Usage of GPS Data for Real-time Public Transport Location Visualisation

Aleksejs Zacepins, Egons Kalnins, Armands Kviesis and Vitalijs Komasilovs

Department of Computer Systems, Faculty of Information Technologies, Latvia

University of Life Sciences and Technologies, Jelgava, Latvia

Keywords:

Public Transport, Smart City, Smart Transportation.

Abstract:

The concept of the smart city has been fashionable in the political arena in recent years. Cities are trying

to be modern and provide various ICT based services for their citizens. An efﬁcient public transportation

service is critical for the citizens, but trafﬁc congestions are still a problem in cities and are one of the main

reasons for public transport delays. Therefore, it is important for citizens to know where the needed public

transport vehicle is located at the moment, to know if the transport has already passed the stop or not. Authors

of this research propose a real-time public transport tracking system using a global positioning system (GPS)

technology module to receive the location of the vehicle in a real-time. System is based on the Raspberry Pi

3, which is used to transfer positioning data received from GPS module to the remote database. Based on

received data, the location of the bus is visualised in the developed Web system.

1 INTRODUCTION

The concept of Smart city is not novel, but in the re-

cent years it rose up to a new level by using ICT (In-

formation and Communication Technologies) and IoT

(Internet of Things) to build and integrate critical in-

frastructures and services of a city (Nam and Pardo,

2011). Smart city can be deﬁned in many ways (Par-

tridge, 2004; Harrison et al., 2010; Hall et al., 2000;

Gifﬁnger, 2007; Washburn et al., 2010; Caragliu et al.,

2011; Albino et al., 2015), but the main idea of the

Smart city is to provide efﬁcient services for the citi-

zens. Smart city consists of several multidimensional

components (like Smart energy, Smart government,

Smart economy etc.), and Smart mobility is one of

them (Nam and Pardo, 2011). In its turn, Smart pub-

lic transportation is a signiﬁcant part of the Smart mo-

bility dimension in Smart cities, as one of the main

problems of urban centres today is mobility of citi-

zens (Lima et al., 2017). Efﬁcient urban transporta-

tion systems are widely accepted as essential infras-

tructure for smart cities, and they can highly increase

a city’s vitality and convenience for residents (Liu

et al., 2017).

Governments on the national and municipalities

on the local level are trying to motivate citizens to use

public transport service instead of private vehicles by

promoting the development of journey planning tech-

nologies in order to optimise commuter interactions

with transportation systems (Cohen et al., 2017). Still

huge amount of people are using private vehicles for

transportation in the cities and bus passengers jour-

neys are decreasing. For example in 2016/2017, 2.20

billion passenger journeys were made by local bus in

England outside London, down 0.8% compared with

2015/16. In London decrease of 2.3% is observed and

passenger journey number is decreasing for several

years in a row (information by Department for Trans-

port, Annual bus statistics: England 2016/17

). Sit-

uation in author’s home country Latvia is similar, in

year 2016 passenger ﬂow is decreased by 2.8% com-

pared to 2015 and tendency is to have passenger num-

ber decrease by 2% each year (statistics by Latvian

road transport directorate

Many citizens using public transportation have ex-

perienced time losses because of waiting at the stops

(Jadhav et al., 2017), which is not corresponding to

the efﬁcient service. Citizen does not get any idea of

current location of a bus or exact timing of arriving

bus. So citizen have to wait for a bus at the bus stop

for several tens of minutes or even hours, when con-

sidering regional buses (Khot and Yadav, 2016). Pub-

lic transport is suffering from a number of uncertain

conditions for the possible delays, like trafﬁc conges-

https://assets.publishing.service.gov.uk/government/

uploads/system/uploads/attachment_data/ﬁle/666759/

annual-bus-statistics-year-ending-march-2017.pdf

www.atd.lv/sites/default/ﬁles/Info_2016_9_menesi_

05122016.pdf

Zacepins, A., Kalnins, E., Kviesis, A. and Komasilovs, V.

Usage of GPS Data for Real-time Public Transport Location Visualisation.

