MapMatching Algorithm: Trajectory and SequentialMap Analysis on Road NetworkKanta Prasad Sharma1, Ramesh C. Poonia1, Raghvendra Kumar2, Surendra Sunda3,and Dac-Nhuong Le4,∗

1Amity Institute of Information Technology, Amity University, Rajasthan, India2Department of Computer Science and Engineering, LNCT College, Jabalpur, India3Indian Space Research Organization, Ahmedabad, India4Faculty of Information Technology, Haiphong University, Haiphong, Vietnam

Abstract

The Global Positioning System (GPS) tracking data is essential for sensor data sources. It plays an importantrole for various systems like Traffic assessment and Prediction, routing and navigation, Fleet managementetc. Trajectory data accuracy is key factor for sampling based vehicle movement using existing GPS alertingsystems. GPS navigation process is not reliable because of weak signaling transmission, weather scenario, specially, tall buildings area and drass sectors in Indian scenario. Map matching finding a path betweenavailable points on the active road segment, enhance road data accuracy through minimize frechet distancefor future purpose. Therefore, accurate road data, become necessary for fast map matching outcomes. Thiswork provides to locate the frechet distance on available free space for accurate path finding. This work alsocontributes to measuring frechet distance, trajectory data error estimation and finding free space surface onroad network with sequential map computational method.

Keywords: Weak Frechet Distance, Error Aware, Map Matching, Trajectory Data, Adaptive Clipping, Free Surface Area

Received on 02 September 2018; accepted on 19 November 2018; published on 29 November 2018Copyright © 2018 Kanta Prasad Sharma et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

doi:10.4108/eai.29-11-2018.155999

1. IntroductionWireless sensor are very helpful for environment mon-itoring and communication process; researchers facechallenges like computational constraints, link fail-ure, fault tolerance for coverage network which wouldimprove with wireless sensor coverage performance andnext generation sensor technologies is necessary formobile communication systems which will helpful onhigh speed mobility and tracking services [1]. GlobalPosition System (GPS) receivers are unified into vari-ous systems like Navigation system, Vehicle telemetricsystem, Intelligent Transport System (ITS), Smartphoneetc. GPS services are not appropriate for real timenavigation on the road network especially in drass

∗Corresponding author. Email: nhuongld@dhhp.edu.vn

sectors, high forestry sectors and tall building areas inIndian scenario. This era GPS users needs appropriatenavigation approach without any tracking pitfalls. Mapmatching concept is useful for position finding on roadnetwork [2]. GPS receiver data is very necessary forposition observing on digital road map. Navigationapplications seriously work for accurate and reliableposition sampling, analysis using sophisticated mapmatching algorithms, which result are not reliable dueto available trajectory data quality.

This paper exploits GPS tracking trajectory dataerrors to pare down on the road network, presumablyin the right tracking approach for high accuracyusing fast map matching algorithm and adaptiveclipping algorithm, which compute approximate realpath between nearest road polygon’s circumferences.GPS trajectory data errors are finding; and reduce bycomputing frechet distance, between active surfacesof polygon circumferences [3]. Existing AdaptiveClipping algorithms provide local and global map

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Research Article EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

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matching approach using Strong Frechet distance.Weak frechet distance is very important for localmapping approach; adaptive clipping approach neglectweak frechet distance for large network due torunning time, storage complexity [4]. We also analysisoutput sensitive variant for weak frechet map usingerror-aware selection strategies for reliable navigationon curve polygon circumferences on road network.Trajectory Data- trajectory data is vehicle movingreal time information like latitude, longitude, height,velocity, starting moment position, ending positionelapsed time, etc, [5] which can easily collected usingtechniques such as Floating Car Data from manysources with appropriate file formats (OSM , XMLfiles). Newson & Krumm [6] provides sophisticated mapmatching results based on available trajectory data,for real time position mapping on Google digital mapwhich are not reliable for mobility time interval.

To address the challenges of map matching fromnoisy, low- frequency sampled trajectory data. Letwe consider the large urban road network with partof different characteristic data. To compute smoothand optimized map between starting and endingmoving points. The adopted approach; explore vehicletrajectories to analyzed segment and reconstruct theunderline movements of network. This approach allowsus to analysis input data set into groups of homogenoustrajectory data and delay network.

