Traffic Expression through Ubiquitous and Pervasive Sensorization

Smart Cities and Assessment of Driving Behaviour

Fábio Silva

, Cesar Analide

and Paulo Novais

Computer Science and Technology Center, University of Minho, Braga, Portugal

Department of Informatics, University of Minho, Braga, Portugal

Keywords: Pervasive Monitoring, Indicator Assessment, Sustainable Development.

Abstract: The number of portable and wearable devices has been increasing in the population of most developed

countries. Meanwhile, the capacity to monitor and register not only data about people’s habits and locations

but also more complex data such as intensity and strength of movements has created an opportunity to their

contribution to the general wealth and sustainability of environments. Ambient Intelligence and Intelligent

Decision Making processes can benefit from the knowledge gathered by these devices to improve decisions

on everyday tasks such as planning navigation routes by car, bicycle or other means of transportation and

avoiding route perils. Current applications in this area demonstrate the usefulness of real time system that

inform the user of conditions in the surrounding area. Nevertheless, the approach in this work aims to

describe models and approaches to automatically identify current states of traffic inside cities and relate

such information with knowledge obtained from historical data recovered by ubiquitous and pervasive

devices. Such objective is delivered by analysing real time contributions from those devices and identifying

hazardous situations and problematic sites under defined criteria that has significant influence towards user

well-being, economic and environmental aspects, as defined is the sustainability definition.

1 INTRODUCTION

Ambient Intelligence (AmI) is a very active area of

knowledge and constitutes a multi-disciplinary

subject which takes advantage of advances in

sensing systems, pervasive devices, context

recognition and communications. Nowadays, AmI

applications can be found in fields ranging from

home, office, transport, tourism, recommender and

safety systems, among many others (Sadri, 2011).

There is also a renewed concern towards the quality

of living and well-being inside great cities. It is

forecasted that most people will be living inside

cities until 2050. If true, such statement would

predict the increase of road traffic in cities that were

neither originally designed nor prepared to handle

such influxes of traffic. Current identification

systems compromise direct evaluation and costly

field studies to assess and evaluate the impact of the

flux of traffic in certain cities. Additionally,

simulation experiments provide possible scenarios

under which some assessment can be made.

However, the downside of simulations is the use of

simplified models that are thought to mimic reality

when in fact they may differ to some degree. Even

more, the accuracy of these simulations is made by

measuring the current state which in itself can

include measurement errors and possible virtual

scenarios under the realistic behaviours.

Ubiquitous Sensorization may be used to assess

current traffic conditions, avoiding the use of costly

field studies. The nature of those ubiquitous devices

enables the possibility of direct analysis of driver

behaviour and community habits (points of

congestion, high speed areas and hazardous corners)

assessed trough the statistical treatment of driving

records. Such model has a direct impact diagnosing

the current state of traffic and traffic behaviours to

each route that may be used in modern GPS

navigation systems as an additional parameter.

Approaches to real time safety assessment can be

found in the research community, for instance in

(Laureshyn, Svensson, and Hydén, 2010) and

(Minderhoud and Bovy, 2001) a set of indicators is

used to assess driving safety through the use of

indicators. Such indicators take into consideration

time of reaction, vehicle breaking time and whether

or not there is a collision course. Yet, the analysis is

still in real time, local to the surrounding area and

activities such as identification of other vehicles

within the nearby visible space. These capabilities

are produced with the help of video interfaces and

Silva F., Analide C. and Novais P..

Trafﬁc Expression through Ubiquitous and Pervasive Sensorization - Smart Cities and Assessment of Driving Behaviour.

DOI: 10.5220/0005242500330042

In Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2015), pages 33-42

ISBN: 978-989-758-084-0

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

disregards other sources of information.

In the case of transport applications, an area also

known as Smart Cars the AmI system must be aware

of not only the car situation, but also the driver's

intention, his physical and physiological conditions

and the best way to deal with them (Rakotonirainy

and Tay, 2004). The driver's behaviour is, thus, of

key importance. Some authors have used machine

learning and dynamical graphical models for

modelling and recognizing driver's behaviours (Sun,

Wu, and Pan, 2009). Examples of applications

integrating AmI and ubiquitous principles in driving

and traffic analysis were already purposed in the

literature. In (Li et al., 2012), it is described a

monitoring and analysis system to approach

personalized driving behaviour, for emerging hybrid

vehicles. The system is fully automated, non-

intrusive with multi-modality sensing, based on

smartphones. The application runs while driving and

it will present personalized quantitative information

of the driver's specific driving behaviour.