DOI: 10.5220/0007350902770282

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 277-282

ISBN: 978-989-758-374-2

277

tions, trafﬁc accidents and others (Suganya and Valar-

mathi, 2017). But still improvement and expansion of

the public transportation service is a key factor to min-

imise trafﬁc congestions in urban cities (Garg et al.,

2017).

In today’s smart world in line with all fast emerg-

ing technologies public transport system must be im-

proved. One of the technological solutions, which can

improve transportation system quality, is GPS (Global

Positioning System). GPS allows the precise posi-

tioning of an object using satellite signals. There are

a many applications of this technology in various sci-

entiﬁc ﬁelds including transportation sector (Mintsis

et al., 2004). GPS has nearly global coverage, that al-

lows to detect location almost everywhere, and nowa-

days technology demonstrates high robustness (Dani

Reagan Vivek et al., 2017). Using GPS data, trans-

port system can also keep track on driver’s perfor-

mance while s/he is driving a bus. Tracking of all

buses helps to reduce chances of vehicle theft opera-

tions and if any accident happens with bus system, it

can be easily identiﬁed (Garg et al., 2017). GPS data

are not just useful for travel time prediction and lo-

cation identiﬁcation, but can also provide information

about trafﬁc, e.g., the existence of congestions (Bacon

et al., 2011), therefore it can extend the ITS (Intel-

ligent Transport System) concept, which is basically

used to improve the safety and efﬁciency of the trafﬁc

management system (Nasim and Kassler, 2012).

There are many approaches and algorithms of bus

arrival time prediction based on GPS data (Kviesis

et al., 2018; Amita et al., 2015; Yin et al., 2017; Fan

and Gurmu, 2015)), but author’s idea is to give citi-

zens an easy tool to see the public transport location

in real-time and make decisions by themselves. In

overall, collection of public transport GPS data can

be as a one source for the data mining in Smart cities,

as data mining is one of the three core pillars (Nasim

and Kassler, 2012) for the smart city together with IoT

and mobile wireless networks.

So the main aim of this paper is to describe ap-

proach for real-time GPS data collection with its fur-

ther application in public transport vehicles for ser-

vice quality improvement by visualisation of real-

time vehicle location. Such system will help to im-

prove interest of citizens towards usage of public

transportation system.

2 MATERIAL AND METHODS

Within this research authors developed GPS data col-

lection system for demonstration of real-time public

bus location. For GPS data visualisation additional

Figure 1: Delay of the transport, experienced by the respon-

dents.

WEB system is developed for use in any device with

Internet connection. Before the development process,

authors conducted a survey to know the Latvian soci-

ety opinions and their needs to know the public trans-

port real-time location and approximate bus arrival

time at the speciﬁc bus stop. As well existing intel-

ligent transportation systems in Latvia are described

and compared with some international systems.

2.1 Survey about Research Topicality

To get to know the Latvian society opinion on the

importance of knowing the public transport real lo-

cation and approximate bus stop arrival time authors

conducted a survey using the Google Forms. For the

survey citizens of Riga (capital of Latvia) were asked

to answer 13 questions about Riga main public trans-

portation service provider "Rigas Satiksme" (RS

Survey included questions about how often they use

public transport, how often transport has delays and

how those delays affects citizens plans. There were

also some questions about GPS technology and pos-

sible beneﬁts of its usage. 163 respondents replied

to the survey. Respondent group consisted of 78.5%

women and 21.5% men, average age was 25.4 years.

Analysis of the answers concluded, that bus has

the most delays. As well more than 30% of respon-

dents marked that their used public transport has de-

lays almost every day. Regarding the delays, 54.6%

said that delay was less than 5 minutes, but other

36.4% marked that delay was more than 5 minutes

(4.3% said that delay was more than 15 minutes) (see

Fig 1).

