Real Time Traffic Systems

(1)

Situational Analysis in

Real Time Traffic Systems

(2)

Affidavit

I hereby declare by oath that I have written this paper myself. Any ideas and concepts taken from other sources either directly or indirectly have been referred to as such. The paper has neither in the same nor similar form been handed in to an examination board, nor has it been published.

Dietrich Leihs

(3)

Acknowledgement

Herewith I have to thank Dr. Martin LINAUER, the head of the Vienna based department

“Dynamic Transport Systems” at Österreichisches Forschungs und Prüfzentrum Arsenal Ges.m.b.H. (AIT Austrian Institute of Technology) for providing traffic data.

Dr. Seweryn Habdank-Wojewódzki explained me in long discussions several innovative methods of generating normal time series.

Professor Andrzej ADAMSKI from the Cracow AGH University of Science and Technology encouraged me to write this paper, having plenty of stimuli that always hit the nail.

Most of all I have to thank my wife Astrid for her patience while watching the emerging of this

paper during night investigations or early morning sessions and knowing that the next day

will be an ordinary working day.

(4)

0 Table of contents

0 TABLE OF CONTENTS ... IV

0.1 L

IST OF FIGURES

...

VI

0.2 L

IST OF

T

ABLES

...

VIII

0.3 L

IST OF ABBREVIATIONS

...

VIII

1 ABSTRACT - STRESZCZENIE... 1

1.1 S

TRESZCZENIE

... 1

1.2 A

BSTRACT

... 2

2 INTRODUCTION... 3

2.1 I

NITIAL

S

ITUATION

... 3

2.1.1 Transport policy ... 3

2.1.2 Hierarchical Models... 4

2.1.3 Traffic Management Systems ... 9

2.1.4 Traffic Information ... 18

2.2 T

HESIS

... 26

3 PROCESS CHAIN... 28

3.1 G

ENERATION OF STANDARD TIME SERIES

... 28

3.2 Z-

TRANSFORMATION OF STANDARD TIME SERIES

... 29

3.2.1 Z-transformation on DSPs ... 29

3.2.2 Z-transformation in high level language... 32

3.3 D

ETECTION OF ZEROS

... 32

3.3.1 DSP considerations ... 34

3.4 D

IGITAL FILTER DESIGN

... 35

3.4.1 Determine filter coefficients... 35

3.4.2 Classification Stage ... 36

3.5 P

ARALLEL FILTER CASCADE

... 37

4 MATHEMATICAL BACKGROUND ... 38

4.1

Z

-

TRANSFORMATION AND

F

OURIER SERIES

... 38

4.2 F

ILTER MODELLING

... 41

4.3 D

IGITAL FILTERS

... 45

4.3.1 Elements of digital filters ... 45

4.3.2 Basic structures of digital filters ... 46

4.3.3 FIR filters... 50

5 SYSTEM DESIGN... 54

5.1 U

SE

-C

ASE

... 54

5.1.1 TCC ... 55

5.1.2 Gather Traffic Data... 55

5.1.3 Supervise Traffic Behaviour... 55

5.1.4 Real Time Incident Detection... 56

(5)

5.1.5 Generate Normal Time Series ... 57

5.1.6 Design Traffic Situation Filters... 57

5.1.7 Traffic Situation Filtering ... 57

5.2 C

LASS

D

IAGRAM

... 58

5.2.1 TCC and related objects ... 59

5.2.2 Interface to TCC... 60

5.2.3 Normal Time Series Creator and related objects ... 60

5.2.4 Filter Cascade and related object ... 61

5.2.5 Filter Designer and related objects... 62

5.3 S

EQUENCE

D

IAGRAMS

... 63

5.3.1 Normal Time Series Creator ... 63

5.3.2 Filter Creator... 64

5.3.3 Real Time Filtering ... 65

6 VALIDATION ... 66

6.1 T

EST SITE

... 66

6.2 N

ORMAL

T

IME

S

ERIES

... 69

6.3 D

ISCRETE

T

RANSFORMATION

... 75

6.3.1 Matlab ... 75

6.3.2 Visual Basic ... 77

6.3.3 Comparison of Matlab and Visual Basic ... 80

6.3.4 Sensitivity Analysis... 81

6.4 D

ETECTION OF ZEROS

... 83

6.5 C

ALCULATING THE FILTER COEFFICIENTS

... 84

6.5.1 Matlab ... 84

6.5.2 Visual Basic ... 86

6.5.3 Filter realization ... 88

6.6 F

ILTER CHARACTERISTICS

... 88

6.6.1 Filter transfer function ... 88

6.6.2 Frequency response of normal time series... 91

6.6.3 Auto correlation ... 95

6.6.4 Finite Precision Effects... 98

6.6.5 Discussion... 100

7 CONCLUSION ... 102

8 FUTURE DEVELOPMENT... 103

9 BIBLIOGRAPHY ... 104

(6)

0.1 List of figures

Fig. 1: The traffic control system pyramid model... 5

Fig. 2: Multi-layer multi-level hierarchical traffic management, surveillance and control system... 8

Fig. 3: Topology of a nation wide Traffic Management Centre, example Austria ... 10

Fig. 4: Data distribution of the TMSC, example Austria... 11

Fig. 5: Calculation of the traffic status according to FGSV ... 14

Fig. 6: Multi-Layer Perceptron for the traffic situation prediction... 18

Fig. 7 Traffic situation during the morning peak (left) and during the night time (right) ... 20

Fig. 8 Example route consisting of four road segments... 21

Fig. 9 Normal time series and respective variances of a major road ... 24

Fig. 10 Fuzzy clustered centres in a time series of traffic quantities over a period of seven days... 25

Fig. 11: Structure of the FIR filter for transforming normal time series... 30

Fig. 12: Digital filter synthesized from the zeros of the transformed normal time series ... 36

Fig. 13: Set-up of the chain for real time classification of time series ... 37

Fig. 14: Parallel filter cascade... 37

Fig. 15: General depiction of the complex number a in the Gaussian complex number plane ... 39

Fig. 16: The Euler formula and the unit circle... 40

Fig. 17: Discrete coefficients on the unit circle ... 41

Fig. 18: Example of a frequency spectrum with 3 zeros at 0°, 90° ( π /2) and 270° (3 π /2) ... 42

Fig. 19: Analogue signal and the corresponding Dirac impulse series... 42

Fig. 20: Spectrum of the input signal before (left) and after (right) the sampling... 44

Fig. 21: Digital filter with distributed summers... 46

Fig. 22: Digital filter with global summers at the input and output... 47

Fig. 23: Digital filter with a global summer at the output ... 47

Fig. 24: Cascading of digital filters ... 49

Fig. 25: DF1 FIR filter with n

^th

order ... 52

Fig. 26: DF2 FIR filter with n

^th

order ... 52

Fig. 27: Use Case diagram of the Situational Awareness Treatment in ITS ... 54

Fig. 28: Class diagram of the Situational Awareness Treatment in ITS... 58

(7)

Fig. 29: Extract of the class diagram: The TCC and its related objects ... 59

Fig. 30: Extract of the class diagram: The Interface to the TCC ... 60

Fig. 31: Extract of the class diagram: The Normal Time Series creator and related objects... 61

Fig. 32: Extract of the class diagram: The Filter Cascade and related objects ... 61

Fig. 33: Extract of the class diagram: The Filter Designer and related objects ... 62

Fig. 34: Sequence diagram of the creation process of normal time series ... 63

Fig. 35: Sequence diagram of the creation process of normal time series ... 64

Fig. 36: Sequence diagram of the algorithm part “Design FIR filter”... 65

Fig. 37: Sequence diagram of the RT filtering process... 65

Fig. 38: Test site on the A23 close to exit „Gürtel“, heading south ... 66

Fig. 39: Magnification of the test site on the A23 close to exit „Gürtel” ... 67

Fig. 40: Speed-volume-scatter of the test site on the A23 ... 68

Fig. 41: Time profile of velocities and volumes of the test site on the A23... 69

Fig. 42: Normal time series of two weeks... 70

Fig. 43: Time series of all working days: Mo…Thu (top) and Fri (bottom) ... 72

Fig. 44: Time series of all Week-end days: Sat (top) and Sun amended by the normal time series for Sundays (below) ... 73

Fig. 45: Time series of all Wednesdays, amended by the normal time series for Wednesdays (top) and comparison of all normal time series (bottom)... 74

Fig. 46: Result of the z-transformation of the two normal time series for Wednesdays and Sundays as a frequency spectrum (top) and 3D on the unit circle for Wednesdays (bottom) ... 76

Fig. 47: Result of the z-transformation of the normal time series of Wednesday and Sunday with Visual Basic ... 79

Fig. 48: Comparison of the spectra generated with Visual Basic and Matlab ... 80

Fig. 49: Sensitivity analysis of the Matlab command fft ... 82

Fig. 50: Post comma resolution effects in Visual Basic to the spectrum... 83

Fig. 51: Filter coefficients as a result of the polynomial expansion... 86

Fig. 52: Comparison of the transformed normal time series for Sunday (top) and the filter characteristics of the respective FIR filter (below)... 90

Fig. 53: Comparison of the filter characteristics for the normal time series of Wednesday and Sunday ... 90

Fig. 54: Filter response to various time series... 92

Fig. 55: Comparison of the transformed normal time series and the filter characteristics

under consideration of the 5 lowest local minima ... 94

(8)

Fig. 56: Filter responses with the Wednesday filter: comparison of the week days Wednesday and Sunday with the respective normal time series ... 96 Fig. 57: Difference of the filter response to single weekdays and to the normal time

series ... 97 Fig. 58: Comparison of the frequency responses of the same DF1 FIR filter with

various word lengths ... 99 Fig. 59: Square of the difference of the filter responses to the actual measuring values

and to the respective normal time series ... 101

If not stated otherwise, all graphics are own graphics.

0.2 List of Tables

Tab. 1: Traffic Levels according to MARZ; Example for two lanes ... 12 Tab. 2: Zero polynomial for the normal time series of Wednesday and Sunday ... 85

0.3 List of abbreviations

ASFINAG...Autobahnen und Schnellstraßen Finanzierungs AG (the Austrian Highway Operator)

ATIS ...Advanced Traveller Information System ATMS ...Advanced Traffic Management System AVL ...Automatic Vehicle Location

DRG ...Dynamic Route Guidance DSP...Digital Signal Processor DSS...Decision Support System FCD...Floating Car Data

FFT...Fast Fourier Transformation

FIR ...Finite Impulse Response

FPGA ...Free Programmable Gate Array

GPS...Global Positioning System

HGV ...Heavy Goods Vehicle

IIR...Infinite Impulse Response

ILS...Integrated Logistic Systems

ITS...Intelligent Transport Systems

(9)

LQ ...Level of Quality LTI ...Linear Time Invariant

MDS ...Monitoring and Detection System MLP...Multi-Layer Perceptron

NNWP ...Network Nominal Working Point NWP...Nominal Working Point

PIACON...Polyoptimal Integrated / Intelligent Adaptive Control PIS ...Parking Information System

P&R...Park and Ride

RDS-TMC ...Radio Data System – Traffic Message Channel RMS ...Root Mean Square

RT ...Real Time

TMSC ...Traffic Management, Surveillance and Control system TCC...Traffic Control Centre

TIC ...Trffic Information Centres

UML...Unified Modeling Language

VDS...Video Detection System

VMS ...Variable Message Signs

(10)

1 Abstract - Streszczenie

1.1 Streszczenie

Predykcja stanu ruchu drogowego jest istotnym problemem w nowoczesnych inteligentnych systemach transportowych (Intelligent Transport Systems – ITS). W szczególnosci wykrywanie w czasie rzeczywistym oraz poprawna ocena incydentów moze ocalic zycie ludzkie i przyczynic sie do utrzymania droznosci sieci transportowej. Jakkolwiek czynniki oddzialujace na ruch drogowy sa wielorakie. Mozna tutaj przytoczyc: zmienne natezenie zapotrzebowania na zasoby sieci drogowej, stan pogody w tym zmiennosc zwiazana z porami roku itd. Te wszystkie wplywy powoduja, ze uzyskanie stosownej jakosci predykcji i oceny stanu ruchu drogowego w czasie rzeczywistym jest trudne. Niniejsza rozprawa koncentruje sie na fakcie, ze mozliwe wzorce ruchu drogowego – przeksztalcone do postaci szeregów czasowych – zmieniaja sie nieznacznie reprezentujac specyficzna sytuacje na drodze lub znormalizowany szereg czasowy. Wykazano, ze uzycie inteligentnego, dedykowanego cyfrowego przetwarzania sygnalów oraz kanalów komunikacji pozwala na skuteczna i szybka poprawe mozliwosci analizy sytuacyjnej w czasie rzeczywistym.

Cyfrowe filtry o skonczonej odpowiedzi impulsowej (Finite Impulse Response – FIR) zostaly uzyte do analizy danych pomiarowych z czujników w sposób pozwalajacy ciagla ocene aktualnego stanu ruchu drogowego oraz detekcje nietypowych zachowan. Filtry FIR uzyte w postaci kaskady, gdzie kazdy filtr reprezentuje specyficzny znormalizowany szereg czasowy przedstawiajacy dzien roboczy, dni wolne od pracy, okresy deszczowe czy tez stan ruchu w zimie. Reprezentacja kazdego znormalizowanego szeregu czasowego jest osiagnieta poprzez zastosowanie transformacji Z tegoz szeregu w celu otrzymania widma czestotliwosciowego i struktury filtra z wlasciwa odpowiedzia czestotliwosciowa.

Rozdzial 3 opisuje ewolucje pojedynczego filtra FIR z oryginalnego znormalizowanego szeregu czasowego do pelnej kaskady filtrów. Umiejscowienie filtrów moze miec miejsce w centrach kontroli ruchu drogowego (Traffic Control Centres – TCC) gdzie wystepuje wystarczajaca moc obliczeniowa lub tez w obudowach sterowników miejscowych czujników opartych o procesory sygnalowe (Digital Signal Processors – DSP). Wdrozenie zostalo opisane w rozdziale 5.

Rzeczywiste dane o ruchu drogowy byly pozyskane z autostrady, gdzie byly uzywane do

oceny poprawnosci procesu, co zostalo opisane w rozdziale 6. Oba sposoby wdrozenia –

uzycie jezyków wysokiego poziomu jak równiez platforma DSP zostaly uzyte aby podkreslic

sposób konstrukcji i zachowanie sie filtrów.

(11)

1.2 Abstract

The prediction of traffic situations is a vital issue in modern Intelligent Transport Systems (ITS). Particularly the real time detection and proper assessment of incidents may save live and may contribute to keep the transport network available. However, the influence factors of traffic are multi variate; the influence factors are varying traffic demand, weather, seasons, etc. This allows only a very poor performance in real time assessment of traffic situations.

This thesis focuses on the fact that the possible traffic patterns – depicted as time series – vary only very little on each site, representing specific traffic situations or “normal time series”.

This thesis says that the use of intelligent dedicated digital signal processing systems and communication media is in a position to improve the requirement of Real-Time traffic situational analysis in an efficient and effective way.

Digital Finite Impulse Response (FIR) filters are used to analyse sensor measurement data in a way that allows an instantaneous assessment about the actual traffic situation and the detection of abnormal behaviour. A FIR filter cascade is used, each filter represents a specific normal time series like working day, weekend day, rain, winter, etc. The representation of each normal time series is achieved by discrete transformation (z- transformation) of the normal time series in order to generate its frequency spectrum and by designing a filter structure with a corresponding frequency response. Chapter 3 describes the genesis of a FIR filter from the original normal time series to the complete filter cascade.

The deployment of the FIR filters can be in Traffic Control Centres (TCC) with a vast amount of computational power as well as in the controller cabinets of local sensors on the basis of Digital Signal Processors (DSP). The deployment is described in chapter 5.

Real traffic data that were gathered on a highway were used to validate the process chain in

chapter 6. Both deployment variants – high language ambient as well as a DSP ambient

were used to show the creation and behaviour of the filters.

(12)

2 Introduction

2.1 Initial Situation

2.1.1 Transport policy

The necessity controlling traffic systems derives from a variety of political, social and economical constraints. Just to mention the most important ones, such constraints are

• Location factors. Modern economical systems rely exceedingly on the free exchange of goods and the freedom to travel. Economical prosperity as a source for social wealth is considered to be enhanced if goods can be freely exchanged. In Europe the legal basis on that is the customs union [1], the prohibition of customs duties [2] and the prohibition of quantitative restrictions between member states [3].

As a consequence, most member states consider the provision of traffic infrastructure as a core task, requiring funding on the construction and maintenance of road and rail infrastructure. In reverse, if a country does not provide traffic infrastructure, it is discriminated in the international competition. As a consequence it is in the interest of regions, countries and states to provide and maintain an effective traffic network.

The effectiveness of traffic networks is diminished by the force of nature (damages due to avalanches, floods; obstruction of traffic due to weather like snow or heavy rain) and by the action of humans (high traffic demand causing congestions;

accidents causing congestions or damages to infrastructure). The diminishing of networks sometimes can be related to socio-economic costs [4] [5]. Reducing the diminishing factors means increasing the effectiveness of existing traffic networks.

• Traffic Safety. The approach of assigning socio-economic costs is found with casualties and injured persons due to accidents here as well [6]. In the European Union the direct measurable costs of all fatalities in 2005 (~ 41.500) sum up to 45 billion € a year. Consequently, in many countries the main purpose of ITS is to enhance the safety level on the roads. On the European level, the “-50%”-objective claims a political commitment and individual responsibility of each Member State [7].

In particular the addressees of this claim are the national governments and local authorities, but also the car industry. On In terms of measures this means active and passive safety of cars, enforcement and infrastructural issues like traffic control and management.

• Protection of the environment and the quality of life. In particular since CO

2

emissions from road transport is considered as a source of global warming, the

(13)

control of traffic becomes more and more an issue of quality of life. Due to a significant rise of fossil energy prices, the European Union [8] promotes a policy change towards energy efficiency where transport plays a role. Policy is primarily thinking of car manufacturing industry to take the lead, and still it is not clear in many countries which role ITS may play in this context, but an indication are measures already taken in order to fulfil other environmental requirements like the reduction of noise along major roads by innovative traffic control.

Regardless of the respective political characteristic of governments, transport policy will always make use of certain instruments in order to fulfil the above mentioned transport policy constraints. These instruments can roughly be divided into the following categories [9]:

• Provision of infrastructure. This aims primarily at the construction and maintenance of road networks, ITS, rolling stock, but also at the provision of information like public transport schedules or real time traffic information.

• Regulatory policy. By command and prohibition, traffic gets controllable.

Appropriate measures are red lights, speed limits, sectoral ban on driving, etc. Static traffic control uses traffic signs and “in-sensitive” traffic lights, dynamic traffic control makes use of these instruments in case of sensitive traffic light control or variable speed control.

• Market mechanism. By assigning an adequate price to scarce goods (i.e.

infrastructure), demand may be controlled. The technical response is electronic fee collection systems. The scarcity of transport infrastructure may be derived by traffic volume, exhaust emissions, travel times, etc. The charges can be fixed either static or dynamic.

It can be observed that ITS gets more and more an instrument of policy, be it in urban areas, on major corridors or be it in protected zones. However, the right mix of measures will decide upon if and how policy constraints will be fulfilled [9] as traffic problems usually are a combination of various symptoms. General speaking policy is not in a position to select processes or technologies so in the late 1990’s framework architectures and hierarchical approaches came up as a top-down instrument.

2.1.2 Hierarchical Models

The European ITS Framework Architecture [10] offers a tower model, which describes a hierarchical control framework for the control of traffic (Fig. 1). The European ITS Framework Architecture model describes four layers [11]:

• At the top level strategy and policy plans are made that capture the vision of the

properties that are desired from the whole traffic control system;

(14)

• Once the policy and strategy has been agreed, they will be passed down to device the control tactics that will achieve them;

• The tactic for each scenario will manifest itself as a plan of control measures, and these have to be prepared in detail and

• The bottom level relates to the execution of the control measures, either autonomously or through the action of operators.

Fig. 1: The traffic control system pyramid model Source: [10]

In strategic traffic management, the traffic management strategy, the reference map and the traffic services / applications needed are defined. All relevant stakeholders in the area (both inter-urban and urban) reach consensus on these definitions and choices. In tactical traffic management, the traffic management tactics (based on traffic services) and accompanying traffic management scenarios (based on traffic applications) are defined. These scenarios describe the optimal combination of traffic applications desired to cope with foreseen situations. The scenarios are based on the strategies previously defined in strategic management. In operational traffic management, the traffic actually is managed. Traffic management applications are activated autonomously or by operators, based on the scenarios.

Practically, in urban networks the various actors are interrelated. The implementation of

policy strategies turns out to be the search for a multi criteria optimum, mostly with divergent

targets. The ITS related actors are road network operators (controlling traffic lights and traffic

regulations); public transport operators (controlling schedules and pricing); toll operators

(15)

(controlling access prices), parking charge operators, information service providers, etc.

Divergent targets appear even on the policy level like for instance assuring mobility versus environmental damage; accessibility of road networks versus accidents; enabling mobility as a vital part of society versus congestion; economic development versus energy consumption [12]. An integrated approach offers essential benefits, like system-wide synchronized response to the full range of mobility needs with the opportunity to bridge multi-modal demand management and real-time supervision and control over different time horizons.

However, the challenges are

• complex phenomena (uncertainty randomness, complex interactions, structural instabilities, very fast dynamics, essential non-linearities, human behavioural anisotropy) appear in large-scale three dimensional networks (networks * time * space) [13] [14];

• Decision making turns complex as multi-goal decisions have to be taken in a robust manner in a multi-time horizon;

• The existence of non-predictable operational incidents is demanding an intelligent detection, identification and system-wide reaction in a very short response time While the ITS framework architecture gives a scheme of the hierarchical inter-relation of the actors, an enhanced view of integrated hierarchical multi-layer TMSC is given [15] [16] [17], see Fig. 2. As far as they are relevant for this thesis, integrated hierarchical multi-layer TMSC system tasks are as follows:

• The aim of the Planning, Coordination and Management Layer is to improve the operating efficiency of the network in the macroscopic meaning through the provision of traffic and travel information. To achieve this aim it is necessary to have a comprehensive monitoring network, information gathering and dissemination facilities and an ATMS (Advanced Traffic Management System) capable of generating an integrated ITS network-wide traffic management strategy. The tasks involve system- wide real time traffic incidents/abnormal events recognition and identification as well as an influence classification. Beneath others this is based on congestion patterns, incidents characteristics, the knowledge about critical network components or the prediction of travel demand. In this level, ATIS plays an important role in order to influence the user’s behaviour by getting system information.

• The main realized tasks in the Adaption Layer concern the prediction of journey times, the estimation and prediction of congestion, the updating of model parameters providing the adequate representation for a real-time evaluation and finally assessing and control processes. The task involves adequate performance indicators, calibrated functions as well as the system-wide real-time information dissemination (e.g. VMS- warnings, advises, RDS-TMC, etc.)

• In the Optimization Layer the above preferences are represented by corresponding

criteria (selection of NWP for groups of controlled intersections) and preference

cones for multi-criteria PIACON (Polyoptimal Integrated / Intelligent Adaptive

(16)

CONtrol) methods [18] in the mesoscopic or microscopic sense. For instance, in the PIACON method the 2-D random fields of vehicle trajectories traffic representation is used with – single reference trajectory / several trajectories / stream of trajectories as corresponding models This enables very efficient the solution of the traffic prediction requirement.

• In the Surveillance and Control Layer the multi-criteria control problem is solved

microscopically in a robust way. The PIACON traffic control method for urban areas

meets most of the needs of modern real traffic decision making and control

processes.

(17)

Fig. 2: Multi-layer multi-level hierarchical traffic management, surveillance and control system

Source: [12]

(18)

A quick and reliable estimation of traffic situations is of crucial importance for these systems in order to provide timely reliable information for the TMSC and for the users. The TMSC has to recognize and react to critical traffic situation symptoms by professional tuning of management and control tools based on traffic prediction. In this context, the Intelligent Supervisor plays an important role [19], operating in the Adaptation, Optimization and Control Layer. He offers a traffic incident diagnosis and a counter measures selection.

Certain incidents like accidents are rare (causing a difficult classification of accident patterns) and multi-causal (patterns are hardly transferable to similar sites and in time on the same site). In general, incidents are with a high degree situation specific. Hence, the Intelligent Supervisor has to make use of all data and information available in order to recognize and identify incidents in a multi-criteria ranking process on each site.

2.1.3 Traffic Management Systems

The general task of TMSC is to keep transport infrastructure available, to reduce the negative effects of traffic and to maintain or raise the safety level on the roads – all with the means of ITS. This covers the gathering of traffic raw data, the managing of the traffic flow, and the detecting of incidents. The quality of service of a TMSC can be expressed in it’s operating cost in relation to the time to reaction or in relation to the rate of disturbance reduction. The treatment of incidents requires real time data and a situation analysis in real time or near real time. However, incidents have to be judged in the context of the general (traffic) situation in order to determine their severity and relevance. The state approach of art of this situation awareness is shown on the deployment level of existing TMSC as well as on the scientific level.

The first nation wide TMSC was the one in Austria [20]. It is located in Vienna and is the core of the national traffic management and information system for the Austrian motor- and expressway network with the length of some 2.100 km. After the planning period of several years, the TMSC was tendered in 2003.

The strategic constraint of the TMSC is to increase the road capacity by optimizing existing resources, to increase the road safety by avoiding or reducing the number of accidents and to decrease the external costs especially those which are generated by delays and congestion as well as environmental follow-up costs. The main functions on the tactical layer are gathering of traffic data, traffic monitoring and control and traffic information distribution.

The measures are to manage incidents by providing early warnings about tailbacks, bad weather, bad pavement conditions, road works and wrong way drivers, to manage information and inform drivers via VMS and RDS-TMC and finally to recommend routes and provide estimated travel times.

The topology of this TMSC (see Fig. 3) consists of a hierarchy between several regional sub-

centres and the national centre. The sub centres could work autonomous or under control of

the national centre which is staffed 7 x 24 and overrules the sub centres. Apart of that there

is an interlink to other operators like urban traffic centres that are not under the regime of the

(19)

TMSC, P&R operators, radio broadcasters or the supra-regional operators of neighbouring countries. According to the operator’s self image, the core of the TMSC is a complex data distribution system that allows an effective, near real-time interchange of mass data between external sources, internal databases, analysis workstations and control workstations (Fig. 4).

Fig. 3: Topology of a nation wide Traffic Management Centre, example Austria

Source: [20]

(20)

Fig. 4: Data distribution of the TMSC, example Austria Source: [20]

Fig. 3 gives an indication of the existence of an intelligent supervisor. The data processing of the TMSC can be done in an automatic, a semi-automatic or a manual way. Apart of that there, a couple of non-standard traffic events are foreseen [21]:

• Automatic Processing covers action like speed harmonizing; speed limits due to high speed differences on adjacent lanes; congestion warning; fog warning;

warning of wet pavement in combination with the ban of passing for HGV; decree a ban of passing for HGV due to heavy traffic or low visibility; decree a permanent ban of passing for HGV; warning of side winds; noise pollution control; control of interlacing by speed limits on the right lane; warning of low distance.

• Semi Automatic Processing covers black ice warning; the release of breakdown lanes for the traffic; the warning of wrong way drivers and the control of interlacing by closing the right lane before the intersection. In this case the pre-processed information has to be treated by the operator.

• Manual operation concerns measures in the case of black ice and frequent special events like systematic police controls of HGV. The non-standard events are treated in a manual way as well. Such are for instance construction works or the management in case of accidents or car break downs.

After a decision is taken in this way, a prioritization is taken among all measures. The control tactics covers an optimisation for several lanes and road sections plus a fall-back-strategy.

The control optimum is when a stabile condition is achieved, otherwise the fall-back strategy

will be taken. In fact, the adaptation to local specialities is done with parametric models. The

traffic state analysis and forecasting is done with standard time series. The determination of

(21)

the actual traffic state is mainly done according to the German standards MARZ

¹

and FGSV

²

standard 358 [21].

The MARZ guidelines for traffic centre applications define processing steps and algorithms for data collection, data management, traffic and environment state estimation, incident detection and the decision logic [22]. According to MARZ, the traffic situation is classified into 4 levels according to defined mean velocities and the local traffic density on the various lanes. An example for two lanes is shown in Tab. 1. It is worth emphasizing that the traffic situation is modelled according to few “hard wired” conditions (i.e. velocity and density). The resulting traffic level is insensitive towards the effects of the road topology (mountainous or flat; high rates of junctions or long homogeneous passages) or the effects of weather.

1

^st

lane 2

^nd

lane

v D v D

Traffic Level [km/h] [veh/km] [km/h] [veh/km]

1 Free Flow ≥ 80 ≥ 0 ≤ 20 ≥ 80 ≥ 0 ≤ 30 2 Dense Traffic ≥ 80 >20 ≤ 50 ≥ 80 >30 ≤ 60 3 Tenacious Traffic ≥ 30 <80 ≤ 50 ≥ 30 <80 ≤ 60 4 Congestion <30 >50 <30 >60

Tab. 1: Traffic Levels according to MARZ; Example for two lanes Source: [22]

A more sensitive approach to determine the traffic situation is delivered by FGSV 358 [23].

Here the actual traffic status on a respective road section is calculated on the basis of the fundamental diagram (speed-volume-scatter) according to the following procedure (see also Fig. 5)

1. in the first step the stability of the traffic situation is determined according to the mean local vehicle speed v(i) at the measuring site i

• In case v ( i ) ≥ f

₁

∗ V

₀

( A ) the traffic flow is considered as stable

• In case v ( i ) < f

₁

∗ V

₀

( A ) the traffic flow is considered as instable

1

„Merkblatt für die Ausstattung von Verkehrsrechnerzentralen und Unterzentralen“ – bulletin on the equipment of traffic control centres and sub centres

2

„Forschungsgesellschaft für Straßen- und Verkehrswesen“ – German Road and Transportation

Research Association

(22)

2. In the second step the section related traffic density D(A) is determined depending on the stability of the traffic flow:

• In case of stable flow: D ( A ) = D ( i )

• In case of instable traffic flow:

) (

) ( ) ) (

(

^max ⁰

i Q

A D A A Q

D

B

∗

= while D ( A ) ≤ 2 ∗ D

₀

( A )

(1)

3. The third step smoothes the trend prognosis of D(A), the result is D

P

(A).

4. In the fourth step the section related velocity V

P

(A) is calculated:

• If D

_P

( A ) ≤ f

₂

∗ D

₀

( A ) is the case:

) ) (

( ) ( ) ) (

( ) (

0

D A

A D

A V A A V

V A

V

_P _f ^f

−

_P

−

=

• If D

_P

( A ) > f

₂

∗ D

₀

( A ) is the case:

) (

) ( )

( ) ) (

(

⁰ ^max

A D

A Q A D

A A D

V

P B P

P

=

(2)

(23)

Fig. 5: Calculation of the traffic status according to FGSV Source: [23]

f

1

, f

2

... selectable parameters [0,5...1,5]

D(A)... density [veh/km] along section A D(i)... density [veh/km] at detector site

D

0

(A)... density [veh/km] along section A at maximum capacity D

P

(A) ... smoothed density [veh/km]

Q

B

(i) ... volume [veh/h] at detector site

Q

Bmax

(A)... volume [veh/h] along section A at maximum capacity V(i) ... mean local velocity

V

f

(A) ... velocity [km/h] along section A at free flow V

P

(A)... actual velocity [km/h] along section A

V

0

(A) ... velocity [km/h] along section A at maximum capacity

The parameters V

0

(A), Q

Bmax

(A), D

0

(A) and V

f

(A) can be derived from the speed-volume-

scatter. The assignment of measuring sites (i) and road sections (A) has to be done

according to the geographic context. The location of the sensor has to be representative for

the situation along the section under consideration. This approach allows a more sensitive

consideration of external influences like road topology (mountainous; urban) or general

disturbances. Such effects are covered with the two selectable parameters f

1

, f

2

. Although

this is just a very short excerpt from the rules given in the standard, it does say nothing about

(24)

shifting variances like weather or seasons. “Calibrating” the selectable parameters is more or less up to the experience of the intelligent supervisor. In reality it turned out that once the parameters are fixed to a certain value, this will work for a while, but sometimes there is no correspondence with reality at all. The main reason for this effect is that there is no situation sensitivity.

The most recent approach of traffic situation awareness in traffic control is the definition of standard time series [21]. The basis are the actual volume measurements in combination with an event calendar. This calendar considers the day of the week, specific holidays (Christmas, 1

^st

of May) or the general holiday. For each group of the day a specific time series is generated by averaging the actual data into the historical ones, the result is an average standard time series for the respective type of day. The averaging takes place only if a selectable correlation is the case. If this is not the case, a new standard time series is started. The disadvantage is that congestion events due to rare incidents (accidents, first rain in autumn, …) can be considered only by means of manual treatment of the intelligent supervisor.

This empirical description of the situation of a specific day is the basis for long time prognosis as well as for a trend estimation of short term incidents. If the actual traffic volume deviates from the standard time series, it is assumed that the actual time series will slowly converge with the standard time series according to an attenuation function:

) ( ) ( )

( t

₀

Q t

₀

Q t

₀

Q = −

_st

∆ (3)

∆Q... Difference of the traffic volumes [veh/h]

t

0

... actual time of the day Q ... actual traffic volume [veh/h]

Q

st

... traffic volume of the standard time series [veh/h]

t p

st p prog

e

p

t Q t

Q t

Q ( ) = ( ) + ∆ (

₀

)

⁻^α ^∆

(4)

Q

prog

... forecasted traffic volume [veh/h]

t

p

... forecast time

α

p

... attenuation factor [0,1…1,0]

∆t ... time difference t

^p

-t

0

[h]

With an attenuation factor α

p

= 0,5 the traffic volume difference will be 60% of the original

value. With α

p

= 1,0 the value will be 36%. As an alternative a linear attenuation can be

foreseen.

(25)

The limitation of this standard time series approach is the inflexible correlation procedure. By determining a correlation coefficient between the historical time series and the actual measuring values it is possible to estimate whether or not the actual traffic behaviour corresponds to the normal time series of the respective site. The correlation coefficient is calculated as follows [21]:

 





 



 −

 





 



 −

 

 



 −

= ∑ ∑ ∑ ∑

∑ ∑ ∑

∑

n Q Q

n Q Q Q

Q r

hist hist

2 2

(5)

r ... correlation coefficient Q ... traffic volume [veh/h]

Q

hist

... historical traffic volume [veh/h]

n... number of time intervals of the time series

This practise offers various points of discussion

• The correlation procedure is very simple. However it does not reflect a similarity of traffic

“patterns”. The correlation coefficient is just a rough estimation as it is a measure of a linear correlation dependence, but typical traffic relations are non-linear.

• Incidents can be foreseen only at special days which are defined according to the event calendar (e.g. special behaviour of May 1

^st

or December 25

^th

).

• A day as such is evaluated. Historical time series are generated for the whole day, starting at midnight and ending at midnight. Unforeseen incidents are probably hidden in the time series of the whole day. Frequently, incidents affect just fragments of days but not the whole day. So it is more then dubious if incidents can be assessed that way. An alternative would be to use shorter historical time series. By applying (4) it can be determined that the actual measurement parameters are (temporarily) out of tune which indicates any incident.

• A single correlation coefficient for the whole day serves for evaluating if the actual traffic behaviour is in line with the history or not. This becomes problematic when little or no data are available (e.g. during the night time) and when the behaviour of the day changes during the day (e.g. the weather gets worse).

As a conclusion it has to be stated that although this procedure aims at the traffic situation

evaluation, it is hardly possible to qualify and quantify specific situation as they occur. Even if

just fragments of days are assessed, no distinction of incidents like weather or accidents is

(26)

foreseen. Apart of that the detection of incidents by means of a correlation coefficient like this does not meet real time requirements as it is very hard to separate real incidents from noise.

The scientific community shows a more ambitious approach towards the traffic situation identification. Neural network models are used for predicting the traffic situation under adverse conditions [24] [25]. Since the neural network has the potential of solving nonlinear problems it can tackle an input-output mapping, it may be used for predicting complex traffic situations [26] [27] [28].

Multi-Layer Perceptrons (MLP) showed a good performance with prediction models, the architectural graph of a MLP is shown in Fig. 6. Back-Propagation is one of the algorithms to train the MLP and to obtain the weight of each link. Basically, error back-propagation learning consists of two passes through different layers of the network: a forward pass and a backward pass. In the forward pass, an input vector is applied to the sensory nodes of the network, and its effect propagates through the network layer by layer. Finally, a set of outputs is produced as the actual response of the network. During the forward pass, the synaptic weights of the networks are all fixed.

( ) _∑

=

ⁿ

i ij i j

j

X w x w

net

1

, (6)

net

j

... network function X ... Inputs vector

x

i

... Inputs vector for unit i

w

j

... Weight vector associated to unit j

w

ij

... Weight vector for the connection between unit i and j n... Number of units connected to j

During the backward pass the synaptic weights are adjusted in accordance with an error correction rule which is specific to the respective application. The error that is generated propagates backwards through the network, against the direction of synaptic connections - hence the name “error back-propagation.”

∑ ⁻

=

j

x

d

E ( )

²

2 1 (7)

E ... Error

d ... Desired output for unit j

(27)

The synaptic weights are adjusted to make the actual response of the network move closer to the desired response in a statistical sense.

Fig. 6: Multi-Layer Perceptron for the traffic situation prediction Source: [24]

The trials showed that Neural Networks are suitable for modelling traffic situations under multiple conditions like weather (rain, snow), construction works (closed lanes) and incidents, allowing a longer and more reliable prediction than conventional time series models.

Networks can be expanded according to the input parameters and the length of routes.

However, the precondition is the availability of historical data.

2.1.4 Traffic Information

Frequently Traffic Information Centres (TIC) are an appendix to TMCS, but there are also

information centres that are run by own operators like radio broadcasters or sometimes

mobile communication operators. In the case of radio operators, traffic information is either

spoken or it is transmitted via digital radio. In both cases, information that is relevant for the

(28)

driver has to be transmitted. This information is neither the punctual velocity at a measuring site nor it’s traffic volume but rather the traffic status. The most important information for TIC operators is the travel time or the loss of travel time due to disturbances. For spoken radio messages the main challenge is to break the total amount of traffic incidents down to a few messages that describe a perceivable traffic situation. This demands the distinction of traffic situations under adverse conditions and like in the case of a TMSC a judgement of the severity and relevance of incidents in the context of the overall situation. But also for digital radio broadcast of traffic information, the situation specific treatment of traffic behaviour is relevant, in particular with the real time detection of incidents and with the long range prediction of the traffic status. Particular if RDS-TMC messages are to be used in dynamic car navigation systems, the messages do not contain raw measurement data but assessed estimated situation descriptions like for instance the indication of a traffic disturbance with the travel time delay of a certain extent. These messages undergo a rigid evaluation by the car driver who frequently is caught in the respective situation in real life. Due to the ubiquitous manner of traffic data and traffic information, the real time approach and the situation specific treatment is given here even more than in TMSC!

In order to fulfil this requirement, new methods of the traffic status determination deploy Floating Car Data FCD [29] [30], allowing a real time and spatial detection of traffic data at reasonable cost. There the positioning data of a number of probe vehicles are transmitted to a centre and used for calculating the velocity. If the number of probe vehicles is adequate, the velocity and traffic status can be determined on single road sections. The localisation technology can be GPS, the positioning data derived from the GSM cellular mobile system [31] or the identification incidents in beacons [32]. FCD can be fused with stationary sensor data.

Since 2003, the City of Vienna is undertaking a project, in which the actual traffic situation is calculated on the basis of FCD. The anonymised positioning data of some 2.500 probe vehicles are used to gain a real time operational picture. This monitoring / detection system (MDS) is showing the actual drive speeds as a 3-step level of service on the major roads of the city, the actualisation is every 15 minutes. The data are used as a source for the RDS TMC service and for the intermodal travel assistance service of the local transport association.

Fig. 7 shows the operational picture of June 7th, 2006; the left picture shows the morning

peak at 9:00 am, the right picture nearly free flowing traffic at 11.00 pm.

(29)

Fig. 7 Traffic situation during the morning peak (left) and during the night time (right) Source: [33]

It is the main task to generate the vehicle trajectories-based traffic attribute “velocity”.

Therefore, the GPS localisation stamps of every single vehicle are assigned to the respective road segment by means of a map matching algorithm. The individual velocity of a single vehicle is determined by calculating the velocity between two localisation points along the effective route according to the road map [34] [35]: After assigning the GPS localisation stamps to a road segment, the length of the effective route is gained and the individual velocity can be calculated according to (7).

i k

j j

i

t

l v

∑

=

¹

⁽⁸⁾

v

i

... velocity

l

j

... effective length of the segment

k ... number of segments between two GPS localisation stamps t

i

... time

For determining the individual velocity of a probe car, a routing process is necessary, if the

two GPS localisation stamps are not located on the same road segment. Then, the

calculated velocity will be assigned to the effective segments between the two GPS

(30)

localisation stamps. As the target is to determine the traffic situation on a certain road segment, the vehicle trajectory from origin and destination is not relevant.

After determining the individual velocity of the probe cars, the velocity values from one or more probe cars are available on the road segment covered. For every road segment the mean travel velocity will be calculated [36] [37]. All velocity values of a defined time interval (e.g. 15 minutes) will be deployed for averaging the mean travel velocity on each road segment for each time interval: Every single individual probe car velocity will be weighted according to the ratio of the respective time and the overall time corresponding to (9).

∑

∑ ∑

∑

=

_n

i i n

i n i

i n

i i i i n Sha

t s

t v t v

1 1 1

1

_

(9)

n

v

Sha_{_}

... Harmonic mean value of n values on the road segment v

i

... i

^th

individual velocity value on the road segment s

i

... Assigned distance to the i

^th

velocity value on the road segment (constant)

t

i

... Assigned time to the i

^th

velocity value on the road segment

The harmonic mean value is normally used for averaging if the values to be averaged are in a certain relation to each other. A velocity is the ratio of distance and time, thus for calculating the mean velocity the harmonic mean value will be applied! If a route consisting of several road segments is considered, the harmonic mean value of the individual velocities depicts the ratio of the overall route and the overall time (i.e. travel time). Hence this mean velocity can be considered as the travel velocity on the route. The travel velocity is formally defined as the “mean velocity of a vehicle as a quotient of the covered distance and the required time within an observation interval” [30].

Fig. 8 Example route consisting of four road segments

The Example route in Fig. 7 is consisting of four road segments. The distances, times and

velocities of every segment are known. The travel time tt of the overall route is tt = t1 + t2 +

(31)

t3 + t4. The mean travel velocity of the route is determined by the quotient of the overall distance and the overall time according to (10).

4 3 2 1

t t t t

s s s tv s

+ + +

+ +

== + (10)

tv ... travel velocity

s

i

... assigned distance to the i

^th

velocity value on the i

^th

road segment

t

i

... assigned time to the i

^th

velocity value on the i

^th

road segment

This corresponds to the harmonic mean value in (11). In order to obtain the travel velocity on a route that is consisting of several road segments, the individual velocities of each road segment have to be averaged harmonically.

∑ ∑

∑

=

= =

=

⁴

1 4

1

i i

i i i i

i

i i i

i

ha

t v t t

t t s t

s

v (11)

v

ha

... harmonic mean velocity

s

i

... assigned distance to the i

^th

velocity value on the i

^th

road segment

t

i

... assigned time to the i

^th

velocity value on the i

^th

road segment

If the assigned distances are equal ( s

_i

= s ∀ i ), the harmonic mean value is determined

according to (12), however if the assigned times are constant ( t

_i

= t ∀ i ), the harmonic

mean value is calculated according to (13) which corresponds to the arithmetic mean value.

(32)

∑

=

= = = =

= ₌

=

n

i i

n

i i n

i n

i i n

i

i n

i i

i n

i i n

i i

v n

s t n

s s t

s

s s t

s

s s t s

t s v

const s i

1 1

(12)

∑ ∑

∑

=

= = = =

= ₌

=

_n

i i n

i i

n

i n

i i

n

i n

i i

n

i i n

i i i

i

n

i i n

i i

n v n

t s

t t t s

t s v

const t i

1 1

1

1 (13)

The determined individual velocities can be considered neither as a local velocity nor as an instantaneous velocity. They are the travel times of single vehicles on single road segments.

If more than one travel time is available on a road segment, the arithmetic mean value will be calculated. This corresponds to the harmonic averaging of the given travel times (determined by the required time for the passed distance) on the respective road segment.

The mean travel velocities on every road segment will be gathered for a longer period of time (typical more than 3 months), are being filtered and time series will be generated [38] [35].

Generating time series can be effected with various methods like a sorting according to certain criteria like the day of the week, the season, the weather, special incidents, etc. and averaging [39]. Other clusterisation methods consider the similarity of each time series to all other time series or fragments of it on the respective road element.

In Fig. 9 the normal time series according to the 4 categories working day, week end, no

holiday and holiday on a random road segment in Vienna are shown. By combining these

four categories the four indicated clusters are built. In addition to every velocity value the

variance is indicated. The calculation period is January to June 2005, the aggregation

method is the median (percentile 50). The period length is 15 minutes. The period with the

smallest sample has the size of 1358 measurement values – i.e. the number of velocity

values, and is during night time on week ends in holidays.

(33)

Fig. 9 Normal time series and respective variances of a major road Source: [33]

An innovative approach within the scientific community is e.g. fuzzy clustering [40] [41] [42].

The Fuzzy clusterisation features are its global range and the circumstance that the clusters are fuzzy, so the decision is not very crisp, thus small changes of the data will not highly influence the result. However the algorithm is complicated and a high computational power in case of optimization is needed. On the other hand, the hierarchical fuzzy subtractive clusterisation is efficient and demands lower computational power demand but needs a manual stop. The combination of the two methods is unsupervised fuzzy clustering with multi-centre clusters [41], supplying proper results. By not looking for multiple centres – which is the case for single centre traffic patterns – the clusterisator is working based on minimizing the function shown in (14).

( ) ( )

( )

∑

∑∑

=

= =

=

+

=

β

β β

µ

ω µ

1

1 1

* ,

, , 1

, ,

k i k

j i ji

n

i j

j i n

i j

j i ji cm

f

cc x E

cc cc E cc

x E J

(14)

(34)

* ,cm

J

f

... objective function as a measure of the differences between the data points and the cluster centres

µ

ji

... membership function x ... data points

cc ... cluster centres

The choice of the data samples for the clusterisation is crucial. By fuzzy clustering a time series of a whole day, one centre for the whole period will be found. This allows that the centre points of whole time series can be compared, which is particularly of little help when determining the actual morning peak. The new approach is to look for fragments of time series that are similar to other fragments within the same time series and within other fragments of other time series. By building floating sequences of e.g. 4 measuring values of a period length of 15 minutes each, time windows of 1 hour length can be compared. As a result of the clusterisation process, a “toolbox” of typical scenarios in the time domain. Fig.

10 shows a data sample of a traffic quantity measurement that lasted for 7 days. The sample went through the clusterisation process, the resulting scenarios are marked within the sample.

Fig. 10 Fuzzy clustered centres in a time series of traffic quantities over a period of seven days

The cluster length is 1 hour (i.e. 4 measuring values). Source: [33]

(35)

Generally speaking, the result of the clusterisation processes is a situation inventory, having available the normal time series that describe the traffic behaviour in various situations. This inventory is the basis for a long range prognosis [32] as well as for the incident detection [43]

[37].

The time series e.g. in Fig. 9 and Fig. 10 show certain characteristics for each of the clusters.

However, taking the variance of every cluster into consideration, it is obvious that the assignment of a random measurement value to one of the clusters might be rather difficult.

There are times of the day when the distance of one normal time series to another is rather low, a situation in which the determination of the actual scenario according to the actual measurement value will fail.

Hence, a real time process that is in a position to consider the history of the actual measurement series as well as the history of the normal time series is required.

2.2 Thesis

Real time or near real time treatment of traffic data in combination with their situation sensitive treatment is a crucial requirement for modern ITS like ATMS, ATIS or ILS where 2-D traffic phenomena are very complex and include almost all difficulties (e.g. human behavioural interactions, very fast stochastic dynamics). In practice, the synthetic modelling of traffic situations is common, facing all given restrictions of multivariate systems. However, a situation sensitive modelling of traffic situations is up-coming. Once there is an empirical model as shown in the previous chapters, a RT process is required that is able to assess the actual measuring data according to these models. So far there is a lack of processes that are able to assess actual traffic data in this way in real time. A remarkably important feature of such processes is the detection of incidents and the judging of the relevance and severity of the incidents already detected.

Thesis: The use of intelligent dedicated digital signal processing systems and communication media is in a position to improve the capabilities of Real-Time traffic situational analysis in an efficient and effective way.