The quality of devices used to perform monitoring

has a direct relationship to the quality of

measurements, thus, in this case, they are the main

source of measurement error which needs to be

controlled and contained to known values of error in

order to make this study effective to production use.

Other advantages include the possibility to increase

information quality and create new routing

algorithms in existing navigation systems taking into

consideration aspects such as driver’s driving style

or hazardous event rate during the routing and

planning phase.

In (Paefgen, Kehr, Zhai, and Michahelles, 2012) a

mobile application assesses driving behaviour, based

on critical driving events, giving feedback to the

driver. The Nericell system (Mohan, Padmanabhan,

and Ramjee, 2008), from Microsoft Research,

monitors road and traffic conditions using the

driver’s smartphone and corresponding incorporated

sensors, but it can also detect honking levels and

potholes on roads. The I-VAITS project

(Rakotonirainy and Tay, 2004) is an example that

pretends to assist the driver appropriately and

unobtrusively, analysing real-time data from the

environment, from the car and from the driver itself,

by the way the driver uses the different elements of

the car, their movements or image processing of

their face expressions. In (Bosse, Hoogendoorn,

Klein, and Treur, 2008), in the context of a car

safety support system, an ambient agent-based

model for a car driver behaviour assessment is

presented. The system uses sensors to periodically

obtain information about the driver's steering

operation and the focus of the driver's gaze. In the

case of abnormal steering operation and unfocused

gaze, the system launches proceedings in order to

slow down, stop the car and lock the ignition.

In what refers to devices used, there are today a wide

range of options that can be used. The most effective

should be devices that are always present and can

perform complex tasks while not requiring user’s

direct attention. In such list, there are devices like

smartphones, smartwatches, and intelligent

wristbands. Those offer the advantage of

accompanying user from one situation to another,

but there are devices that can be used that are

situation and local specific such as the internal

computer of a car. In this last case the object itself

becomes part of the car which might increase its

production cost while on the other hand multi-

purpose portable devices suffice to the work

described.

Another important subject in these studies is the

preservation of privacy, which may matter in order

to make user trust the system. Privacy concerns can

be included in many steps from the information

collection and protection to the disclosure of

aggregated information. Framework have been

proposed in the literature to raise awareness to the

potential causes that impair privacy. Martínez-

Ballesté et. al (Martinez-Balleste, Perez-martinez,

and Solanas, 2013) discusses a five dimensional

framework to handle privacy which can be applied

these projects. In this study, privacy concerns the

identity of users, their queries in the system, their

location, their footprint generated by sensors and the

information they own. In the study portrayed in this

article, privacy disclosure is maintained locally to

the device the user holds and in the central server

requires user’s token to access is own information.

Aggregated results are disclosed coupled with

different user and with no reference to user, location

or records used. Though privacy issues are being

addressed, as stated in the literature, in order to

ensure privacy it is felt the need of a minimum

community users which have to have overlapping

routines to mask their own habits statistically.

2 TRAFFIC ASSESSMENT

Traffic assessment is directly related to trending

topics such as smart cities and sustainable services.

It is an increasing concern not only the quality of life

of people inside big cities with millions of

inhabitants but also its sustainability. Such concern

has already derived the construction of sustainable

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

development indicators and sustainable indicators

that directly target this theme.

2.1 Road Traffic Analysis

2.1.1 Slow and Congested Sites

The definition of congested sites refers places in

which traffic flow becomes affect by the overflow of

cars that affect negatively usability of such roads to

a point where the movement and flow becomes

much slower than the norm of is even stopped.

Aside from the stressful environment the

concentration of pollution gases in traffic jam can be

a negative influence for one’s health. The

identification of traffic jams, and more importantly

their avoidance can help mitigate its influence over

the social dimension of sustainable driving. With the

advent of the paradigm Smart Cities, health has

become one of the topics of concern (Solanas et al.,

2014). Smart assumption with information from

vehicle configuration, speed and fuel can be used to

grossly estimate the gas emissions to the

atmosphere. Toxic emissions from vehicle exhaust

fumes can be tracked both by the number of vehicles

identified in a certain area and by the time the

vehicle is present in such area. This can also be used

to monitor air quality, even if grossly, it can give

meaningful information to healthcare frameworks

and help raise awareness for such problems in

mobility and city planning.

