Complex Road Network Research Articles

Geometrical and Functional Criteria as a Methodological Approach to Implement a New Cycle Path in an Existing Urban Road Network: A Case Study in Rome

Most road accidents occur in urban areas and notably at urban intersections, where cyclists and motorcyclists are the most vulnerable. In the last few years, cycling mobility has been growing; therefore, bike infrastructures should be designed to encourage this type of mobility and reduce motorized and/or private transport. The paper presents a study to implement a new cycle path in the existing cycle and road network in Rome, Italy. The geometric design of the new path complies with Italian standards regarding the technical characteristics of bicycle paths, while the Highway Capacity Manual has been considered for the traffic analysis. In particular, a before-after approach has been adopted to examine and compare the traffic flow at more complex and congested intersections where the cycle path will pass. Trams, buses, cars, bikes and pedestrians were the traffic components considered in each analysis. The software package PTV VISSIM 8 allowed the simulations of traffic flows at traffic-light intersections; an original linear process has been proposed to model dynamic intelligent traffic controls, which are not admitted by the software used. The traffic analysis allowed the identification of the best option for each of the five examined intersections. Particularly, the maximum queue length value and the total number of passed vehicles have been considered in order to optimize the transport planning process. The results of this study highlight the importance of providing engineered solutions when a cycle path is implemented in a complex road network, in order to avoid negative impacts on the citizens and maximize the expected advantages.

Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

The road networks provide key information for a broad range of applications such as urban planning, urban management, and navigation. The fast-developing technology of remote sensing that acquires high-resolution observational data of the land surface offers opportunities for automatic extraction of road networks. However, the road networks extracted from remote sensing images are likely affected by shadows and trees, making the road map irregular and inaccurate. This research aims to improve the extraction of road centerlines using both very-high-resolution (VHR) aerial images and light detection and ranging (LiDAR) by accounting for road connectivity. The proposed method first applies the fractal net evolution approach (FNEA) to segment remote sensing images into image objects and then classifies image objects using the machine learning classifier, random forest. A post-processing approach based on the minimum area bounding rectangle (MABR) is proposed and a structure feature index is adopted to obtain the complete road networks. Finally, a multistep approach, that is, morphology thinning, Harris corner detection, and least square fitting (MHL) approach, is designed to accurately extract the road centerlines from the complex road networks. The proposed method is applied to three datasets, including the New York dataset obtained from the object identification dataset, the Vaihingen dataset obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D semantic labelling benchmark and Guangzhou dataset. Compared with two state-of-the-art methods, the proposed method can obtain the highest completeness, correctness, and quality for the three datasets. The experiment results show that the proposed method is an efficient solution for extracting road centerlines in complex scenes from VHR aerial images and light detection and ranging (LiDAR) data.

A Tree Based Approach for Data Pre-processing and Pattern Matching for Accident Mapping on Road Networks

Numbers of road accidents are increasing day by day and it is necessary to keep record of all these accidents, for the purpose of providing road safety. The records maintained by the police might have some inaccurate data. For this purpose, different authors have proposed different techniques, in which the accidents are mapped to the correct road segment. The existing accident mapping algorithms have many limitations such as (1) algorithms developed are for the specific datasets only, (2) improper mapping in case of complex road networks and (3) modular approach is used for different types of roads, instead of any general rule to be applied, (4) learning algorithm developed involve lot of cost and efforts, without guaranteeing the mapping of accident to the exact location. The challenge is to identify this inaccuracy in data and correct it with least amount of cost and effort involved. Significance of the attributes having inaccuracy plays an important role in mapping accidents to the correct location. In this paper, our main aim is to (1) map the accidents on the correct location, (2) finding missing values, (3) calculation of the erroneous values in police recorded data and (4) estimation of the attribute significance. The proposed algorithm can map the accidents to correct location instead of mapping an accident to a road segment, junction, and candidate link. The basic concern is that the algorithm being developed should be generalized and can be applied on any type of a road network. To achieve this aim, we have proposed the minimum bounded region based tree technique, which used for pattern matching methodology.

Efficacy of local attack strategies on the Beijing road complex weighted network

Understanding the complex network response to nodes failure is important to protect them and guarantee their functioning. Most existing studies considered binary networks where the intensity (weight) of the links is not accounted for. However, in real world networks the intensity of the links widely differ so that a realistic description of complex networks is only possible if the heterogeneity present in edge weights is taken into account.Further, the vast majority of the existing studies investigates the efficacy of the attack strategies when all the nodes are removed. Despite it is important to study the network behavior in such a circumstance, real-world networks are more likely to experience local failures involving a small amount of nodes with a decrease in the system functioning not causing an overall collapse. This is the case of crossroad closing in complex road networks. For this reason it is interesting to investigate the networks response focusing to small node removals.In this paper we analyze the response of the real-world road networks of Beijing, the capital of China, to the removal of 1 and 2 nodes and accounting the weighted structure of the network. We use both classic binary node properties and network functioning measurement, introduced a weighted rank for node importance (node strength or page rank), and used a measurement for network functioning that accounts for the weight of the links (weighted efficiency).We find that the weighted node attack strategy Str (i.e. strength attack), which removes nodes according to their strength, is the most efficient to blemish the system when evaluating the damage with a weighted efficiency. Differently, betweenness attack strategy Bet, which removes nodes according to their centrality is the most efficient when using the binary efficiency to compute the network damage. These results indicate that adopting weighted or binary efficiency to measure networks functioning changed the efficacy of the attack strategies to damage the system. Last, we found a non-negligible fraction of random removals to be more efficient than the attack strategies; given that the main goal of the attack strategies is to individuate important nodes in the system, i.e. nodes which removal triggers highest damage, our results would indicate how in order to find the most important nodes may be more efficient to simulate an higher number of random nodes removal.

Traffic Flow Modeling on Road Networks Using Cellular Automata Theory

The paper deals with mathematical modeling of traffic flows on urban road networks. The original model is based on the cellular automata theory and presents a generalization of Nagel-Schreckenberg model to a multilane case.Numerical realization of the model is represented in a form of the program package that consists of two modules: User Interface and Visualization module (for setting initial conditions and modelling parameters and visual representation of calculations) and Computation module (for calculations).Computations are carried out for each element of the road (i.e. T or X type intersection, straight road fragment) separately and in parallel, that allows performing calculations on various complex road networks. Different kinds of average characteristics (e.g. the capacity of the crossroad) can be also obtained using the program package.

Link Restriction: Methods of Testing and Avoiding Braess Paradox in Networks Considering Traffic Demands

AbstractBraess paradox is a well-known paradox in transportation researches. In urban cities, there are many different kinds of complex road networks. Unavoidably, some of them fall into the Braess...

A Representation Method for Complex Road Networks in Virtual Geographic Environments

Road networks are important for modelling the urban geographic environment. It is necessary to determine the spatial relationships of road intersections when using maps to help researchers conduct virtual urban geographic experiments (because a road intersection might occur as a connected cross or as an unconnected bridge overpass). Based on the concept of using different map layers to organize the render order of each road segment, three methods (manual, semi-automatic and mask-based automatic) are available to help map designers arrange the rendering order. However, significant efforts are still needed, and rendering efficiency remains problematic with these methods. This paper considers the Discrete, Crossing, Overpass, Underpass, Conjunction, Up-overlap and Down-overlap spatial relationships of road intersections. An automatic method is proposed to represent these spatial relationships when drawing road networks on a map. The data-layer organization method (reflecting road grade and elevation-level information) and the symbol-layer decomposition method (reflecting road covering order in the vertical direction) are designed to determine the rendering order of each road element when rendering a map. In addition, an “auxiliary-drawing-action” (for drawing road segments belonging to different grades and elevations) is proposed to adjust the rendering sequences automatically. Two experiments are conducted to demonstrate the feasibility and efficiency of the method, and the results demonstrate that it can effectively handle spatial relationships of road networks in map representations. Using the proposed method, the difficulty of rendering complex road networks can be reduced.

Dynamics of functional failures and recovery in complex road networks.

We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks

Most of mobile object trajectory clustering analysis to date has been focused on clustering the location points or sub-trajectories extracted from trajectory data. This paper presents TraceMob , a systematic approach to clustering whole trajectories of mobile objects traveling in road networks. TraceMob as a whole trajectory clustering framework has three unique features. First, we design a quality measure for the distance between two whole trajectories. By quality, we mean that the distance measure can capture the complex characteristics of trajectories as a whole including their varying lengths and their constrained movement in the road network space. Second, we develop an algorithm that transforms whole trajectories in a road network space into multidimensional data points in a euclidean space while preserving their relative distances in the transformed metric space. This transformation enables us to effectively shift the clustering task for whole mobile object trajectories in the complex road network space to the traditional clustering task for multidimensional data in a euclidean space. Third, we develop a cluster validation method for evaluating the clustering quality in both the transformed metric space and the road network space. Extensive experimental evaluation with trajectories generated on real road network maps of different cities shows that TraceMob produces higher quality clustering results and outperforms existing approaches by an order of magnitude.

