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Objectives: Ridesharing is an essential part of shared mobility for improving passengers' travel efficiency in cities. The existing studies usually determine shareable trips based on whether the arrival sequence of vehicles in more than one pick-up and drop-off points can meet the predefined spatiotemporal constraints. Such a simple approach cannot quickly and comprehensively find all the potential shareable trips under scenarios involving large-scale car-sharing requests. Methods: Based on the modeling method of space-time prism and the spatial-temporal expression of passengers' sharing willing, we first propose a potential spatiotemporal path area model of travel. Then, we apply the topological relation between the spacetime prisms of trips for ridesharing identification, and quantify the strength of ridesharing of trips. Finally, two ridesharing matching strategies are proposed to simulate the ridesharing matching process in real-world transport environment. Results: The proposed ridesharing identification model can accurately delineate the potential space-time accessibility of vehicular travel, which makes it easier to discover all potential shareable trips and to realize the accurate and effective ridesharing identification. Conclusions: This study can be helpful for vehicle dispatching and passengers' travel planning in shared urban mobility system. [ABSTRACT FROM AUTHOR]

Road network selection is an important part of cartographic generalization and the existing selection methods only consider the static characteristics of road network. A hybrid multi-feature selection method based on trajectory data is proposed to solve these problems. Methods: The proposed method takes stroke as the basic selection unit. Firstly, a dual graph is constructed to describe the structural relationship of the road network. The static characteristics of the road network are evaluated using such indicators as length, degree, betweenness and closeness. Secondly, based on the characteristics of the trajectory data, the dynamic characteristics of the road network are evaluated using such indicators as vehicle flow, vehicle speed and vehicle density near the road intersection. Finally, the weight of each indicator and the comprehensive importance value of each road are calculated by the method of criteria importance through intercriteria correlation. At the same time, the Voronoi diagram is introduced to divide the road network, and the density characteristics are obtained, which are taken as the constraint indicator of road network selection. Results: The experimental results show that this method can maintain the overall structure, taking into account the connectivity and density distribution, and can combine the traffic characteristics of the trajectory data. Conclusions: The selection results are in accordance with the actual situation and have certain practicability. [ABSTRACT FROM AUTHOR]

Objectives: The k nearest neighbor query method under the road network environment plays an important role in geographic information system, point of interest discovery, smart city, data mining and material distribution. The existing methods mainly deal with the query problem that the query object and data object are single data type. However, in reality, the data object type of the target object set is often not single, and the query problem will become more complex. The existing nearest neighbor query methods in road network cannot directly solve the neighbor query problem in which the query object is a point and the data object includes the data mixed with points and line segments. In order to make up for the shortcomings of the existing methods, the nearest neighbor query algorithm of mixed complex data in the road network is proposed. Methods: The query process is divided into three parts: Preprocessing, data set reduction and data set refining. Firstly, the data set is preprocessed, the line segments are transformed into points. A mapping relationship is formed between the intersection of line segments in the road network and line segments, and the corresponding line segments are found by querying the location of the points. The query points, data points and Hilbert values are stored using arrays. Secondly, the data object set is refined preliminarily by pruning method and shortest path theorem to reduce the number of data objects, and the data set filtering algorithm is given. Finally, the points in the data set are further pruned to calculate the shortest path network distance from the query point to the points in the candidate set. Compared with these distances, the shortest path distance can be determined, and then the nearest neighbor is accurately found. Results: Experimental results show that the proposed algorithm effectively reduces the data set and the amount of calculation in the query process, further refines the nearest neighbor candidate set and reduces the query scope and the query time. There is no need to store the distance information between all boundary nodes in the road network, so a large amount of redundant distance information is eliminated. The data points and road network data that meet the pruning conditions around the query points are pruned. Therefore, the increase of road network scale has little impact on the time cost of the algorithm. Conclusions: The proposed algorithm can directly deal with the nearest neighbor query of mixed data in the road network environment. When the number of data objects and the scale of the road network are large, the algorithm has obvious advantages in central processing unit running time and input/output cost. [ABSTRACT FROM AUTHOR]

Objectives：Road network is the basis of various road traffic analysis and visualization. It is difficult to extract road centerlines automatically in analyzing the internal of complex polygonal road surface and difficult road surface segmentation. An algorithm of constructing road network is proposed to distinguish road sections and intersections, which can simplify data preprocessing based on the topological relationship among blocks.Methods：Firstly, through performing logical negation operation on road surface, complex polygons with holes are converted into independent and simple block polygons. Secondly, neighbor points among them are calculated, and neighboring blocks of each block can be obtained using the neighbor analysis. Consequently,the topological relationship among common road sections or common intersections is determined, and road sections and intersections are discriminated. Then, it is obtained that two common blocks on both sides of each section and all blocks which around each intersection, so the geometric positions of centerlines of road sections are calculated by means of applying the unit circle rolling tracking algorithm. Finally, according to the type of intersection, the positions of intersection associated with road sections are determined to complete the construction of the road network digital model.The algorithm is verified by developing experimental software which uses urban road surface data in a city topographic map with scale of 1∶1 000 as experimental data.Results：The experimental result shows that the topological relation‐ ship of the final road traffic network is accurate and the matching relationship among blocks is correct. The positions of road centerlines are accurate and every line shape is smooth. There are no excess centerlines or missing centerlines. The proposed method works well in dense and sparse neighborhoods. Compared with traditional algorithms, the algorithm for constructing road network by using street topology relationship doesn't need to perform manual segmentation or road surface edge line encryption. It simplifies complex graphics by negating the road surface and obtains road sections information according to topological relation‐ ship among polygons. The road centerlines are calculated by using unit circle rolling algorithm，so the geometric characteristic of the road centerlines and the accuracy of the topological relationship can be guaranteed. The experimental result shows that the method construct a road network with complete topology, ac‐ curate location and smooth shape.Conclusions：In the process of constructing the road section‐block topology using the method, the road sections and intersections are distinguished. It solves the problems of segmentation of the road sections and provides a good basis for the extraction of road centerlines. It is suitable for the construction of geometric road networks in dense road networks and dense blocks. [ABSTRACT FROM AUTHOR]