2.1.2 Dangerous and Hazardous Routes

There are different approaches to identify hazardous

routes or places. Depending on the approach results

may vary which indicates that the analysis and

classification of this parameter can become complex

and include coverage problems, where the definition

and identification process might miss classes of

spots due to incomplete definition or incomplete

detection systems.

Traditional approaches consider the number of

accidents or the number of fines issued. In this case,

they fail to identify situations like preponderance to

aggressive driving, dangerous events such as high

variance in velocity in short periods of time or

dangerous maneuverers like high speed cornering

sudden near full or full stops.

2.2 Driving Analysis

2.2.1 Driving Profiles

The usage of roads can be affected by driver’s

driving patterns. It is accepted that, if the majority of

drivers have a predisposition to drive more

aggressively in certain areas than others, then those

areas are more dangerous. Our approach uses this

thought to gather the driving records from a

community of users and use them to calculate

potential hazardous spots inside cities. Most

evaluations are made using standard driving

attributes, matured in the literature over a number of

studies across different authors and projects. Thus,

the list of most used attributes is:

 Time

 Average velocity

 Standard deviations of velocity

 Number of breaks

 Standard deviations of breaks

 Number of accelerations

With those measures, a complete profile can be

designed and executed in applications that monitor

current driver’s performance. In (Ericsson, 2001),

(Ericsson, 2000) other parameters were used to

collect data from ordinary drivers in real traffic

situations, such as wheel rotation, engine speed,

ambient temperature, use of breaks and fuel

consumption. In these studies, GPS data was also

monitored, where each driving pattern was attributed

to street type, street function, street width, traffic

flow and codes for location in the city (central, semi-

central, peripheral). It was concluded that the street

type had the most influence on the driving pattern.

The analysis of the 62 primary calculated

parameters, resulted in 16 independent driving

pattern factors, each describing a certain dimension

of the driving pattern. When investigating the effect

of the independent driving pattern factors on exhaust

emissions, and on fuel consumption, it was found

that there already studies with a common number of

factors amongst the literature. As seen on table 1,

there some studies across time that share the same

factors.

Due to the decision to implement a pervasive system

over mobile sensorizaion the work here described

will account the attributes that are able to be

collected by smartphone applications.

While these attributes characterize driving in a long

term analysis, such strategy might miss spontaneous

events that occur sporadically. An example of such

is a sudden break with high intensity. In order to

deal with these one-off events, other attributes are of

relevance:

 Force exerted in the car

 Slope of the line connecting initial to final

velocity during breaking and accelerating

events

TrafficExpressionthroughUbiquitousandPervasiveSensorization-SmartCitiesandAssessmentofDrivingBehaviour

 Degree of the curvature of the road and force

exacted in the car

Table 1: Attribute Study.

Attribute Study References

Fuel

Consumption

(Ericsson, 2001); (Mohan et al., 2008);

(Kuhler and Karstens, 1978)

Velocity

(Ericsson, 2001); (Mohan et al., 2008);

(Kuhler and Karstens, 1978);

(Johnson and Trivedi, 2011)

Acceleration

(Ericsson, 2001); (Mohan et al., 2008);

(Kuhler and Karstens, 1978);

(Johnson and Trivedi, 2011)

Street Type

(Ericsson, 2001); (Mohan et al., 2008);

(Kuhler and Karstens, 1978)

Trip Duration

(Ericsson, 2001); (Mohan et al., 2008);

(Kuhler and Karstens, 1978);

(Johnson and Trivedi, 2011)

Wheel

Rotation

(Ericsson, 2001); (Kuhler and

Karstens, 1978)

Motor

Monitoring

(Ericsson, 2001); (Kuhler and

Karstens, 1978)

Hour of day

(Ericsson, 2001); (Mohan et al., 2008);

(Kuhler and Karstens, 1978);

(Johnson and Trivedi, 2011)

This kind of analysis is only possible with a

dedicated user community that constantly updates

and makes use of the platform supporting these

models. With the general availability of internet of

things and the advent of portable and wearable

devices with the ability to sense and store user

locations, speed, gravitational force, sound and force

among some examples.

2.2.2 Context Awareness

Aside from driving study, another complex analysis

can be made with the help of context conditions.

Such conditions include weather, traffic congestion

and time of day, for instance. Each example can

have significant influence on the safety and on the

assessment of attributes related to driving.

Aggressiveness and dangerous behaviour has

different meanings in any of these conditions and

while some concepts are broad enough to be used by

all, others are situation specific meaning that what is

dangerous in one situation might not be in another.

