Data Management in Vehicular Telematics System ... being applied to vehicular telematics system. ... value-added information, to design a new sophisticated.
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies
An Intelligent Data Processing Engine for Spatial Data Management in Vehicular Telematics System Animesh Tripathy
Subhalaxmi Das
Prashanta Kumar Patra
Assistant Professor, Department of Computer Science Engineering, KIIT University, Bhubaneswar, INDIA
Research Associate, Department of Computer Science Engineering, KIIT University. Bhubaneswar, INDIA
Professor, Department of Computer Science Engineering. CET, BPUT, Bhubaneswar, INDIA
A killer application of mobile peer-to-peer networks [7] is resource discovery in transportation. For example, the mobile peer-to peer network approach can be used to disseminate the information for a cab driver to find a cab customer, or vice versa. Or, the driver may use this approach to get the traffic conditions (e.g. average speed) one mile ahead. Safety information (e.g. a malfunctioning brake light in a vehicle) can also be disseminated in this fashion[5]. Furthermore, the approach can be used in social networks; when two singles whose profiles match are in close geographic proximity, one can call the other's cell phone and suggest a short face-to-face meeting[3].
Abstract In this paper we propose an intelligent data processing engine for spatial data management. The spatial data management scheme is being applied to vehicular telematics system. In this paper we also propose the data model for database management of spatiotemporal resource information in centralized telematics system. The platform includes spatiotemporal resource data model, its corresponding entity relationship model, and query model for information prioritization. The proposed query model and its queries provide incentive for peers to participate as information suppliers and intermediaries, information usage strategies, and transaction management. The proposed system has the potential to create a completely new information marketplace. The paper shows how the central call server, call customer, and empty taxi co-ordinates together. With this designed model we have a central call server that works in response to the customer call as well as for an empty taxi. Location history data are required for the central call server to provide the map display of needy customer spot to an empty taxi. Location tracker tracks the location of the taxi and stores it in inmemory structure which provides a history of location traveled by the taxi as to use it in future to generate reports which helps in post accident analysis. Other mechanism such as state tracer and pickup pattern provides further analysis information of the taxi location and speed.
Vehicle Telematics is a term used to define connected vehicles interchanging electronic data [3, 6]. These systems may be used for a number of purposes, including collecting road tolls, intelligent transportation systems, pricing auto insurance, tracking fleet vehicle locations (fleet Telematics), cold store logistics, recovering stolen vehicles, providing automatic collision notification, location-driven driver information services — and more particularly, dedicated short range communications DSRC [2,8] in-vehicle early warning (car accident prevention) notification alerts.
Keywords: Data Processing Engine, Telematics, Global Positioning System, Location Tracker.
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II.
INTRODUCTION
The term ‘Telematics’ is the integrated use of telecommunication and informatics. More specifically it is the science of sending, receiving and storing information via telecommunication devices. With the development of geographic information system (GIS) and telematics technologies, a lot of location based services is penetrating into our everyday life [5]. Real-time location tracking, efficient path finding and vehicle dispatch services also makes a large amount of services location data available to produce value-added information, to design a new sophisticated service. More recently, telematics have been applied specifically to the use of Global Positioning System (GPS) technology integrated with computers and mobile communications technology in automotive navigation systems.
978-0-7695-3915-7/09 $26.00 © 2009 IEEE DOI 10.1109/ACT.2009.132
RELATED WORK DONE
In Vehicle Telematics System, an infrastructure-based telematics networks, each vehicle is equipped with a cellular network interface through which location record is periodically sent to the central call server[1,4]. Each record consist of taxi ID and status fields in addition to the basic GPS data such as time stamp, latitude, longitude, direction, and speed. Map match module searches the link closest to the received coordinate from the digital map, and calculates the position ratio in the link in position field [6]. Based on this classification of stays and destinations, this system investigates two probabilistic models, both with and without first-order markovian conditioning. In the past, location histories have been reconstructed by archaeologists and historians looking at migrating populations [3] or census takers tracking demographics, at temporal resolutions of decades or centuries and spatial resolutions of tens or hundreds of kilometers. 517
Recent advances in location-aware technology, however, allow us to record location histories at a dramatically increased resolution through technologies such as GPS [7], radio triangulation, and localization through mobile phones, 802.11 wireless systems, and RFID tags, it becomes feasible to track individual objects at resolutions of meters in space and seconds in time–in some cases, even greater resolution is possible[8]. III.
