DESC:
FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR PREDICTING TIME OF ARRIVAL OF A VEHICLE

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under The Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application claims priority to Indian Provisional Patent Application No. 3210/MUM/2014, filed on October 09, 2014, the entirety of which is hereby incorporated by reference.

TECHNICAL FIELD
[002] The present disclosure described herein, in general, relates to system and method for predicting time of arrival of a vehicle at a location. More particularly, the present disclosure relates to a system and method for predicting time of arrival of a vehicle using historical data and time of arrival of the vehicle at the previous location on the particular day

BACKGROUND
[003] It has been observed that passengers often have to wait for longer period of time for arrival of a passenger vehicle. This is because the passengers are unknown to one or more delaying factors that delay the arrival of the passenger vehicle. The one or more delaying factors are unexpected traffic congestion, road blocks, road construction, engine breakdown or any other unknown reasons.
[004] Irrespective of a delaying factor, of the one or more delaying factors, there are various time prediction techniques available in the existing state-of-art that facilitates to predict the Expected Time of Arrival (ETA) for passenger vehicle. One of the time estimation techniques is based on a navigation system in which a Global Positioning System (GPS) tracks location of the passenger vehicle and thereby estimate the ETA for a particular passenger pickup point of the passenger vehicle. It may be observed that such time estimation technique may facilitate to estimate the ETA but does not take into consideration various unplanned halts that the passenger vehicle may make along its respective route.
[005] Though, it may be understood that the time prediction techniques estimate the ETA but lacks applicability specifically in developing countries due to randomness and uncertainty of public transport and unavailability and inaccuracy in reported data pertaining to traffic congestion, weather condition, and road condition.

SUMMARY
[006] Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to system and method for predicting arrival time for a passenger vehicle and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of subject matter nor is it intended for use in determining or limiting the scope of the subject matter.
[007] In one implementation, a system for predicting time of arrival of a vehicle is disclosed. In one aspect, the system may comprise a processor and a memory coupled to the processor for executing a plurality of modules present in the memory. The plurality of modules may comprise a receiving module, an analysis module and a prediction module. The receiving module may be configured to receive historical data comprising of latitude-longitude coordinates of a vehicle at a plurality of locations on a route. The analysis module may be configured to determine a first median speed and a second median speed of the vehicle based upon the historical data. It may be understood that the first median speed indicates a speed of the vehicle between a location and a previous location. It may be further understood that the second median speed indicates a speed of the vehicle during a predefined time interval. The analysis module may be further configured to calculate coefficients using the first median speed, actual velocity between the location and the previous location on a reference day and an average speed of the vehicle during the predefined time interval. It may be understood that the average speed of the vehicle is computed based on the second median speed. The prediction module may be configured to predict the time of arrival of the vehicle at the location on a particular day. It may be understood that the time of arrival is predicted based upon time of arrival of the vehicle at the previous location on the particular day, distance between the location and the previous location, and predicted velocity of the vehicle between the location and the previous location on the particular day. It may be understood that the predicted velocity is computed based upon the first median speed between the location and the previous location, the coefficients and the average speed of the vehicle during the predefined time interval on the reference day.
[008] In one implementation, a method for predicting time of arrival of a vehicle is disclosed. The method may comprise receiving, by the processor, historical data comprising of latitude-longitude coordinates of a vehicle at a plurality of locations on a route. The method may further comprise a step for determining, by the processor, a first median speed and a second median speed of the vehicle based upon the historical data. Further, the first median speed indicates a speed of the vehicle between a location and a previous location and the second median speed indicates a speed of the vehicle during a predefined time interval. The method may further comprise a step for calculating, by the processor, coefficients using the first median speed, actual velocity between the location and the previous location on a reference day and an average speed of the vehicle during the predefined time interval. Further, the average speed of the vehicle is computed based on the second median speed. The method may further comprise step for predicting, by the processor, time of arrival of the vehicle at the location on a particular day. Further, the time of arrival is predicted based upon time of arrival of the vehicle at the previous location on the particular day, distance between the location and the previous location, and predicted velocity of the vehicle between the location and the previous location on the particular day. Further, the predicted velocity is computed based upon the first median speed between the location and the previous location, the coefficients and the average speed of the vehicle during the predefined time interval on the reference day.