Usually, driving pattern is defined and associated to

the speed profile of the driver, but it can be

expanded to other variables, as gear changing, and

big changes on the acceleration (Ericsson, 2001). In

1978, Kuhler and Karstens (Kuhler and Karstens,

1978) introduced a set of ten driving pattern

parameters. Later, in 1996, André (André, 1996)

reviewed those parameters, and reviewed some of

the most common parameters such as action

duration, speed, acceleration, idle periods and

number of stops per kilometre. Experiments with

communities are often used to provide real time

analysis of geographic conditions and events, with

examples of such in the Waze platform (Waze Ltd,

2014). However, they are the lacking features of

historic analysis and historical supported

suggestions.

The aim of this work is to focus on intangible and

soft attributes which we define as attributes that are

not directly observed by data records but rather

computed with techniques from static analysis and

machine learning processes. Such attributes should

be used to find hidden patterns of road usage that

might be missed in standard traffic flow simulations.

Examples of such errors in simulation include

driving aggravation due to unforeseen events even

with normal traffic conditions.

2.3 Sustainable Driving

Traffic assessment is directly related to trending

topics such as ubiquitous and pervasive methods that

allow the balancing of economic, environmental and

social factors needed for sustainable development. A

new emerging and interdisciplinary area, known as

Computational Sustainability, attempts to solve

problems which are essentially related to decision

and optimization problems in correlation to welfare

and well-being. Due to its importance, some

researchers have discussed and proposed

quantification methods, and modelling process for

sustainability (Todorov and Marinova, 2011),

(Kharrazi, Kraines, Hoang, and Yarime, 2014).

Often, decision and assessment are based on

measurements and information about historical

records. Indicator design provides an explanation on

why such decisions are being made and it often uses

information fusion to create and update its values.

From a technological point of view, indicator

analysis uses different and sometimes nonstandard

data which sounds feasible by technological data

gathering software that collect, store and combine

data records from different sources. In the case of

transportation systems, the assessment of the impact

of a given driving pattern is made over sustainability

indicators, like fuel consumption, greenhouse gas

emissions, dangerous behaviour or driving stress in

each driver’s profile.

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

A system to estimate a driver profile using

smartphone sensors, able to detect risky driving

patterns, is proposed in (Eren, Makinist, Akin, and

Yilmaz, 2012). It was verified whether the driver

behaviour is safe or unsafe, using Bayesian

classification. It is claimed that the system will lead

to fuel efficient and better driving habits. In (Healey

and Picard, 2005), and in addition to car sensory

data, physiological data was continuously collected

and analysed (heart rate, skin conductance, and

respiration) to evaluate a driver's relative stress. The

CarMa, Car Mobile Assistant, is a smartphone-based

system that provides high-level abstractions for

sensing and tuning car parameters, where by

developers can easily write smartphone applications.

The personalized tuning can result in over 10%

gains in fuel efficiency (Flach, Mishra, Pedrosa,

Riesz, and Govindan, 2011). The MIROAD system,

Mobile-Sensor-Platform for Intelligent Recognition

Of Aggressive Driving (Johnson and Trivedi, 2011),

is a mobile system capable of detecting and

recognizing driving events and driving patterns,

intending to increase awareness and to promote

safety driving, and, thus, possibly achieving a

reduction in the social and economic costs of car

crashes.

3 EVALUATION APPROACH

In the work presented in this paper, the evaluation of

city traffic is made using a platform named PHESS

Driving. This platform inludes smartphone

aplications, server backends for adminisrators and

users and webpages for data visualization.

Management is made either by platform managers or

users of the platform in distinct, non-overlapping

areas. Platform managers are responsible for the

integrity of the data collected and the monitoring of

processes while users can manage their own records

(Fábio Silva, Analide, Gonçalves, and Sarmento,

2014).

This platform is responsible for synchronizing

driving data from user’s smartphones with a central

server that analyses the behaviour of a community of

users to deliver metrics and information about traffic

and risky driving patterns in these areas. In figure 1,

it is depicted the general flow of data used in the

system. The user smartphone acts both as sensor

collection device and as a display of information

about user metrics. Internal metrics and indicators

are supported at the server level which by

coordination and conjunction of web services

delivers processed information towards user’s

displays.

Assessment of metrics and indicators is made using

data fusion processes detailed in sections 3.1 and

3.2. Such metrics and indicators provide the value

added information of this system and have a

preventive objective to warn the user about potential

dangerous routes and avoid its use.

