Design and Implementation of Transportation Management System

I. Ashour, M. Zorkany

and M. Shiple

National Telecomminication Institute (NTI), Cairo, Egypt

Keywords: Intelligent Transportation System (ITS), Arrival Time Prediction, Neural Network, Android Application.

Abstract: This paper proposes an effective method of transportation management system. This proposed system is

designed to interconnect public transport vehicles and bus stations to “Central Room” to monitor the vehicles

& traffic status. Based on the collected data and via analyzing road condition, estimated arrival times are

computed and transmitted to all relevant stations. The main structure of proposed system consists of Bus unit,

station unit and main control centre with servers. Bus and station unit can be hardware unit or mobile android

unit. Monitoring Busses based on GPS and GPRS applications. The data transferred between Bus units, station

units and the main servers are managed via GPRS/UMTS link. At the server (Central Room) and based on

the collected data from buses and via analyzing road condition, accurate arrival times will be computed (Via

Neural network (NN) / Kalman Filter (KF)) and transmitted to all relevant stations. In this paper, we proposed

a modified technique to predict bus arrival time depending on the two algorithms (NN & KF) simultaneously

to take advantage of historical data (NN) with current data (KF). Achieving these main features will cause

major improvements in public transport convenience and safety. Field tests were performed under real traffic

situations in order to test the system.

1 INTRODUCTION

Intelligent Transportation Systems (ITS) domains

include many areas as public transportation control

framework, road traffic management and the

application of traffic control. Vehicle monitoring and

transportation management systems fall under the

category of (ITS).

Many previous studies and systems have been

addressing intelligent transportation and vehicle

monitoring systems (Han and Huh, 2011; Doğan et

al., 2010; Hickman and Hanowski, 2011). Intelligent

transportation systems enable various technologies to

be applied in management of transportation and is

defined as the use of information and communication

technologies to collect, process, and transmit traffic

data to transport users and operators (Qin et al.,

2008). Vehicle monitoring systems, however, only

take vehicles into account; for example, auto-

positioning systems can be applied to vehicle

monitoring, vehicle control, and vehicle

management. Addressing the problem of public

transportation commuters in Egypt waiting for long

time piling at the middle of the streets, struggling to

catch a bus with all the suffering they meet, the delay

they cause and the accompanying traffic digestion.

When travelling with buses, the passengers want

to know the predicted bus arrival time at the bus stop.

Long waiting time at bus stops may drive away the

anxious travellers and make them reluctant to take

buses. Accurate arrival time of next bus will allow

travellers to take alternative transport choices instead,

and thus mitigate their anxiety and improve their

experience.

Towards this aim, many commercial bus

information providers offer the real-time bus arrival

time to the public. Providing such services, however,

usually requires the cooperation of the bus operating

companies (e.g., installing special location tracking

devices on the buses), and incurs substantial cost (F.

Li Y. et al., 2011; Pengfei et al., 2012; Dihua et al.,

2007). Many research on implementing tracking

systems based on android applications (Manav and

Anupam, 2012; Ruchika and BVR, 2011), but most

of them concentrated on tracking system not fully

management transportation system and estimate

arrival time predilection espials in countries

complicated in traffic as Egypt. Our proposed system

can be implemented based on hardware units or

mobile application units.

The proposed transportation system is designed to

interconnect public transport vehicles and bus

stations to “Central Room” to monitor the vehicles &

Ashour I., Zorkany M. and Shiple M..

Design and Implementation of Transportation Management System.

DOI: 10.5220/0005430200110018

In Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS-2015), pages 11-18

ISBN: 978-989-758-109-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

traffic status. Monitoring vehicles based on GPS and

GPRS applications. Via analyzing and processing the

collected data at the control center, we can predict the

bus arrival times at relevant stations.

The prediction times to the next bus will be

announced on screens to passengers on the bus stops.

The next bus stop will be notified to the passengers in

buses using visual & audio announcements.

Achieving these features of the proposed system will

cause major improvements in public transport

convenience and safety, especially in Egypt and

countries like Egypt in transportation. Most vehicle

arrival time prediction in intelligent transportation

system depend on many parameters like: Bus speed,

occupancy, traffic flow, traffic incidents, weather

conditions, daily, weekly and seasonal and many

other parameters which can affect on quality of

prediction times. These large number of parameters

make bus arrival time predictions very complex and

difficult to reach optimal accuracy. Some of these

parameters can be classified as on line predictions

like Bus speed, occupancy, traffic flow, traffic

incidents, weather conditions and other parameters

can be classified as history like daily, weekly and

seasonal.