The main contribution of this work focus on Proposedsequential map matching algorithm, Proposed vehiclelocation tracking algorithm, it provide optimized roadnetwork, based on original input data and thresholdvalues, This work introduce, proximity turn sampledbased algorithm for similar turns analysis on theroad network and useful for create intersection nodeson available trajectory data [6]. Focus on optimizedresults using real data set according our researchscenario to show the performance of proposed methodsoutperformance the current state of art.

This paper introduces, map matching issues andalgorithms including optimum outcomes. Section 2provides background details and Frechet Distance andissues are discuss in Section 3. Section 4 surmisedExisting Map Matching Issues and in Section 5, we willpropose Sequential Map Matching Algorithm. Section6 Provides Results and Discussion and finally wesurmised conclusion in section 7.

2. BackgroundMap matching algorithms are use full for trajectorydata process and exact location finding on appropriatetime period. Adaptive Map Matching Algorithms worksas Incremental and Global Map Matching Algorithmfor vehicle location tracing in different road situations.Quddus, et.al [7] provides, an incremental algorithm

principle based on greedy strategy with historicalmapping results, for active surface area in roadnetwork similarly Goh C. Y et.al [8] & He Z. Cet.al [9] provides, incremental algorithm with slidingwindow and recursive principle; Sliding window dividetrajectory data into the smallest points, for sequentialprocessing when sliding window size continuouslyupdated with segment points is large, algorithmprovides optimal results.

Karagiorgou et.al [10] introduces the look aheadmethod for delay analysis between each segment points.Yin et.al [11] proposed, a high quality map matchingmethod for computing nearest point’s trajectory data;on active surface area of the road network which isindicates through a weighted graph, every weightededges are decoding the distance between vehiclesposition for calculate shortest Path value usingDijkstra’s Algorithm.

Wenk.C et. al [12] introduce, an appropriate route-finding approach using road and geometric analysisfor Point to point map matching approach or linesegment tracking in different whether situation on roadnetwork. Mostly route tracking research is based onGPS trajectory [13] for vehicle’s variability which ismore reliable than personal trajectory data collectionapproach Similarly, Chen. L, et.al [14] proposed,personal trajectory data collection approach whichwould face adequate challenges for example thediversity of trajectory data on each movement. Frechetdistance is necessary parameter for accurate routefinding between road networks. A global algorithm forfinding all possible polygon circumferences (circularpath) on appropriate velocity of vehicle [15].

The objective is to enhance the performance of globalmap matching algorithm for kinetic and precise pointpositioning for real time tracking.

3. Frechet DistanceCurve matrix space provide computed results betweenpolygons in O(p × q × log2p × q) time where p and q arecurve polygon segments for example, suppose a manwalking a dog in forward direction with continuousvelocity where belt’s minimum distance (between manand dog) indicate strong frechet path on active segmentlength and weak frechet path indicate belt’s length [15].Strong and weak path depend on belt’s variant, forexample suppose distance (ε > 0) means, distance isfixed or not [27]. Alt H & Godau [16] provides a binarysearch method for curve circumference’s radius variantsat ε distance or > ε distance.

Figure.1 represent road network on right side andvehicle trajectory explicitly indicated with related pathfrom lower left to upper right corner, indicated by a linein right side.

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Map Matching Algorithm: Trajectory and Sequential Map Analysis on Road Network

Figure 1. Road Network (left ) and White surface (right )

4. Map Matching Issues with AlgorithmsMap Matching is a used for continuous and smoothnavigation process. Existing navigation systems areworking on positional data such as longitude, latitudebut, mostly systems are neglect altitude for routemapping approach on the horizontal surface; itbecomes imbalance for sophisticated regions likeground road network, for example, hill station routes,rural region and tall building area. Map matchingalgorithms appropriate results are depend on trajectorydata and digital map quality.

4.1. ProblemLet T = (Tn|n = 1, 2, .., N ) sequence of N vertices of theroad network.

Every point (Tn) identify necessary parameterslike Longitude, Latitude, Speed and Time stamprespectively (Tn.Lon), (Tn.Lat), (Tn.v), (Tn.t).

Procedure for computing GPS points threshold [17],let T = time interval between vertices and GPS log pointunder a certain threshold:

∆t, T : P1 → P2 → P3 → ...→ Pn (1)

where Pi ∈ L, means contains (latitude, longitude andtime stamp) in range 0 < pi+1.t − pi .t < (1 ≤ n).