In order to provide a smart analysis for smart cities,

the indicators are used in squares of terrain in city

maps and the data used to classify that square is an

aggregation of the indicators that fit geographically

inside such square. For classification a three level

classification was used from initial sample data

present in the PHESS platform. In this sample data

the focus was on outlier data present in over the 80%

quartile representing the most extreme cases. Such

cases are them flagged as yellow or red depending if

they are between the 80% - 95 % or over 95%

quartile respectively.

Figure 1: Conceptual architecture of the PHESS platform.

3.1 Driver Evaluation

In order to produce information about traffic flow

and route safety it is necessary to gather information

about relevant driving patterns in city areas. The

focus of our analysis was derived from indicators

accepted from the literature review and already

present is the system PHESS Driving. Towards this

effect it was considered:

TrafficExpressionthroughUbiquitousandPervasiveSensorization-SmartCitiesandAssessmentofDrivingBehaviour

 Intensity of acceleration and breaking;

 Number of breaking and accelerating events;

 Curve driving analysis;

The intensity of acceleration and breaking events is

a challenging task. Due to the usage of mobile

smartphones, sensor access is not easily controllable.

Efficiency measures make data reading uneven in

time meaning sometimes there is oversampling

where others there is under sampling. In order to

mitigate such problem the assessment is made using

the linear slope from the line connecting an initial

and final velocity as presented in equation 1. Such

slope provides a mean to assess intensity that is

independent of the size of the time interval.

















(1)

The classification of the intensity of breaking is

made using a green, yellow and red scale, derived

from the initial sample as depicted in table 2.

Table 2: Indicator for slope of line between two different

velocities.

Behaviour Slope Sample Coverage

Green < 0.0017 80 %

Yellow 0.0017 – 0.0079 15 %

Red > 0,7 5 %

Number of breaking and accelerating events are

measured in time windows. Defining an event

window is helpful because only accelerations and

breaking inside such window are considered and can

be analysed and compared between time windows.

Table 3 provides information about classification for

breaking and accelerating events.

Table 3: Indicator defined for acceleration and breaking

events per 10 minutes.

Behaviour Average Sample Coverage

Green < 42 80 %

Yellow 42 - 63 15 %

Red > 63 5 %

Lastly, curve driving is a special event due to

characteristic and driving difficulty. Due to car

uneven handling, driving inside curvatures can

present a risky task specially if driven at too much

speed. For this analysis a special strategy is

employed which monitor the degree trough

smartphone sensors. Equation 2 demonstrates the

formula used to track angle difference in the

direction between two points. As the curvature

becomes more intense the road curvature is

identified as potentially more dangerous than others.

  tanh



sin













∗ cos









, cos



∗sin





sin



∗ cos 



∗ cos















(2)

In table 4 it is depicted the assessment made by the

extended PHESS platform to the degree of curvature

found using mobile sensorization.

Table 4: Indicator defined for assessing degree of

curvature.

Behaviour Average Sample Coverage

Green < 24º 80 %

Yellow 24º – 47º 15 %

Red > 47º 5 %

3.2 Dynamic Modelling and Historic

Information

There is information that is dependent on external

conditions of traffic and not related with driving

itself. The platform developed will try to assess

external condition using context estimation from the

data gathered. Again the strategy is made with the

help of indicators. The indicators defined are:

 Road congestion;

 Road high speed.

In this case, the velocity recorded by users is

aggregated inside each square of terrain. After an

initial sample of the system the average speed is

classified in a grid map identifying high speed

squares and low speed, congested, squares. For such

analysis it is considered the squares with average

speed over the 80% quartile and the inverse for the

selection of low speed squares.

Value added information produced in the system is

published using a range of public web services.

These web services provide public information

about current traffic and driving conditions as well

as, modelling analysis based on the historical data

available in the platform.

4 RESULTS AND DISCUSSION

The model described in this article was tested as a

complimentary module to the sustainability

framework PHESS (Fabio Silva, Analide, Rosa,

Felgueiras, and Pimenta, 2013). Its aims are to

produce and generate knowledge that can be used to

perform decisions and suggestions that have a direct

impact on sustainability and the sustainability of

user’s actions. More than a responsibility

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

framework, it is intended to increase awareness to

sustainable problems that arise from user’s own

actions and road usage by drivers.