[009] Yet in another implementation a non-transitory computer readable medium embodying a program executable in a computing device for predicting time of arrival of a vehicle is disclosed. The program comprises a program code for receiving historical data comprising of latitude-longitude coordinates of a vehicle at a plurality of locations on a route. The program further comprises a program code for determining a first median speed and a second median speed of the vehicle based upon the historical data. The first median speed indicates a speed of the vehicle between a location and a previous location. The second median speed indicates a speed of the vehicle during a predefined time interval. The program further comprises a program code for calculating coefficients using the first median speed between the location and the previous location, actual velocity between the location and the previous location on a reference day and an average speed of the vehicle during the predefined time interval. The average speed of the vehicle is computed based on the second median speed. The program further comprises a program code for predicting time of arrival of the vehicle at the location on a particular day, wherein the time of arrival is predicted based upon time of arrival of the vehicle at the previous location on the particular day, distance between the location and the previous location, and predicted velocity of the vehicle between the location and the previous location on the particular day. The predicted velocity is computed based upon the first median speed between the location and the previous location, the coefficients and the average speed of the vehicle during the predefined time interval on the reference day.

BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, there is shown in the present documents example constructions of the disclosure; however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and drawings.
[0011] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
[0012] Figure 1 illustrates a network implementation of a system for predicting time of arrival of a vehicle, in accordance with an embodiment of the present disclosure.
[0013] Figure 2 illustrates architecture diagram of the system, in accordance with an embodiment of the present disclosure.
[0014] Figure 3 illustrates functional representation of the system, in accordance with an embodiment of the present disclosure.
[0015] Figure 4 illustrates a method for predicting arrival time of a vehicle at a location, in accordance with an embodiment of the present disclosure.
[0016] Figure 5 illustrates speed variation of each segment over a period of time, in accordance with an embodiment of the present disclosure.
[0017] Figure 6 illustrates time slot wise speed variation over a period of time, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0018] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0019] The present disclosure relates to a system and method for predicting time of arrival of a vehicle. In one aspect the latitude and longitude coordinates of the vehicle on a route are collected using a sensor. In general, the sensor may comprise a Global Positioning System (GPS) deployed in the vehicle or present in the Smartphone of the driver driving the vehicle configured to specifically sense time-stamp and vehicle location (latitude-longitude coordinates). The sensor may further sense other data relating speed of the vehicle and number of passengers boarding and getting down from vehicle based on card swiping time-stamp.
[0020] Subsequent to receiving the raw sensor data from the GPS, the data is preprocessed to eliminate missing values, inaccurate data and incompatible data readings. Further, based upon the data received the median speed value is determined for the vehicle between two locations. Likewise, median speed for a predefined interval of time is also determined for the vehicle. In one aspect of the disclosure it is known that, the speed of the vehicle varies with respect to both location and time. Location and time coefficients are derived by using least square approximation method and assumption that a linear relation exists between time, location and speed. The average value of the location coefficient and time coefficient are considered final location and time coefficients for vehicle.
[0021] In order to predict the time of arrival of the vehicle at the location on a particular day mathematical calculation is made based on the time of arrival of the vehicle on the previous location and distance between the location and the previous location, and the predicted velocity of the vehicle between the location and the previous location on the particular day.
[0022] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0023] Referring to Figure 1, a network implementation 100 of a system 102 for predicting arrival time for vehicle is illustrated, in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 captures the historical data based on latitude-longitude coordinates of a vehicle at a plurality of locations on a route. The system 102 analyzes and pre-processes the historical data to determine inaccurate and missing values and correction of reference location. Further, the system 102 based on the historical data determines the first median speed and second median speed of the vehicle. The system 102 further calculates the location wise speed and time slot wise speed to establish time and location coefficient respectively. Subsequently, the system 102 predicts the time of arrival of a vehicle based on the time of arrival of the vehicle at the previous location, distance between the location and the previous location and the predicted velocity of the vehicle between the location and the previous location on the particular day.