Segment. Let R is the mapping point on polyline(Figure 2) which indicates curve segments on the road.Number of nodes are lies on a line are indicate as(p1, p2, .., pm), where each node contain Longitude andLatitude where segments with

• Road width (r, w).

• Speed limit(r, v)

• Bi-direction travel [r, d ∈ T rue, False].

Figure 2. GPS Trajectory

Digital map. Map is a combination of many segments(k) of a road network. It provides route and locationof the vehicle. Let G = {rk |k = 1, 2, k}. The objective isto find out corresponding trajectory point (T) betweensegments (G).

4.2. Map matching algorithmMap matching performance can enhance using somestrategies such as graph optimization, error awarenesswith trajectory data, and result in the sensitive processusing adaptive clipping technique.

Algorithm 1 Turn and Segment AnalysisInput: Trajectory set (T) and speed categories (C)Output: A set of trajectory segment based on speed.BEGIN

Tsegm ← ∅For Tsegm ∈ T

For each (LJ ∈ T )U (Li)← median(U (LJ − w)), V (Tsegm) ∈ CTsegm ← Tsegm ∪ Lg , C

End forEnd forReturn Tsegm

END

To analyzed the tracking trajectories input data,which split into subsets with difference characteristicsas per speed of moving objects. When set theinput parameters of proposed algorithm for computedifference between overlapping area of the networkwith higher accuracy. So, that it can be mergethe subsets to produce the complete network. Thisapproach helps to construct virtual network. Foranalysis, the various speed for example slow, medium,and fast. The input trajectories split or filtered then anobject may have to move with different speed acrossdifferent subset of trajectories. A native method toachieve such condition: need to assign speed values foreach line segment of trajectories. This value computebased on segment length on time interval durationbetween starting and ending points.

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Graph Optimization algorithm. A traditional approachof curve polygon’s frechet distance computation onvariant frames in Θ(m, n) time duration, here m, n arecurve polygon conditions where active points are searchout using graph traveling technique [18]. GPS trajectoryposition complexity for shortest path computationbetween nearest active points on road segment inO(m ×nlog(m ×m)) time and Θ(m × n) space complexity forevery shortest path ε < ε∗, where ε∗ indicate optimalfrechet distance and ε represent weak frechet distance.

White surface indicate active surface area for optimalpath analysis, where every pair of vertices (v) connectedby an edge (e) for a path (v, e).

Sensitiveness algorithm. An optimal shortest path isimportant for historical data on previous trackinggraph. Hash table maintain historical information onO(1) time for each entity similarly output sensitivealgorithm requires O(k × logk) time, where K indicaterequired free space for every shortest path betweenstart (e, p0) and end (f , pn) nodes, e and f are roadswith p0 and pn position points. Sensitiveness methodfor shortest path finding with trajectory data refiningprocess for upgrading map matching performance [19].

Error aware map matching. Real time tracking applica-tions services are not reliable due to GPS signal fre-quency and receiver performance which is causes oftracking in GPS restricted areas, tall buildings region,narrow streets, high drass sector in our scenario. Onemost causes of GPS error is signal reflection when trans-mitted on Satellite Based Augmentation System (SBAS)due to satellite’s health or weather circumstances.

The following Lemma encodes the observed trajec-tory properties for the road network for signal trajec-tory:Lemma 1: The positive vehicle trajectory of the

vertices q1..qm in a road network that has encoded to thevertices trajectory p1..pn including following properties

• Initial Stage: q(1) ∈ A(pi)

• Final Stage: q(m) ∈ A(pi)

• Intersection: q1..qm for Intersect A(pi) 1 ≤ 0 ≤ n

• Containment: if q1 ∈ A(pi) and qi+k ∈ A(PJ+1)

Proof:

• Starting vertex q1 is belongs to active area A(p1) incurve (polygon).

• Ending vertex qm is belongs to active area A(pn)

Each active vertex (n) must intersect on free spaceincluding regular moving position from start vertex piand end vertex pi+1 with measurement errors means[q1 ∈ A(pj ) and qi+k ∈ A(pj+1)].

Adaptive Clipping Algorithm. Adaptive Clipping algo-rithm working for localized map matching on weakfrechet distance, for example, suppose trajectory edgebetween pi and pi+1 is pipi+1 on velocity (v) and sam-pling rate (r) with every pi in active area A(pi). Oncurve center pi with radius µ or measurement error (µ)[20] and Dijkstra’s Shortest Path Finding algorithm isimplementing on two conditions.