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

Traffic Forecasting Model for a Road Section

The paper presents a traffic forecasting model for a road section based on a computer model of the transportation system of a traffic gravity section. Traffic forecasting is an integral part of the process of designing of road facilities, starting from investment feasibility study to developing of working documentation. Determination of transportation and distribution of cars in sections are performed under a set of interrelated factors. Complete and valid consideration of these factors for complex road networks is possible only by means of mathematical models and corresponding software applications. Accuracy and consistency of the forecast determines validity of almost all the main characteristics of the designed object starting from direction of the route and location of connection points to the existing elements of the road network, ending with specific planning solutions for road facilities.

GIS-Modeling of Multimodal Complex Road Network and Its Traffic Organization

The article throws light on spatial, structural and functional characteristics of Ust-Luga Multi Modal Complex (ULMMC). Geodatabase and cartographic basis of the above complex are established in ArcGIS environment. Network models of roads and railway lines of the above complex are realized via the use of Network Analyst analyzer. Upon investigation of generic rules of mobile facilities behavior within the territory of sea ports and industrial enterprises a part of these generic rules has been reflected (as an example) in geodatabase and road networks. Consideration was given to three-dimensional modeling of ULMMC multiple-bridge intersections and Traffic Management Facilities (TMF) arrangement on them.

Predicting Melbourne ambulance demand using kernel warping

Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data sparsity at high resolutions and the need to respect complex urban spatial domains. To provide spatial density predictions for ambulance demand in Melbourne, Australia, as it varies over hourly intervals, we propose a predictive spatio-temporal kernel warping method. To predict for each hour, we build a kernel density estimator on a sparse set of the most similar data from relevant past time periods (labeled data), but warp these kernels to a larger set of past data irregardless of time periods (point cloud). The point cloud represents the spatial structure and geographical characteristics of Melbourne, including complex boundaries, road networks and neighborhoods. Borrowing from manifold learning, kernel warping is performed through a graph Laplacian of the point cloud and can be interpreted as a regularization toward, and a prior imposed for, spatial features. Kernel bandwidth and degree of warping are efficiently estimated via cross-validation, and can be made time- and/or location-specific. Our proposed model gives significantly more accurate predictions compared to a current industry practice, an unwarped kernel density estimation and a time-varying Gaussian mixture model.

Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

Intelligent traffic control systems optimized using meta-heuristic algorithms can greatly alleviate traffic congestions in urban areas. Meta-heuristics are broadly used as efficient approaches for complex optimization problems. Comparing the performance of optimization methods on different applications is a way to evaluate their effectiveness. The current literature lacks studies on how performance of traffic signal controllers is affected by utilized optimization algorithms. This paper evaluates the performance of three meta-heuristic optimization methods on an advanced interval type-2 adaptive neuro-fuzzy inference system (IT2ANFIS)-based controller for complex road networks. Simulated annealing (SA), genetic algorithm (GA), and the cuckoo search (CS) are applied for optimal tuning of IT2ANFIS controller. Optimizations methods adjust the parameters in a way to reduce the total travel time of vehicles in the road network. Paramics is used to design and simulate urban traffic network models and implement proposed timing controllers. Comprehensive simulation and performance evaluation are done for both single and multi-intersection traffic networks. Obtained results reveal significant superiority of IT2ANFIS trained using CS method over other controllers. The average performance of the CS-IT2ANFIS is about 31% better than the benchmark fixed-time controllers. This is 17% and only 3% for GA-IT2ANFIS and SA-IT2ANFIS controllers respectively.