It is worth to emphasize that plenty of respon-

dents (65%) said that they missed their work, school

or meeting due to transport delays. 43.6% noted, that

this really spoiled their mood. The next question was

https://www.rigassatiksme.lv/en/about-us/

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278

Figure 2: RS web system for minibus location visualisation.

about the citizens’ wish to know not only the transport

scheduled times, but also real location showed that

this is very actual, because 92.7% respondents replied

positively. This allows to re-plan their schedules, in-

form others about possible delays, and consider other

transportation options.

2.2 Existing Intelligent Transportation

Systems in Latvia

Today GPS data is used in many logistic enterprises

to monitor the transport, calculate fuel consumption,

driver work load etc. As well, taxi companies use

transport positioning data for optimising operational

efﬁciency of the driver, but mainly this data is not for

a public use, but for private enterprise needs. It is

found that in Latvia only two public transport service

providers started to analyse GPS data and provide it

for the public usage. In Riga, Rigas Satiksme devel-

oped mobile and web applications for real-time GPS

data visualisation. Each vehicle, which is equipped

with GPS device is demonstrated on the map, based

on OpenStreetMaps. To get GPS data from vehi-

cle, GET query is sent to the speciﬁc web address:

https://marsruti.lv/rigasmikroautobusi/gps.txt. Using

this text ﬁle, it is possible to get vehicle number and

it’s exact location. GPS data is updated with 5 sec

intervals. Web system for public use (see Fig 2) is

accessible online

. At this moment, only location of

minibuses is shown.

In authors’ home town Jelgava (Jelgava is the

fourth largest city in Latvia, a historical centre of

Zemgales region, distance from Riga is 42 km, res-

idents number is approx. 62 000), local public trans-

port operator "Jelgavas Autobusu Parks" (JAP

) also

implemented feature of demonstrating real GPS posi-

tion of the buses. In Jelgava there are twenty local bus

routes. JAP also uses the same approach as RS, using

the Mapon service. But the visualisation approach is

different, JAP shows approaching buses in 30 minutes

time interval to the selected bus stop and not showing

all bus real-time location on map. As well provided

https://marsruti.lv/rigasmikroautobusi/#minibus/map/en

https://www.jap.lv/?lang=en

Figure 3: JAP web system for bus location visualisation.

Figure 4: TRAVIC web system.

information about bus location is only textual, not vi-

sual (see Fig 3).

Comparing to local systems in the world, there

are TRAVIC (Trafﬁc Visualisation Client), which is

developed by geOps company collaborating with the

University of Freiburg (see Fig 4). This product is

a web-based solution for real-time transport location

visualisation and route demonstration by the static

transport schedule. The application combines static

and real-time schedule data and calculates the current

position of the transport vehicles. TRAVIC solution

uses publicly available open data, which is stored in

General Transit Feed Speciﬁcation format. To this

moment, system collects data from more than 700

sources mainly from North America and from Cen-

tral Europe (Germany, Switzerland, Netherlands). 66

sources are for real-time data (in the system marked

with green line of the circle) other are static, based on

transport schedule. Real time data is collected using

the GTFS RT format, which is extension of GTFS.

Data transfer is made using Google developed plat-

form independent protocol.

3 RESULTS AND DISCUSSION

As a result of authors developed approach efﬁcient

tool for extracting valuable trafﬁc data from vehicles

is presented. Within this research authors demonstrate

Usage of GPS Data for Real-time Public Transport Location Visualisation

279

Figure 5: GPS module GP-20U7.

simpliﬁed approach for GPS data collection, which

can be used in public transport sector without addi-

tional need to buy expensive GPS commercial hard-

ware. The GPS accuracy was accepted to be within

10 meters as the primary use would be for public bus

tracking. This is easily obtained with the most GPS

sensors on the market. As a result, authors demon-

strate developed system and user interface for GPS

data visualisation.

3.1 Developed System for GPS Data

Collection

In authors’ case Raspberry Pi 3 with additional GPS

module GP-20U7 was used (see Fig 5). Based on

technical speciﬁcation module precision is 2.5m, but

during experiments authors observed location preci-

sion about 9m. An update of GPS coordinates was im-

mediate, when data from GPS module was received,

module sent coordinate updates each second. The

GP-20U7 GPS module is energy efﬁcient and has

only 40mA power consumption. GPS module and

Raspberry Pi is connected using UART interface, and

only 3 wires were necessary – Vcc, GND and RX.

