in one model. Computational experiments using six road networks in Santa Barbara, CA, showed that the new model is selective and reduces false matches more than existing optimized conflation models. On average, it achieves a precision of 94.7% with over 81% recall and achieves a 99.4% precision when enhanced with string distances.

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

Optimizing road networks in underdeveloped regions for improving comprehensive efficiency integrated by accessibility, vulnerability and socioeconomic interaction

Previous studies on performance assessment and optimization of road networks (RNs) have focused on either normal performance related to accessibility or extreme performance related to vulnerability. However, these two aspects are rarely considered together, and the impact of socioeconomic interaction has not been addressed. Therefore, the RN may be unable to adapt to the construction of future resilient infrastructure, leading to unstable and inefficient operations. Besides, the neglect of the role and development of RNs in underdeveloped regions can lead to unbalanced regional development. This study develops an optimization model of RNs in underdeveloped regions for comprehensive efficiency to solve the regional coordinated development problem. Specifically, a quantitative assessment method of the comprehensive efficiency integrating accessibility, vulnerability, and socioeconomic interaction is proposed to measure the performance. Then, a two-stage bi-level programming model for underdeveloped regions focusing on optimizing comprehensive efficiency is proposed. Finally, a case is studied to verify the model's effectiveness and enlighten the assessment and planning of RNs. Results show that compared with the original RN regarding invulnerability, reachable time, and socioeconomic interaction, optimization results of Stage 1 are optimized by 14.81 %, 31.62 %, and 0.03 %, respectively, and Stage 2 are optimized by 44.64 %, 42.87 %, and 57.71 %, respectively.

Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance

Electric vehicles (EVs) not only serve as significant loads for the power grid but also play a crucial role in the operation of the traffic. Their travel and charging behaviors have an impact on both the power grid and the road network. In order to address the potential impacts of a large-scale deployment of EVs on the power grid and the exacerbation of traffic congestion, this paper first establishes a dynamic road network model based on graph theory and time-varying traffic data combined with a road impedance model. Then, the spatio-temporal distribution characteristics of EV travel are modeled. Furthermore, by incorporating real-time road network data, the traditional Dijkstra’s algorithm for finding the optimal path is improved. At each node, the current real-time road impedance is used as the objective for EV path updates, thus accurately capturing the energy consumption of the EVs. Finally, using a standard testing problem on a typical working day based on data from a real case, the impacts of EV travel and charging behaviors on power distribution network operation and traffic congestion are analyzed under scenarios with no guidance and guidance for the shortest travel time. The results show that this method can significantly reduce the time cost by approximately 18% in travel time, which is of particular concern to users. This method balances the load of the charging stations, elevates the voltage level within the safety requirement of 7%, and simultaneously alleviates traffic congestion near the stations.