Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

scholarly journals 3D Underground Property Rights of Transportation Infrastructures: Case Study of Piraeus Metro Station, Greece

2021 ◽  
Vol 13 (23) ◽  
pp. 13162
Author(s):  
Dionysia-Georgia Perperidou ◽  
Konstantinos Sigizis ◽  
Agkronilnta Chotza
Keyword(s):  
Property Rights ◽  
Spatial Data ◽  
Transportation Networks ◽  
Transportation Network ◽  
Three Dimensional ◽  
Legal Framework ◽  
Metro Station ◽  
Wide Range ◽  
Underground Transportation ◽  
Cadastral System

Underground development covers a wide range of underground uses, transportation and infrastructures networks; water and energy storage facilities; municipal spaces, housing, business and manufacturing facilities; and overall exploitation of Urban Underground Space (UUS). According to the Greek legal framework on properties underground, transportation networks, such as the metro, are developed deep enough that no compensation is due to surface parcel owners, which are usually a public entity. The current Greek cadastral system is two-dimensional and there are no records for underground transportation networks. As the need for the exploitation of UUS is arising, especially in densely populated Greek cities, such as Athens, the detailed documentation of transportation networks 3D underground property rights is essential. Herein is presented the technical and legal definition of the 3D underground property rights of the Piraeus Metro Station that is constructed in Piraeus Municipality UUS. Three-dimensional underground models for both Piraeus Station and official cadastral parcels are created so as to identify their 3D spatial intersection. For the identification of their legal and spatial status in 2D, the UUS was subdivided into layers in respect to the station’s vertical infrastructure and then correlated to the current cadastral 2D spatial data. The presented 3D underground property rights of Greece’s major urban underground transportation network facilitates its registration in the current 2D Greek cadastral system and contributes to the better understanding and the identification of legal and technical aspects of UUS rights in Greece.

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

scholarly journals On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

2018 ◽  
Vol 26 (2) ◽  
pp. 237-267 ◽  
Author(s):  
Chao Qian ◽  
Yang Yu ◽  
Ke Tang ◽  
Yaochu Jin ◽  
Xin Yao ◽  
...  
Keyword(s):  
Negative Impact ◽  
Fitness Function ◽  
Evolutionary Optimization ◽  
Function Evaluation ◽  
Noisy Optimization ◽  
Running Time ◽  
Analysis And Design ◽  
True Value ◽  
Wide Range ◽  
The Impact

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple ([Formula: see text]) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (1[Formula: see text]1)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of [Formula: see text] for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of [Formula: see text]. We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.

Network dynamics on the Chinese air transportation multilayer network

2021 ◽  
pp. 2150070
Author(s):  
Yong Yang ◽  
Kai-Jun Xu ◽  
Chen Hong
Keyword(s):  
Success Rate ◽  
Transportation Networks ◽  
Human Mobility ◽  
Transportation Network ◽  
Air Transportation ◽  
Transfer Number ◽  
Multilayer Network ◽  
Scheduling Model ◽  
Network Mechanism ◽  
Random Failures

Air transportation networks play important roles in human mobility. In this paper, from the perspective of multilayer network mechanism, the dynamics of the Chinese air transportation network are extensively investigated. A multilayer-based passengers re-scheduling model is introduced, and a multilayer cooperation (MC) approach is proposed to improve the efficiency of network traffic under random failures. We use two metrics: the success rate and the extra transfer number, to evaluate the efficiency of re-scheduling. It is found that a higher success rate of passengers re-scheduling can be obtained by MC, and MC is stronger for resisting the instability of the capacity of links. Furthermore, the explosion of the number of extra transfer can be well restrained by MC. Our work will highlight a better understanding of the dynamics and robustness of the Chinese air transportation network.

Analysis of Identification Methods of Key Nodes in Transportation Network

2022 ◽  
Author(s):  
Qiang Lai ◽  
Hong-hao Zhang
Keyword(s):  
Transportation Networks ◽  
Transportation Network ◽  
Practical Significance ◽  
Node Degree ◽  
Connected Subgraph ◽  
Network Efficiency ◽  
Identification Methods ◽  
Identification Method ◽  
Bus Networks ◽  
Key Nodes

Abstract The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted k-shell degree neighborhood identification method (KSD), the degree k-shell identification method (DKS), and the degree k-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

scholarly journals Trajectory optimization of UAV based on Hp-adaptive Radau pseudospectral method

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yi Cui ◽  
Xintong Fang ◽  
Gaoqi Liu ◽  
Bin Li
Keyword(s):  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Tracking Error ◽  
Pseudospectral Method ◽  
Planning Problem ◽  
A Algorithm ◽  
Planning Scheme ◽  
Path Search ◽  
Simulation Results

<p style='text-indent:20px;'>Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.</p>

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

Aerial VHF tracking of wildlife using an unmanned aerial vehicle (UAV): comparing efficiency of yellow-eyed penguin (Megadyptes antipodes) nest location methods

2019 ◽  
Vol 46 (2) ◽  
pp. 145 ◽  
Author(s):  
Chris G. Muller ◽  
B. Louise Chilvers ◽  
Zane Barker ◽  
Kelvin P. Barnsdale ◽  
Phil F. Battley ◽  
...  
Keyword(s):  
Cryptic Species ◽  
Single Channel ◽  
Unmanned Aircraft ◽  
Search Range ◽  
Very High Frequency ◽  
Nest Location ◽  
Search Effort ◽  
Wide Range ◽  
Aerial Vehicle ◽  
Location Methods

Context Locating cryptic animals is an important aspect of many wildlife management programs and research studies. However, this process can be inefficient, time-consuming and expensive. Unmanned aerial vehicles (UAVs), unmanned aircraft systems (UASs) or drones fitted with a camera are increasingly being used for counting and monitoring wildlife; however, these are often not suitable for cryptic species. Very high-frequency (VHF) radio-tracking is commonplace; however, single-channel VHF receivers mean that animals must be tracked individually, or scanning receivers must be used; but this raises the possibility of signals being missed. Aims We aimed to test the effectiveness of aerial VHF tracking using a multi-channel receiver for locating wildlife. Methods We tracked wildlife fitted with VHF transmitters operating on individual frequencies, by means of a UAV with a multi-channel VHF receiver to simultaneously monitor all frequencies. This offered distinct advantages over traditional single-channel scanning receivers. To test and compare this novel method, yellow-eyed penguins (Megadyptes antipodes) were located on nests hidden under vegetation on Enderby Island in the New Zealand subantarctic, using manual ground searching, unassisted ground VHF tracking, as well as using location flights by the UAV Drone Ranger system. Key results The UAV system allowed for faster nest location than did all other methods, with a higher cumulative success (number of nests found each day) and lower search effort required (person hours per nest). Conclusions Aerial VHF tracking can greatly extend the search range and minimise search effort compared with ground VHF tracking or manual searching. Implications This technology has applications for locating and tracking a wide range of wildlife, particularly cryptic species that may be difficult to find using other methods.

scholarly journals Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Sihong Chen ◽  
Jianchao Xi ◽  
Menghao Liu ◽  
Tao Li
Keyword(s):  
Spatial Structure ◽  
Complex Network ◽  
Transportation Networks ◽  
Transportation Network ◽  
Tourism Development ◽  
Scientific Basis ◽  
Complex Network Theory ◽  
Passenger Flow ◽  
Service Areas ◽  
Utilization Potential

Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

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