Authors chose to use Raspbian as Raspberry Pi op-

erating system. Software, to communicate with GPS

module, was developed using Python programming

language. Software aim was to get raw GPS data

from GPS module and send values to dedicated cloud

server for further data analysis and visualisation. For

system tests Internet access was shared from mobile

phone, but for the ﬁnal solution additional 3G/4G

module should be connected to the Raspberry Pi for

persistent Internet access.

Example of data that is sent to the remote database

is shown below. For data transfer to the cloud server

PUT request is used.

{"id":"5ad39422c57f652a8827e225","vehicleId":

"11001","serialNumber":1337,"routeId":"riga_trol_

27","vehicleTypeId":800,"geoData":{"latitude":

56.92166,"longtitude":24.091107,"speed":

0.0,"provider":null,"date":"2018-04-16T19:55:

05.832Z"}}

For public transport GPS data visualisation stand-

alone web system was developed. It is possible to see

real-time transport location based on selected route.

For system development several technologies were

used: NoSQL database MongoDB for data storage,

.NET Core SignalR library, for data transfer from

server to client, web API for GPS data collection. Sig-

nalR library was chosen to implement data exchange

using websockets protocol which allows to faster up-

date data in real time using one websocket connec-

tions. Web API was implemented in REST archi-

tecture style and its main purpose is to allow using

HTTP protocol for getting, updating and adding new

data in database. GPS receivers use web API to up-

date location data and each time corresponding API

method is called, it also calls SignalR hub method

that distributes data to clients who has joined group,

which has been identiﬁed by selected route. All ve-

hicle data on given route identiﬁer is distributed to all

clients that has subscribed for vehicle data method.

C# and JavaScript languages were used for server and

client application development. For server-side .NET

Core 2.0 framework version was used. Client side was

made using JavaScript Angular 2 framework. Client

application is integrated in the same solution and de-

ployed on one Azure web application service. Archi-

tecture of the developed system is demonstrated in Fig

3.2 Web System for GPS Data

Visualisation

User interface of the developed system is shown in

Fig 7. Basic idea is, that user choose the needed pub-

lic transport route and stop, afterwards system shows

on the map where is the nearest transport. All RS

public transport routes and stops were integrated from

their publicly available GTFS feed

. Feed informa-

tion was converted from .txt to JSON format, us-

ing Javascript library for further data upload to local

database. Google Maps services were used to display

map and location. In case when there is no GTFS feed

for public transport agency, it is possible to retrieve

route data from Google Directions service and with

custom algorithm extract waypoint information and

draw approximate route polylines in map and mark

stops. Based on provided information system user can

see if the public transport has already passed the stop

or is still approaching it.

http://saraksti.rigassatiksme.lv/riga/gtfs.zip

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280

Figure 6: Architecture of the developed system for GPS data visualisation.

Figure 7: User interface of the developed system.

4 CONCLUSIONS

Implementation of GPS data monitoring for pub-

lic transport needs can signiﬁcantly increase service

quality for its customers and give additional value for

service provider internal needs.

The data provided by the GPS records can be

translated into reliable indicators that will show the

exact position of the bus on the route.

Developed system prototype demonstrates easy

and relatively cheap solution for public transport lo-

cation visualisation for service users based on GPS

data. Developed system eliminates the need for a third

party to maintain the infrastructure and management

of the system.

The GPS location reported by the unit was almost

always accurate to within 10 meters and no loss of sig-

nal was experienced during the testing phase. Know-

ing real-time transport location helps citizens to avoid

unpredictable transport delays and helps to re-plan

their scheduled route.

ACKNOWLEDGMENTS

Scientiﬁc research, publication and presentation are

supported by the ERANet-LAC Project „Enabling re-

silient urban transportation systems in smart cities

(RETRACT, ELAC2015/T10-0761)”.

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