The proposed algorithm for prediction times relies

on real-time location data (Kalman filter) and takes

into account historical travel times (Neural Network)

as well as temporal and spatial variations of traffic

conditions as presented in our paper (M. Zaki et al.,

2013). Although the two separated algorithms gave us

accepted results as shown in our paper (M. Tantawy

and M. Zorkany, 2014), but still the two algorithms

work individually.

In this paper, we proposed a modified technique

to predict bus arrival time depending on the two

algorithms (NN & KF) simultaneously to take

advantage of historical data (NN) with current data

(KF). So the proposed prediction time method is a

hybrid scheme that combines a neural network (NN)

that infers decision rules from historical data with

Kalman Filter (KF) that fuses prediction calculations

with current data.

This paper is organized as follows. The proposed

Transportation management system is presented in

Section 2. Communication Protocols and Frame

Structure are given in Section 3, System based

Hardware units is presented in section 4, System

based Android applications is presented in section 5.

Arrival time prediction is proposed in section 6.

Simulation results are discussed in section 7 and

finally conclusions are drawn in Section 8.

2 PROPOSED

TRANSPORTATION

MANAGEMENT SYSTEM

The proposed main structure of transportation

management system are given in figure 1, where data

transferred between the buses, the main servers, and

the end users are managed via mobile networks and

Internet. The hardware and firmware units in the

busses collect, control, and process the data while

hardware and firmware units at bus stops “Bus station

Unit” controls and display data received from server.

The bus and station units can be a hardware-based unit

or Android-based unit. The servers host the system

management and processing algorithms, system

database, and web applications. A portal is included to

give the necessary information to the end-users.

For In-Bus units and Station Units, Our proposed

system can be implemented based on hardware units

(In-Bus & Station units) or mobile Android units

(Android unit as In-Bus unit and Android unit as

station) or mix units (hardware and Android).So the

proposed system could be presented in the market in

two different versions: System based customized

Hardware and System based Android application.

At server and based on the collected data from

buses, bus arrival times will be computed (Via Neural

network / Kalman Filter) and transmitted to all

relevant stations. the prediction time for the next

bus(s) to arrive will be displayed on screens on the bus

station. The proposed system is composed of three

main parts: Servers, In-bus unit and Bus stop (Station)

unit.

Figure 1: Main structure of proposed system.

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems

2.1 Proposed Network Infrastructure

The proposed framework uses a layered or modular

approach to communications standards, similar to the

layering approach adopted by the Internet and the

International Standardization Organization (ISO).

In general, data communication between system

units in buses, stations, and servers in “Central Room”

can be considered to involve the following primary

levels: Information level, application level, transport

level, sub-network level, and plant level. These levels

can be compared to both of ISO open system

interconnection seven layers model and The

American National Transportation Communication

for ITS Protocol (NTCIP) framework published by

American Association of State Highway and

Transportation Officials (Aashto, 2009).

The proposed framework extends beyond the

communications stack to include informational data

and interfaces to physical communication

infrastructure. Figure 2 shows how the proposed

framework relates to the OSI model.

Figure 2: Proposed Protocol Architecture.

2.2 Communication Sequence

Figure 3 shows the communication sequence for the

proposed system. After system initialization, the

system admin customize the processing servers by

entering the routes, the stations, the authenticated

International Mobile Equipment Identifier (IMEI) for

every Bus/station unit.

When the bus/station units registered to the

processing server in the central room the processing

server can upload the required audio files to the bus-

units. Then location, velocity and other information

for all buses are forwarded to the processing server in

the central room.

The server processed the received information,

using neural network and Kalman filter, and

estimating the arrival time, sending the results to the

database server, the corresponding stations, and to the

portal web site.

Figure 3: Proposed Communication Sequence.

3 COMMUNICATION

PROTOCOLS AND FRAME

STRUCTURE

In general, data communication between in-bus unit

and servers in “Central Room” uses TCP/IP suite,

which run over a GPRS mobile operator network and

higher technology as UMTS. The transmitted frames

to/from the server and busses are classified into three

types of packets.

3.1 Frames between Buses and Server

In the proposed system, three packet frames are used

for communication between buses and sever; bus

registration frame from bus to server, bus route frame

from sever to bus, and bus periodic frame from bus to

sever. The following figure demonstrates the

communication sequence between buses and server.

Figure 4: Sequence diagram of bus unit and server.

DesignandImplementationofTransportationManagementSystem

3.1.1 Bus Registration Frame

In the designed system all busses are defined in the

server database though the admin GUI using the IMEI

of the GSM module of unit installed in each bus. The

IMEI is the unique GSM serial code that can be

extracted through android applications. IMEI is typed

by the manufacturer on the GSM module and can be

used as the in-bus unit serial number.

This proposed method gives ease for remote and

flexible control and management of all busses in the

designed system. All buses in the designed system

remotely load and save the route stations data which

gives the following privileges:

 Reducing sever processing overhead as the

calculation of the bus station zone is done in each

bus through in-bus unit.

 Optimizing the use of the network capacity as the

announcement and display of the stations through

in-bus display interface and audio system is done

automatically through the in-bus unit.

 Overcome problems due to network connection

failure as in-bus units, in this case act as a stand-

alone system that can announce and display the bus

route stations directly as the processing is done

through the in-bus unit.

 Sending bus periodic package frame (explained

below) to the sever directly as the bus enters a station

without waiting for the fixed periodic delay which

could result is bad system accuracy (bus can enter

and leave the station before the next periodic frame).

This is done as the calculation of the bus station zone

is done automatically via the in-bus unit.

 In the beginning of operation the route stations of

each bus is loaded remotely using the following bus

registration request frame.

3.1.2 Bus Route Frame

This frame is sent to the bus either as an answer to

request due to start of operation bus request as shown

in the previous section or directly from the sever in

case of admin request of bus route change (i.e. the bus

route is changed remotely according to admin

requirements). Bus route frame given below contains

all bus route stations data; namely; stations

coordinates (longitude, latitude), names, and audio

codes.

Figure 5: Bus route frame.

3.1.3 Bus Periodic Frame

These periodic packets are sent from bus to server

with constant delay, unless bus enters a station zone.

The periodic packets are sent directly without delay

within station zone. This frame is used to manage

buses and process and calculate the bus arrival time.

The following frame shows the designed bus periodic

packet format example sent from the buses to the

server. and table 1 shows each field indication and size

of each field.

B1,I352134010613025,s00064*{103032.000t,A,3002.6816

,N,03119.3300,E,45.98v,8S, 062000.000 ,001#

Table 1: Bus periodic frame packet fields.

Field & Length (Byte) Indication

B1 Start of Packet / Bus Number

I352134010613025

Identification number of GSM

module

s00064 packet sequence

*{ start of GPS data

103032.000t Time

A (or V)

Status: data valid (A) or not valid

(V),

(valid if number of satellites ≥ 3)

3002.6816,N North (Latitude)

03119.3300,E East (Longitude)

45.98v Velocity

8S Number of satellites

062000.000 Time2: time of first station in Route

001 Audio Code

# End of Packet

3.1.4 Connection between Server and

Stations

The connection between stations and server is

constructed via web page design application. The

server can detect each zone of station and

automatically open the web page of this station to

display the prediction times of busses of this station.

Using this type of connection, we didn’t need and

extra connection between server and stations as

connection between buses and servers.

4 PROPOSED SYSTEM UNITS

The proposed bus and station units can be a hardware-

based unit or Android-based unit. The system based

hardware is divided into two entities; one in the

moving buses and one at the Bus station. The exact

functions performed by the In bus unit are different

from those performed by the Bus station unit.

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems

4.1 In-Bus Unit

The "In-Bus Unit" basic function is tracking the bus

during motion by sending periodic messages (every 30

seconds) to the main processing server, including its

new location; unit is divided to sub-units as shown in

Figure 6, where the display system is optional for the

In-Bus unit. Figure 7shows our In-Bus unit which

fully designed and implemented at NTI by the project

research team.

Operation of In-Bus Unit:

Main controller communicates with GSM/GPRS via

AT commands. The GSM/GPRS module is

configured to define the mobile service provider

access point, connection selection (TCP/IP), and

server IP address and port. Each unit has a unique ID

which is the GSM/GPRS module IMEI (International

Mobile Equipment Identity). The ID is sent by the In-

Bus Unit after connection with server is established.