Figure 2. Data model for Telematics System
PROPOSED DATA MANAGEMENT
Pick-up Pattern scrutinizes the consecutive reports from the taxi, and catches the status change. Call customer calls to the central call server for an empty taxi and in reply of this call, server searches for nearest empty taxi and sends the taxi ID and approximate time to reach to the customer and also sends a message to the taxi with the customer location and road network[3,7].
A. Structure of Vehicular Telematics System We have adopted the architecture of Taxi Telematics System for describing the Vehicular Telematics System in our paper. There is a central server named central call server and two clients one named is call customer and other one is empty taxi. Central call server has location history data and a spatial data processing engine [2], which is capable of efficiently analyzing the large amount of location history data. The server has a unit which displays the status of each taxi and there is also an empty taxi’s list is maintained by server, which makes easy to find the nearest taxi. In central call server there are some units which also work together for providing the necessary inputs for the spatiotemporal analysis [9]. These units are such as State Tracer, Location Tracker, and Pick-up pattern. State Tracer keeps the track of status of a taxi whether it is empty or not, and pick-up, drop-off and some other services also. This is done through the GPS device fetched with taxi. And it is the duty for driver to change the status of taxi accordingly. State Tracer helps to maintain the list of empty taxis. Location Tracker plots the sequential location change of each vehicle on the digital map and relays the user interaction to the appropriate component. According to time flow the tracker changes the location of each vehicle, replaying the past movement history for the purpose of trajectory analysis. Hence the in-memory structure maintains the location list of vehicles currently active in analysis process. After all, the tracker reads each record from database one by one, finds the vehicle in memory list, updates the location, and refreshes the map. Location tracker flags the vehicle in the in-memory data structured when the map is refreshed. This procedure is very useful to find the vehicles which have passed the specific area or stayed at some facilities such as a gas station or a building.
. Figure 1. Architecture of Taxi Telematics System
B. Three Layered Database Architecture This model generates solutions to transactional and consistency issues that arise in report dissemination, and minimize dependence on any centralized structure. The database architecture has been divided into three layers as shown in Figure 2. The bottom is the data layer, which implements the data model for the system. Above the data layer is the support layer. This layer defines how the data is disseminated and how queries are processed. It also contains transaction management. Transaction Management handles all problems relating to concurrency control of spatial queries in the system. The top is the utility layer, which contains the modules relevant to utilization of the resource information, including relevance evaluation, query language, economic model, and usage strategies. IV.
PROPOSED DATA MODEL
We introduce a unified data model for spatial resources in Telematics System. We illustrate how the data model can be used to represent various resource types even though these resource types are utilized in quite different ways. We propose an opportunistic approach for data dissemination. In this approach, we keep track of a customer and a called taxi. The taxi register at the base station and the transport office handles all requests from customer. These techniques provide a total data model for this approach starting from the traditional ER Model. The ER Model is then mapped to spatial data model. Further this model is represented in terms of unified modeling language.
A. A. The Entity Relationship Model There are many design tools available for conceptual data modeling, but the ER model is one of the most popular ones. The ER model integrates seamlessly with the relational data model, which in turn is one of the most prevalent logical models. Besides the ER model and propelled by the success of the object-oriented design methodology, the UML is another popular conceptual modeling tool. Here we model the Vehicular Telematics System example using the ER model as shown in
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Figure 4. An ER Diagram for Vehicular Telematics System using Pictogram.