Taking into consideration a test city with a

community of 10 users, it is possible to assess the

sample metrics and indicators defined. The

community of users was chosen from volunteers

who recorded their driving routes during their daily

habits without restrictions to the rate of recordings,

duration of trips or locations. The nature of

recording device was by definition their own android

smartphones as long they were equipped with GPS,

accelerometers, internet connection and able to

install the application to gather driving records. Due

to the strain on the recording device and their

availability, cross-validation with approaches from

other projects over the same information was not

assessed in this study. Additionally, the

unavailability of access to tools stated by research

conducted in the community increases the difficulty

comparing results with these studies. The nature of

the results presented is also different relying on zone

assessment instead of street assessment, due to the

fact that street close to traffic jam tend to suffer the

same problems as the main street. These results

express areas according to the square granularity

defined in the system and, as result may identify

portions of a street englobing its assess routes within

the same zone. Platforms such as Waze (Waze Ltd,

2014), currently do not do such assessment instead

when indicated as congested, the full street is

assigned a congested parameter and its assess routes

are not automatically classified as well.

Nevertheless, the use of a decentralized capture

devices presented no problems recording user’s

driving records. Analysing the flow of data, the

bottleneck of the system should be the central server.

However, during this experiments no problem was

detected and measures to handle synchronization

between device and central server can be mitigated

with additional servers and load balancers.

The coverage of cities and metropolitan areas is

dependent on the community. Not only its size, but

also frequency, duration and length are determinant

to allow users to produce information that can be

used to assess cities. Its calculation is rather

difficult, but considering the most popular and

influential routes used by the vast majority of users,

then small communities can produce meaningful

results, which users can relate to. That was the case

with our own experiments.

Statistical results from the data collected according

to the principles defined in section 3 can be found in

this section. Over table 5 and table 6, it is

represented the summary of green, yellow and red

evaluation according to each non-conformant

behaviour defined in the system. In this specific case

information about indicators in section 3.1 is used

aggregating classification rounding the average

classification in each square.

Table 5: Analysis of the indicator defined for acceleration

per User per Trip.

Behaviour Average Number of

Accelerations and

Decelarations

Standard

Deviation

Green 38 3

Yellow 12 4

Red 3 8

Table 6: Analysis of Curvature and Intensity per User per

Trip.

Behaviour Average Curvature Standard Deviation

Green 10 4

Yellow 12 3

Red 15 2

These results provide evidence that our initial

sampling strategy to define green, yellow and red

accelerations and curvatures hold in a real scenario.

This means, the proportion of events classified is not

excessively different from expected. A

demonstrative example of the classification of

events is made in figure 2 and its impact on the grid

map on figure 3.

Figure 2: Individual Trip Event Classification.

In order to analyse the classification and demons-

trate that the scale has been appropriated to detect a

small but significant set of yellow and red events.

Such detection mechanisms can be improved with

more technical data about dangerous events or even

TrafficExpressionthroughUbiquitousandPervasiveSensorization-SmartCitiesandAssessmentofDrivingBehaviour

adjust the quartiles used for classification,

nevertheless the proposed approach provides

satisfactory results.

Figure 3: Individual Trip Impact on Community Map.

Each event is characterized in the map, and for the

user it is possible to see the information relevant to

that assessment. A set of filters with each indicator

defined may alter the map zone classifications

according to whether or not they are selected.

Current available filters are acceleration and

decelerations and curvature, as shown in the results

of this paper. Speed information is available but as a

heat map index as speed classification is dependent

of traffic regulations directly enforced on each

street. Due to this, the approach presented cannot

accurately classify according to speed but it can

clearly indicate faster zones than others.

On the other hand, figure 4 does not provide event

level explanation but rather aggregations of the

driving records of users that have been in such

zones. The set of filter according to each indicator is

also present but instead of providing information

about individual records, they also use the

aggregated values from the community of users in

the same location.

The identification of only yellow and red locations

over the grid helps mitigate privacy problem as it

provides the user with relevant aggregated

information but holds a significant portions of the

data, green evaluations (±80% of the total records).

Without this information it becomes more difficult

individual user identification as the information not

displayed may or may not exist in the system.

Nevertheless, the critical information that can be

used by drivers and for studies of mobility and

transport planning is presented.

Figure 4: Aggregated User Data on Community Map.

The map covered by the classification results is

within the expected range but varies according the

time of day. However, the location of squares is

preserved to allow direct comparison by time and

day if necessary. Each square can have different

averages, according to the time and classifications,

Our approach, identifies such metrics on daily basis

but the identified spots are within 10% to 15% of the

visible map.