For a first time attachment on the system network,

the main processing server will send all information

about the route which this bus belongs to including an

ordered list of the coordinates of all bus station on the

route; the microcontroller will save this information in

the main controller ROM for further calculations.

GPS module scans the satellites and finds the real-

time coordinates then these coordinates are compared

with the pre-stored coordinates of the respective bus

stations. If the coordinates match, the result is shown

as Station Name on LCD display and also

announcement using the speakers. At the same time

these coordinates send to the main processing server

using GPS/GPRS module.

Due to the problems that have been noticed due to

GPRS link failure according to signal coverage which

required the use of dual SIM card holder controller

design to switch between different GSM service

providers in case of link failure

Figure 6: Block Diagram of in-bus unit.

Figure 7: In-Bus Unit.

4.2 Bus Station Unit

The "Bus Station Unit" is installed inside public bus

stations to display a list of the new arriving buses and

the expected time to arrive at. This list is received from

the main server after gathering information from the

bus fleet and making prediction calculations. The unit

is divided to sub-units. The Bus station Unit and the

In-Bus Unit are the same; nevertheless the In-Bus Unit

has GPS and the announcement system circuits. The

main processing server can send the arriving buses list

to the concerned station after registering its IMEI

number.

5 SYSTEM BASED ANDROID

MOBILE APPLICATION

a new features was added to the system by developing

In-Bus and station units redesigned based on Android

mobile application. In general, data communication

between electronic android units in buses, stations,

and servers in "Central Room" uses TCP/IP suite

which run over GPRS/UMTS mobile operator

network. The following subsections show how re-

implement system based android application.

5.1 GPS Implementation on Android

GPS

Location-based services or LBS refer to 'a set of

applications that exploit the knowledge of the

geographical position of a mobile device in order to

provide services based on that information.

LBS can be classified in three categories: Public

Safety / Emergency Services, Consumer Services and

Preferred Location Search.

There are two methodologies to implement LBS

on android applications:

 To process location data in a server and to forward

the generated response to the clients.

 To find location data for a mobile device-based

application that can use it directly.

DesignandImplementationofTransportationManagementSystem

The location of Android unit can be computed by

3 methods:

1) Mobile Phone Service Provider Network-

Using cell ID.

2) GPS Satellites

Using GPS satellites system.

3) Assisted-GPS (A-GPS)

Integrates the mobile network with the GPS to

give a better accuracy.

In Android, location-based services are provided

by the Location Manager class located in the android.

location package. Using the Location Manager class,

we can obtain periodic updates of the device's

geographical locations as well as fire an intent when it

enters the proximity of a certain location. Location-

based service is another key functionality that gets

used in smart phone applications. It is often combined

with maps to give a good experience to the user about

their location [18].

5.2 Display Android Phone's Screen on

Monitors

A popular feature on several Android phones is the

ability to connect the phone to an High Definition

Multimedia Interface (HDMI) monitor. The phone

must have an HDMI connector, and need to buy an

HDMI cable.

6 PROPOSED ARRIVAL TIME

PREDICTION

This proposed transportation system presents an

effective method to predict the bus arrival time at

individual bus stops along a service route. This

method depend on famous methods in prediction

domain, Kalman filter and Neural Network. The

proposed method combines Kalman Filter (KF) that

fuses prediction calculations with current GPS

measurements with a neural network (NN) that infers

decision rules from historical data.

The proposed algorithm relies on real-time

location data (Kalman filter) and takes into account

historical travel times (Neural Network) as well as

temporal and spatial variations of traffic conditions as

presented in paper (M. Zaki et al., 2013).

Although the two separated algorithms gave us

accepted results as shown in paper (M. Tantawy and

M. Zorkany, 2014), but still the two algorithms work

individually.

In this paper, we proposed a modified technique to

predict bus arrival time depending on the two

algorithms (NN & KF) simultaneously to take

advantage of historical data (NN) with current data

(KF). In the proposed method, we used three available

previous data from the buses in the same period and

the same link and the fourth data from learned neural

network to predict time using Kalman filter algorithm.

