A Future of Traffic Management

Toward a Hybrid System of Roadside and Personal Mobile Devices

Tom Thomas

Center of Transport Studies, University of Twente, p.o.box 217, 7500 AE Enschede, The Netherlands

t.thomas@utwente.nl

Keywords: Travel Time, Travel Demand, Urban, Prediction, Detection Loops, GPS.

Abstract: In the last few decades, increasing traffic has led to serious problems in urban areas. Travel times for

travellers have increased and the liveability in residential areas has declined due to pollution and issues

concerning safety. Whereas new road and rail infrastructure in densely populated areas is costly, a better

utilisation of existing infrastructure appears to be more attractive. This becomes increasingly possible due to

huge developments in the world of Information and Communication Technology (ICT). However,

implementing ICT solutions for the purpose of traffic management remains challenging. First, it is quite a

task to set up a system in which infrastructure, travel vehicles and travellers can communicate with each

other. Secondly, it is quite difficult to gather and exchange traffic and travel information in such a way that

the traffic situation improves significantly. This paper deals with the latter issue, and it provides an outline

for the possible architecture of a future traffic management system. It concludes that in such a system both

mobile devices, like smartphones and navigation systems, and roadside devices, like loop detectors and

cameras, need to be included to arrive at optimal results.

1 INTRODUCTION

Congestion has increased significantly in the last

few decades. The efficient use of existing

infrastructure by dynamic traffic management

(DTM) is one of the strategies to reduce congestion

and related problems like air pollution. An important

requirement is the availability of detailed traffic

information such as travel demand and travel times.

In the Netherlands and many other developed

countries, highway data are collected by a high

concentration of detection loops which yield

information on traffic intensities and travel times. In

urban areas traffic information is much scarcer and

only since quite recently, traffic data gathered by

roadside devices like detection loops have become

available in traffic information centres (e.g., Hasberg

and Serwill, 2000, Kellerman and Schmid 2000,

Leitsch, 2002). For urban areas, the traffic

circulation is usually estimated by a combination of

intensity measurements and traffic models.

However, with new measurement methods by which

individual vehicles are identified (Blokpoel and

Vreeswijk, 2011), accurate roadside measurements

of travel times and routes will become possible.

At the same time, the use of mobile sensor data

such as GPS and GSM has been increasing rapidly,

which have led to separate travel time estimators

(e.g., Google Traffic, 2013). Because these data are

gathered by personal devices such as navigation

systems and smartphones, travel information and

advice can be personalized and adapted to the

preferences of the individual traveller (e.g., Bie et

al., 2012). In case of smartphones, travel information

does not have to be limited to car trips, but may also

include several other travel modes.

Roadside and mobile sensor data techniques both

have their specific strengths. In this paper, a possible

architecture for future DTM is put forward in which

both data sources are combined at some point. In

section 2, the requirements for a future DTM system

are provided. Then, in section 3 and 4, the

advantages, disadvantages and possibilities of

mobile and roadside sensor data are described

respectively. Based on the requirements for a future

DTM system and the strengths and weaknesses of

roadside and mobile sensor data, section 5 describes

an outline for the architecture of a possible future

DTM system.

271

Thomas T.

A Future of Trafﬁc ManagementToward a Hybrid System of Roadside and Personal Mobile Devices.

DOI: 10.5220/0004776402710277

In Proceedings of the Third International Symposium on Business Modeling and Software Design (BMSD 2013), pages 271-277

ISBN: 978-989-8565-56-3

2 DTM REQUIREMENTS

The requirements for a modern DTM system should

be based on two pillars. First, the traffic system,

usually the responsibility of a traffic manager,

should run smoothly. This may mean several things,

but in general the objective is to minimize the total

delay on the (road) network and to minimize

external costs caused for example by traffic

accidents or air pollution. Secondly, the traffic users,

i.e. travellers, want to travel smoothly. This may

also mean various things for individual travellers,

but in general travellers want to minimize their

(perceived) travel time and cost while perceiving the

journey as being safe and comfortable.

In other words, the traffic manager’s objective is

to optimize the traffic system as a whole, while

travellers want to optimize their individual journeys.

Both things are not necessarily resulting in the same

traffic equilibrium, and in some cases they are even

clearly conflicting with each other. One of the main

challenges of a modern DTM is therefore to

reconcile the two.

Meeting the objectives of the traffic manager and

individual traveller also requires different

information needs. An individual traveller needs to

have traffic information about the possible travel

modes and routes that are relevant to him, i.e. those

that can be used to reach the preferred destination.

Detailed information only needs to be available

during the time of traveling. This is by no means

trivial, because it still requires a prediction of the

traffic situation in the near future, i.e. until the

journey is expected to be completed.

However, this task is relatively easy compared to

optimizing management objectives. Whereas the

individual traveller is merely influenced by

surrounding traffic, the traffic manager is

influencing traffic itself, which effects are much

harder to predict. Moreover, the effects of traffic

operations like traffic lights are not necessarily

instantaneous, but may show delays. For example, a

traffic measure in one part of a city can have an

effect in another part half an hour later. In the ideal

case, the traffic situation should therefore be

predictable for the whole network, for different

traffic management scenarios, and during a longer

period of time, for example a whole peak hour.

Fulfilling the needs of individual travellers on

the other hand has its own challenges. While there is

one traffic manager with one set of objectives, there

are many individual travellers, all with their own

perceptions, preferences and habits that play an

important role in the decision making process. In

this modern individual oriented society, traffic

information and travel advice of one fits all is

becoming less acceptable. By using individual

devices like smartphones, it is also becoming

technically possible to provide personalized traffic

information and travel advice.

From the aforementioned considerations, one

can arrive at the following requirements:

1. Traffic management requires accurate

predictions about the traffic situation for the

whole network, different traffic management

scenarios and whole (peak) periods.

2. Individual travellers need personalized multi-

modal travel advice based on their preferences

and habits.

3. A traffic management measure should not lead to

the perception of travellers that they are worse

off due to the measure or are harmed unfairly by

it.

The third requirement tries to reconcile differences

between the interests of traffic manager and

individual travellers. Of course, it is impossible to

satisfy all travellers. However, it might be possible

to introduce measures such that travellers do not

notice they are worse off and therefore do not

change their behaviour, or such that travellers do not

perceive the alternative they switch to as worse or

unfair.

3 MOBILE SENSOR DATA

Mobile sensors like GPS and GSM are widely used

in smartphones and navigation systems nowadays.

Initially, they were used for navigation, but as their

numbers increase, they are now also being used for

estimating travel times on road trajectories (e.g.,

Google Traffic, 2013). There are however more

applications: they can reveal travel patterns of

individuals and groups of travellers.

In most countries, including the Netherlands, the

understanding of people’s travel behaviour is based

on cross-sectional travel surveys where in general

only one day is surveyed for each respondent in

representative periods (Ortuzar et al., 2010). From

these data, origin destination matrices, modal split

(mode choice) and route choice are estimated and

used in models that model urban traffic flows.

However, this is not enough to gain a proper

understanding of the dynamics in travel behaviour.

More specific, cross-section data do not give any

information to ascertain how choices will vary over

Third International Symposium on Business Modeling and Software Design

272

time (i.e. policy response) if the system changes.

Studies with GPS-devices show a strong variation in

multi-day travel behaviour (e.g., Stopher amd

Zhang, 2011). People are shown to visit new places

even after several months of monitoring

(Schönfelder and Axhausen, 2010). Apart from

determining destination and mode choice patterns

over longer periods of time, GPS data are

increasingly used to study route choice (e.g., Jan et

al. 2000, Zhu and Levinson 2009, Papinski and Scott

2011).

Derived from standard economics it is often

assumed in transport modeling that travelers are

rational decision makers and have perfect

knowledge on all available choice alternatives.

There is increasing recognition that these

assumptions are debatable. In reality, people may

have limited knowledge and constrained cognitive

abilities, leading to prejudiced reasoning and

seeming randomness in choice behavior (e.g.

Avineri and Prashker, 2004). This has been

described as bounded rationality or satisficing

behavior, first introduced by Herbert Simon (Simon,

1955) , and also found its way into transportation

research (Mahmassani and Chang, 1987,

Jayakrishnan et al., 1994). Since then, multiple

studies suggested that these irrational behaviors are

neither random nor senseless; they are systematic,

consistent, repetitive, and therefore predictable

(Tversky and Kahnemann, 1981, Ariely, 2009).

A well-known mechanism derived from the

principles of bounded rationality is the notion of

indifference band (Mahmassani and Chang, 1987).

According to the theory of indifference bands,

drivers will only alter their choice when a change in

the transportation system or their trip characteristics,

for example the travel time, is larger than some

individual-situation-specific threshold.

More in general, travelers appear to make their

decisions based on their perception of alternatives,

which is biased according to the ‘choice-supportive

bias’. That is, people are more likely to attach

positive feeling to options they choose and attribute

negative features to options they reject (Mather et

al., 2003, Henkel and Mather, 2007) even if that

would be irrational. In terms of travel choices this

suggests that travelers have different perceptions of

options they frequently use than options they hardly

use (Vreeswijk et al. 2013).

