Study of fuzzy logic and particle swarm methods in map matching algorithm

Abstract

Navigation system is used in deploying the real-time position of vehicles on the map of route with the help of Global Positioning System (GPS). However, the imprecision is included in the map of route due to erroneous GPS signal (i.e., inclusion of errors related to receiver and/or propagation). Also, the current navigation system involves dense road network areas having higher probability of imprecise inputs. The map matching (MM) method requires capability of inherent tolerant to these imprecise inputs and is capable of suggesting certain vehicles more likely to run on one specific link than other links in navigation system. The fuzzy inference system (FIS)-based technique converting the qualitative terms into quantitative values is very effective to deal with human intervention and linguistic vagueness in MM algorithms. In case of highly unreliable positioning errors included in MM, the particle swarm optimization (PSO) is used as an option for continuously updating sensor information to find vehicle motion and respective route link on map. This has directed us to do the comparative study of both the above two methods with the other methods. Simulation results show that FIS-and PSO-based MM algorithm significantly outperforms the previous algorithms in terms of running time, accuracy and recall.

Some Issues for Location Dependent Information System Query in Mobile Environment

“…Further research work aims to propose a framework for implementing software as a service for people with disabilities or older people with reduced cognitive abilities. In [9], Fuzzy inference and recommendations are implemented in MM algorithms to remove navigation system errors and get correct FIS based navigation systems. Also, a framework is designed using PSO integrated into MM algorithm for unreliable navigation systems having very high MM related errors in positioning.…”

A Literature Review of Location-Aware Computing Policies

“…TostartadiscussiononcachereplacementinLBS,onemustbeawareofthedifferencebetween temporalandspatialcachereplacement.Thetemporal-dependentcachereplacementschemeconsiders temporalpropertiessuchasaccessrate,updaterate,etc.fortheevictionofthedataitem.Contraryto this,thespatial-dependentcachereplacementschemeconsidersspatialpropertiessuchasmovement direction,movementspeed,movementpattern,etc.todecidedataitemeviction.Forthebettercache hit ratio (Gupta & Shanker, 2020) in the system, the cache replacement policy should consider temporalaswellasspatialparameters.ProbabilityArea(PA) (Zhengetal.,2002)isaTemporal cachereplacementwhileFarthestAwayReplacement(FAR) (Ren&Dunham,2000)isaspatial cachereplacementpolicy.PAisadvantageousduetolowercomputationcomplexity,butitsupports theonlytemporalproperty.FARhaveitsprosasitconsiderstheclient'scurrentaswellaspossible futuremovementdirection;however,frequentdirectionchangesresultinpoorperformance.Moreover, thispolicyavoidstemporalproperties.…”

A Comprehensive Review of Map-Matching Techniques

“…Real-time positional data (Gupta & Shanker, 2020d) is used by navigational applications to give direction or location-relevant information. The major services include navigation assistance such as fleet management and path planning.…”