scholarly journals Fast Computation of Clustered Many-to-many Shortest Paths and Its Application to Map Matching

2019 ◽  
Vol 5 (3) ◽  
pp. 1-20
Author(s):  
George R. Jagadeesh ◽  
Thambipillai Srikanthan
Keyword(s):  
Shortest Paths ◽  
Map Matching ◽  
Fast Computation

scholarly journals Two Techniques for Fast Computation of Constrained Shortest Paths

2008 ◽  
Vol 16 (1) ◽  
pp. 105-115 ◽  
Author(s):  
Shigang Chen ◽  
Meongchul Song ◽  
S. Sahni
Keyword(s):  
Shortest Paths ◽  
Fast Computation ◽  
Constrained Shortest Paths

scholarly journals Path complexity and bicyclist route choice set quality assessment

2019 ◽  
Author(s):  
Thomas Koch ◽  
Luk Knapen ◽  
Elenna Dugundji
Keyword(s):  
Shortest Paths ◽  
Route Choice ◽  
Large Set ◽  
Map Matching ◽  
Demand Modeling ◽  
Urban Road ◽  
Modeling Process ◽  
Choice Sets ◽  
Minimum Number ◽  
Choice Set

AbstractEveryday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. Building route choice sets is a difficult task. Even including detailed attributes such as the number of left turns, the number of speed bumps, distance and other route choice properties we still see that choice set quality measures suggest poor replication of observed paths. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which could potentially be considered to be important for route choice in a similar way. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions are shown to be significantly different so that the choice sets do not reflect the traveler preferences, this is in line with classical choice set quality indicators. Secondly, we investigate often used choice set quality methods and formulate measures that are less sensitive to small differences between routes that can be argued to be insignificant or irrelevant. Such difference may be partially due to inaccuracy in map-matching observations to dense urban road networks.

Simple Map-Matching Algorithm Applied to Intelligent Winter Maintenance Vehicle Data

2005 ◽  
Vol 1935 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Carola A. Blazquez ◽  
Alan P. Vonderohe
Keyword(s):  
Performance Measures ◽  
Shortest Paths ◽  
Decision Makers ◽  
Map Matching ◽  
Public Agencies ◽  
Matching Algorithm ◽  
Winter Maintenance ◽  
Vehicle Data ◽  
Data Points ◽  
Goals And Objectives

Intelligent winter maintenance vehicles are equipped with automatic vehicle location (AVL) technology, including differential Global Positioning System (DGPS) receivers and various additional sensors that collect equipment status and material use data. DGPS data points are associated with the nearest roadway centerline by calculating minimum perpendicular distances between each roadway centerline representation and the DGPS data points. Highly accurate roadway centerline maps and DGPS measurements are not always available. Thus, spatial mismatches may occur at converging and diverging roadways, divided highways, and intersections. Decision makers use winter maintenance performance measures to evaluate achievement of goals and objectives and to improve winter maintenance operations in public agencies. These performance measures are sensitive to spatial mismatches, which need to be resolved before calculations are done. This paper presents a simple map-matching algorithm that resolves spatial ambiguities by determining the correct roadway centerline on which the vehicle is traveling. The algorithm computes shortest paths between snapped DGPS data points using network topology and turn restrictions. A path is considered viable, and locations for the snapped DGPS data points correct, if similarity exists between values of calculated and recorded vehicle speeds. If a path is not feasible, DGPS points are snapped to alternative roadway centerlines contained within their buffers, shortest paths are recalculated, and speeds are again compared. Examples are presented to illustrate the implementation and effectiveness of the algorithm.

scholarly journals Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory

2021 ◽  
Vol 13 (22) ◽  
pp. 12820
Author(s):  
Zhengang Xiong ◽  
Bin Li ◽  
Dongmei Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Shortest Paths ◽  
Low Frequency ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy

In the field of map matching, algorithms using topological relationships of road networks along with other data are normally suitable for high frequency trajectory data. However, for low frequency trajectory data, the above methods may cause problems of low matching accuracy. In addition, most past studies only use information from the road network and trajectory, without considering the traveler’s path choice preferences. In order to address the above-mentioned issue, we propose a new map matching method that combines the widely used Hidden Markov Model (HMM) with the path choice preference of decision makers. When calculating transition probability in the HMM, in addition to shortest paths and road network topology relationships, the choice preferences of travelers are also taken into account. The proposed algorithm is tested using sparse and noisy trajectory data with four different sampling intervals, while compared the results with the two underlying algorithms. The results show that our algorithm can improve the matching accuracy, especially for higher frequency locating trajectory. Importantly, the method takes into account the route choice preferences while correcting deviating trajectory points to the corresponding road segments, making the assumptions more reasonable. The case-study is in the city of Beijing, China.

scholarly journals Map-Matching Using Shortest Paths

2020 ◽  
Vol 6 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Erin Chambers ◽  
Brittany Terese Fasy ◽  
Yusu Wang ◽  
Carola Wenk
Keyword(s):  
Shortest Paths ◽  
Map Matching

Control of Autonomous Mobile Robot Using Map Matching with Optimized Search Range

2013 ◽  
Vol 133 (5) ◽  
pp. 502-509 ◽  
Author(s):  
Kouhei Komiya ◽  
Shunsuke Miyashita ◽  
Yutaka Maruoka ◽  
Yutaka Uchimura
Keyword(s):  
Mobile Robot ◽  
Map Matching ◽  
Autonomous Mobile Robot ◽  
Search Range

eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

2019 ◽  
Author(s):  
Ruslan N. Tazhigulov ◽  
James R. Gayvert ◽  
Melissa Wei ◽  
Ksenia B. Bravaya
Keyword(s):  
Web Application ◽  
Shortest Paths ◽  
Coarse Grained ◽  
Decay Parameter ◽  
Electron Hole ◽  
Web Based ◽  
Aromatic Amino Acid Residue ◽  
Pathways Model ◽  
Visualization Tools ◽  
Effective Decay

<p>eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one effective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be specified by the users. The software searches for the shortest paths connecting the user-specified electron/hole source to either all surface-exposed ETA residues or to the user-specified target. The identified pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p>

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