Condition 1. Dijkstra on free space graph, finding theshortest path between start (pi) to end (pi+1) vertices.Free space graph vertices (p1, e), (v, p1p2), (p1, e) here,

• v is vertex;

• e is edge for (AP 1P 12) [continuous changing radiusof curve] and

• e is an edge for A(P2) [means Active region].

Condition 2. Dijkstra on (n − 1 vertices) for all availablevertices (pi , e), (v, pipi+1), pi+1, e) on free spaces graph,here,

• v is vertex;

• e is an edge in A(pipi+1) and

• e is an edge in A(pi+1)[(v, pipi+1)] for continuousmoving position [12].

Now, we can filter GPS trajectory data (error free)for sensitive results on the continuous ways on the rodnetworks.

Figure 3. Methodology for GPS Signal detection Process on L2Frequency Data

The aim of this method, to reduce frechet distancecomputation time on curve circumferences radius,which also contain computed location matching statusvehicle movement on the road network. Table.1indicated the comparative analysis with differentexisting map matching algorithms.

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Table 1. Existing Map Matching Algorithms

5. Proposed Map Matching Approach forSequential Map5.1. Bayes ApproachThis approach provide periodic information withlikehood [32]. When we, compute time differenceparameter based on filtering algorithms upon tradeof between accuracy and complexity. The Kalmanfilter algorithm [35, 37] is adopt for simplicityimplementation, tractability and robustness [33].

5.2. Proposed FilterTo estimate the position of vehicle (VO) in fixed timeinterval, existing research work neglect the motionof vehicle [23]. Now, consider the focus on dynamicoutputs, we adopt Bayesian filter for particle-trackingnodes position tracking.

X0(k) = [x0(k), y0(k), s0(k), h0(k)] (2)

Here,

• x0(k), and y0(k) is vehicle position coordinates oftime k.

• s0(k) is the vehicle speed (m/sec).

• h0(k) is consider as vehicle heading (in ration)form the x-axis to consider the direction andmeasure the positive angle [40].

The state space is consider based on

x0(k) = x0(k − 1) + Ts0(k − 1)cos(ho(k − 1)) (3)

y0(k) = y0(k − 1) + Ts0(k − 1)sin(h0(k − 1)) (4)

s0(k) = s0(k) + wk (5)

h0(k) = h0(k − 1) + h + wk (6)

Here,

• T is time interval between k − 1 and k (in seconds).

• ∆h is estimated heating change route during eachinterval (1 sec).

• sk is estimated average speeds with in timeinterval when noise is define both heading andspeed as Gaussian distance.

• wk is consider as normal distribution have zeromean variations and measure equation define by2(k) for composite vector of time k.

To compute individual GPS position

p0(k) = p0 − |s ∝ log ‖xa(k) − x0(k)‖ v0(k)| (7)

Here, v0 is vehicle position uncertainty.

xak = [xa, ya]T (8)

Gaussian distribution with mean xa as the measure ofGPS position and [xa, ya] measure GPS position basedon k derivation time interval.

5.3. Map InformationIn case, road map is available [22], then computedifference between road and no road area. The proposedcomputation method adopt open start map (osm) whereeach roads are linked by a single edge line withoutconsider the width of road. We optimized the outputbased on 10 m road segment as input and roaddirections are consider as similar or opposite. This workkeeps the highest value to select road site trajectorynodes.

5.4. Lane Level MapThe road map information is considering lane level datato scaling suitability between micro scale dense points,which are more suitable or reliable for intelligentvehicles location tracking. These dense points aremore accurate without long delay [27]. The Map’sprototypes are utilized to cover 4 km in Ahmedabad &2 km another network. The proposed sequential map-matching algorithm provides high quality outcomes.

Road Geometric Information. Polyline provides the geom-etry information of vehicle moving segment. The seg-ment is split into various subsets of segment withsegment heads.

Road Lane Marking. Each lane having Meta data withrespect to markings delimiting, Geometric descriptionprovides some extra information such as solid lines fortracked segment [28].

Road Lane/ Segment Connections. No real time accuratetracking/navigation information in terms of connectionb/w segment heads/links provide by existing mapmatching approach. Here we compute a unique path onthe road network along current vehicle position.