Figure3. An ER Diagram for Vehicular Telematics System
C. Extending the ER model with Spatial Concepts The translation of spatial attributes in an ER diagram to spatial table does not take full advantage of the spatial data types. It simply treats spatial attributes as any non-spatial attributes. We now describe emerging trend toward extending ER diagrams with pictograms to provide spatial treatment to spatial data types. This reduces clutter in ER diagram, as well as in resulting relational schema, while improving the quality of spatial modeling. The spatial relationship for example Allocation and Verify can be omitted from the ER diagram and made implicit. The relation representing multivalued spatial attributes and M:N spatial relationship may not be needed in relational schema. Many extensions have been proposed to ER model to make the conceptual modeling of spatial applications easier and more intuitive. The idea is to add constructs that capture and convey the semantics of spatial reasoning and at the same time keep the graphical representation simple. Using pictograms to annotate and extend ER diagram hag been recently proposed. The relational schema for Figure 4 can be simpler than the Figure 3 and implicit relationship as well as spatial data types were can be omitted.
Figure 3. The diagram shows six relationships. Entity Transport-Office participates in five relationships. Entity RouteDetails participates in only one relationship, namely Maintains. The cardinality constraints show that each Route-Details maintained by one Base-Station. Many are implicit in this diagram. For example Request relationship between a Registered-Customer and Transport Office is shown explicitly, but Allocation and Verify is implicit. B. The Relational Model The relational model is one of the most popular logical data models. The popularity and power of this model is a consequence of the simplicity of its structure. We explain the terminology of the relational model in the context of the Vehicular Telematics System example below. Registered-Customer (C_id, C_name, C_add, Mob_no), Taxi (T_id, D_id, Cost, Capacity, Type), Base-Station (B_id, Name, Loc, Src, Dest), Route-Details (R_no, B_id, Src, Dest), Request (Req_no, C_id, Name, Src, Dest, Time, Date), Registration (Regi_no, Time, Date) Transport-Office(Name,C_id,T_id,B_id,D_id,R_no,Time, Date), The spatial attributes and the space-varying attributes in the ER diagram have to be handled in a special way in the relational model. New domains, for example, spatial objects, are represented as new relations. The primary key for these relations is used as foreign keys in relations representing entities containing attributes type using these domains. Corresponding to each one of the attribute, there is a relation: Point and can be represented by
D. Data Modeling with UML UML is one of the emerging standards for conceptual level modeling for object-oriented software design corresponds closely with the concept of classes in the class diagrams of UML as shown in Figure 5. Both entities ad classes have attributes,and may participate in relationships, such as inheritance and aggregation. Classes can also include “methods” besides attributes. Methods are functions encapsulating logic and computational recipes which are usually not part of entities in the ER Model. In UMLCD there is a notion of methods which can be used to modify the behviour of the state of the classes.Some concepts from the ER model are not available in class diagrams. One such concept is weak entities, which depend on another entity for unique identification. Consider electronic mail addresses consisting of a user name and a domain name. Each entity instance is characterized by a user-defined explicit identity,
Point (Pointid, Latitude, Longitude). The Point table has three attributes: pointid, latitude, and longitude. Though there are many other reference systems, the latitude-longitude system is the most familiar, and all other reference systems can be derived from it.
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request to the server. Server Message Handler phase handles the messages send to the server. Initialization A set of call customer locations point CLP = {cCLPj} for j := 1 to m A set of taxi locations point TLP = {tLPi} for i:=1 to n A set of taxiID = {tIDi} for i := 1 to n Boolean EMPTY I {True, False} for i := 1 to n Set EMPTY I := True for all i Algorithm Customer Request Handler: If Customer Request cCLPj 0 Then Repeat Loop Until find a Taxi tIDi If EMPTYi = True Then Process cCLPj { shortestDistance(cCLPj , tIDi, tLPi) allotTaxi(cCLPj, tIDi) EMPTYi := False } Else messageToSERVER(x := Wait for Traffic Signal Change) EndIf EndLoop Else Loop for all tIDi for i := 1 to n If Taxi EMPTY I = True Then hotspotMap(tIDi) EndIf EndLoop EndIf Taxi Request Handler: If Taxi tIDi Request EMPTYi = True Then messageToSERVER(y := Find a nearest Customer Location) EndIf SERVER Message Handler: messageToSERVER(message) If message = x then Process message { Wait Refresh Location Map } Else Customer Request Handler EndIf Figure 6. Proposed Algorithm Details
Figure 5. The Vehicular Telematics System example in UML class diagram
while in UMLCD it is assumed that a system will generate a unique identity for the instantiated classes. V.