Indicators of reliability and vulnerability: Similarities and differences in ranking links of a complex road system

This article investigates two performance attributes of road networks, reliability and vulnerability, analyzing their similarities as well as the differences that justify distinct definitions, based on consolidation of recent studies. We also discuss the indicators found in the literature for these two performance attributes. Since various authors treat vulnerability as an aspect of reliability instead of a specific attribute, we carried out an application to a complex road network representative of the city of Rio de Janeiro to check the suitability of this approach. The results show that the vulnerability indicators are more strongly affected by the characteristics of alternative routes while the reliability metrics are more sensitive to the congestion level. The conclusion is that reliability and vulnerability should be treated distinctly for evaluating the performance of road network links.

Optimized Route Selection Method based on the Turns of Road Intersections: A Case Study on Oversized Cargo Transportation

For oversized cargo transportation, traditional transportation schemes only consider road length, road width, the transportation cost as weight values in analysis and calculation of route selection. However, for oversized trucks, turning direction at road intersections is also a factor worth considering. By introducing the classical algorithm of Dijkstra into the model of road network, this research considers the size of turning angle at intersections as the weight value of the edge in the auxiliary network based on the weight values of road corners, upon which the shortest path analysis is performed. Then, an optimal path with minimum time cost was eventually obtained. The proposed algorithm was analyzed and compared with the traditional shortest path algorithm and it reported that our method could reduce the time for oversized trucks to pass through intersections. In addition, the proposed algorithm could be adapted to the complex and diverse road networks and provide a reliable scheme for route selection of oversized trucks.

Intelligent traffic signal controller based on type-2 fuzzy logic and NSGAII

Intelligent traffic signal control (TSC) system is important for the alleviation of traffic congestion. Usually, most of the researches about TSC focused on single intersection based on type-1 fuzzy set. Compared with type-1 fuzzy logic controller (FLC), type-2 FLC can deal with more uncertainties in the road traffic control system. Therefore, a type-2 FLC optimized by NSGAII (T2-NSGAII) is designed for TSC in a complex road network, in which the intersection's traffic signal time is dynamically adjusted by its own and adjacent intersections' traffic volumes to reduce global delay time and traffic congestion. In T2-NSGAII, the expert rule set and the parameters of the fuzzy membership functions are simultaneously optimized by NSGAII to achieve less time delay and traffic congestion. In the simulations of a six-intersection traffic network with different vehicular arrival rates, it is demonstrated that T2-NSGAII has better performance compared with vehicle actuated controller based on fixed-time control (FTC), type-1 FLC, type-2 FLC and isolatedly optimized Type-2 FLC and the total delay time could be reduced by 76.3%, 65.1%, 58.3% and 35.4% respectively.

Curvedness feature constrained map matching for low-frequency probe vehicle data

ABSTRACTMap matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments.

A Statistical Multiplexing Method for Traffic Signal Timing Optimization in Smart Cities

Urban road traffic is the heart of many problems: more recent years, this critical aspect involved every day is unfavorable to many fields, such as economics or ecology. For these reasons, the Intelligent Transportation Systems (ITS) have emerged to best optimize the expenditure of the user on often complex road networks. In this paper, after studying the backgrounds of such systems, we propose a system of control of traffic lights through the use of statistical multiplexing technique based on fixed and vehicular networks of wireless sensors. We will see that this architecture can be flexible within the framework of ITS and participate in low cost to obtain interesting results. The simulation results prove the efficiency of the traffic system in an urban area with an adaptable and dynamic traffic road, because the average waiting time of cars at the intersection is sharply dropped when the red light duration is 65 s and the green light time duration is 125 s. DOI: http://dx.doi.org/10.11591/telkomnika.v15i1.8092

Vehicle Moving State Recognition Method Based on Anomaly Detection and Its Applications on Dynamic In-car Navigation Systems

In order to efiectively determine whether a vehicle is turning or not, we propose a method to map arbitrary consecutive GPS heading information to two dimensional feature space. Through analyzing the distribution of the data, we decide to use two dimensional Gaussian distribution to build the anomaly detection model. After that, the feature space can be divided into two categories (one indicates the vehicle is traveling on a straight road and the other indicates it is doing a turning maneuver) by setting the threshold of the model. Finally, we make use of the labeled data and F-measure method to choose the threshold value. The experimental results show that the model built in this way has good generalization. Based on the above research achievement, a vehicle moving state recognition learning system for Dynamic In-Car Navigation Systems is designed and implemented, and this system is applied to improving the map-matching algorithm. The improved algorithm is tested on a complex urban road network and the experimental results show that the new algorithm can improve the performance of the junction match.