Current map-matching algorithms for transport

Current map-matching algorithms for transport

applications:State-of-the art and future research directions Mohammed A.Quddus a ,Washington Y.Ochieng

b,*,Robert B.Noland b a

Transport Studies Group,Department of Civil and Building Engineering,Loughborough University,Leicestershire LE113TU,United Kingdom b Centre for Transport Studies,Department of Civil and Environmental Engineering,Imperial College London,

London SW72AZ,United Kingdom

Received 15November 2006;received in revised form 30April 2007;accepted 2May 2007

Abstract

Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines)to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link.A map-matching algo-rithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS).The required horizontal positioning accuracy of such ITS applications is in the range of 1m to 40m (95%)with relatively stringent requirements placed on integrity (quality),continuity and system availability.A number of map-matching algorithms have been developed by researchers around the world using di?erent techniques such as topological analysis of spatial road network data,probabilistic theory,Kalman ?lter,fuzzy logic,and belief theory.The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data.However,these algorithms are not always capable of supporting ITS applications with high required navigation performance,especially in di?cult and complex environments such as dense urban areas.This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements.The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them.This paper also highlights the potential impacts of the forthcoming Euro-pean Galileo system and the European Geostationary Overlay Service (EGNOS)on the performance of map matching algorithms.Although not addressed in detail,the paper also presents some ideas for monitoring the integrity of map-matching algorithms.The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’information (i.e.based on either cost or time).Clearly,such data would result in relatively simple map-matching algorithms.

ó2007Elsevier Ltd.All rights reserved.

Keywords:Map-matching;Transport applications;Research directions

0968-090X/$-see front matter ó2007Elsevier Ltd.All rights reserved.doi:10.1016/j.trc.2007.05.002

*Corresponding author.Tel.:+4402075946104.

E-mail addresses:m.a.quddus@https://www.wendangku.net/doc/5615792672.html, (M.A.Quddus),w.ochieng@https://www.wendangku.net/doc/5615792672.html, (W.Y.

Ochieng).

Transportation Research Part C 15(2007)

312–328

M.A.Quddus et al./Transportation Research Part C15(2007)312–328313

1.Introduction

A range of intelligent transport system(ITS)applications and services such as route guidance,?eet man-agement,road user charging,accident and emergency response,bus arrival information,and other location based services(LBS)require location information.For instance,buses equipped with a navigation system can determine their locations and send the information back to a control centre enabling bus operators to pre-dict the arrival of buses at bus stops and hence improve the service level of public transport systems.The hor-izontal positioning accuracy for such ITS applications is in the range of1m to40m(95%,i.e.at the2r level), with relatively high requirements on integrity,continuity and system availability.

In the last few years,the Global Positioning System(GPS)has established itself as a major positioning tech-nology for providing location data for ITS applications.Zito et al.(1995)provide a good overview of the use of GPS as a tool for intelligent vehicle-highway systems.Deduced Reckoning(commonly referred to as‘Dead’Reckoning or DR)sensors consisting of an odometer and a gyroscope are routinely used to bridge any gaps in GPS positioning(Kubrak et al.,2006).This information is then used with spatial road network data to deter-mine the spatial reference of vehicle location via a process known as map matching.

Map-matching algorithms use inputs generated from positioning technologies(such as GPS or GPS inte-grated with DR)and supplement this with data from a high resolution spatial road network map to provide an enhanced positioning output.The general purpose of a map-matching algorithm is to identify the correct road segment on which the vehicle is travelling and to determine the vehicle location on that segment(Greenfeld, 2002;Quddus et al.,2003).Map-matching not only enables the physical location of the vehicle to be identi?ed but also improves the positioning accuracy if good spatial road network data are available(Ochieng et al., 2004).This means that the determination of a vehicle location on a particular road identi?ed by a map-match-ing algorithm depends to a large extent on the quality of the spatial road map used with the algorithm.A poor quality road map could lead to a large error in map-matched solutions.

A map-matching algorithm can be developed generically for all applications or for a speci?c application. For example,Taylor et al.(2006)developed a map-matching algorithm referred to as Odometer Map Matched GPS(OMMGPS)applicable to services where the most likely path or route is known in advance.In this paper,only generic map-matching algorithms are reviewed.A map-matching algorithm can also be developed for real-time applications or for those where post-processing is su?cient.For instance,Marchal et al.(2005) developed an e?cient post-processing map-matching method for large GPS data.In the review presented in this paper,only real-time map-matching algorithms are considered as most ITS services require a map-match-ing algorithm that can be implemented in real-time.

It is essential that the map-matching algorithm used in any navigation module meet the speci?ed require-ments set for that particular service.Although the performance of a map-matching algorithm depends on the characteristics of input data(Chen et al.,2005),the technique used in the algorithm can enhance overall per-formance.For instance,the performance of a map-matching algorithm based on fuzzy logic theory may be better than that of an algorithm based on the topological analysis of spatial road network data if all else are equal.There are at least35map-matching algorithms produced and published in the literature during the period1989–2006,most of which are recent re?ecting the growth in the need for ITS services.The posi-tioning accuracy and quality o?ered by these algorithms has also improved over the years.This is mainly due to the use of advanced techniques in the algorithms such as Kalman?ltering,fuzzy logic,and belief theory, and the improvement in the performance of positioning sensors and the quality and quantity of spatial road network data.

Another important operational consideration is the sampling frequency.Although most ATT services(nav-igation and road guidance,distance-based road pricing,etc.)require a sample frequency of1Hz,some ATT services(such as bus arrival information at bus stops)only require a sample frequency of0.3Hz or lower.This can obviously in?uence the design of an optimal map-matching algorithm,as the performance of some nav-igation sensors vary,for example with speed.This aspect of the performance of map-matching is measured in part by the required navigation performance parameter of continuity.

Di?erent algorithms,however,have di?erent strengths and weaknesses.Some algorithms may perform very well within suburban areas but may not be appropriate for urban areas and vice versa.A review of the liter-ature suggests that existing map-matching algorithms are not capable of satisfying the requirements of all ITS

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applications and services.For instance,bus priority at junctions requires a2-D positioning accuracy of5m (95%)with integrity.None of the existing algorithms can meet this positioning requirement,especially,within dense urban areas.This implies that apart from other elements including input data sources,further improve-ments to map-matching algorithms are essential.To accomplish this,it is necessary to identify the constraints and limitations of existing map-matching algorithms for further research.Therefore,the objectives of this paper are to perform an in-depth literature review of existing map-matching algorithms and then to uncover the constraints and limitations of these algorithms.In addition to this,the paper also recommends ideas for future research to overcome these limitations.The potential impacts of the European Geostationary Overlay Service(EGNOS)and the forthcoming Galileo system on the performance of map-matching algorithms are highlighted also.It is important to emphasise that this paper is intended to serve as a key reference for future research and development of map-matching algorithms by bringing together existing knowledge and de?ning future research directions.

The remainder of the paper is structured as follows.First,an in-depth literature review of map-matching algorithms is presented,followed by a presentation of the performance of some existing map-matching algo-rithms.The next section describes the constraints and limitations of existing map-matching algorithms.This is followed by a discussion of the potential impacts of Galileo and EGNOS on the performance of map matching algorithms.Conclusions summarise the key constraints and limitations of existing algorithms and provide some thoughts on future research directions.

2.Literature review

As stated above,map-matching algorithms are used to determine the location of a vehicle on a road.Most of the formulated algorithms utilise navigation data from GPS(or GPS integrated with DR sensors)and dig-ital spatial road network data.One of the common assumptions in the literature on map-matching is that the vehicle is essentially constrained to a?nite network of roads.While this assumption is valid for most vehicles under most operating conditions,problems may be encountered for o?-roadway situations such as car parks or on private land.Most of the studies also report that the digital spatial road network data used for map-matching should be of a large scale in order to generate position outputs with fewer errors(e.g.,Zhao, 1997;Quddus et al.,2006a).

Procedures for map-matching vary from those using simple search techniques(Kim et al.,1996),to those using more advanced techniques such as the use of an Extended Kalman Filter,fuzzy logic,and Belief Theory (El Najjar and Bonnifait,2005;Quddus et al.,2006b).Approaches for map-matching algorithms in the liter-ature can be categorised into four groups:geometric(Bernstein and Kornhauser,1996),topological(White et al.,2000;Joshi,2001;Greenfeld,2002;Chen et al.,2003;Quddus et al.,2003),probabilistic(Zhao,1997; Ochieng et al.,2004),and other advanced techniques(El Najjar and Bonnifait,2005;Pyo et al.,2001;Yang et al.,2003;Jagadeesh et al.,2004;Syed and Cannon,2004;Li and Chen,2005;Quddus et al.,2006b;Wang et al.,2006).The following sections brie?y describe these algorithms.

2.1.Geometric analysis

A geometric map-matching algorithm makes use of the geometric information of the spatial road network data by considering only the shape of the links(Greenfeld,2002).It does not consider the way links are con-nected to each other.

The most commonly used geometric map-matching algorithm is a simple search algorithm.In this approach,each of the position?xes are matched to the closest‘node’or‘shape point’of a road segment.This is known as point-to-point matching(Bernstein and Kornhauser,1996).A number of data structures and algorithms exist in the literature to select the closest node or shape point of a road segment from a given point (e.g.,Bentley and Mauer,1980).This approach is both easy to implement and very fast.However,it is very sensitive to the way in which the spatial road network data were created and hence can have many problems in practice.That is,other things being equal,arcs with more shape points are more likely to be properly matched. In a straight arc with two end nodes,all positioning points above the arc match only to the end nodes of the arc.

M.A.Quddus et al./Transportation Research Part C15(2007)312–328315 Another geometric map-matching approach is point-to-curve matching(Bernstein and Kornhauser,1996; White et al.,2000).In this approach,the position?x obtained from the navigation system is matched onto the closest curve in the network.Each of the curves comprises line segments which are piecewise linear.Distance is calculated from the position?x to each of the line segments.The line segment that gives the smallest distance is selected as the one on which the vehicle is apparently travelling.Although this approach gives better results than point-to-point matching,it has several shortcomings that make it inappropriate in practice.For example, it gives very unstable results in urban networks due to high road density.Moreover,the closest link may not always be the correct link.