Road networks are the skeleton of a city, its pattern recognition plays an important role in urban landscape analysis, municipal planning, and traffic flow analysis. Road networks pattern recognition is an intelligent recognition problem that combines road geometric features, semantic features, and contextual relationships. The current road pattern recognition methods are mainly using geometric measures and statistical indicators to detect pattern recognition rules, and based on the edge ⁃node graph structure and the polygon group structure of road network.As the expression of road patterns is influenced by human spatial cognition and visual psychology, rule⁃based reasoning and statistical analysis are difficult to obtain results consistent with human judgment. Driven by artificial intelligence technology, this paper introduces a novel deep learning model for graph structure, namely graph convolution network (GCN), to identify the grid ⁃pattern in a road network. Firstly, to construct the graph structure, the road intersections are taken as nodes and the roads are taken as edges, all nodes are classified into grid and non⁃grid to act as the label and prediction target of the model. Road segments are then broken into consecutive linear units to extract the characteristics of the nodes which are also the input of the model. Then, the grid pattern is recognized by extracting the roads whose endpoints are all predicted to be a grid. The experimental results show that the accuracy of the method can reach 89.2% for road network’s node classification and 86.1% for road segment classification, so it is effective for identifying grid pattern in a road network. [ABSTRACT FROM AUTHOR]

A new composite index for efficiently reducing the saturation problem of nighttime light data has been proposed in this study. It introduces the elements of road network and buildings. It assumes that the lighting of road network and buildings contributes more to the DN(digital number) values of the image. The results were analyzed and compared to EANTLI （enhanced vegetation index adjusted nighttime light index） in three aspects: （1） The ability to distinguish objects within city; （2） The degree of fitting with radiation calibration data; （3） The ability to estimate electricity consumption. Experimental results show that composite index has obvious advantages in the details of the interior of the city and the ability to distinguish objects. And the determination coefficient for single row can be up to 0.928, which can be increased by 0.1 compared to EANTLI. And the determination coefficient can reach 0.901 in the estimation of electric power consumption. In conclusion, the composite index can effectively reduce nighttime light data saturation in urban centers and has certain reliability. [ABSTRACT FROM AUTHOR]

The distance metrics under a network context such as road network distance, and travel time measurement have been commonly applied in the domains of spatial analysis and spatial statistics. In practice;however, it might be difficult to calculate these metrics properly due to the limits of data accessibility and accuracy problems. The Minkowski distance function is a generalized distance metric in the Euclidean space, and it may present various kinds of metrics when different values of the power parameter p are specified. In this article, we use the Minkowski distance function to approximate the road network distance taking advantage of its generality and flexibility. We also explored the relation­ships between the varying optimal values of p and a set of quantitative characteristics including road network density, curvature, etc. in accordance with road networks of distinct features. The results show that network distance could he approximated better by the Minkowski distance with an optimized power parameter p than Euclidean distance, i. e. straight line distance. In addition, the optimum val­ue of pcanbe affected largely by the curvature of a road network, which might provide an important clue for selectingMinkowski distance for approximation. We take the geographical weighted regression (GWR) technique as an example, and calibrate a GWR model with Euclidean distance.and Minkowski distance with an optimal power value p and travel time, respectively. The results show that the esti­mates with the optimal Minkowski distance provided closer coefficient estimates to the values calibra­ted with travel time than those from the calibration using Euclidean distance. [ABSTRACT FROM AUTHOR]

From the visual attention mechanism when people are looking for the sameroad, this paper proposed a road network matching method based on hierarchical stable strokes. The method adopts the principle of matching from obvious to vital, and constructs larger matching unit “stroke” instead of “road section”. First, the correspondence of strokes is calculated; Then, the detailed node correspondence is further identified. The results show that: when calculating similarity, the differences rate between candidate strokes and the largest reference solution is more significant than that of road segment. In this example,10 percent of the first layer strokes can ensure the stability of the matching relationship. Layered stable reference and hierarchical strokes improves the ability to identify matching relation, and it has obvious advantages in the region with similar local structure and obviously different global strokes. [ABSTRACT FROM AUTHOR]

A Stroke in a road network is defined as a set of one or more road segments in a non-branching, connected chain, according to the good continuity principle. The Stroke plays an important role in road network generalization, topology analysis, pattern recognition and schematic map generation. This paper demonstrates ways to improve the self-best-fit strategy in two aspects. One is that the processing order of segments is determined based on their importance rather than random order, another is extending the definition of best-fit, proposing that segment which satisfying the concatenation criterion and the most important segment is defined as the best-fit segment instead of segment with the smallest deflection angle. The Shenzhen road network at the scale of 1 : 10 000 was used as experimental data. An evaluation from the visual cognition and network functionality points of view shows that: (1) the Stroke set generated by the modified self-best-fit strategy is usually unique. (2) From the visual cognition point of view, the modified self-best-fit strategy performs better than the every-best-fit strategy. In a Stroke set generated by modified self-best-fit strategy the Strokes are normally longer and have more global properties. (3) From the network functionality point of view, the modified self-best-fit strategy performs better than the self-best-fit strategy, as the self-fit strategy and the every-best-fit strategy performs less efficiently than the optimum solution of the self-best-fit strategy and the self-fit strategy in the experiment. [ABSTRACT FROM AUTHOR]