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

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

A Novel Network Screening Methodology for Rural Low-Volume Roads

Low-volume roads (LVRs) are an integral part of the rural transportation network providing access to remote rural areas and facilitating the movement of goods from farms to markets. These roads pose unique challenges for highway agencies including those related to safety management on the highway network. Specifically, traditional network screening methods using crash history can be effective in screening rural highways with higher traffic volumes and more frequent crashes. However, these traditional methods are often ineffective in screening LVR networks due to low traffic volumes and the sporadic nature of crash occurrence. Further, many of the LVRs are owned and operated by local agencies that may lack access to detailed crash, traffic and roadway data and the technical expertise within their staff. Therefore, there is a need for more efficient and practical network screening approaches to facilitate safety management programs on these roads. This study proposes one such approach which utilizes a heuristic scoring scheme in assessing the level of risk/safety for the purpose of network screening. The proposed scheme is developed based on the principles of US Highway Safety Manual (HSM) analysis procedures for rural highways and the fundamentals in safety science. The primary application of the proposed scheme is for ranking sites in network screening applications or for comparing multiple improvement alternatives at a specific site. The proposed approach does not require access to detailed databases, technical expertise, or exact information, making it an invaluable tool for small agencies and local governments (e.g. counties, townships, tribal governments, etc.).

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Monitoring subsidence rates along road network by persistent scatterer SAR interferometry with high-resolution TerraSAR-X imagery

Ground subsidence is one of the key factors damaging transportation facilities, e.g., road networks consisting of highways and railways. In this paper, we propose to apply the persistent scatterer synthetic aperture radar interferometry （PS-InSAR） approach that uses high- resolution TerraSAR-X （TSX） imagery to extract the regional scale subsidence rates （i.e., average annual sub- sidence in mm/year） along road networks. The primary procedures involve interferometric pair selection, interfer- ogram generation, persistent scatterer （PS） detection, PS networking, phase parameterization, and subsidence rate estimation. The Xiqing District in southwest Tianjin （China） is selected as the study area. This district contains one railway line and several highway lines. A total of 15 TSX images covering this area between April 2009 and June 2010 are utilized to obtain the subsidence rates by using the PS-InSAR （PSI） approach. The subsidence rates derived from PSI range from -68.7 to -1.3 mm/year. These findings show a significantly uneven subsidence pattern along the road network. Comparison between the PSI-derived subsidence rates and the leveling data obtained along the highways shows that the mean and standard deviation （SD） of the discrepancies between the two types of subsidence rates are 0.1 and 4-3.2 mm/year, respectively. The results indicate that the high-resolution TSX PSI is capable of providing comprehensive and detailed subsidence information regarding road networks with millimeter-level accuracy. Further inspections under geo- logical conditions and land-use categories in the study area indicate that the observed subsidence is highly related to aquifer compression due to groundwater pumping. Therefore, measures should be taken to mitigate groundwater extraction for the study area.

Short-Term Traffic Flow Prediction Based on Road Network Topology

Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).

Spatial Accessibility of Road Network in Wuhan Metropolitan Area Based on Spatial Syntax

Based on space syntax theory, the spatial accessibility of the road network in Wuhan Metropolitan Area has been quantitatively analyzed by building a series of accessibility variables. Topologic connectivity in the accessible rings appears to be broken;traffic axis network is in spatial structure of hub-and-spoke and fishbone-like. Meanwhile, the differences in classified road network have led to inefficiency of its network servo and its ever-worsening capability to respond to traffic jams. Besides, two band-like integrated cores of which one is east to west along the Yangtze River and the other is north to south along Beijing to Guangzhou Railway, have become the first level traffic axis in the whole network, which is responsible for the connectivity of the entire metropolitan area network. This consequently has strengthened the dominant position of Wuhan which is located on the bands’ crossing. In short, the spatial accessibility of that classified space morphology, the urban system, the transport infrastructure as well as the social and economic development of Wuhan Metropolitan Area are highly interrelated to each other, especially to the high level highway network featured by freeways, the development level of which is well in line with that of road network accessibility.

Spatial Structure,Hierarchy and Formation Mechanisms of Scientific Collaboration Networks:Evidence of the Belt and Road Regions

Scientific collaboration has become an important part of the people-to-people exchanges in the Belt and Road initiative,and remarkable progress has been made since 2013.Taking the 65 countries along the Belt and Road(BRI countries)as the research areas and using collaborated Web of Science(WOS)core collection papers to construct an international scientific collaboration matrix,the paper explores the spatial structure,hierarchy and formation mechanisms of scientific collaboration networks of 65 countries along the Belt and Road.The results show that:1)Beyond the Belt and Road regions(BRI regions),Central&Eastern Europe,China and West Asia&North Africa have formed a situation in which they all have the most external links with other countries beyond BRI regions.China has the dominant role over other BRI countries in generating scientific links.The overall spatial structure has changed to a skeleton structure consisting of many dense regions,such as Europe,North America,East Asia and Oceania.2)Within the Belt and Road regions,Central&Eastern Europe has become the largest collaboration partner with other sub-regions in BRI countries.The spatial structure of scientific collaboration networks has transformed from the‘dual core’composed of China and the Central&Eastern Europe region,to the‘multi-polarization’composed of‘one zone and multi-points’.3)The hierarchical structure of scientific collaboration networks presents a typical‘core-periphery’structure,and changes from‘single core’to‘double cores’.4)Among the formation mechanisms of scientific collaboration networks,scientific research strength and social proximity play the most important roles,while geographical distance gradually weakens the hindrance to scientific collaboration.