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The trajectory satisfies:

T (α) : [0, 1]→ xij (9)

with

T (0) = (0, 0)T (10)

andT (1) = xlocation, ylocation (11)

The trajectory will initially- input by curvature.Now, need to analysis that some mapping M

(Cartesian coordinates):

T (α)M↔ ft (α, qt) =

[fx (α, qt)fy (α, qt)

](12)

f (α, qt) Indicate simulation model being t showntrisection and q are recursive parameters for process.

Consider the global reference frames, when need tore align location with the regional mapping trajectoryat every time slot means

xrt = prt = 0 (13)

The simulation model can be simplified

xrt+1 = (∆n + εn), yt+1 = 0 (14)

Now, the updated local map (OSM- Open Street Map)to be consider with the vehicle location can be measuredas [29] (∆n,∆φ) including associated uncertainty σ∆.

The given matrix provides movements computationparameters

[xt+1, yt+1, 1]t = T −1[xt , yt , 1]T (15)

Here

T =

cos(∆φ) −sin(∆φ) ∆nsin(∆φ) cos(∆φ) 0

0 0 1

(16)

Suppose mapping is restrict M to be a linear function,the simulation parameters of the form

f (α,φ) = θA(α) (17)

Now, the new coordinate parameters are [30]

xt+1 = T xt = T f (α, qt) = T qtA(α) = QA(α) (18)

Qt+1 = T qtA(α) (19)

The frequency of this computation is much betterthan existing process shown in Table 2.

Table 2. GPS Raw Estimation information

5.5. Segment Fatching ApproachThe proposed algorithm is categorized on hierarchylevel, which are given below:

1. Map (.osm) data files are deploy for mapping thevehicle location according GPS sensor trackingtrajectory- the velodyne H10Z-64 laser sensorused for evaluate sensor’s generated pit cloud

2. The Road generating algorithm provides roadboundary’s information by vehicle sensor datathen compute using RANSAC/ least- sequencesapproach for optimal trajectory point’s analysis.

3. Vehicle should be implement odometer sensors-to estimate local map updated information aftervehicle movements

4. The reference trajectory utilized steer the vehiclefor local maps using segment traversal approach.

Figure 4. Working Methodology for Road Analysis and TrackingProcess

Here, we compute two important possibilities thatare outcomes from the fact, which we are planningon the local road network. In the algorithm 2, step 1indicate the GPS location which may be inaccurate thatnavigating precisely to related coordinate in step 2.

The vertex to be attend on the graphical map maybe accurate or sufficiently mismatch to GPS or Sensor’scomputation of reliable path from vehicle locationpoints in all direction (forward, backward, left, right).Those issue projecting the targeted of smooth path onthe free space of road network.

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Algorithm 2 . Road Analysis and Tracking ProcessInput: Segment heads, road missing data, Odometer-calculated inputs.BEGIN

Step 1. Check current position of vehicle on road networkwhile (current_possition ) , osm_possition) do

Step 2. Check current GPS Position on purified Trajectory of road network.GPS input , purified trajectory input

Step 3. Simulate trajectory input/ receiver type or single type (PPP-Precise Point Position/STATIC/Dynamic)Step 4. End of outer whileStep 5. Compute new trajectory data based on GPS sensor inputStep 6. Compute probabilistically force reference trajectoryStep 7. End of outer while

END

Algorithm 3 . Map Matching Vehicle TrackingInput:

- Global Position Data (.GPS)- Updated Road map data (.OSM)

Output: Estimate optimized path with tracking nodes and moving vehicle direction.BEGIN

Step 1. Get initial position and uncertainty from individual data.Step 2. Initialized trajectory frechet distance free space randomly.Step 3. Measure moving object speed and continuously change segment head from initial to next.

For each segment doCompute sample speed error;Compute sample heading error;Compute node displacement update state;

End ForStep 4. Check map conditions

Minimized the node frechet distance;Check the curve type;Manage historical moving nodes location;

Step 5. Analyzed the positionTrack real time GPS location (long, Lat);Purify the uncertainty of signals using Gaussian probability mechanism ;

Step 6. Check thresholdIf (Sum (weight) < zero_threshold) then

Repeat Step 5;Else

Compute optimized weight;Compute optimized speed;

End IfStep 7. To resampling threshold on the path

If sum(weight2 )<resampling threshold thenNo need to lowest weight nodes;

ElseConsider highest threshold nodes;

End IfStep 8. Mapping Status

Get optimized map;Compute tracking nodes on map;Measure the velocity (speed );

Step 9. ExitEND

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Now, we consider the corresponding speed categoryto each segment. The nature of vehicle movement; itpreserve the high degree of fragmentation and refiningthe data set unusable. When start the movement,velocity (speed) is consider low down due to intersectionor other kinds of issues but, we adopt sliding windowcross the trajectory and replacing the speed value ofeach segment based on median computation over aseries of consecutive line segment (smooth head) value[35]. This approach will help us to avoid excessivefragmentation of trajectory due to short change inspeed. The process highlighted some objectives:

• Every line segment lj on each trajecotry T step 1.