PROPOSED ALGORITHM
As per our proposed model there are two clients one is call customer and other one is empty taxi. First we take the mechanism goes on between Call customer and Central call server. When a Call customer makes a call to the Central call server for an empty taxi, the server checks the empty taxi list and there arises three conditions first one –list is empty, means there is no empty taxi available at that time of instance. Second one is there is only one taxi in the list, and the last third one is there are more than one taxi in the list. For the first one condition when list is empty, the server simply waits for traffic signal change. For second condition, when there is only one empty taxi in the list, the server allots that taxi to the customer. And for third one condition the server find the nearest taxi among the list of empty taxi, and allot the nearest taxi to the customer and sends all other taxi the hot customer spot. Now if we take the mechanism between the empty taxi and Central call server then there is only two conditions, either there is any Call customer or no Call customer. When there is any Call customer at the server then server starts the process to allot the taxi to the call customer and when there is no Call customer at server then server send the hot customer spot map to the taxi. As per the algorithm in initialization we take a set of call customer location points, a set of taxi location points, a set of taxi ID, and a Boolean variable EMPTY, which set true initially.
VI.
QUERY PROCESSING
With opportunistic peer-to-peer, each peer maintains a local reports database. The collection of the local databases of all the peers forms a virtual database to the database application in each peer. In this section we discuss the query interface to this virtual database and the query processing issue. In order to present a design of our proposed model a query language using the design is proposed. First let us an example: Consider a transportation application where a passenger needs to transfer from one route to another. Assume that buses can wait for transfer passengers for certain amount of time. Now a transfer passenger Bob wants to transfer to route #8 at a certain intersection P. Bob expects to arrive at P at 10:10. Usually a driver is willing to wait at a stop for a transfer passenger for at most 2 minutes. So Bob wants to notify a route #8 bus to wait him if the bus arrives at P between 10:08 and 10:10. We believe that declarative languages like SQL are the preferred way of express such queries.
This algorithm has three phases Customer Request Handler, Taxi Request Handler, and SERVER Message handler. All these phases are working parallel as the threads. In Customer Request Handler when customer request comes, the server handles all the three conditions and provides an empty taxi to the customer. In this phase we use a function shortestDistance which finds the nearest taxi from the Call customer location. Taxi Request Handler phase handles the empty taxi
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VIII.
DRIVE uses the following query template. SELECT select-list [FROM reports] WHERE where-clause [GROUP BY gb-list [HAVING having-list]] [EPOCH DURATION epoch [FOR time]] [REMOTE query-destination-region [BUDGET]]
The SELECT, FROM, WHERE, GROUP BY and HAVING clauses are very similar to the functionality of SQL. The relation name reports represent the virtual database. The optional EPOCH DURATION clause specifies the time between successive samplings and for how long the query lasts .The optional REMOTE clause specifies whether the query is to be answered by the local database or is to be evaluated in a remote geographic region. If the REMOTE clause is used, then a query destination-region should be further specified; it indicates that the query should be disseminated to all the nodes in the specified region. If the REMOTE clause is omitted, then the query is processed locally. BUDGET specifies how much budget in virtual currency the user is willing to spend for disseminating the query and collecting the answers. If BUDGET is omitted, then the database system automatically sets a budget based on the distance to the query-destination-region, the size of the query destination-region, the peer density Finally, we define a member function Rel() for each report description data type. This function takes as input a set of attributes and it returns the relevance using the input as the relevance attributes. Now we illustrate how our query template can be used to express the query examples given above.