[0024] Although the present disclosure is explained considering that the system 102 is implemented as a server. It may be understood that the system 102 may also be implemented as a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server and the like. In one implementation, the system 102 may be implemented in a cloud-based environment in which the user may operate individual computing systems configured to execute remotely located applications. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user 104 hereinafter, or applications residing on the user devices 104. In one implementation, the system 102 may comprise the cloud-based computing environment in which a user may operate individual computing systems configured to execute remotely located applications. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[0025] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0026] Referring now to Figure 2, an architecture diagram of the system 102 is shown in accordance with an embodiment of present disclosure. In one embodiment, the system 102 may include at least one processor, an input/output (I/O) interface, and a memory. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions or modules stored in the memory.
[0027] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface may allow the system 102 to interact with a user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0028] The memory 206 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, a compact disks (CDs), digital versatile disc or digital video disc (DVDs) and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0029] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a receiving module 212, an analysis module 214, a prediction module 216, and other modules 218. The other modules 218 may include programs or coded instructions that supplement applications and functions of the system 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102.
[0030] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a database 220 and other data 222. The other data 222 may include data generated as a result of the execution of one or more modules in the other modules 218.
[0031] In one implementation, at first, a user may use the client devices 104 to access the system 102 via the I/O interface 204. The user may register themselves using the I/O interface 204 in order to use the system 102. In one aspect, the user may accesses the I/O interface 204 of the system 102 for predicting time of arrival of a vehicle based on time of arrival of the vehicle at the previous location, distance and the predicted velocity of the vehicle between the location and the previous location. In order to determine the speed of the vehicle, the system 102 may employ the plurality of modules i.e. the receiving module 212, the analysis module 214, and the prediction module 216. The detailed working of the plurality of modules is described below referring to Figures 3 – 5.
[0032] Now referring to Figures 3, it may be understood that a Global Positioning System (GPS) may either be deployed in the vehicle or may be carried by driver driving the vehicle. Since the GPS may either be carried by the passenger or deployed in the vehicle, the receiving module may capture one or more parameters associated to speed and the corresponding location of the vehicle by using the GPS. The one or more parameters may include, but not limited to, data relating speed of the vehicle, number of passengers boarding and getting down from vehicle based on card swiping time-stamp, latitude- longitude coordinates of a location from a plurality of location on a route. The one or more parameters are captured corresponding to a time stamp, corresponding to one or more location. In this manner, the receiving module 212 facilitates to receive the one or more parameters that may further be processed in order to derive historical data.
[0033] It is to be understood when the vehicle is moving, the values measured using that GPS may not always capture the required data. This approach to collect the sensor data may have several errors such as missing values, noise, erroneous readings or data compatibility (unequal sampling, reverse order latitude-longitude reading) format. Therefore, the GPS sensor data is required to be received and pre-processed by the receiving module 212 before further analyzing the data. Such errors in data are eliminated in order to determine the correct latitude-longitude data to analyze the speed pattern with respect to time and location. In order to identify the inaccurate sensor data / readings and missing values, small grids along the route of the vehicle are considered. If there exists no data for more than two consecutive grids the sensor data is discarded for the day. Otherwise, the missing values are interpolated to determine the sensor reading. Further, the erroneous vehicle stop location along the route of the vehicle is corrected by considering the speed of the vehicle in a grid. The grid wherein the average speed and speed variance of the vehicle is less is considered as vehicle stop location. In this manner, the receiving module facilitates to receive the correct one or more parameters that may further be processed in order to derive historical data.
[0034] In one embodiment, the route of a vehicle may be divided into one or more segments wherein the length of each segments (distance between two consecutive locations) is estimated using Google map by mentioning intermediate location to avoid multi path options. Further, nearest time-stamp reading for each location is identified. Subsequently the average speed of the vehicle for each segment may be calculated.
[0035] The analysis module 214 may be configured to determine the first median speed wherein the first median speed is indicative of the speed of the vehicle between a location and a previous location.
