scholarly journals A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

2020 ◽  
Vol 10 (5) ◽  
pp. 1625
Author(s):  
Zhonggui Zhang ◽  
Yi Ming ◽  
Gangbing Song
Keyword(s):  
Prediction Accuracy ◽  
Road Network ◽  
Second Step ◽  
Hotspot Analysis ◽  
Network Distance ◽  
New Approach ◽  
The Road ◽  
Spatial Weights ◽  
Accuracy Index ◽  
Spatial Weights Matrix

In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI). This new approach has three major steps. The first step is to build the INDSWMI by forming the road network, extracting the intersections from road junctions, and constructing the INDSWMI with road network constraints. The second step is to build the KDSWMCI by obtaining the adjacency crashes for each intersection. The third step is to perform intersection hotspot analysis (IHA) by using the Getis–Ord Gi* statistic with the INDSWMI and KDSWMCI to identify CHIs and test the Intersection Prediction Accuracy Index (IPAI). This approach is validated by comparison of the IPAI obtained using open street map (OSM) roads and intersection-related crashes (2008–2017) from Spencer city, Iowa, USA. The findings of the comparison show that higher prediction accuracy is achieved by using the proposed approach in identifying CHIs.

scholarly journals Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation

2021 ◽  
Vol 1 (3) ◽  
pp. 570-589
Author(s):  
Andy H. Wong ◽  
Tae J. Kwon
Keyword(s):  
Road Network ◽  
Kriging Model ◽  
Regression Kriging ◽  
Network Distance ◽  
True Nature ◽  
The Road ◽  
Statistical Measures ◽  
Network Screening ◽  
Effective Network ◽  
The Cost

Winter conditions create hazardous roads that municipalities work hard to maintain to ensure the safety of the travelling public. Targeting their efforts with effective network screening will help transportation managers address these problems. In our recent efforts, regression kriging was found to be a viable and effective network screening methodology. However, the study was constrained by its limited spatial extent making the reported results less conclusive and transferrable. In addition, our previous work implemented what has long been adopted in most of conventional studies—the Euclidean distance; however, use of the road network distance would, intuitively, result in further improving kriging estimates, especially when dealing with transportation problems. Therefore, this study improves upon our previous efforts by developing a more advanced kriging model; namely, network regression kriging using the entire state of Iowa with the significantly expanded road network. The transferability of the developed models is also explored to investigate its generalization potential. The findings based on various statistical measures suggest that the enhanced kriging model vastly improved the estimation performance at the cost of greater computational complexity and run times. The study also suggests that regional semivariograms better represent the true nature of the local variances, though an overall model may still function adequately if higher fidelity is not required.

scholarly journals GEOMETRIC ENHANCEMENT OF THE OPENSTREETMAP ROAD NETWORK

2021 ◽  
Vol V-4-2021 ◽  
pp. 33-39
Author(s):  
F. Z. Belhouari ◽  
I. Boukerch ◽  
K. Si youcef
Keyword(s):  
Delaunay Triangulation ◽  
Road Network ◽  
Geometric Accuracy ◽  
Local Transformation ◽  
Collaborative Project ◽  
New Approach ◽  
The Road ◽  
The Mean ◽  
Network Quality ◽  
Transformation Methods

Abstract. OpenStreetMap (OSM) is a collaborative project to create a free and editable map of the world. You can think of OSM as the 'Wikipedia' of cartography. An important geospatial component of this database is the road network quality, which is important for applications such as routing and navigation.The objective of this work is the geometric enhancement of the OSM road network using a standard national map as a reference. We use two transformation methods, the global transformation and the local transformation (Delaunay triangulation).This study aims to present a new approach to improve the OSM road network geometrically. To this end, we present a three-step approach based on two techniques that leads to the enhancement of the geometric accuracy of the OSM road network. The first step is the global transformation of the OSM road network. The second step consists of applying the local transformation (Delaunay triangulation) on the OSM road network. In the last step, a comparison between the two methods is examined by calculating the mean and the standard deviation of the checkpoints in order to justify which is the best technique for the geometric enhancement of the OSM road network. We will be particularly interested in the application of this approach in the geometric enhancement / correction where each node of the OSM network will have a newly calculated position. Both approaches have been tested in the region of Oran in Algeria as testing example. The reference data is a city map produced by the National Institute of Cartography and Remote Sensing (INCT) in 2006. The proposed techniques show a clear improvement in geometric accuracy.

Road network link prediction model based on subgraph pattern

2020 ◽  
Vol 31 (06) ◽  
pp. 2050083
Author(s):  
Bin Wang ◽  
Xiaoxia Pan ◽  
Yilei Li ◽  
Jinfang Sheng ◽  
Jun Long ◽  
...  
Keyword(s):  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Road Network ◽  
Network Embedding ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Network Link ◽  
Subgraph Pattern

Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

scholarly journals INFERRING ROUTING PREFERENCES OF BICYCLISTS FROM SPARSE SETS OF TRAJECTORIES

2018 ◽  
Vol IV-4/W7 ◽  
pp. 107-114
Author(s):  
J. Oehrlein ◽  
A. Förster ◽  
D. Schunck ◽  
Y. Dehbi ◽  
R. Roscher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Shortest Path ◽  
Road Network ◽  
Transport Infrastructure ◽  
New Approach ◽  
City Councils ◽  
The Road ◽  
Shortest Path Algorithms ◽  
The Given ◽  
Single Set

<p><strong>Abstract.</strong> Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.</p>

scholarly journals Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaolei Ru ◽  
Xiangdong Xu ◽  
Yang Zhou ◽  
Chao Yang
Keyword(s):  
Information Acquisition ◽  
Prediction Accuracy ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Route Planning ◽  
Travel Speed ◽  
Traffic Information ◽  
Operational Conditions ◽  
The Road

Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.

scholarly journals A route pruning algorithm for an automated geographic location graph construction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph Schweimer ◽  
Bernhard C. Geiger ◽  
Meizhu Wang ◽  
Sergiy Gogolenko ◽  
Imran Mahmood ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Road Network ◽  
Triangle Inequality ◽  
Geographic Location ◽  
Second Step ◽  
Pruning Algorithm ◽  
Agent Based ◽  
The Road ◽  
Wide Range ◽  
On The Road

AbstractAutomated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is $$\mathscr{O}(L^3)$$ O ( L 3 ) , which enables the computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible.

scholarly journals A Route Pruning Algorithm for an Automated Geographic Location Graph Construction

2021 ◽  
Author(s):  
Christoph Schweimer ◽  
Bernhard C. Geiger ◽  
Meizhu Wang ◽  
Sergiy Gogolenko ◽  
Imran Mahmood ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Road Network ◽  
Triangle Inequality ◽  
Geographic Location ◽  
Second Step ◽  
Pruning Algorithm ◽  
Agent Based ◽  
The Road ◽  
Wide Range ◽  
On The Road

Abstract Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is σ(L3), which outperforms the complexity of σ(L4 log L) to enable computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible.

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