The other geometric approach is to compare the vehicle’s trajectory against known roads.This is also known as curve-to-curve matching(Bernstein and Kornhauser,1996;White et al.,2000;Phuyal,2002).This approach?rstly identi?es the candidate nodes using point-to-point matching.Then,given a candidate node,it constructs piecewise linear curves from the paths that originate from that node.Secondly,it constructs piece-wise linear curves using the vehicle’s trajectory,and determines the distance between this curve and the curve corresponding to the road network.The road arc which is closest to the curve formed from positioning points is taken as the one on which the vehicle is apparently travelling.This approach is quite sensitive to outliers and depends on point-to-point matching,with the consequence of sometimes giving unexpected results(Quddus, 2006).

Taylor et al.(2001)propose a novel method of map-matching referred to as the road reduction?lter(RRF) algorithm,which uses GPS,height-aiding1from the digital spatial road data and virtual di?erential GPS (VDGPS)corrections.Due to the use of height-aiding,they report that one less GPS satellite is required for the computation of the vehicle position(i.e.,height-aiding removes one of the unknown parameters). The initial matching process of this algorithm is based on the geometric curve-to-curve matching proposed by White et al.(2000)which is quite sensitive to outliers.

2.2.Topological analysis

In GIS,topology refers to the relationship between entities(points,lines,and polygons).The relationship can be de?ned as adjacency(in the case of polygons),connectivity(in the case of lines),or containment(in the case of points in polygons).Therefore,a map-matching algorithm which makes use of the geometry of the links as well as the connectivity and contiguity of the links is known as a topological map-matching algorithm (e.g.,Greenfeld,2002;Chen et al.,2003;Quddus et al.,2003;Yin and Wolfson,2004;Blazquez and Vonder-ohe,2005;Meng,2006).

Greenfeld(2002)reviews several approaches for solving the map-matching problem and proposes a weighted topological algorithm.This is based on a topological analysis of a road network and uses only coor-dinate information on observed positions of the user.It does not consider any heading or speed information determined from GPS.This method is very sensitive to outliers as these can lead to the calculated vehicle head-ing being inaccurate.Care must be taken,however,in the use of vehicle headings calculated from the coordi-nates of the vehicle,as GPS position?xes are less reliable at speeds of less than3.0m/s(Taylor et al.,2001; Ochieng et al.,2004).At low speed,the uncertainty in the vehicle position could contaminate the derivation of heading based on displacement,over several epochs depending on the frequency of matching.

Meng(2006)also uses a topological analysis of the road network to develop a simpli?ed map-matching algorithm.This algorithm is based on the correlation between the trajectory of the vehicle and the topological features of the road(road turn,road curvature,and road connection).A number of conditional tests are applied to eliminate road segments that do not ful?l some pre-de?ned thresholds.The thresholds are obtained from statistical analysis of?eld-test data.The algorithm is implemented using navigation data from GPS/DR and spatial road network data including information on turn restrictions at junctions to improve performance. The algorithm does not work well at junctions where the bearings of the connecting roads are not similar.In these circumstances,the algorithm switches to a post-processing mode to identify the correct link,making it 1Height data obtained from a Digital Elevation Model(DEM)are used as a forcing function in the navigation di?erential equations to generate position,speed and heading.

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unsuitable for real-time applications.However,a qualitative decision making process based on the speed of the vehicle,the direction of the vehicle from a turn-rate gyro,the distance travelled by the vehicle,the quality of the digital map,and the errors associated with the navigation sensors could be utilised to correctly identify the correct link in real-time applications for such circumstances.

Quddus et al.(2003)developed an enhanced topological map-matching algorithm based on various simi-larity criteria between the road network geometry and derived navigation data.However,the objectives of the research are to use fewer inputs and to make the algorithm as simple and as fast as possible.The similarity criteria developed by Greenfeld(2002)are applied also.To improve the performance of the algorithm,the weighting scheme is enhanced by introducing additional criteria and other parameters including vehicle speed, the position of the vehicle relative to candidate links,and heading information directly from the GPS data string or the integrated GPS/DR system.Di?erent weighting factors are used to control for the importance of each of these criteria in determining the best map-matching procedure.

2.3.Probabilistic map-matching algorithms

The probabilistic algorithm requires the de?nition of an elliptical or rectangular con?dence region around a position?x obtained from a navigation sensor.This technique was?rst introduced by Honey et al.(1989)in order to match positions from a DR sensor to a map.Zhao(1997)discusses this technique in the case of GPS and suggests that the error region can be derived from the error variances associated with the GPS position solution.The error region is then superimposed on the road network to identify a road segment on which the vehicle is travelling.If an error region contains a number of segments,then the evaluation of candidate seg-ments are carried out using heading,connectivity,and closeness criteria.While such criteria are conceptually bene?cial,Zhao(1997)does not go into the details of their implementation.Indeed,there are many other parameters such as speed of the vehicle and distance to the downstream junction that can be used to further improve the map-matching process.

Ochieng et al.(2004)developed an enhanced probabilistic map-matching algorithm.In this algorithm,the elliptical error region is only constructed when the vehicle travels through a junction(in contrast to construct-ing it for each position?x as suggested by Zhao(1997)and there is no need to create the error region when the vehicle travels along a link.This makes the algorithm faster as there are a number of processes involved in the creation of the error region and hence the identi?cation of the correct link.This method is more reliable as the construction of an error region at each epoch may lead to incorrect link identi?cation if other links are close to the link on which the vehicle is travelling.Ochieng et al.also developed a number of criteria based on empirical studies to detect a turning manoeuvre of the vehicle at a junction.This helps to e?ectively identify the switching of the vehicle from one link to another.This enhanced probabilistic algorithm also takes into account the inaccuracy of the heading from the navigation sensor when the vehicle travels at low speeds.This e?ectively assists the algorithm to correctly match the position?xes at low speed,especially in urban areas where there are frequent stops.Moreover,an optimal estimation for the determination of vehicle location on a link is developed.This technique takes into account various error sources associated with the navigation sensor and the spatial road network data quality.

2.4.Advanced map-matching algorithms

Advanced map-matching algorithms are referred to as those algorithms that use more re?ned concepts such as a Kalmam Filter or an Extended Kalman Filter(e.g.,Krakiwsky et al.,1988;Tanaka et al.,1990;Jo et al., 1996;Kim et al.,2000;Li et al.,2005;Obradovic et al.,2006),Dempster–Shafer’s mathematical theory of evi-dence2(e.g.,Yang et al.,2003;El Najjar and Bonnifait,2005),a?exible state-space model and a particle?lter (Gustafsson et al.,2002),an interacting multiple model(Cui and Ge,2003),a fuzzy logic model(e.g.,Zhao, 1997;Kim et al.,1998;Kim and Kim,2001;Syed and Cannon,2004;Quddus et al.,2006b;Obradovic et al., 2See Dempster(1968)and Shafer(1976)for details.

M.A.Quddus et al./Transportation Research Part C15(2007)312–328317 2006),or the application of Bayesian inference(Pyo et al.,2001).Some of these algorithms are described brie?y below.

Kim et al.(2000)developed an integrated navigation system consisting of GPS,DR,and a map-matching technique for ITS applications.Their study assumes that several ITS services require2-D horizontal position-ing accuracy of5–10m(95%).They attempt to achieve this accuracy by the e?cient use of digital road maps. First,a simple point-to-curve matching approach is used to identify the correct link.Then an orthogonal pro-jection of the position?x onto the link is used to obtain the location of the vehicle.Due to the projection,the cross-track error(i.e.,the error across the width of the road)is reduced signi?cantly.However,the along-track error remains a key issue.An extended Kalman?lter(EKF)is then used to re-estimate the vehicle position with the objective of minimising the along-track error.The inputs to the EKF come from GPS position?xes. The performance of such a?lter may depend on the quality of spatial road network data,speci?cally how road curvature is represented.As stated earlier,the point-to-curve method is not su?cient to select the correct link especially in dense urban road networks.If the identi?cation of the link is incorrect,then the inputs to the Kalman?lter will also be inaccurate which inevitably leads to further positioning errors.The method can be improved using a more e?cient technique for the selection of the correct link taking into account the head-ing and speed of the vehicle as well as a topological analysis of the road network.

Gustafsson et al.(2002)developed a framework for positioning,navigation and tracking problems using particle?lters(a Recursive Bayesian Estimation).One of the applications of their method is vehicle position-ing by map-matching in which a digital map is used to constrain the possible vehicle positions.The only other input to the algorithm is wheel speed.The paper suggests that an erroneous initial position(on the order of km’s)is improved to one metre accuracy by the use of particle?lters.Their method could be used to supple-ment or replace GPS.In this method,the initial position of the vehicle is marked by the driver or obtained from a di?erent source such as a terrestrial wireless communications system or GPS.The initial area should also cover a region not extending more than a couple of kilometers to limit the number of particles to a real-izable number.With in?nite memory and computation time,no initialization of the algorithm is necessary. The performance of this algorithm is evaluated against the performance of GPS.In open-space,both provide similar results,but in urban areas the particle?lters provide better results.However,it is unclear how the per-formance in urban areas is measured.

Cui and Ge(2003)propose a constrained solution to tackle the problem associated with GPS in urban can-yon environments where GPS signals are often blocked by high-rise buildings and trees.Their study solves this problem by approximately modelling the path of the vehicle as pieces of curves such as straight lines,arcs,and polynomials.The vehicle path is assumed to be constrained to a known segment of road as the vehicle enters an urban area.With this constraint only two GPS satellites are necessary to obtain the positioning information.To identify the correct road segment after entering a junction,their method then uses a probabilistic algorithm which is further integrated with an EKF to estimate the location of a vehicle at junctions and to identify the correct road from all the roads meeting at a particular junction.Since this algorithm only requires two satellites, the availability of position?xes within an urban area increases.The algorithm fails in the case where either one or no satellite is available.Further testing of this algorithm is essential to evaluate its performance.