[0036] In order to understand the working of the analysis module 214 to determine the first median speed, consider an example wherein a vehicle deployed with GPS is moving from location i to location j on a route. The average speed of the vehicle with respect to location is determined for an extended period. The speed variation of the vehicle from location i to location j on a route may be recorded for a time period of four months. Figure 5 shows the segment wise speed variation. Since there may be variation of speed between the location, median speed value (VLi,j) may be calculated using Equation 1.
[0037] …………………..……………………… (1)
[0038] where i corresponds to previous location and j corresponds to location. di,j is the distance between previous location and location. ti,j is the travel time interval between previous location and location. N is the total number of days.
[0039] The analysis module 214 may further be configured to determine the second median speed wherein the second median speed is indicative of speed of the vehicle during a predefined time interval.
[0040] In one aspect of the invention speed pattern of the vehicle with respect to time is calculated based on the distance covered by the vehicle in the predefined time interval. Similar to the location, median speed of the vehicle (VTi,t’) for the predefined time interval is considered as the representative speed and calculated using Equation 2.
[0041] …………………..……………………. (2)
[0042] where i varies from one location to another on the route, t’ is the predefined time interval, di,t’ is the distance covered into ith predefined time interval and N is the total number of days.
[0043] In order to understand the working of the analysis module 214 to determine the second median speed, consider an example wherein a vehicle deployed with GPS is moving from previous location to a location in a time interval of fifteen minutes. The distance travelled by the vehicle in fifteen minutes may also be determined. Figure 6 shows the speed variation for each fifteen minutes time interval. The speed of the vehicle during a predefined time interval is collected for a period of four months. Based on the distance travelled by the vehicle in fifteen minute time interval median speed of the vehicle (VTi,15) for each fifteen minute time interval is considered as the representative speed and calculated using Equation 2 as:
[0044] ………………..…………………… (2a)
[0045] where i varies from one location to another on the route, ti,15 is fifteen minutes time interval, di,15 is the distance covered in ith fifteen minute time interval and, and N is the total number of days.
[0046] It may be understood that the speed of the vehicle varies with respect to both location and time. After obtaining the first median speed (VLi,j) and second median speed (VTi,t’) from the Equation 1 and Equation 2 respectively, coefficients comprising of time coefficient (ß) and a location coefficient (a) are calculated by the analysis module 214. The time coefficient and location coefficient are based on the linear relationship between time, location and speed. In one aspect, the time coefficient (ß) and a location coefficient (a) may be determined by Equation 3 by using least square approximation method.
[0047] ……………..…………………… (3)
[0048] where i corresponds to previous location and j corresponds to location. a and ß are coefficients for the location and the time respectively. is kth reference day actual velocity of vehicle between two consecutive locations and VLi,j is the representative first median speed of vehicle speed between two consecutive locations. In one aspect, VTl,t’,k is the speed in fixed time interval, wherein the VTl,t’,k is determined by Equation 4 given below:
[0049] …………..……………………..….… (4)
[0050] where mth interval to nth interval correspond to time interval between two consecutive location on kth reference day and the average speed in mth to nth intervals are calculated as per Equation 4.
[0051] It is to be understood that variation in the values of time coefficient (ß) and location coefficient (a) is less over a period of time with respect to location of the vehicle and previous location of the vehicle. Therefore the analysis module 214 considers average value of a and ß as the final a and ß for the vehicle.
[0052] Subsequent to the computation of the time and location coefficient, the prediction module 216 predicts the arrival time of the vehicle on a particular day by using the Equation 5 given below:
[0053] ……………..…………………….………… (5)
[0054] where, Tj,k is the arrival time of the vehicle at location. Ti,k is the arrival time of the vehicle at previous location; di,j is the distance between the location and the previous location , Vi,j,k is calculated using Equation 3.
[0055] Referring now to Figure 4, a method 400 for predicting arrival time of the vehicle based on the time of arrival of the vehicle at the previous location, distance between the location and the previous location and predicted velocity of the vehicle between the location and the previous location on the particular day, in accordance with an embodiment of the present disclosure. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400 may be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0056] The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400 or alternate methods. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 400 may be considered to be implemented in the above described in the system 102.
[0057] At block 402, historical data comprising time and location of the vehicle may be received. In one implementation, historical data samples may be received by the receiving module 212 from GPS sensor deployed in the vehicle. The historical data may be stored in the database 220.