• The median speed is computed over a slidingwindow of width (w)2 in step 2.

• All segments are assign to related classes withminimum and maximum speed computation step5 and step 6.

6. Results and DiscussionWe were trying to calculate precise position withinany appropriate accuracy, need to input necessaryinformation such as longitude, latitude, height andtimestamp of the base station or GPS receiver positionfor reliable data collection on geodetic and ellipsoidalheight.Simulation tool: RTKLIB1 is an open source program

package for standard and precise positioning withGNSS (Global Navigation Satellite Systems). RTKLIBconsists of a portable program library and several APs(Application programs) utilizing the library. It supportsstandard and precise positioning algorithms with: GPS,GLONASS, Galileo , QZSS, BeiDou and SBAS

• It supports various positioning modes withGNSS for both real-time and post-processing:Single, DGPS/DGNSS, Kinematic, Static, Moving-Baseline, Fixed, PPP-Kinematic, PPP-Static andPPPâĂŘFixed.

• It supports many standard formats andprotocols for GNSS: RINEX 2.1, 2.11, 2.12OBS/NAV/GNAV/HNAV/LNAV/QNAV, RINEX3.00 , 3.01, 3.02 OBS/NAV, RINEX 3.02 CLK,RTCM

• It supports several GNSS receivers’ proprietarymessages: NovAtel: OEM4/V/6, OEM3, OEM-Star, Superstar II, Hemisphere: Eclipse, Cres-cent, u-blox: LEAâĂŘ4T/5T/6T, SkyTraq: S1315F,JAVAD/ GRIL/GREIS,

1www.rtklib.com/

• It supports external communication via: Serial,TCP/IP, NTRIP, local log file (Record and playback)and FTP/HTTP (Automatic download).

• It provides many library functions and APIs(Application program interfaces)

Data Set: Give data set is deploying for locationtracking simulation process. Here longitude, latitudeindicate location of vehicle movements within fixedtime interval and moving directions are indicate by Xand Y respectively left and right direction.

Table 3. Dataset information

Here, observation represents the variations on timeintervals for moving direction as well as location onadopted open street map. The rapidly changes on thetracking node depend on the longitude and latitudevalues.

Figure 5. Solution status position on carrier phase L1

Figure 5 provide available GPS signal strength withsufficient quality for all satellite position in different

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color bar where gray represents not used, orangemeans waiting for connection, deep green meansconnecting or running, light green means data activeor processing, red indicate communication errors, deeppick upcoming activation status message.

After input observation, compute the positionsolution for GPST status or X/Y /Z means longitude,latitude and height component including ration factorof ambiguity validation in the figure because unreliableinput data become causes of high level pitfalls such asposition error, residual error.

In case, computing precise position on L1 or L2 carrierphase; results are not up to mark due to weak GPSsignaling indicated in Figure.6.

Figure 6. PPP signal status on carrier phase L1

In addition, when computing precise position pointswith high frequency carrier phase L5, GPS signals arenot recognized for other input stream L5 represented inFigure 7.

Figure 7. PPP signal status on carrier phase L5

The road segment and traveling points are indicatedin Figure 8 and technical summary is shown in Table 2.

Figure 8. Vehicle location movements on L2 frequency signals

7. Conclusion and Future Work

This work introduces a fast and accurate map matchingalgorithm and adaptive Clipping algorithm for allpossible curves navigation with minimum weak frechetdistance in local frechet space. We introduce map-matching issues with solution strategies for errorawareness solution and provide mathematical exposurefor process analysis. The proposed method rawestimation summary is providing in the given Table 2.

In addition to the future work, we would liketo compute precise position for narrow streets, hillstation routes, rural region and tall building area withGPS services on L1/L2 carrier phase signals or highquality frequency L5 carrier phase signals using anothersatellite system (IRNSS) which is helpful for precisenavigation in our scenario.

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