Road and Tunnel Extraction from SPOT Satellite Images Using Neural Networks

Road extraction from the satellite images can be used in producing road maps as well as managing and developing geospatial databases. In this work, the extraction of roads from SPOT satellite images using artificial neural network has been studied. Then the location of tunnel is extracted from image using digital elevation information. Also it is tried to enhance the precision of the road extraction method using spectral information as well as texture and morphology. The method was implemented on SPOT satellite images of Tabrizand Miyaneh (Iran). The results of this research indicate that it would be possible to promote the precision of road extraction by using texture and morphology in image classifycation using neural networks. Finally the location of tunnel was extracted by digital elevation information. Junctions of roads and mountains have high potential in locating the tunnel. For this reason, in this study, the junctions of roads and mountains were also detected and used.

Seismic damage to road networks subjected to earthquakes in Nepal,2015

The Gorkha earthquake in Nepal led to many landslides and severe damage to the transportation infrastructure. After careful comparison of the aerial photographs and satellite images before and after the earthquake, partially verified by a field study, more than 2,064 landslides and many road failures were observed. Many bridges, especially steel-truss and suspension bridges, suffered little damage from inertia loads during the earthquake, but were severely damaged due to rockfalls. Potential geological hazards hindered the delivery of supplies in mountainous areas, and road closures impeded the overall speed of rehabilitation.

Effects of aggregating forests, establishing forest road networks, and mechanization on operational efficiency and costs in a mountainous region in Japan

We investigated forest road networks and forestry operations before and after mechanization on aggregated forestry operation sites. We developed equations to estimate densities of road networks with average slope angles, operational efficiency of bunching operations with road network density, and average forwarding distances with operation site areas. Subsequently, we analyzed the effects of aggregating forests, establishing forest road networks, and mechanization on operational efficiency and costs. Six ha proved to be an appropriate operation site area with minimum operation expenses. The operation site areas of the forest owners＇ cooperative in this region aggregated approximately 6 ha and the cooperative conducted forestry operations on aggregated sites. Therefore, 6 ha would be an appropriate operation site area in this region. Regarding road network density, higher-density road networks increased operational expenses due to the higher direct operational expenses of strip road establishment. Therefore, road network density should be reduced to approximately 200 m.

Privacy Protection with Dynamic Pseudonym-Based Multiple Mix-Zones Over Road Networks

In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance user privacy. It has been revealed that changing pseudonym at improper time and location may threat to user's privacy. Moreover, certain methods related to pseudonym change have been proposed to attain desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes. By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based multiple mix zone(DPMM) technique, which ensures highest level of accuracy and privacy. We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number of pseudonym change.

The Shortest Path Analysis Based on Road Network

Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two points also affects people’s daily travel.Taking Heze Railway Station to Heze College for example,with remote sensing image data as the base map,we conduct vectorization and topological analysis on roads in the target area.With Dijkstra as theoretical basis of shortest path algorithm,we use ArcG IS network analysis method to build road network,and calculate the planning program of the shortest distance path,the shortest path by driving and the shortest path by walking.

Vital Nodes Evolution Study on Railway Network of Silk Road Economic Belt

In order to study the nodes importance and its evolution process of the railway network of SREB (Silk Road Economic Belt), we construct the network (RNSREB) based on Graph Theory, which focuses on the time intervals according to actually railway network, railway project under construction and the national railway network of medium-and long-term plan. The algorithms for vital nodes evaluation are analyzed, the evaluation method on nodes importance of RNSREB is proposed, the quantized values of each node are calculated with Pajek, and TOP20 core nodes of the network with different coefficients and time intervals are determined respectively. Then the evolution process of TOP20 critical nodes with 4 periods is contrasted and analyzed. It is indicated that some vital nodes newly discovered (Geermu, Maduo, Ruoqiang) should be concerned.

Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect.

Spatiotemporal Patterns of Road Network and Road Development Pri-ority in Three Parallel Rivers Region in Yunnan,China:An Evaluation Based on Modified Kernel Distance Estimate

Road network is a critical component of public infrastructure,and the supporting system of social and economic development.Based on a modified kernel density estimate(KDE)algorithm,this study evaluated the road service capacity provided by a road network composed of multi-level roads(i.e.national,provincial,county and rural roads),by taking account of the differences of effect extent and intensity for roads of different levels.Summarized at town scale,the population burden and the annual rural economic income of unit road service capacity were used as the surrogates of social and economic demands for road service.This method was applied to the road network of the Three Parallel River Region,the northwestern Yunnan Province,China to evaluate the development of road network in this region.In results,the total road length of this region in 2005 was 3.70×104km,and the length ratio between national,provincial,county and rural roads was 1∶2∶8∶47.From 1989 to 2005,the regional road service capacity increased by 13.1%,of which the contributions from the national,provincial,county and rural roads were 11.1%,19.4%,22.6%,and 67.8%,respectively,revealing the effect of′All Village Accessible′policy of road development in the mountainous regions in the last decade.The spatial patterns of population burden and economic requirement of unit road service suggested that the areas farther away from the national and provincial roads have higher road development priority(RDP).Based on the modified KDE model and the framework of RDP evaluation,this study provided a useful approach for developing an optimal plan of road development at regional scale.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

A Trajectory-Oriented Carriageway-Based Road Network Data Model, Part 1: Background

This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpose of the series is to develop a trajectory-oriented road network data mode l, namely carriageway-based road network data model (CRNM). Part 1 deals with t he modeling background. Part 2 proposes the principle and architecture of the CR NM. Part 3 investigates the implementation of the CRNM in a case study. In the p resent paper, the challenges of managing trajectory data are discussed. Then, de veloping trajectory-oriented road network data models is proposed as a solution and existing road network data models are reviewed. Basic representation approa ches of a road network are introduced as well as its constitution.