[0058] At block 404, historical data associated with the vehicle may be corrected based upon preprocessing of the historical data. In one implementation, the jerk historical data may be corrected by the receiving module during preprocessing. The corrected historical data may be stored in the database 220.
[0059] At block 406, first median speed of the vehicle may be determined based upon speed of the vehicle between a location and a previous location. In one implementation, the first median speed of the vehicle may be determined by the analysis module 214.
[0060] At block 408, second median speed of the vehicle may be determined during predefined time interval. In one implementation, the second median speed of the vehicle may be determined by the analysis module 214.
[0061] At block 410, time coefficient (ß) and a location coefficient (a) may be calculated. In one implementation, the coefficient may be calculated by the analysis module 214.
[0062] At block 412, time of arrival of vehicle at a location may be predicted based upon the historical data and time of arrival of the vehicle at the previous location. In one implementation, the time of arrival of vehicle at a location may be predicted by the prediction module 216.
[0063] Although implementations for methods and systems for predicting arrival time of a vehicle have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for predicting arrival time of a vehicle at a location.
[0064] Thus, in this manner explained, the aforementioned methodology is advantageous for predicting the arrival time of the vehicle using minimum input data for computation. The method and system can be used in multiple areas such as school/college transport services, cargo/logistics management and the like.
[0065] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0066] Some embodiments enable a system and a method to predict time of arrival of a vehicle in real-time.
[0067] Some embodiments enable a system and a method to predict the time of arrival based on simple, light-weighted historical data model using limited feature set.
[0068] Some embodiments enable a system and a method is computationally inexpensive due to optimal set of parameters selected to predict the time of arrival. ,CLAIMS:WE CLAIM

1. A method for predicting time of arrival of a vehicle, the method comprising:
receiving, by the processor, historical data comprising of latitude-longitude coordinates of a vehicle at a plurality of locations on a route;
determining, by the processor, a first median speed and a second median speed of the vehicle based upon the historical data, wherein the first median speed indicates a speed of the vehicle between a location and a previous location, and wherein the second median speed indicates a speed of the vehicle during a predefined time interval;
calculating, by the processor, coefficients using the first median speed, actual velocity between the location and the previous location on a reference day and an average speed of the vehicle during the predefined time interval, wherein the average speed of the vehicle is computed based on the second median speed; and
predicting, by the processor, time of arrival of the vehicle at the location on a particular day, wherein the time of arrival is predicted based upon
time of arrival of the vehicle at the previous location on the particular day,
distance between the location and the previous location, and
predicted velocity of the vehicle between the location and the previous location on the particular day, wherein the velocity is computed based upon the first median speed between the location and the previous location, the coefficients and the average speed of the vehicle during the predefined time interval on the reference day.
2. The method of claim 1, wherein the historical data is received from a Global Positioning System (GPS) sensor.
3. The method of claim 1, wherein the historical data collected is corrected for missing values or erroneous readings or data compatibility.

4. The method of claim 1, wherein the coefficients further comprises a time coefficient (ß) and a location coefficient (a), and wherein the coefficients are calculated using a least square approximation method, wherein the coefficients represents a linear relationship between time, location and speed.
5. The method of claim 1, wherein the time of arrival of the vehicle at the location is predicted using a formula, , wherein, ‘Tj,k’ is the arrival time of the vehicle at the location; ‘Ti,k’ is the time of arrival of the vehicle at the previous location; ‘di,j’ is the distance between previous location and the location, and wherein ‘Vi,j,k’ is the velocity of the vehicle between the location and the previous location on the particular day.
6. The method of claim 5, wherein the velocity of the vehicle between the location and the previous location on the particular day is computed using a formula,
wherein i corresponds to previous location and j corresponds to location, t’ is the predefined time interval, a and ß are coefficients for the location and the time respectively; ‘Vi,j,k’ is kth reference day actual velocity of vehicle between two consecutive locations and ‘VLi,j’ is the representative of vehicle speed between two consecutive locations, ‘VTl,t’,k’ is the speed in fixed time interval.