According to Kalam filter prediction algorithm

[11-12], the prediction time t(k+1) depends on four

pervious data (t(k), t(k-1), t(k-2), t(k-3))

t(k+1)=f ( t(k), t(k-1), t(k-2), t(k-3) ) (1)

In the proposed method, we replace t(k-3) by

estimated time computed from neural network. and the

equation (1) will be:

t(k+1)=f ( t(k), t(k-1), t(k-2), t(NN) ) (2)

As well as if the previous data are not available, we

can replace it by learned neural network in Kalman

filter algorithm. For example, if two previous data are

available only, we use the other two data from learned

neural network as shown in equation (3).

t(k+1)=f ( t(k), t(k-1), t(NN), t(NN) ) (3)

7 SIMULATION RESULTS

7.1 Network Traffic

The network traffic is estimated mainly as a result of

bus periodic frame (from bus to sever). All other

frames do not represent any load on the network

because they will be sent either at the beginning of the

operation or in case of changing bus route. The effect

of this frame on network traffic is given as the

following. According to the periodic packet format

from bus to server, the total traffic per bus per packet

is given by:

Total traffic per bus per packet = IP packet

payload + IP packet header

(4)

IP packet payload = Total application packet

length + TCP segment header

(5)

Where:

IP packet header = 20 bytes

Total application packet length = 91 bytes (Table

(1))

TCP segment header = 20 bytes

As IP packet payload should be divisible by 8, extra

zero pads are added (in our case = 1 byte is added)

Then: The Total traffic per bus per packet = 132 byte

Figure 8 and figure 9 show the average IP traffic

transmitted from one bus varying with the inter-

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems

transmission period and the total traffic received at the

server versus number of buses respectively.

Figure 8: Average IP traffic transmitted from one bus vs.

duration between IP packets transmission.

Figure 9: Total traffic received at the “Central Room” server

vs. number of buses.

It should be noted that the real data traffic will be

slight higher than that in Figure 6 and figure 6 due to

GSM/GPRS overhead and the acknowledge packets

transmitted back from the server to the bus.

7.2 Testing Bus Arrival Time

Prediction Technique

The proposed prediction time algorithm was tested

based on the available data, which was collected

during more than three months for units have been

installed in three Busses of the lines of NTI fleet (Our

Institute) in different three routes (about 18 stations).

Taking into account that these lines in different areas

to cover different cases of traffic in Egypt.

For Example, figure 10 shows prediction arrival

time calculation result between two stations "Ahmed

badawy and Massara station" using Kalman Filter,

Neural Network and Hybrid method. Where, RMSE

"Route mean square error" =1.28 mint for Neural

network, 1.92 for Kaman filter and 1.44 for hybrid.

A comparison between arrival time prediction

algorithms (Kalman filter, neural network and Hybrid

NN&KF) for some stations is summarized in Table 2

and figure 11.

Figure 10: Arrival Time Prediction between two stations.

Figure 11: RMSE for KF, NN and Hybrid NN&KF.

Table 2: RMSE (Root-Mean-Square Error).

Station

KF NN Hybrid

1.92 1.28

1.44

2.65 1.96

2.20

3.26 2.61

2.9

3.66 4.14

3.29

0.99 1.56

0.92

From the field test results using NTI fleet (low trip

rate), it can be noted that calculating the arrival time

using neural network algorithm gives us better results

than Kalman filter algorithm in most different

conditions. Nerveless Kalman filter has show

negligible improvement than neural network

algorithm in some stations under test. In case of heavy

daily trip rates the kalman filter algorithm shows

better results.

8 CONCLUSIONS

This paper presents the Transportation Management

system. The system was designed and implemented

based on Hardware units and redesigned using

Android based mobile application.

Based on the collected data from buses and via

analyzing road condition, accurate arrival times

computed (Via Neural network / Kalman Filter) at the

123456789101112

RMSE(minutes)

TripNo.

Real

Time

KFTime

Time

12345

RMSE(minutes)

LinkIndex

Hybrid

2000

4000

6000

1 100 10000

TotalTrafficreceived

fromthebuses(kbps)

NumberofBuses

0,0

500,0

1000,0

1500,0

0100200

TotalTrafficTransmitted

fromonebus(bps)

DurationbetweenIPpackettransmission

(sec)

DesignandImplementationofTransportationManagementSystem

server and transmitted to all relevant stations. The

network traffic is estimated mainly as a result of bus

periodic frame (from bus to sever). All other frames

do not represent any load on the network because they

will be sent either at the beginning of the operation or

in case of changing bus route. A Comparison between

implementing transportation system based on

hardware unit viruses Android unit are presented. The

system was tested using NTI Fleet for a field test.