5 CONCLUSIONS

The use of pervasive devices already adopted by

communities of users possess enough information

and computing ability to build collaborative systems

to tackle complex tasks. City traffic evaluation is

one of such problems that are costly to audit and

diagnose structural problem but can be simplified

with crowd computing. Results are seem as

satisfactory are reliable with the possibility to adjust

according to specifics needs or needed improvement.

The use of mobile sensors does constitute an

additional effort to mitigate external influences such

user involuntary movement, measurement and

coverage errors. Nevertheless, the outputs generated

in this platform were also found of relevance to the

study of sustainability, where the intangible metrics

and the structures employed to the indicator analysis

pave the way to building sustainability assessing

indicators able to join general purpose sustainability

assessment frameworks such as the platform PHESS

in discussion in this work.

In future iterations there are plan to update from grid

analysis to road detection and road analysis

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

becoming more accurate. Also, the validation of

experiments on other cities are planned in order to

prove both resilience and adaptation of the system.

Integration of metrics found by this platform in

common navigation systems are planned on the long

term project, thus influencing routing options of

people and acting as a true pervasive and ubiquitous

object directing people away from dangerous

situations into more comfortable and safe

environments.

ACKNOWLEDGEMENTS

This work was developed in the context of the

project CAMCoF - Context-aware Multimodal

Communication Framework funded by ERDF -

European Regional Development Fund through the

COMPETE Programme (operational programme for

competitiveness) and by National Funds through the

FCT - Fundação para a Ciência e a Tecnologia

(Portuguese Foundation for Science and

Technology) within project FCOMP-01-0124-

FEDER-028980 and PEst-OE/EEI/UI0752/2014.

Additionally, it is also supported by a doctoral grant,

with the reference SFRH/BD/78713/2011, issued by

FCT and included in the financial program

POPH/FSE in Portugal.

REFERENCES

André, M. (1996). Driving Cycles Development:

Characterization of the Methods. doi:10.4271/961112.

Bosse, T., Hoogendoorn, M., Klein, M. A. C. A., and

Treur, J. (2008). A Component-Based Ambient Agent

Model for Assessment of Driving Behaviour. In F.

Sandnes, Y. Zhang, C. Rong, L. Yang, and J. Ma

(Eds.), Ubiquitous Intelligence and Computing (Vol.

5061, pp. 229–243). Springer Berlin Heidelberg.

doi:10.1007/978-3-540-69293-5_19.

Eren, H., Makinist, S., Akin, E., and Yilmaz, A. (2012).

Estimating driving behavior by a smartphone. In 2012

IEEE Intelligent Vehicles Symposium (pp. 234–239).

IEEE. doi:10.1109/IVS.2012.6232298.

Ericsson, E. (2000). Variability in exhaust emission and

fuel consumption in urban driving. URBAN

TRANSPORT SYSTEMS. PROCEEDINGS …, (1980),

1–16. Retrieved from http://trid.trb.org/view.aspx?

id=715489.

Ericsson, E. (2001). Independent driving pattern factors

and their influence on fuel-use and exhaust emission

factors. Transportation Research Part D: Transport

and Environment, 6(5), 325–345. doi:http://dx.doi.org/

10.1016/S1361-9209(01)00003-7.

Flach, T., Mishra, N., Pedrosa, L., Riesz, C., and

Govindan, R. (2011). CarMA. In Proceedings of the

9th ACM Conference on Embedded Networked Sensor

Systems - SenSys ’11 (p. 135). New York, New York,

USA: ACM Press. doi:10.1145/2070942.2070957.

Healey, J. A., and Picard, R. W. (2005). Detecting Stress

During Real-World Driving Tasks Using

Physiological Sensors. IEEE Transactions on

Intelligent Transportation Systems, 6(2), 156–166.

doi:10.1109/TITS.2005.848368.

Johnson, D. A., and Trivedi, M. M. (2011). Driving style

recognition using a smartphone as a sensor platform.

In 2011 14th International IEEE Conference on

Intelligent Transportation Systems (ITSC) (pp. 1609–

1615). IEEE. doi:10.1109/ITSC.2011.6083078.

Kharrazi, A., Kraines, S., Hoang, L., and Yarime, M.

(2014). Advancing quantification methods of

sustainability: A critical examination emergy, exergy,

ecological footprint, and ecological information-based

approaches. Ecological Indicators, 37, Part A(0), 81–

89.

doi:http://dx.doi.org/10.1016/j.ecolind.2013.10.003.