Yang et al.(2003)developed an improved map-matching algorithm based on Dempster–Shafer’s(D–S)the-ory of evidence using rule based logical inference systems.The inputs to the algorithm(i.e.,vehicle positions from GPS)are smoothed with a Kalman?lter(KF).The distance between a GPS position?x and the sur-rounding road segments is obtained using the point-to-curve matching concept.Weights are then given to seg-ments based on the calculated distances.Their results suggest that the algorithm identi?es96%of the road segments correctly(based on1075position?xes).However,this may not always be the case in urban areas, as the point-to-curve method does not fully consider the topology of the road network.

Najjar and Bonnifait(2003)developed a novel road-matching algorithm to support real-time car navigation systems.The study?rst describes the integration of DGPS with ABS(Anti-lock Braking System)sensors for continuous positioning information.The vehicle,in this case is not equipped with any additional extra sensors such as a gyroscope.Their map-matching method is based on several criteria using Belief Theory.These are the proximity criterion(based on the distance between the position?x and the link)and the heading criterion (based on the di?erence between the heading of the vehicle and the direction of the candidate segment).Based on each criterion,a degree of Belief(yes,perhaps,no)is assigned to each link.These criteria are then combined

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using Demspter–Shafer’s rule.If a link is associated with a large Belief to the yes hypothesis from both criteria, then it is selected as the correct link.The method however,can give inaccurate results in parallel streets,as it does not consider the topology of the road network.The algorithm does not take into account the errors asso-ciated with the navigation sensors and the digital spatial road network data.Therefore,the location of the vehicle estimated by the algorithm may not be robust as it will be adversely a?ected by these errors.

Syed and Cannon(2004)also describe a map-matching algorithm based on a fuzzy logic model.The algo-rithm consists of two sub-algorithms:(1)?rst?x mode,and(2)tracking mode.In the?rst sub-algorithm,a fuzzy inference system(FIS3)is used to identify the correct link for the initial position?x.Following the iden-ti?cation of the?rst link and the location of the vehicle on it,the algorithm then goes into the second sub-algo-rithm.Another FIS is used to see whether the subsequent position?xes can be matched to the link identi?ed in the?rst?x mode.The inputs are proximity,orientation and distance travelled by the vehicle along the link.The algorithm normally takes about30s in order to complete the?rst?x mode.This is too long for some services such as en-route guidance where the vehicle can travel through several junctions within this time period.In addition the map matching algorithm does not take into account the error sources associated with the naviga-tion sensors and the digital maps when estimating the location of the vehicle on the identi?ed road segment.

Fu et al.(2004)propose a hybrid map-matching algorithm by analysing the geometry of the road network.

A fuzzy logic model is used to identify the correct link among the candidate links.Two inputs used in the FIS are:(1)the minimum distance between the position?x and the link,and(2)the di?erence between the vehicle direction and the link direction.The single output of the fuzzy inference system is the possibility of matching the position?x to a link.This simple fuzzy logic model is sensitive to measurement noise.Moreover,the vehi-cle heading obtained from GPS is inaccurate at low speed.This has not been taken into account.As the algo-rithm selects a link for each position?x with no reference to historical trajectory,there is a high possibility of selecting a wrong link,especially at junctions.

Pyo et al.(2001)developed a map-matching algorithm using the Multiple Hypothesis Technique(MHT). The MHT,which uses measurements from a validation region,is re-formulated as a single target problem to develop the map-matching method.Pseudo-measurements are generated for all links within the validation region as de?ned using the error ellipse derived from the navigation sensors(GPS/DR).Pseudo-measurements (position and heading)are de?ned as the projected points of the GPS/DR positions on the links.The topo-logical analysis of the road network(connectivity,orientation,and road design parameters)together with the pseudo-measurements are used to derive a set of hypotheses and their probabilities for each GPS/DR sen-sor output.The main disadvantage of this map-matching algorithm is that it does not have a method for initial map-matching.The performance of the subsequent matching largely depends on the initial matching and thus potential mismatches are likely.

Quddus et al.(2006b)developed a fuzzy logic map-matching algorithm that attempts to overcome some of the limitations of the existing map matching algorithms described above.The algorithm uses a number of new input variables(at no extra cost).These are:(1)the speed of the vehicle,(2)the connectivity among road links, (3)the quality of position solution,and(4)the position of a?x relative to a candidate link.These inputs are incorporated into the fuzzy rules in order to improve the performance of the algorithm.Three sets of knowl-edge-based fuzzy rules are formulated when the navigation solution comes from either stand-alone GPS or GPS/DR.The?rst set(six rules)is for an initial map-matching process(IMP),the second set(thirteen rules) is for subsequent map-matching on a link(SMP-Link),and the third set(four additional rules)is for subse-quent map-matching at a junction(SMP-Junction).Zhao(1997)developed a total of eight rules in the case of a navigation solution obtained from a DR sensor.These eight rules do not represent completely the stand-alone GPS or the integrated GPS/DR scenarios.

2.5.Performance of existing map-matching algorithms

The formulations of most of the existing map-matching algorithms are not accompanied by methodologies for performance assessment.The few performances reported are captured in Table1.

3A fuzzy inference system(FIS)is the process of formulating the mapping from a given input to an output using fuzzy logic.

The navigation sensors used in the algorithms,the test environments,the percentage of correct link iden-ti?cation,and the 2-D horizontal accuracy are shown in columns two,three,four,and ?ve respectively.The most frequently used navigation sensors are either GPS or GPS/DR.The percentage of correct link detection ranges from 86(White et al.,2000)to 99(Quddus et al.,2006b ).The 2-D horizontal positioning accuracy ranges from 18m to 5.5m (95%).Very few studies state the scale of the digital spatial road network data used to estimate performance.As shown in Zhang et al.(2003)and Quddus et al.(2006a),the quality of spatial data can have a large impact on map-matching performance.

3.Constraints and limitations

Some of the map-matching algorithms discussed in the previous sections possess the capability to support the navigation module of many ITS applications and services.For example,a positioning accuracy up to 5.5m (95%)is achievable within suburban areas using some algorithms.Among these algorithms,the fuzzy logic map-matching algorithm provides the best performance both in urban and suburban areas.However,as reviewed in the previous sections,there are a number of issues that hinder the maximum exploitation of the current map-matching algorithms.These are discussed in the following sections.

3.1.Problems with initial map-matching processes

Most of the map-matching algorithms start with an initial matching process.The purpose of this process is to select road segments that fall within an error ellipse 4based on the errors associated with the navigation device.The initial matching process ?rst selects all nodes (i.e.,road junctions)or shape points (i.e.,road topol-ogy)that are within the error ellipse.The segments that originate from (or are destined to)these nodes or shape points are considered to be candidate segments.Although this process normally identi?es the correct segment near a junction or a shape point,there may be some circumstances in which the initial matching pro-cess needs to start on a road segment which is further from the junctions or shape points.The error ellipse then does not contain any junctions or shape points (see Fig.1).Consequently,the initial matching process com-monly used in the literature would identify no road segments and would assume that the vehicle is o?the known road network.This is incorrect as the vehicle can be on link AB in the case of Fig.1.This type of sce-Table 1

The performance of some existing map-matching algorithms

Authors and year of

publication

Navigation sensors Test Environments Correct Link Identi?cation (%)Horizontal Accuracy (m)Kim et al.(2000)

GPS Suburban –10.6(100%)Kim and Kim (2001)

GPS/DR Urban and suburban –15m (100%)White et al.(2000)

GPS Suburban 85.8–Pyo et al.(2001)

GPS/DR Urban and suburban 88.8–Taylor et al.(2001)

GPS +Height Suburban –11.6(95%)Bouju et al.(2002)

GPS Suburban 91.7–Yang et al.(2003)

GPS Suburban 96–Quddus et al.(2003)

GPS/DR Map scale –1:2500Urban and suburban 88.618.1(95%)Syed and Cannon (2004)

GPS/DR Urban and suburban 92.8–Ochieng et al.(2004)

GPS/DR Map scale –1:2500Urban and suburban 98.19.1(95%)Quddus et al.(2006b)GPS/DR Map scale –

1:2500Urban and suburban 99.2 5.5(95%)

4An error ellipse is determined based on the assumption that the error has a Gaussian distribution.

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nario can occur if the map-matching process needs to be re-initialised between the middle of two junctions (e.g.,on a motorway)and if there is no historical information available.This type of ambiguity needs to be resolved.

A possible solution would be to consider an error circle with a radius equal to the semi-major axis of the error ellipse.This gives an assurance that the error circle selects all segments that would have been selected by the error ellipse.Moreover,the error circle is relatively easy to formulate compared to an error ellipse.Alter-natively,a 99%or more positioning probability circle which is equal to the 3-drms (distance root mean square error)or the 2.6?CEP (circular error probable 5)could be used to formulate the error circle.A straight line representing the road segment can then be formed using the coordinates of the starting node and end node.This is a one-o?o?ine process that could be performed for all road segments within the spatial digital map data.Therefore,the problem of identifying road segments within an error ellipse then transforms to the prob-lem of identifying whether a line intersects a circle or not.This is known as a circle-line intersection problem and should eliminate the issues related to the initial map-matching processes discussed above.

3.2.Problems with threshold values

A variety of thresholds are used to make the correct decision during various decision-making processes within host map-matching algorithms.For instance,the threshold for the minimum speed at which the head-ing of the vehicle from stand-alone GPS is incorrect is taken as 3m/s by Taylor et al.(2001)and Quddus et al.(2003).The threshold values are commonly derived empirically from a series of ?eld observations.A more analytical approach may be needed to improve this value.Moreover,the values of various weighting param-eters used in map-matching algorithms can vary based on di?erent operational environments.For example,the values of a and b (the weighting parameters for heading and relative position respectively)used in the algo-rithm developed by Quddus et al.(2003)are based on using data from London.These values may be di?erent when they are derived using data from another city and may also be dependent on the type of sensor equip-ment used.The transferability of the method to determine weighting parameters for map matching in di?erent operational scenarios is a key research issue.

3.3.Problems at Y-junctions

The techniques used in existing map-matching algorithms may fail to identify the correct road segment at or near a Y-junction as shown in Fig.2.Given that a map-matching algorithm identi?es the correct link,AB,for the position ?xes P1and P2,the identi?cation of the correct link for the ?x P3may be incorrect if the

perpen-51CEP =1.17?(1-sigma standard error).

320M.A.Quddus et al./Transportation Research Part C 15(2007)312–328

dicular distance from P3to link BC and BD is almost equal ,and the heading of the vehicle from the navigation sensor is 90°.

This type of hypothetical road network may be observed in motorway diverging scenarios.Further improvements of map-matching algorithms should focus on this type of scenario.One possible suggestion is to consider the sign of the di?erence in heading between ?xes P3and P2as an additional input to the map-matching algorithm.For instance,a positive heading change between ?xes P3and P2implies that the vehicle is on link BD and a negative heading change implies that the vehicle is on link BC.

3.4.Consideration of road design parameters in map-matching

Road design parameters such as turn restrictions at junctions,roadway classi?cation (such as one-way,two-way),width of the carriageway,number of lanes,and overpass and underpass information are normally not included as inputs to existing map-matching algorithms as the data have not been readily available.The availability of such attribute data could potentially improve the performance of map-matching algorithms especially at junctions.

3.5.Height data from the navigation sensors

Map-matching algorithms normally do not make use of height data from a navigation sensor.6This height data together with the data from a 3-D digital road network map can e?ectively identify the correct road seg-ment at a section of roadway with ?y-overs.However,this will largely depend on the accuracy of height data and the availability of a high-quality 3-D road map.

3.6.Spatial road network data quality

The review of the literature suggests that spatial road network data have both geometric and topological errors (Noronha and Goodchild,2000;Kim et al.,2000;Zhang et al.,2003).It is envisaged that the position ?xes from a stand-alone GPS,speci?cally in an open-space environment,could be better than the map-matched positions if a poor quality map is employed in the map-matching algorithm.Therefore,the quality of the spatial road map data that is used may a?ect the performance of a map matching method.Quddus et al.(2006a)demonstrated this to an extent by evaluating the e?ects of the digital spatial road network data on the performance of map-matching algorithms using map scales 7of 1:1250,1:2500,and 1:50000.Further

research 6

Note that height data from GPS are not as accurate as horizontal positioning data.

7Map scale can be de?ned as the ratio of distance on a map over the corresponding distance on the ground,represented as 1:M where M is the scale denominator.Map scale is an issue because as scale becomes larger the amount of detail that can be presented in a map is also increased.M.A.Quddus et al./Transportation Research Part C 15(2007)312–328321

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is required to characterise the link between spatial data quality and the performance of map-matching algo-rithms.For example,in addition to the work in Quddus et al.(2006a),other scales should be studied,e.g. 1:5000,1:10,000,and1:25,000.

Topological errors due to features which are omitted or simpli?ed in some road maps are typically ignored by many of the algorithms.These features can include roundabouts,junctions,medians,and curves,amongst others.For example,large roundabouts might be represented by a single node(i.e.a point).Before implement-ing a map-matching algorithm,a thorough check needs to be carried out to identify such?aws in any spatial road network data.The error budget associated with spatial road network data can be derived from?eld experiments and can be incorporated into the map-matching process.In addition to this,it would be interest-ing to see how other topological features of road maps such as a multi-centreline representation of a carriage-way and a representation of a roundabout by a node,a?ect the performance of a map-matching algorithm. Initial results by Meng(2006)suggest that spatial road network data that represent a carriageway by multiple centrelines(i.e.,a centreline for each lane)provide incorrect results for identifying road segments.Further research is essential to quantify such errors in terms of the estimation of the location of a vehicle on a link.

3.7.Techniques used in the map-matching processes

The methods used in the map-matching algorithms vary greatly from using simple search techniques to highly mathematical approaches.The performance and speed of the algorithms in turn largely depend on the technique used in the algorithm.For instance,it has been found that the fuzzy logic based map-matching algorithms provide better performance compared with other methods for the same inputs.Other potential techniques would be to employ a pattern recognition approach,or a hierarchical fuzzy inference system opti-mised by a genetic algorithm(GA),or a hybrid method.Currently,map-matching algorithms generate out-puts exactly on the centreline of a road segment.This may be desirable for many ITS applications. However,some ITS applications require more accurate positioning information.Therefore,the current meth-ods introduce large errors in the location estimation,especially in the case of low resolution spatial road net-work data.A method should be developed so that the?nal positioning outputs from the map-matching algorithm can optimally be determined anywhere within the edges of the carriageway.

3.8.Validation issues

Validation of a map-matching algorithm is essential to derive statistics on its performance in terms of cor-rect link identi?cation and vehicle location determination.A precise vehicle reference(true)trajectory is required in order to assess performance.Very few existing map-matching algorithms provide a meaningful val-idation technique.Although some studies report on the accuracy of map-matching algorithms,it is unclear how this is determined.Kim et al.(2000)use code-based DGPS to obtain reference vehicle positions.How-ever,the performance of DGPS is strongly a?ected by signal multipath,among other factors,and varies according to the surrounding environments.The typical accuracy of DGPS is of the order of0.5–5m (95%)(US DoA,2003).Consequently,the vehicle positions obtained from DGPS may not be suitable to derive the reference trajectory of the vehicle.Quddus et al.(2004)employ high accuracy GPS carrier phase observations in order to validate the performance of a map-matching algorithm.However,it is not always possible to obtain GPS carrier phase observations in dense urban areas due to inherent problems associated with GPS signal masking and multipath error.A tightly coupled integrated navigation system employing GPS and a high-grade Inertial Navigation System(INS)could be used to obtain reference vehicle trajectories in urban areas at the centimetre level.This is a key area for further research to validate new and existing algo-rithms.Currently the carrier phase measurements are di?erenced across receivers and satellites(double di?er-ences)in order to mitigate the e?ects of some of the error sources.In future,there is the possibility of using the undi?erenced measurements made by a single receiver to realise centimetric positioning accuracy.This is refereed to as real-time precise point positioning(PPP).

The PPP concept is based on the(near)real-time availability of precise GPS satellite orbit and clock data (Heroux et al.,2001;Shen and Gao,2002).This is a relatively new area of research in the?eld of positioning and navigation and is based on the processing of un-di?erenced pseudo-range and carrier phase observations

M.A.Quddus et al./Transportation Research Part C15(2007)312–328323 from a single GPS receiver.It has the potential to provide global position accuracy at the level of decimetres to centimetres in stand-alone kinematic and static modes.A number of researchers and institutions around the world are developing models for predicting ephemeris and satellite clock corrections,which would help to make real-time PPP possible.

The e?ectiveness of the use of a carrier phased based reference trajectory for the measurement of the per-formance of map-matching algorithms should be evaluated for the two components of map-matching:link identi?cation and accuracy of map-matched position.The?rst is a?ected directly by the quality of the spatial database in addition to other factors.Hence,even if a GPS/High-grade INS is used,there is the real possibility of error in link identi?cation.One way of dealing with this is to use the carrier phase based trajectory together with the calibration of the spatial database by the concept of‘way points’or the adoption of human generated second-by-second link data.Note that the latter may have problems of both spatial and temporal referencing to the map-matched data.The second(i.e.,accuracy)can be measured e?ectively by the use of the‘truth’from an integrated GPS/High-grade INS.Note also that this approach(carrier phase based validation)has the?ex-ibility to operate with the expected improvement in the quality of digital spatial data.

3.9.Integrity(level of con?dence)

The integrity of a map-matching algorithm directly re?ects the level of con?dence that can be placed in the map-matched position.Integrity measures can be used to detect a failure in the map-matching process.The detection capability can be utilised to provide a timely warning to the driver that the position solution should not be used for navigation or positioning,and to aid the algorithms in recovery from the failure mode.Quddus (2006)describes a simple empirical method to derive the integrity of a map-matching algorithm.Yu et al. (2004)developed a technique to detect wrong map-matched solutions and recover from such circumstances. Although the performance of these integrity methods are good for the test route used,it is essential to further investigate their performance with other test routes,especially in urban areas.An alternative to the empirical approach would be to consider more rigorous statistical approaches similar to that used in autonomous integ-rity monitoring systems such as Receiver Autonomous Integrity Monitoring(RAIM)which would be based on consistency checking and redundancy measurements(outlier detection capability)within a sensors/data integration architecture.

3.10.Implementation issues

Map-matching algorithms can be implemented in two ways depending on the application:(1)within an in-vehicle unit;and,(2)within a central station(i.e.,a central server).In the case of the former(e.g.,for route guidance),each vehicle must have a map-matching processor along with other navigation devices and spatial road network data.A communication link with the central server is only required if other information(such as roadway tra?c conditions)is essential for the application.In the case of implementing the algorithm in a cen-tral server(e.g.,for?eet management),each vehicle contains the navigation sensors.The positioning data (easting,northing,speed,and heading)computed by these sensors are then sent to the central server to improve the accuracy of the vehicle position,and to obtain data on physical location(i.e.,addresses)and road related information using a suitable map-matching algorithm.In this case,communication between the vehicle and the central server is essential.Clearly,there is the potential that the type of implementation including the available functionality will in?uence not only the performance of the map matching algorithms but also the service as a whole.Therefore,a careful trade-o?is required to consider a number of issues including commu-nications requirements and bandwidth,computation load,data management including storage,security,cost, etc.

3.11.Summary

The areas listed above are the ten primary topics where further enhancement of map-matching algorithms is necessary.It is expected that future map-matching algorithms that account for all of these shortcomings will be able to make a signi?cant contribution to the realisation of a system that supports the navigation require-

324M.A.Quddus et al./Transportation Research Part C15(2007)312–328

ments of many ITS services with a high degree of accuracy,integrity,continuity and availability in di?erent operational environments.

4.Impacts of Galileo and EGNOS

The Galileo System will be an independent,global,European-controlled,satellite-based navigation system that will provide a number of services to users equipped with Galileo-compatible receivers.Galileo will also provide a number of navigation and search and rescue(SAR)services globally(ESA,2002).The most relevant services to surface transport are:the Open Service(OS)which will provide positioning,navigation and timing services,free of charge,for mass market navigation applications(such as GPS–SPS),and the Commercial Ser-vice(CS)which will generate revenue by providing added value over the OS including dissemination of encrypted navigation related data(1KBPS),and ranging and timing for professional use with service guar-antees.The additional data provided by Galileo may improve the accuracy of map matching with existing algorithms.

Although GPS provides users with their locations and other derivatives in real-time,limitations on system performance and potential political considerations suggest that stand-alone GPS cannot always meet all the requirements of a range of ITS services and other safety-of-life(SOL)applications.Moreover,the US DoD, which operates GPS,does not guarantee the Standard Positioning Service(SPS).One solution to this is to develop an augmentation to GPS to improve accuracy,integrity,continuity,and availability(Ledinghen and Auroy,2001).Therefore,Europe is developing a satellite-based regional augmentation system,known as the European Geostationary Overlay Service(EGNOS).The development of EGNOS began in the early 1990’s initiated by the Tripartite Group(ETG),comprising the European Space Agency(ESA),European Community(EC),and EUROCONTROL.The initial operation of EGNOS began in July2005(http:// www.essp.be),with full operational capability expected in2007.

The following sections examine the potential impacts of the forthcoming European Galileo and EGNOS systems on the performance of map matching algorithms and whether this will further improve the capability to support the navigation module of ITS services.Note that although the other current and potential satellite-based navigation systems such as(Kaplan and Hegarty,2006)Russia’s GLONASS,China’s Bei-Dou,India’s GAGAN(GPS and Geo-Augmented Navigation)system and IRNSS(Indian Regional Navigation Satellite System),and Japan’s GZSS(Quasi-Zenith Satellite System),have not been addressed here due to a lack of maturity and/or uncertainty(as a result of economic and institutional concerns),they should result in even higher navigation performance particularly with respect to integrity,continuity and availability with limited improvement in accuracy.Such an improvement in navigation performance should improve further the per-formance of map-matching algorithms.

4.1.The impact of Galileo

Most ITS services will be supported by the combination of Galileo Open Service(OS)and the commercial service(CS)which will provide a horizontal positioning accuracy of4m95%of the time when a dual fre-quency Galileo receiver is used.According to US DoD(2001),stand-alone GPS provides an accuracy of 13m(95%).8Hence,the integration of Galileo with DR will provide better positioning?xes than GPS/ DR,thereby improving the performance of map matching algorithms if map quality allows.

The United States and the European Union(EU)signed an agreement to harmonize their respective satel-lite navigation systems:the existing US GPS system and the planned European Galileo system during the US-EU summit in Ireland(https://www.wendangku.net/doc/5615792672.html,eu.be/Galileo).As a result,this opened the door for future navigation receivers that use both systems.This will provide the capability of computing the receiver location using a sig-ni?cantly increased number of satellites.Therefore,the deployment of Galileo and the modernization of GPS over the next few years will have a profound impact on future GNSS receiver design.Currently,ITS applica-tions use single frequency GPS receivers.In the future it will be possible to have a?ordable multi-frequency 8Although higher accuracy is generally achieved in practice particularly in open areas.

M.A.Quddus et al./Transportation Research Part C15(2007)312–328325 receivers.Such receivers will provide higher positioning accuracy and will be able to acquire and track lower strength signals compared to the current single frequency GPS receivers.Furthermore,the impact of RF inter-ference could be reduced using techniques including the use of special antennas(e.g.beam steering)and/or redundant sensors.In general,the cost of a future integrated GNSS receiver(GPS+Galileo)will be on the order of10–20%more than a single-system receiver(Rizos,2005).

However,the navigation module of any ITS services will still require the support of a robust map-matching algorithm.This is because the positioning?xes obtained from an integrated GNSS receiver will still need to be placed on a known-road network where a spatial digital map is used as a physical reference for the vehicle. Although the availability of satellites from a particular point on the earth will be increased,the e?ect of mul-tipath and satellite blockage(including weak satellite con?gurations)on the performance of the quality of nav-igation solutions will remain a critical issue,especially in urban areas.The implication of this will be that augmentation with other sensors such as DR will still be required.A good example of this is the urban canyon where although more satellites will be visible,the geometry of the satellites with respect to the receiver will still be relatively weak.In such cases,a map-matching algorithm will be required not only to identify the physical location of the vehicle but also to improve accuracy and availability of the positioning service.

4.2.The impact of EGNOS

EGNOS was designed to provide potential users with the following services(Sauer,2004).

?GEO ranging:Under this service,3GEO satellites transmit GPS-like L1band signals which improve the availability.

?Integrity channel:GPS/EGNOS integrity information is available in this service.This is expected to satisfy the integrity requirement for civil aviation up to Category I(CAT I)Precision Approach.

?Wide area di?erential:This service includes the broadcast of di?erential correction data to users.This enhances the accuracy of GPS/EGNOS.

Ledinghen and Auroy(2001)provide an overview of how Europe-wide civil aviation can bene?t from EGNOS.Other transport sectors,especially land transport can also bene?t from the use of EGNOS.The fol-lowing scenarios are considered to discuss the potential impact of EGNOS on the performance of map-match-ing algorithms.

Scenario1(GPS+DR+Map Matching):This is the basic scenario on which current map-matching research is based.In this scenario,a map matching algorithm takes inputs from an integrated GPS/DR and a road map.The quality of the position solution from the GPS/DR depends among other things on the duration of GPS outage.In such cases,the performance of a map-matching algorithm depends on the per-formance of DR sensors,especially in a dense urban area where the visibility of GPS satellites is poor.

Scenario2(GPS+EGNOS+DR+Map Matching):In this scenario,a GPS receiver will be capable of receiving data as transmitted by the di?erent services provided by EGNOS.This will facilitate obtaining good quality GPS data due to the availability of di?erential corrections and marginally better geometry due to the additional signal(GEO L1)for some instances where the EGNOS GEO satellites will be visible.This will lead to a marginally better positioning accuracy.Therefore,the improvement in the performance of map-matching algorithms is expected to be marginal,especially in urban areas.

Scenario3(GPS+EGNOS(SISNet)+DR+Map Matching):In this scenario,the problem of the unavail-ability of GEO satellites in urban areas(scenario2)is addressed to some extent by the development of an internet solution called SISNeT9which provides wide-area di?erential correction data via the internet.SIS-NeT gives access to the corrections and the integrity information of EGNOS.Any user with access to the internet(usually through wireless networks e.g.,GSM or GPRS)can access EGNOS through SISNeT.This will provide EGNOS di?erential correction data continuously.Therefore,it will be possible to have better quality GPS data for visible satellites at all times.This will always lead to better quality positioning data.With 9http://esamultimedia.esa.int/docs/egnos/estb/sisnet/sisnet.htm

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SISNet,visibility of a GEO satellite will not be required,therefore,the GEO L1data is e?ectively lost,and there will be no improvement in satellite geometry.However,there will be signi?cant improvement in position-ing accuracy when the geometry allows.Consequently,the improvement in the performance of the map-matching algorithm will be signi?cant and only constrained by the spatial road network data quality.

Scenario4(GPS+EGNOS(conventional+SISNet)+DR+Map Matching):This is the combination of scenarios2and3.In this scenario,a GPS receiver will be capable of receiving both di?erential correction data via SISNet and the additional data(GEO L1)directly from EGNOS.This will lead to better positioning data at all times and a marginal improvement in geometry for some instances.The performance of map matching algorithms will be improved if the spatial road network data quality allows.

Clearly further research is required to quantify the impact of each of the scenarios above not only on the geometric positioning capability(as inputs to the map-matching process)but also on the overall performance.

On the issue of integrity,the EGNOS integrity service monitors GPS satellite signals to generate integrity information.The latter is broadcast to the users in terms of use/don’t use and other parameters that are used by the user receiver to carry out failure detection.This requires not only the required navigation performance parameters,but also the computation of the protection levels.The drawback of this approach of external integrity monitoring is that it does not account for errors local to the user.The other relevant issue is that of the combined use of data from GPS and other sensors such as DR and digital spatial databases.Clearly, a good approach to monitoring the integrity of map-matching algorithms could account for these issues. Therefore,the autonomous integrity monitoring concept(as highlighted in Section3.9)applied at the user receiver level is appropriate in this case.This process should include methods to determine the test statistic(s), threshold(s)and protection levels.

5.Conclusions

The navigation function of an intelligent transport system can be supported by a map-matching algorithm that integrates positioning data with spatial road network data.This paper has presented an in-depth litera-ture review of map-matching algorithms.A number of di?erent techniques are used in the map-matching pro-cesses such as simple search techniques(e.g.,point-to-point matching,point-to-curve matching)and complex ones including the applications of probability theory,fuzzy logic theory,and belief theory.These algorithms are not always capable of supporting the navigation module of some ITS applications such as bus priority at junctions,especially in dense urban areas.Therefore,to achieve the required navigation performance for some ITS services,further research and improvements to map-matching algorithms are essential.This paper has identi?ed a number of constraints and limitations of existing map-matching algorithms and has suggested key areas for further research.The key constraints and limitations are the problems associated with initial identi?cation of vehicle positions,the problem of matching positioning?xes in complex road lay-outs(such as Y-junctions and?y-overs),performance evaluation,especially in dense urban areas,and development of con?dence indicators.These algorithm enhancements will be aided by enhanced and new systems including EGNOS and Galileo to o?er a signi?cantly improved performance capable of supporting a wide variety of ITS services in di?erent operational scenarios.

In terms of implementation,most of the map-matching algorithms reviewed here have been developed in recent years with details on their actual implementation(for example in commercial applications of advanced traveller information systems)not being available in the public domain.However,the recent increase in col-laboration between industry and research establishments in this area is indicative of a strong interest from industry in advanced map matching algorithms.

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激励机制设计的五个原则

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虚拟演播室方案

虚拟演播室是视频技术于计算机技术结合的产物，把计算机图形图像处理技术与传统的色键技术集合起来形成的。是一种新颖的独特的电视节目制作技术。 虚拟演播室技术原理：虚拟演播室技术与色键技术十分相像，他是由前景主持人为主的画面和背景画面，采用色键的方法构成一个整体，产生人物置身于背景中的组合画面。 虚拟演播室工作原理 虚拟演播室装修的总体要求： 建立一个功能完善的虚拟演播室，需要做到如下基本要求: 1、要求演播室的拾音空间首先具有较好的语言清晰度、可懂度，其次是要有良好的声音丰满度， 2、要求演播室内各处要有合适的响度和均匀度，具有相应的满足拾音要求的混响频率特性。 3、抑制影响听、拾音音质的声缺陷，防止出现声聚焦、驻波、颤动回声、低频嗡声等。 4、演播室内墙面的声学装饰考虑在装饰大方美观、造型新颖的基础上对于高中低各频段的声学处理方式，特别是低频段的声学处理方式方法。 演播室的建声指标：混响时间≤0.6S±0.05S；噪声评价曲线NR-30---NR-35。 设计的隔声门隔声量大于35dB并具有好的密封性。 5、演播室声学建声装饰所选用的材料符合国家相应的强制消防要求，要求采用达到B1、B2级标准的材料。 6、演播室声学建声装饰所选用的材料符合国家相应的强制环保要求，特别是要求甲醛的释放量为＜0。1mg/m3。墙面装饰层内禁止使用不安全和危害性较高的吸声材料。 7、装饰踢脚线兼做视音频线槽并做屏蔽处理。 8、演播室配置录制指示灯和紧急逃生指示灯。 9、装饰层内的综合布线按要求做穿管处理。 10、演播室现有的位置南边部分为玻璃幕墙，不利于演播室的隔声，所以要对原幕墙部分进行隔断，制作隔声封闭处理，在保证整体装饰的美观性和隔声性的同时，还应保证演播室正常的通风换气。 11、导控室地面用防静电地板，装修过程中做好设备布线（强电，弱电），做好防雷，接地各类设施的设计施工。 12、装修预留好空调位置，并配合本台做好空调，配电等设备的安装施工。

促销活动中激励机制如何设置

促销活动中激励机制如何设置您的激励措施达到了激励目的了吗？ 现今，很多商场在做大型促销活动的时候，均设置了动员与激励环节，以期望最大限度地调动活动参与人员的积极性，提高活动的接单量。大量的事实也证明，好的激励方案对活动的成功开展的确起到了积极的作用，值得肯定。但是，也出现了不少“无效激励”的现象，需要进行改良。 问题的关键是激励机制如何制定才能真正实现激发个人或团队斗志，提高团队协同作战能力、营造良好的分享氛围、提高活动接单量等，而不至于出现激励后遗症，这是管理者在制定激励方案时需要仔细斟酌和考量的。 激励措施的分类 激励按性质划分，一般分为正激励与负激励两种；按方式划分，一般分为物质奖励与精神奖励两种；按形式划分，一般分为团队激励与个人激励两种；按周期划分，一般分为短期激励与长期激励两种等等，形式多种多样，在此就不一一赘述。 仔细看了很多商场提交的活动方案，相当一部分的商场在活动期间的激励方案如下： 个人奖励：1—5单以上，奖励20元/单；6—10单以上，奖励30元/单；10—20单以上，奖励45元/单；21—30单以上，奖励60元/单；31—40单以上，奖励80元/单 集体奖励：完成目标180单，奖励集体漂流或旅游一次。 这种做法的好处是简单、易操作，但存在的弊端是过于简单，同时还存在很多漏洞，不一定能达到激励的目的。 制定激励方案 在制定激励方案时，须综合考虑目标可实现性、团队氛围、个人接单能力、活动力度（与自己以往比、与对手比）、活动宣传面、活动开展时间（淡季还是旺季）、以往所采用过的方式方法等因素，同时切忌简单的复制商场的激励方案，否则，就会出现有激励无动力的局面，达不到激励的目的。 总体目标的制定建议分三级设置：确保级（及格线）、力争级（成功线），冲击级（非常成功线），这样的好处是可望又可及。如：保60单、争80单、冲100单，再设置相应的奖励。 团队目标和个人目标的设定上，同样也采用三级别设置，这样的好处是能将总目标分解到具体的每个人，真正实现千斤重担人人挑，人人头上有指标。

虚拟演播室系统方案

VS-VSCENE 虚拟演播室系统方案建议书北京华视恒通系统技术有限公司

北京华视恒通系统技术有限公司 目栩 公司简介................................................................................................................................................................... 3.. . 惊）前悅........................................................................................................................................................................................ 4.. . . 二）系统方案设计.................................................................................................................................................. 4.. . 1、设计原则........................................................................................................................................... 4.. . 2、设计方案........................................................................................................................................... 5.. . 3、系统结构原枞图............................................................................................................................. 7.. . 4、系统功能特点 ................................................................................................................................ 1..0. 5、TOPACK-C抠K 像卡................................................................................................................ 1..2 6、TOPACK-CG/AUD旓IO幕混愃卡 ................................................................................ 1..3 三）软件系统功能................................................................................................................................................. 1..5. 1、系统参数设敢 ................................................................................................................................ 1..5. 2、抠像参数设敢 ................................................................................................................................ 1..7. 3、场景编排.......................................................................................................................................... 1..8. 4、实时控敥.......................................................................................................................................... 2..0. 5、远程旓幕客户端............................................................................................................................ 2..2. 四）设备悪本及效果图........................................................................................................................................ 2..3. 五）系统配敢........................................................................................................................................................................................ 2..4 . 售后服务措施及承诺 ............................................................................................................................................. 2..6.

员工激励机制全套方案设计

封面 作者：ZHANGJIAN 仅供个人学习，勿做商业用途

员工激励机制方案 人力资源是现代企业的战略性资源，也是企业发展的最关键的因素，而激励开发是人力资源的重要手段。企业实行激励机制的最根本的目的是正确地诱导员工的工作动机，使他们在实现组织目标的同时实现自身的需要，增加其满意度，从而使他们的积极性和创造性保持和发扬到最佳状态。建立一套科学有效的激励机制直接关系企业的生存和发展。在企业激励机制的创建中，不能忽视人的需要的作用，只有建立以人为本的激励机制，才能使其在企业的生存和发展中发挥巨大的作用。 一、员工的基本需要（本中心的工资激励制度） 激励来源于需要。作为企业的经营者首先应该了解员工除了薪酬和福利待遇等最基本的需要之外还存在着如安全的需要、归属的需要、社会的需要、自我价值实现的需要等多方面的需求。物质需要仅仅是员工基本需要的一个方面。实际上员工的需要是多种多样的，不同的人有不同的需要，员工共同的需要就是企业的需要。人们有了需求才会有动力，当然员工的需求必须是他经过努力后才能达到的，这样才能起到激励的作用。因此，建立合理有效的激励机制，就必须根据员工的需要对激励的目标和方法进行具体的研究，采取多方面的激励途径和方法与之相适应，在“以人为本”的员工管理模式基础上建立企业的激励机制。从本中心的激励模式来分析，员工的满意度达不到理想的程度，难以留住人才。 二、激励的基本方式 一般来说，根据需求的不同，可将激励分为四大类；成就激励、能力激励、环境激励和物质激励。 (一)成就激励 近代著名管理学家麦克利兰明确的将人在基本需求(生理一安全)之上的部分分为社会交往——权力欲望——成就欲望等三个不同的层次。在人的需求层次中，成就需要是人的一个相对较多的需求层次。成就激励的基本出发点是随着社会的发展、人们的生活水平逐渐提高，越来越多的人在选择工作时不仅仅是为了生存，更多的是为了获得一种成就感，从实际意义上来说，成就激励是员工激励

如何设计一个组织的激励制度

思考：如何设计一个组织的激励制度？ 什么是激励？美国管理学家贝雷尔森（Berelson）和斯坦尼尔（Steiner）给激励下了如下定义：“一切内心要争取的条件、希望、愿望、动力都构成了对人的激励。——它是人类活动的一种内心状态。”人的一切行动都是由某种动机引起的，动机是一种精神状态，它对人的行动起激发、推动、加强的作用。 如何在工作上调动员工的积极性，激发全体员工的创造力，是开发人力资源的最高层次目标。作为企业，需要塑造激发员工创造力的环境和机制：一是创造一个鼓励员工开拓创新精神和冒险精神的宽松环境以及思想活跃和倡导自由探索的氛围；二是建立正确的评价和激励机制，重奖重用有突出业绩的开拓创新者；三是强化企业内的竞争机制，激励人们去研究新动向、新问题，并明确规定适应时代要求的技术创新和管理创新的具体目标；四是要求企业必须组织员工不断学习以更新知识，并好好的引导他们面对现实去研究技术的新动向。同时做到在员工心里，使他们知道工作行为的实际效果，产生员工高效工作、高满足的结果。 对于激励的方式现在学术界有很多种理论和方法，有著名的马斯洛需求层次理论、激励—保健双因素理论，其中激励因素为满意因素，有了它便会得到满意和激励。保健因素为不满意因素，没有它会产生意见和消极行为。其实诸多模式中都不外乎两个方式：正面激励与反面激励。 对此我们可以从上述两个方面入手建立一个适合、有效的激励模式。 薪酬层面： 企业的人力资源管理系统中，薪酬问题无疑是最为敏感的问题之一。长期以来，分配制度上存在的问题一直困扰着众多企业管理系统运行效率与效果。目前，众多国内企业分配制度上都不同程度地存在两个问题，一是分配中的平均主义，这在国企尤为突出；二是薪资支付的随机性，这是众多民营企业的通病。我在公司实践调查中发现，公司老总总是热衷于绩效管理系统的建设，而不愿意对薪酬系统进行相应的变革。他们的理由很简单：进行薪酬系统变革可能对企业绩效没有直接的影响，况且一旦变革，也许就得加工资，这是多数老板们不情愿看到的，因此也就不会搞这个既发精力又增加人力成本的事。所以在激励员工方面是没有到位的。 从总体管理流程来看，薪酬管理属于企业人力资源管理的一个末端环节，特别是在企业最底层的员工，对于他们这个薪酬的激励作用可以说是整个企业中最大的。前面已提到他们大多是从经济水平低的农村来的，所以物质的满足即是他们工作最重要的目的了。针对员工我们可以采用以下方法建立薪酬机制： 其一是废除官僚的行政级别制度建立以市场为导向的薪酬机制 在薪酬制度上企业一般采用行政级别制，在这种制度下员工的发展是极为单向的，要想多赚钱只有“熬”级别，通过对制造企业的岗位分析，其车间员工占一个相当大的比例，余下的或做技术的员工，或做销售的员工，他们不可能都安排担任行政的级别，在这种现状下，上至高层领导、中至车间领导、下至基层员工，三者继续倚老卖老、抱残守缺、继续维系个人利益、裙带利益和派系利益。要想清除这种不良的现象，必须废除官僚的行政级别制建立以市场为导向的薪酬机制，在这种机制下，薪酬不再以行政级别为标准，而是以员工对企业

建立健全激励机制

建立健全激励机制深化国有企业改革（上） ——四川建立健全国企经营管理者激励机制的探索与启示随着改革的深入，四川省委、省政府进一步认识到：国企要摆脱困境，必须建立起一套符合社会主义市场经济要求的企业制度、管理体制和管理模式。在建立现代企业制度进程中，由于出资者缺位等国情特点，搞活国有企业更有赖于高素质的企业经营管理者，尤其是高素质的职业企业家阶层。而要调动他们的积极性和创造性，必须要有一套适应社会主义市场经济体制要求的激励约束机制。由于四川省委、省政府高度重视，大胆改革，使四川国企经营管理者激励机制的探索建立有较大突破，取得了实质性进展。 一、四川探索国有企业经营管理者激励机制的作法及成效 （一）四川对国企经营管理者的主要激励方式 1、物质激励 四川采取的物质激励形式，归纳起来，主要有六种： (1)业绩计提 报酬结构：基薪＋业绩计提 对省属公司制企业的董事长、总经理或非公司制企业的厂长（经理）主要采用这种激励形式，部分市、县也参照这种形式制定国企经营管理者的报酬方案。其基薪主要根据企业规模、本地区和本企业职工平均工资收入水平确定，其公式为： 基薪＝（本企业职工上年度平均工资＋本地区职工上年度平均工资）÷2×系数 公式中“系数”根据企业资产规模等具体情况，确定在2.0-10.0的范围内。业绩计提以基薪为基础，根据其经营实绩确定。赢利企业、亏损企业的考核指标及计算办法有所区别。赢利企业主要考核指标为净资产增值率、净资产收益率和社会贡献率：净资产增值率每增加1%，按基薪的15%计提业绩收入；净资产收益率每增加1%，按基薪的10%计提业绩收入；社会贡献率每增加1%，按基薪的6%计提业绩收入。亏损企业经营管理者业绩收入主要按减亏增盈指标考核，实行分档递减计提的办法，按减亏额的1-2%计提业绩收入。税收指标是计提业绩收入的否定指标，亏损企业的社会贡献率指标也是计提业绩收入的否定指标。各项考核指标基数原则上以上年实际完成数为基础并参考行业和区域内经济效益水平合理确定，考核指标基数一年一定。同时建立国企经营管理者专用帐户，将其业绩收入的50%存入专户，作为经营管理者任期风险保证金，用于抵补经营管理者以后年度可能形成的亏损。经营管理者调动、解聘或退休时，经离任审计一年后，方能兑现余额。 (2)奖售股权 报酬结构：基薪＋股权或股票期权等 对股份制企业，尤其是上市公司的总经理或董事长采用这种激励形式，其他领导班子成员按照小于1的系数进行折算，通过给予不同数量的股权、股票期权来体现其差别。奖售股权分四种情况：一是奖励股。有些市专门设了两种形式的奖励股份：一种是将经营管理者年薪中的部分奖金折算成普通股份；另一种是凡任期内经济效益连续3年上升及做出其他突出贡献的，按企业净资产的含量折算为股份予以奖励。二是出资购买和送配股。长虹集团改制时，要求经营管理人员购买股份，根据职位和工龄，最多8000多股，最少3000多股。其中董事长兼总经理期初持股7900股，逐年送配后现在达65227股。在任期间，这些股票均被“锁定”，不能出售。三是岗位股。南充、眉山等地在国企股份制改造时，对总经理设岗位股，经理班子其他成员按经理的0.5-0.8比例设置。这种岗位股实际上是一种干股，只享有红利分配权。四是股票期权和分配权期权，目前四川正在积极探索试行这种激励方式。 (3)目标定酬 报酬结构：A目标薪金＋B目标薪金等 这种形式没有基薪。对国有宾馆、饭店、旅游公司以及部分商贸企业，尤其是特殊困难亟待解决的企业总经理或兼职董事长一般采用这种激励形式。1999年，绵阳药业集团外聘一位营销部经理，年薪28万元（没有基薪），交30万元股金并作为风险抵押金，确定销售、利润两项目标：完成销售目标任务3200万元，领取销售部分底薪8万元；完成利润目标400万元，领取利润部分底薪20万元。实现销售的超差部分则按同比例增减销售底薪（增加销售薪金的前提是必须完成利润指标）。利润指标完成额未达到400万元，则按比例扣减利润底薪，利润低于274万元，则按未完成额的26.8%扣减收入和风险金；利润超出400万元的部分则按所得税后剩余部分的40%提成，年总收入的25%留在公司作为本人风险抵押金。目标定酬的激励约束作用很大，1-9月，该营销部实现利润470万元，是上年同期利润的8倍，净资产收益率已达71.2%。 (4)准公务员报酬 报酬结构：工资＋津贴、奖金等 对承担政策目标的水、电、气、电信、邮政及对国民经济和国家安全具有特殊战略意义的大型集团

虚拟演播室灯光技术说明

虚拟演播室技术说明 由于虚拟演播室系统不同于传统演播室的抠像,它允许几台摄象机在不同的角度分做推、拉、摇、移等动作。为了保证摄象机在蓝箱中拍摄的人物与计算机制作的虚拟场景通过色键组合成系统准确合成,要求虚拟演播室系统中人物的活动空间(蓝箱)要有非常均匀和柔和的照明,不能有硬的影子出现,所以首先应用柔光灯把蓝箱铺满打匀,形成一个基本光。 根据贵台的实际情况，设计方案如下： 1.篮箱立面墙的布光：在灯具的选择上,虽然近年来国内一些灯 光企业相继推出了虚拟演播室专用灯光设备,但是由于大多数 电视台虚拟演播室是在原有传统演播室中设置的,所以虚拟演 播室的布光可利用传统演播室的灯具进行布光。布光时,我们 首先考虑选用冷光源——4×55W三基色柔光灯9台,由于它是 散射型光源,布光面积大,容易将墙体的光布匀。 2.篮箱地面布光：在虚拟演播室节目制作时,画面如果出人物的 全景,出现虚拟的地面时,这时不但主持人身后和两侧的蓝墙 要有均匀的布光,而且蓝箱的地面也要有非常均匀的照明。本 方案我们采用4×55W三基色柔光灯6台，作为地面布光，使 篮箱地面光线均匀； 3.人物布光：虚拟演播室人物的布光基本方法和对光比的要求, 仍采用传统演播室的三点式布光和对光比的要求,但同时要考 虑到虚拟演播室的特点。灯光人员在布光前要使人物的主光方

向与虚拟场景中的主光方向一致,同时使光的强弱、硬柔、色彩也都要与虚拟场景中的主光方向一致。使人和景在画面上融为一体,看起来真实。方案采用冷热光源混合式布光，用2台6×55W三基色柔光灯作为侧光，4×55W、6×55W三基色柔光灯各2台，1KW透射式聚光灯2台，作为人物的主面光和辅助面光，使拍摄人物更加丰满圆润； 4.吊挂系统采用格珊架式悬挂，充分利用室内空间高度，避免拍 摄全景时发生“穿帮”现象； 5.整个虚拟演播室采用冷热混合光源布光，总功率为9KW、色温 3200k、中心照度900Lux，满足贵台的虚拟演播室拍摄需求。

虚拟演播室方案

SUNUR-VS三维虚拟演播室系统集成方案 一、系统综述 如何在有限的时间内，不用花费大量的精力和财力，就能轻松地搭建出富有创意的演播室，制作出精彩新颖又充满无限魅力的节目？如何在现有的标清环境下选择面向未来的高清系统而不浪费投资？福州索普电子科技有限公司推出的面向未来创新虚拟演播室系统——ＳＵＮＵＲ-ＶＳ，一个先进的、实用的、高度集成的、真三维、全场景的虚拟演播室完整解决方案，可以轻而易举地让您的梦想成真。 SUNUR VS三维虚拟场景解决方案使虚拟演播室系统去除了烦琐的硬件配置和大规模的数据运算，凭借简单的设置和直观的用户界面，使之成为一套功能强大的广播电视节目制作工具。只要利用摄影棚中的一小部分空间搭配绿色或蓝色背景，加上摄影灯光，把人物拍下，通过系统集成的色键器，对摄像机获得的信号与虚拟演播室系统信号进行处理，即可实现演播主体与虚拟场景的合成。从此，不再受狭小空间和景物的限制，使用SUNUR VS三维虚拟演播室系统，充分发挥您的想象力和创造力，便可满足任何电视节目现场直播、后期制作及应用的需要。并且，SUNUR VS 无三维虚拟演播室系统具有颠覆传统的业界最优的性价比。通过极快速的启动时间和极低的成本，SUNUR VS三维虚拟演播室系统能为新闻电视广播、体育、财经、现场访谈、气象、远程教育、娱乐节目、广告、游戏秀以及许多其他应用领域提供理想的硬件和软件解决方案。 二、系统方案设计原则 随着电视业和计算机技术的极速发展，高清制作和播出的要求也离我们越来越

近，虚拟演播室的更新步伐不断加快，大家对节目的制作水平和信号质量要求不断提高，SUNUR VS三维虚拟演播室系统本着"简捷至上"的设计宗旨,充分体现系统的技术先进性、功能完整性、经济实用性、运行可靠性、操作灵活性及系统扩展性,不仅能满足现阶段的需要，同时确保系统在今后相当长一段时间内具有先进性并留有扩展余地。在设计方案的过程中,首先考虑到系统要满足演播室现行技术要求,及其应用领域,同时又符合当今虚拟化的趋势，我们遵循以下几个原则： 1、技术的先进性 SUNUR VS三维虚拟演播室系统是福州索普公司在国外虚拟现实软件的基础上开发而来的真三维虚拟演播室系统，该系统是针对市场反馈，专为广电和电教系统应用量身定做和特别优化设计。 SUNUR VS三维虚拟演播室系统，采用革命性的独特设计，无需传感器，采用独有的虚拟摄像机结构，使得产品的安装、初始调试、使用极其方便，省却了繁琐的安装调试过程，真正作到随架随用，一开就用，迅速快捷。一人即可实现多机位的节目演播操作工作，并且真实人像与实时渲染的三维虚拟背景同步运行。如果用户习惯使用传感器系统时可通过增加传感器实现传统虚拟演播室功能。 SUNUR VS三维虚拟演播室系统一开始设计就采用HDSDI高清输入，并能兼容标清输入。在用户预算可能的情况下可以直接使用高清设备，并实现高清、标清、N制、P制混合输入。并在此基础上开发出基于模拟及HDMI接口输入的配套产品,以满足不同经济条件的用户的不同个性化需求。 2、功能完整性 SUNUR VS三维虚拟演播室系统功能完善。 系统集成了色键器、切换台等多种功能。 您无需使用昂贵的摄像机动作传感器，系统采用独有的虚拟摄像机结构。能够轻松的在3D场景中设置和改变8个不同的虚拟摄像机位置（模拟配置），还可方便地编辑3D场景中摄像机的运动速度和运动轨迹。通过与3D虚拟场景进行实时地无缝结合，可进行多重虚拟摄像机的显示与切换。 在系统配置的动作设计模块中，可以生成实时的镜像反射效果，增强了场景的真实感。

激励机制的设计

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体分析内容如下图所示。 中观环境分析 微观环境的分析即对企业自身的条件进行分析，常用工具是SWOT 分析方法。根据SWOT 分析方法，组织战略目标设定的微观环境分析具体如下表所示。 微观环境分析 优势（S ） 劣势（W ） 1．擅长什么 2．组织有什么新技术 3．能做什么别人做不到的 4．和别人有什么不同 5．顾客为什么来…… 1．什么做不来 2．缺乏什么技术 3．别人有什么比我们好 4．不能够满足何种顾客 5．最近因何失败…… 供应商的议价能力 供方主要通过其提高投入要素价格与降低单位价值质量的能力，来影响行业中现有企业的盈利能力与产品竞争 购买者的议价能力 购买者主要通过其压价与要求提供较高的产品或服务质量的能力，来影响行业中现有企业的盈利能力 替代品的威胁 替代品价格越低、质量越好、用户转换成本越低，其所能产生的竞争压力就强；而这种来自替代品生产者的竞争压力的强度，可以具体通过考察替代品销售增长率、替代品厂家生产能力与盈利扩张情况来加以描述 新进入者的威胁 新企业进入一个行业的可能性大小，取决于进入者主观估计进入所能带来的潜在利益、所需花费的代价与所要承担的风险这三者的相对大小情况。新进入者的威胁程度取决于进入新领域的障碍大小与预期现有企业对于进入者的反应情况 行业竞争者的竞争 行业中的企业的利益都是紧密联系的，各企业竞争战略的目的是使自己的企业获得更加优越的优势，这些构成了 行业竞争者的竞争

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文件：点击文件，选择来自计算机的视频文件，导入完成。 物体：物体是对视频活文件的一个赋予（可以将文件或视频赋予给大屏或主持人） 物体控制：点击此按钮，可对主持人、LOGO或物体，对其进行大小、位置进行改变。 隐藏：对当前选中的对象进行隐藏和显示操作。（一般当前操作的对象会在下方显示） 定向、跟随：在被选对象的旋转操作时，进行定向或跟随操作（建议一般在旋转场景时不要使用跟随）。 16:9：点击按钮，进行4：3和16：9的切换。 抠像设置 应用抠像：对当前对象进行抠像操作应用。

取消抠像：对当前对象取消抠像操作应用。 网格背景：对当前所选对象进行网格背景和黑色背景的切换。黑色背景：对当前所选对象进行网格背景和黑色背景的切换。ALPHA：对所选视频进行alpha通道的抠像。（使要被保留的对象全部变成白色，要去掉的对象全部变成黑色。即黑透白不透）COLOR：对所选视频进行彩色通道的抠像。 黑色通道：对所选视频进行黑色通道抠像的参数调整（不要的对象如背景通过此按钮全部调成黑色） 白色通道：对所选视频进行白色通道抠像的参数调整（要保留的对象如主持人通过此按钮全部调成白色） 边缘色溢：对所选视频进行色溢参数调整（其实是对住要扣的对象进行实际色彩的调整，类似于条白平衡）。 色阀调节：对所选视频进行色阀参数调整（可以理解为对一开始的取色进行色彩饱和度的调整）。另外通过此按钮和白色通道的调整可对要抠对象的边缘进行更加完美的调整。 上边裁边：对所选视频进行上裁边操作。 下边裁边：对所选视频进行下裁边操作。 左边裁边：对所选视频进行左裁边操作。 右边裁边：对所选视频进行右裁边操作。 人物反射：对人物在地面上的反射进行参数调整。 地面反射：对虚拟背景进行在地面上反射参数的调整。

激励机制设计的五个原则

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新媒体公司机构设置薪酬方案及激励机制

新媒体公司机构设置薪 酬方案及激励机制 Document number【SA80SAB-SAA9SYT-SAATC-SA6UT-SA18】

公司机构设置及薪酬方案 为了充分调动公司员工的积极性,增强公司的凝聚力，体现“责、权、利”一致的原则,特拟定该机构设置和薪酬方案：一、公司机构设置 公司拟设置行政部、技术部、编辑部和市场部四个部门。 其中公司设总经理、副总经理两个高管职位，工作职责由董事会决定后授权；行政部下设行政总监等4个岗位，主要负责公司的后勤保障、综合文秘、会务安排、财务管理、人力资源等事务；技术部下设技术总监等5个岗位，主要负责公司“两微一端”及频道技术维护、产品设计和开发、交互设计、运维测试、网络安全等事务；编辑部下设内容总监等3个岗位，主要负责公司“两微一端”及频道内容生产、信息更新、稿件编审、话题策划及版面维护等事务；市场部下设市场总监、频道总监等4个岗位，主要负责频道和项目运营、社群管理、活动策划、项目执行等事务。 二、公司岗位薪酬方案 （一）公司高管及中干 总经理：全面主持公司内容和运营工作。 薪酬：月薪万，根据董事会考核发放年终奖 副总经理：协助总经理以及分管相关工作。 薪酬：底薪万加提成（提成办法见附件），年底公司根据业绩考核发放年终奖。

技术总监：对公司新媒体产品进行研发、设计、制作。 薪酬：年薪20-30万，基本年薪为年薪的70%，余下部分公司考核后发放全额或者部分。 市场总监：带领运营团队全面开展公司的运营工作。 薪酬：底薪分别为1万、万加提成，年底公司根据业绩发放年终奖。 频道总监：带领社群运营团队开展公司的社群运营工作。 薪酬：底薪分别为1万、万加提成，年底公司根据业绩发放年终奖。 内容总监：带领内容团队对公司的两微一端进行内容建设工作。薪酬：底薪分别为1万、万，年底公司根据业绩发放年终奖。 行政总监：统筹管理公司政务、事务、安全保卫、内部服务与对外联络工作。 薪酬：底薪分别为万、1万，年底公司根据业绩发放年终奖。 财务总监：在董事会和总经理的领导下，总管公司会计、报表、公司预算体系建立、经营计划、预算编制、执行与控制工作。薪酬：底薪分别为1万、万，年底公司根据业绩发放年终奖。（二）部门岗位 1、技术部 技术人员：（助理工程师、工程师、高级工程师、首席工程师）

激励机制设计

激励机制设计 （一）激励机制的含义 激励机制的含义是由激励和机制两个含义的有机合成。所谓机制，是指系统内各子系统，各要素之间相互作用、相互联系、相互制约的形式和运动原理以及内在的、本质的工作方式。它包含下几层含义：①机制按照一定的规律自动发生作用并导致一定的结果。②机制不是最终结果，也不是起始原因，它是把期望转化为行动、原因转化为结果的中介。③机制制约并决定着某一事物功能的发挥。 ④在一定的系统中，机制是客观存在的，它所反映的是事物内在的、本质的作用方式和规律，是系统各组成部分之间相互作用的动态关系。⑤机制的优劣是以其作用于系统而导致的系统机能的强弱来评价的。这样，我们就可以把激励机制定义为：在组织系统中，激励主体与激励客体之间通过激励因素相互作用的方式。 （二）激励机制的内容 根据激励的定义，激励机制应包括五个方面的内容。 ⒈诱导因素集合 诱导因素就是用于调动员工积极性的各种奖酬资源。 ⒉行为导向制度 它是组织对其员工所期望的努力方向、行为方式和应遵循的价值观的规定。 ⒊行为幅度制度 它是指对由诱导因素所激发的行为在强度方面的控制规则。通过行为幅度制度，可以将个人的努力水平调整在一定范围之内，以防止一定奖酬对员工的激励效率的快速下降。 ⒋行为时空制度 它是指奖酬制度在时间和空间方面的规定。这方面的规定包括特定的外在性奖酬与特定的绩效相关联的时间限制，员工与一定的工作相结合的时间限制，以及有效行为的空间范围。这样的规定可以防止员工的短期行为和地理无限性，从而使所期望的行为具有一定的持续性，并在一定的时期和空间范围内发生。 ⒌行为归化制度 行为归化是指对成员进行组织同化和对违反行为规范或达不到要求的处罚和教育。组织同化实质上是组织成员不断学习的过程，对组织具有十分重要的意