7. The method of claim 6, wherein the ‘VTl,t’,k’ is computed using a formula, where mth interval to nth interval correspond to time interval between two consecutive location on kth reference day, t’ is the predefined time interval and ‘VTl,t’,k’ is the average speed in mth to nth intervals .
8. A system for predicting time of arrival of a vehicle, the system comprising:
a processor; and
a memory coupled to the processor, wherein the processor executes a plurality of modules stored in the memory, and wherein the plurality of module comprising:
a receiving module configured to receive historical data comprising of latitude-longitude coordinates of a vehicle at a plurality of locations on a route.
an analysis module configured to
determine, a first median speed and a second median speed of the vehicle based upon the historical data, wherein the first median speed indicates a speed of the vehicle between a location and a previous location, and wherein the second median speed indicates a speed of the vehicle during a predefined time interval;
calculate coefficients using the first median speed between the location and the previous location, actual velocity between the location and the previous location on a reference day and an average speed of the vehicle during the predefined time interval, wherein the average speed of the vehicle is computed based on the second median speed; and
a prediction module configured to predict time of arrival of the vehicle at the location on a particular day, wherein the time of arrival is predicted based upon
time of arrival of the vehicle at the previous location on the particular day,
distance between the location and the previous location, and
predicted velocity of the vehicle between the location and the previous location on the particular day, wherein the velocity is computed based upon the first median speed between the location and the previous location, the coefficients and the average speed of the vehicle during the predefined time interval on the reference day.
9. The system of claim 8, wherein the historical data is received from a Global Positioning System (GPS) sensor.
10. The system of claim 8, wherein the historical data collected is corrected for missing values or erroneous readings or data compatibility.
11. The system of claim 8, wherein the coefficients further comprises a time coefficient (ß) and a location coefficient (a), and wherein the coefficients are calculated using a least square approximation method, wherein the coefficients represents a linear relationship between time, location and speed.

12. The system of claim 8, wherein the time of arrival of the vehicle at the location is predicted using a formula, , wherein, ‘Tj,k’ is the arrival time of the vehicle at the location, ‘Ti,k’ is the time of arrival of the vehicle at the previous location; ‘di,j’ is the distance between previous location and the location, and wherein ‘Vi,j,k’ is the velocity of the vehicle between the location and the previous location on the particular day.
13. The system of claim 12, wherein the velocity of the vehicle between the location and the previous location on the particular day is computed using a formula, wherein i corresponds to previous location and j corresponds to location, t’ is the predefined time interval, a and ß are coefficients for the location and the time respectively; ‘Vi,j,k’ is kth reference day actual velocity of vehicle between two consecutive locations and ‘VLi,j’ is the representative of vehicle speed between two consecutive locations, ‘VTl,t’,k’ is the speed in fixed time interval.
14. The system of claim 13, wherein the ‘VTl,t’,k’ is computed using a formula, where mth interval to nth interval correspond to time interval between two consecutive location on kth reference day, t’ is the predefined time interval and VTl,t’,k is the average speed in mth to nth intervals.

15. A non-transitory computer readable medium embodying a program executable in a computing device for predicting time of arrival of a vehicle, the program comprising:
a program code for receiving historical data comprising of latitude-longitude coordinates of a vehicle at a plurality of locations on a route;
a program code for determining a first median speed and a second median speed of the vehicle based upon the historical data, wherein the first median speed indicates a speed of the vehicle between a location and a previous location, and wherein the second median speed indicates a speed of the vehicle during a predefined time interval;
a program code for calculating coefficients using the first median speed between the location and the previous location, actual velocity between the location and the previous location on a reference day and an average speed of the vehicle during the predefined time interval, wherein the average speed of the vehicle is computed based on the second median speed; and
a program code for predicting time of arrival of the vehicle at the location on a particular day, wherein the time of arrival is predicted based upon
time of arrival of the vehicle at the previous location on the particular day,
distance between the location and the previous location, and
predicted velocity of the vehicle between the location and the previous location on the particular day, wherein the velocity is computed based upon the first median speed between the location and the previous location, the coefficients and the average speed of the vehicle during the predefined time interval on the reference day.