REFERENCES [1] E. Kang, G. Park, and J. Lee. “Design and Implementation of a tour planning system for Telematics users”. Lecture Notes on Computer Science, August 2007, 4707: 179-189. [2] G. Park, H. Kim, J. Lee, P. Kim, S. Kim and Y. Yang. “A telematics service system based on the linux cluster”, Lecture notes in computer science, May 2007, 3340:660- 667. [3] SAE, “Dedicated short range communication message set dictionary”, Society of Automative Engineers, Technical Report Standard J2375, 2006. [4] J. Kurmm and S. Shafer. “Data store issues for location based services”, IEEE Computer Society Bulletin of the Technical Committee on Data Engineering, 2005. [5] K. toyama, and R. hariharan. “Project lachesis: Parsing and modeling location histories”, Lecture notes in computer science, 2004, 3234:106-124. [6] A. V. Savkin, P. N. Pathirana, and S. K. Jha. “Mobility modeling and trajectory prediction for cellular networks with mobile base stations”, 4th International Symposium on mobile Ad Hoc Networking and Computing(MobiHOC), Annapolis, MD, 2003, pp. 213-221. [7] A. Bhattacharya, S. K. Das, LeZi-update. “An informationtheoretic approach to track mobile users in PCS networks”. In: Imielinski, T., and Steenstrup, M., eds. 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, WA, 1999, pp. 1-12. [8] C. Rose, Lie, Z. “Wireless subscriber mobility management using adaptive individual location areas for pcs systems”, IEEE International Conference on Communications (ICC), Atlanta, GA, 1998, vol. 3, pp. 1390-1394 [9] Analp Pathak, et..al “Spatial Data Model for Vehicular Telematic System”, IEEE International Conference on Computioanl Sciences, China, pp 796-799, 2009.
The following query notifies route #8 buses to wait if the bus arrives at P between 10:08 and 10:10. SELECT resource_id FROM reports WHERE resource-type=TAXI and report description. route_no=8 and WITHIN_DISTANCE_SOMETIME_BETWEEN (report description. raj, P, 0, 10:08, 10:10) REMOTE route_of_bus_ #8.
Route_no and Traj are two attributes of a Taxi report. Traj is the trajectory of the Taxi moving object; it defines the object's future location as a piece-wise linear function from time to the two dimensional geography. WITHIN_DISTANCE_SOMETIME_BETWEEN (a,b,c,d,e) is a predicate. It is true iff the distance between moving object a and point location b is within c some time between d and e. In our example it is true iff the bus arrives at P some time between 10:08 and 10:10. If a route #8 bus receives the query and it will wait, then the bus sends Bob an answer to the query. VII.
CONCLUSION
Taxi telematics systems are one of the emerging transportation management and service providing systems which is to be implemented to manage the fleet of taxis and customers waiting or requesting taxis in an efficient and systematic way. The management of taxi-customer interaction is one of the core issues of a taxi telematics system, and needs to be addressed in priority to other services being provided. This paper hence presents a unique data management model to manage the interaction in a very simple elegant and efficient manner. In this paper we devised a platform for dissemination of spatial and temporal resource-information in a centralized server environment and as well as in mobile peer-to-peer network environment. The moving objects also serve as routers of queries and answers. The platform includes spatiotemporal resource data model, database maintenance, relevance evaluation for information prioritization, query language and its corresponding query model.
ALGORITHM COMPLEXITY
The complexity of this algorithm is O (E+m) log n. E is the road segments in road map network and m and n are the Call customer location points and Taxi location points respectively. As this approach provide the nearest empty taxi to the Call customer and vice-versa. For finding the nearest empty taxi it calculates the shortest distance between the Call customer location point and the empty taxi location points using Dijkstra Algorithm.
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