These findings may play an important role in

future DTM, especially in fulfilling requirements 2

and 3. Although some travelers may be worse off

when the overall network performance is optimized,

it may be possible to choose DTM measures for

which travelers do not perceive they are worse off or

do not find this a problem. This will only be

possible, however, when travelers get personalized

travel advice based on their preferences and habits.

For this, mobile sensor data appear to be

indispensable.

4 ROADSIDE DEVICES

The use of smartphone, carrying among others a

GPS-sensor, will probably rise in the coming years,

enabling new data acquisition opportunities

(Stopher, 2009, Nitsche et al., 2012). In addition,

there already are numerous smartphone applications

gathering personal travel data (e.g. UbiActive (Fan

et al. 2012), Trip Analyzer (Li et al., 2011), and

tripzoom (Bie et al., 2012)). Finally, smartphones or

navigation devices are used as probes to estimate

travel times on main roads. The question thus arises

whether roadside devices are still necessary in the

future.

To answer this question, we need to consider

requirement 1 from section 2: “Traffic management

requires accurate predictions about the traffic

situation for the whole network, for different traffic

management scenarios and over whole (peak)

periods”.

This requirement implies several things at the

same time. First, information is needed on the traffic

situation. This is much more than travel time alone.

Policy makers are not only responsible for travellers,

but also for the environment that is harmed by

traffic. Pollution, noise hindrance and safety are

important external factors which need to be

considered, especially in dense residential areas or

near locations that attract vulnerable groups such as

schools. This implies that certain vulnerable, busy or

economically important areas, locations or corridors,

may need to be monitored continuously. Because

many external effects depend on traffic intensity,

this important quantity should be included in the

monitoring.

Secondly, predictions are required for the whole

network under various (possible) management

scenarios. This implies that traffic intensities and

travel times should be predictable when the traffic

manager decides to increase or reduce the capacity

of certain roads (for example by giving more or less

green time). Because travel time shows a strong non-

linear dependence on network intensities (demand)

and capacities (supply), it is difficult to predict travel

time when intensities and capacities are unknown,

especially when small changes in intensity have a

A Future of Traffic Management: Toward a Hybrid System of Roadside and Personal Mobile Devices

273

large effect on travel time. This is actually the case

when it matters (i.e. in urbanized areas with a lot of

traffic), while at the same time intensities are known

to show strong variation within days and between

days (Thomas et al., 2008). Accurate travel time

predictions under varying (management) scenarios

are therefore only possible when network demand

and supply are predictable.

From this, it can be concluded that traffic

management requires continuously monitoring of

travel times, traffic intensities and capacities

throughout the network or at least at, in or along

important locations, areas or corridors.

At the moment this is not possible with mobile

sensors. Mobile sensor (GPS) samples for public use

are simply much too small. This may be changing

(e.g. Rieser-Schüssler et al. 2012) somewhat, but the

expectation is that, in general, public GPS samples

will remain limited, (partly) due to privacy

restrictions and commercial interests. In other

words, large amounts of mobile sensor (GPS) data

may remain out of reach for traffic management.

Even if mobile sensor data would increase

substantially for public use, there will always be

some travelers missing from the data. For example,

commuters will be less inclined to use navigation.

Therefore, it can be questioned whether there will

ever be enough mobile sensor (GPS) data to monitor

traffic intensities and capacities of the important

road sections with enough accuracy.

Roadside observations can fill this gap. In urban

areas, single detection loops have long been used to

measure occupation levels and intensities as input

for traffic light operations. Network monitoring is

more difficult with these data, because delays cannot

easily be estimated in saturated conditions (when

queues form near traffic lights), and individual

vehicles cannot be followed through the network. As

a result these measurements don’t provide

information on OD patterns and routes.

However, this is changing due to increasing use

of cameras and new induction detection techniques

that enable the identification of individual vehicles

(Blokpoel and Vreeswijk, 2011). Thus, with these

roadside devices located at important intersections,

travel times, intensities and capacities can be

measured directly throughout the network. Together

with prediction algorithms like neural networks

(e.g., Dharia, and Adeli, 2003, Yin et al. 2002),

pattern matching models (e.g., Bajwa et al., 2004),

extrapolation models (e.g., Wild , 1997, Chrobok et

al., 2004, Thomas et al., 2009) or clustering models

(e.g., Chung, 2003, Weijermars, 2007), more

accurate traffic predictions of intensities and travel

times will then become possible given certain

management scenarios.

5 SYNTHESIS

As we have seen in the previous sections, traffic

managers and travelers use different devices, i.e.,

roadside and mobile devices respectively, to acquire

traffic information. Although mobile devises like

smartphones with GPS become increasingly

important, roadside devices might remain the main

source of information for traffic management,

because besides travel time they are able to provide

accurate information on intensities and capacities.

The traditional use of traffic information by

policy makers and travelers as shown in Figure 1

therefore remains quite realistic. The Figure shows

two independent symmetric systems for both traffic

management and the individual traveler. Both

systems have objectives, i.e., policy objectives for

the traffic manager and travel objectives for the

traveler. By confronting these objectives with traffic

and travel information respectively, traffic

operations are set to manage the traffic system, and

travel decisions are made to execute a trip. The

traffic information is derived from data from

roadside sensors, while travel information is derived

from mobile sensor data.

Of course this Figure is a simplistic illustration

of reality. The division between the use of roadside

and mobile sensors is in reality not this strict, and a

single box could in itself represent a complicated

process with feedback loops. Traffic operations, for

example, represent an interplay between

instruments, such as traffic lights, variable message

signs or route guidance panels, and traffic managers,

while travel decisions may include more than only

the traveller’s behaviour. In fact, nowadays most

travellers are assisted by travel apps that provide the

traveller with information or advice. Travel apps are

therefore implicitly included in travel decisions. The

use of travel information in travel apps is more

subtle than the figure indicates. Mobile sensor data

of other users are used to provide reliable

information on relevant travel modes and routes,

while historical travel choices of the user may be

used to personalize the advice. However, for the

broader picture, these issues do not need to be

considered in detail here.

The weak part of the traditional concept, as

illustrated by Figure 1, is the lack of any interaction

between traffic management and traveller. This leads

to drawbacks regarding all three DTM requirements.

Third International Symposium on Business Modeling and Software Design

274

Figure 1: scheme of traditional use of mobile and roadside sensors.

Figure 2: Scheme of integrating traffic and individual travel information on an operational level.

Surely, the usefulness of advice to the traveller

would be enhanced by knowledge about (future)

traffic operations (requirement 2). By the same

token, the quality of traffic prediction would be

increased when the intentions or likely future

decisions of individual travellers are known to the

traffic manager (requirement 1). Finally, without any

interaction, there is no knowledge about (a change

in) travellers’ perceptions regarding certain traffic

management measures (requirement 3).

In some projects such as SUNSET (Sunset, 2013)

and I-zone (Veenstra et al., 2010), the information

from travellers (mobile sensors) and traffic operators

(roadside sensors) have been combined. This is

shown schematically in Figure 2. Again, the

architecture is more complicated in reality, and both

projects comprise much more than combining

different data sources. However, the general use of

data in these projects is well captured by Figure 2.

Information derived from roadside and mobile

sensor data are combined in one large database.

Third parties, mostly private companies, can retrieve

this information via APIs, and can use this

information in all kinds of apps they develop for

travellers.

The main characteristic of such an architecture is

sharing of underlying sensor data, and providing

these data to the larger public. However, there are

two main drawbacks of such an architecture

regarding DTM. First, travel information on such

A Future of Traffic Management: Toward a Hybrid System of Roadside and Personal Mobile Devices

275

Figure 3: Scheme of integrating traffic and individual travel information on a strategic level.

an operational level is sensitive (regarding privacy)

and is also regarded as quite valuable by companies

that gather the data. It is therefore not very likely

that these important players are willing to share their

data. Secondly, these operational data do not provide

the intentions of traffic operators and travellers. In

fact, the main difference between roadside and

mobile sensor data is not that they necessarily

measure very different things (although not exactly

the same either), but that they are used by very

different users and for very different purposes. The

real strength of sharing information would be on a

higher, strategic, level such as shown in Figure 3.

In Figure 3, there is directly feedback between

traffic operations and travel decision making. Based

on traffic information from roadside devices and

traffic policies, traffic operations are set to manage

the traffic (such as in Figure1). However,

information about travellers’ reactions to and

perceptions of certain management measures are

provided to the traffic operators and are used to

improve the management scenarios. At the same

time, updated management scenarios are provided to

travellers(’ apps), enabling travellers to take specific

traffic measures (including possible incentives for

favourable behaviour) into account when planning

their trip.

Instead of sharing traffic data, the main

characteristic of this DTM vision is sharing of

intentions, plans and measures between traffic

operators and travellers. The corresponding

architecture would connect well to the DTM

requirements mentioned in section 2. The question

would then be: what kind of information is exactly

shared and how is this information shared? Should

the information include detailed operational data

such as green times of individual traffic lights given

specific inflow intensities, or should the information

be provided on a more aggregated and strategic

level? To answer these questions follow up research

is needed, preferably in a European project with as

main aim to develop such a future DTM system for

main European cities.

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