From the field test results using NTI fleet (low trip

rate), it can be noted that calculating the arrival time

using neural network algorithm gives better results

than Kalman filter and hybrid algorithm in most

different conditions. Hybrid neural network with

Kalman filter give better results than Kalman filter. In

case of heavy daily trip rates the hybrid algorithm

shows better results

ACKNOWLEDGMENT

This work concern is a part of research project called

Transportation Management and User Awareness (TMUA)

that financially supported by the National Telecom

Regulatory Authority (NTRA) of Egypt. The project team

are: Prof. A. Ammar, Prof. E.M.Sad, Prof. I.Ashour,

Ass.Prof. M.Tantawy, Dr. M.Zorkany, Dr. M.Shiple, Eng.

A.Nabeil, Eng. M.Sami, N.A.Nagdy and Eng. A.Hamdi.

REFERENCES

HAN, S., HUH, K. Monitoring system design for lateral

vehicle motion. IEEE Transactions on Vehicular

Techology. 2011, vol. 60,no. 4, p. 1394-1403.

DOĞAN, S., TEMIZ, M. S., KÜLÜR, S. Real time speed

estimation of moving vehicles from side view images

from an uncalibrated video camera. Sensors. 2010, vol.

10, no. 5, p. 4805-4824.

HICKMAN, J. S., HANOWSKI, R. J. Use of a video

monitoring approach to reduce at-risk driving behaviors

in commercial vehicle operations. Transportation

Research Part F-Traffic Pshychology and Behaviour.

2011, vol. 14, no. 3, p. 189-198.

QIN, K., XING, J., CHEN, G., WANG, L., QIN, J. The

design of Intelligent Bus Movement Monitoring and

Station Reporting System, In Proceedings of the IEEE

International Conference on Automation and Logistics,

Qingdao, China, September 2008, p. 2822-2827.

F. Li, Y. Yu, H. Lin, and W. Min. “Public bus arrival time

prediction based on traffic information management

system”. In Proceedings of IEEE International

Conference on Service Operations and Logistics, and

Informatics (SOLI), pages 336–341, 2011.

Pengfei Zhou, Yuanqing Zheng, Mo Li, " How Long to

Wait?: Predicting Bus Arrival Time with Mobile Phone

based Participatory Sensing", MobiSys’12,June 25–29,

2012, Low Wood Bay, Lake District, UK.

Dihua Sun, Hong Luo, Liping Fu, Weining Liu, Xiaoyong

Liao, and Min Zhao, “Predicting Bus Arrival Time on

the Basis of Global Positioning System Data”,

Transportation Research Record: Journal of the

Transportation Research Board, No. 2034,

Washington, 2007, pp. 62–72.

Manav Singhal, Anupam Shukla, “Implementation of

Location based Services in Android using GPS and

Web Services”, IJCSI International Journal of

Computer Science Issues, Vol. 9, Issue 1, No 2, January

2012, ISSN: 1694-0814.

Ruchika Gupta and BVR Reddy,” GPS and GPRS Based

Cost Effective Human Tracking System Using Mobile

Phones”,VIEWPOINT, Volume 2, No. 1, January-June

2011.

M. Zaki, I. Ashour, M.zorkany, B. Hesham,"Online Bus

Arrival Time Prediction Using Hybrid Neural Network

and Kalman filter Techniques," International Journal

of Modern Engineering Research (IJMER), Vol. 3,

Issue. 4, Jul - Aug. 2013 pp-2035-2041.

M. Tantawy and M. Zorkany, " A Suitable Approach for

Evaluating Bus Arrival Time Prediction Techniques in

Egypt ", Proceedings of the 2014 International

Conference on Communications, Signal Processing

and Computers.

AASHTO (American Association of State Highway and

Transportation Officials), "National Transportation

Communications for ITS Protocol NTCIP 9001 version

v04," July 2009.

Qusay H. Mahmoud. J2me and location based services.

2004. URL http://developers.sun.com/mobility/apis/

articles/location.

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems