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Routing for Signal Processing
Published in Fei Hu, Qi Hao, Intelligent Sensor Networks, 2012
Wanzhi Qiu, Efstratios Skafidas
With this link metric which characterizes the additive and independent contribution of each link to detection performance, the Chernoff routing [30] finds the optimal route using the shortest path framework. In shortest path routing [36], each link in the network is assigned a link cost γk,j which quantifies the consumed resources of the link Nk to Nj, and the “least cost” route, where the cost of a route is simply the sum of its link costs, is sought. A constant link cost, that is, γk,j = ε, leads to the minimum-hop routing. Alternatively, setting γk,j = ek,j, where ek,j is the link energy consumption given by (10.3), results in the minimum-energy routing. The link cost of the Chernoff routing introduces a weighting factor α ≥ 0 to control trade-offs between detection performance and energy: γk,j=(ek,j−αqj)ε+
A GPS data-based analysis of built environment influences on bicyclist route preferences
Published in International Journal of Sustainable Transportation, 2018
Peng Chen, Qing Shen, Suzanne Childress
Path search methods have explicit procedures for route choice set generation, which are divided into deterministic and stochastic approaches (Bovy, 2009). K-shortest paths and labeling routes are deterministic approaches to generate route alternatives. The K-shortest path3The K-shortest path routing algorithm is a generalization of the shortest path problem. “Path” is a similar term as “route.” approach is restricted to minimize trip distance, which is inadequate for capturing other factors which impact the bicycle route choice. The labeling route4The labeling route approach minimizes costs by creating functions through a linear combination of factors, and labels a route with the prominent factor. approach is endorsed for producing a better bicycle choice set because this approach exploits various road segment5A road segment is defined by the link between two consecutive nodes. features and creates more realistic route alternatives. In recent bicycle route choice studies, a calibration process was applied to improve the existing labeling route methods (Broach, Gliebe, & Dill, 2010). Link elimination6The link elimination approach is described as continually eliminating the shortest path segments from the road network to find the next best route until converged. and link penalty7The link penalty approach is labeled as repeatedly increasing the impedances of the shortest path segments to search the next best route until converged. are simulation (stochastic) methods for enumeration (Bekhor, Ben-Akiva, & cRamming, 2006; Bovy, 2009; Prato, 2009). Link elimination is not an ideal method for enumeration because it may result in an unsolvable road network. For example, if the bridges which connect north and south Seattle are disregarded in the link elimination process, no additional route alternatives can be created based on the remaining road network. In terms of the number of generated comparable routes, the link penalty approach is better. However, when the bicycling distance is long and road network consists of densely connected streets, large numbers of generated route alternatives can be unmanageable. To solve this issue, a recent methodological advancement of route choice modeling is the introduction of a recursive model, which eliminates the requirement of creating a choice set of paths (Zimmermann, Mai, & Frejinger, 2017).