Kuhler, M., and Karstens, D. (1978). Improved Driving

Cycle for Testing Automotive Exhaust Emissions.

doi:10.4271/780650.

Laureshyn, A., Svensson, A., and Hydén, C. (2010).

Evaluation of traffic safety, based on micro-level

behavioural data: theoretical framework and first

implementation. Accident; Analysis and Prevention,

42(6), 1637–46. doi:10.1016/j.aap.2010.03.021.

Li, K., Lu, M., Lu, F., Lv, Q., Shang, L., and Maksimovic,

D. (2012). Personalized Driving Behavior Monitoring

and Analysis for Emerging Hybrid Vehicles. In

Proceedings of the 10th International Conference on

Pervasive Computing (pp. 1–19). Berlin, Heidelberg:

Springer-Verlag. doi:10.1007/978-3-642-31205-2_1.

Martinez-Balleste, A., Perez-martinez, P., and Solanas, A.

(2013). The pursuit of citizens’ privacy: a privacy-

aware smart city is possible. IEEE Communications

Magazine

, 51(6), 136–141. doi:10.1109/MCOM.2013.

6525606.

Minderhoud, M. M., and Bovy, P. H. (2001). Extended

time-to-collision measures for road traffic safety

assessment. Accident; Analysis and Prevention, 33(1),

89–97. Retrieved from http://www.ncbi.nlm.nih.gov/

pubmed/11189125.

Mohan, P., Padmanabhan, V. N., and Ramjee, R. (2008).

Nericell. In Proceedings of the 6th ACM conference

on Embedded network sensor systems - SenSys ’08 (p.

323). New York, New York, USA: ACM Press.

doi:10.1145/1460412.1460444.

Paefgen, J., Kehr, F., Zhai, Y., and Michahelles, F. (2012).

Driving Behavior Analysis with Smartphones: Insights

from a Controlled Field Study. In Proceedings of the

11th International Conference on Mobile and

Ubiquitous Multimedia (pp. 36:1–36:8). New York,

NY, USA: ACM. doi:10.1145/2406367.2406412.

Rakotonirainy, A., and Tay, R. (2004). In-vehicle ambient

intelligent transport systems (I-VAITS): towards an

integrated research. In Proceedings. The 7th

TrafficExpressionthroughUbiquitousandPervasiveSensorization-SmartCitiesandAssessmentofDrivingBehaviour

International IEEE Conference on Intelligent

Transportation Systems (IEEE Cat. No.04TH8749)

(pp. 648–651). IEEE. doi:10.1109/ITSC.2004.

1398977.

Sadri, F. (2011). Ambient intelligence. ACM Computing

Surveys, 43(4), 1–66. doi:10.1145/1978802.1978815.

Silva, F., Analide, C., Gonçalves, C., and Sarmento, J.

(2014). Ubiquitous Sensorization for Multimodal

Assessment of Driving Patterns. In C. Ramos, P.

Novais, C. E. Nihan, and J. M. Corchado Rodríguez

(Eds.), Ambient Intelligence - Software and

Applications (Vol. 291, pp. 143–150). Cham: Springer

International Publishing. doi:10.1007/978-3-319-

07596-9.

Silva, F., Analide, C., Rosa, L., Felgueiras, G., and

Pimenta, C. (2013). Gamification, Social Networks

and Sustainable Environments. International Journal

of Interactive Multimedia and Artificial Intelligence,

2(4), 52. doi:10.9781/ijimai.2013.246.

Solanas, A., Patsakis, C., Conti, M., Vlachos, I., Ramos,

V., Falcone, F., … Martinez-Balleste, A. (2014).

Smart health: A context-aware health paradigm within

smart cities. IEEE Communications Magazine, 52(8),

74–81. doi:10.1109/MCOM.2014.6871673.

Sun, J., Wu, Z., and Pan, G. (2009). Context-aware smart

car: from model to prototype. Journal of Zhejiang

University SCIENCE A, 10(7), 1049–1059. doi:10.

1631/jzus.A0820154.

Todorov, V., and Marinova, D. (2011). Modelling

sustainability. Mathematics and Computers in

Simulation, 81(7), 1397–1408. doi:10.1016/j.matcom.

2010.05.022.

Waze Ltd. (2014). Waze. Retrieved September 23, 2014,

from https://www.waze.com/

PECCS2015-5thInternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems