Estimation of Network Reliability under Traffic
Incidents for ITS Applications

Background andHong Kong Statistics

Ir Prof. William H.K. Lam
Chair Professor of Civil & Transportation Engineering andHead
Department of Civil & Environmental Engineering
The Hong Kong Polytechnic University
E‐mail: william.lam@polyu.edu.hk
Website: www.cee.polyu.edu.hk/~cehklam
Department of Civil & Environmental Engineering

2

The Hong Kong Polytechnic University1970

Background
BackgroundShenzhen• Population: Over 7.23 million• Total area：1,104 km2, about 24% land developed• Car ownership: 75 per 1,000 people, about 10% of the US figure, despite
a similar level of GDP• Urban density: 27,000 persons/km2 (Developed land average)• In comparison: LA – 3,144;Taipei – 9,650; Tokyo – 7,100; Bangkok – 1,301• 13 million daily trips, ~10% of car trips

1980
1990 ‐ 2000
Planned New
Development
Area (2014)

BorderNew Territories

New Town
Development

Hong Kong
International
Airport

Kowloon• Road length = 2,090km

Downtown• No. of licensed vehicle = 700,600 (as at January 2015)

Hong Kong Island• 175,000 commercial vehicles out of 700,600 licensed vehicles inHong Kong in January 2015.
34

Within‐day & Day‐to‐day
Recurrent and Non‐Recurrent Congestion Problems

Hong Kong ‐ A high density populated city

• There are with‐in day and day‐to‐dayrecurrent
andnon‐recurrent
trafficcongestion problems in densely populated
cities such as Hong Kong. It has considerable
impactoneconomic
productivity,environment and safety.
• However, due to the topography of Hong
Kong, there are hardly any feasible sites forfurther expansion of existing road network.
To alleviate the recurrent and non‐recurrent
traffic congestion problems in Hong Kong,
recent attention has been given to develop
intelligent transportation systems (ITS).

56Better Use of New Technologies
Objective“ The use of new technologies will be encouraged
to increase the efficiency of traffic management,
improve the overall capacity of the road system,and enhance road safety . ”

Recent ITS Developmentin Hong Kong

78

Real‐time Traffic Information System (RTIS)

Hong Kong eRouting
Deterministic
travel time
 Single criterion
Update once
every 5 minutes !

Shortest time
Lowest toll
 Shortest distanceAccuracy ofspeed color is90%• Launched in Hong Kong Transport Department’s website inJanuary 2007;• Recently updated in May 2010 with use of the latest road
network in Hong Kong
http://tis.td.gov.hk/rtis/ttis/index/main_partial.jsp

Tam M.L. and Lam W.H.K. (2008) Using
Automatic Vehicle Identification Data for
Travel Time Estimation in Hong Kong.
Transportmetrica, 4(3), 179‐194.

Launched in Hong Kong Transport Department’s website in mid‐2010
http://hkerouting.gov.hk/drss/index.php?lang=EN
9

Journey Time Indication System (JTIS)

Expansion of JTIS to Kowloon

Launched on themajor routes ofHong Kong Islandin mid‐2009 andof Kowloon area
in mid‐2010

10Speed Map Panels (SMP) in the New Territories ofHong Kong
Launched in January 2013

Update once every
2 minutes !

Update once every
2 minutes !

Accuracy of the computed journey time iswithin +/‐ 20% errors with a compliance of 95%
Tam M.L. and Lam W.H.K. (2011) Validation of Instantaneous Journey Time Estimates: A Journey TimeIndication System in Hong Kong. Proceedings of the 9th International Conference of Eastern Asia Society
for Transportation Studies, Vol. 8, 20‐23 June 2011, Jeju, Korea.

11

Accuracy of the computed journey time and speed range (in formof color) is within +/‐ 20% errors with a compliance of 95%
Tam M.L. and Lam W.H.K. (2013) Validation of ATIS Journey Time and Traffic Speed Estimates by
Floating Car Survey. Journal of the Eastern Asia Society for Transportation Studies, 10, 131‐146.

12

Real‐time Traffic Data Collection Technologies
adopted in Hong Kong

Two Types of Traffic Detectors Adopted in SMP• Link speed detector (LSD) by automatic license platerecognitionBA

License plate:AB123
9:20:00am

Automatic License Plate Recognition(ALPR) DetectorLicense plate:AB123
9:30:00am

Journey time = 10 min• Spot speed detector (SSD) by video image processing

There is a need to make use of all
different types of detector data!!!
Radio Frequency Identification(RFID) Reader

Loop Detector

Video Detector

65km/h

58km/h

55km/h1314

Data Filtering Method for Generating Stochastic
Journey Time Windows (at 2‐min intervals)

Filtering of Automatic Vehicle Identification (AVI)
Data Captured by LSD
OriginDestination

Lower limit

Upper limit

2:00

6:00

Valid observation

Invalid observation

Average journey time30BJourney time ofvehicle A = 20 min

25


Journey time ofvehicle B = 50 min

Journey time (min)

A

20

15

10

50
0:00

4:00

8:00

10:00

12:00

14:00

16:00

18:00

20:00

22:00

0:00

Time of day

Journey times on a selected path in Hong Kong (11 May 2009)15

16

Autoscope Speed Data

Speed‐based Method for Estimating Journey Time
• Average speed method

40 km/hTime mean speed• Piecewise linear speed based method• Piecewise non‐linear speed based
method (with consideration of covarianceSpace mean speed

20 km/h

20m500m500m

20m

Signalized
Intersection

relationship of link travel times/speeds)
30 km/h

20 km/h

40 km/h
20 m

500 m

500 m

20 m

17

Integrated Algorithm for Estimation of
Instantaneous Average Journey Times and Traffic Speed Ranges

Offline Travel Time Estimates Average link travel time estimates(t1, t2)
 Spatial variance (12, 22 ) andcovariance (12) relationships oflink travel times

Real‐time Travel Information System (RTIS)(http://tis.td.gov.hk/rtis/ttis/index/main_partial.jsp)

1

2

Real-time LSD data

Real-time SSDdata

Data filtering methodfor travel timeestimation

Speed-based method fortravel time estimation

2

1
Link travel timet2

t1

Offline link travel
time estimates

t2

t1

18

Link travel time

Integration of three instantaneous travel time estimates on road segments

Link 1

Link 2

Lam W.H.K., Chan K.S. and Shi J.W.Z(2002) A Traffic Flow Simulator for Short‐term Travel Time Forecasting. Journal ofAdvanced Transportation, 36(3), 265‐291.
19

Instantaneous average journey time estimates on the selectedpaths and traffic speed ranges (color) on road segments
Tam M.L. and Lam W.H.K. (2008) Using Automatic Vehicle Identification Data for Travel Time
Estimation in Hong Kong. Transportmetrica, 4(3), 179‐194.

20

Instantaneous Average Journey Time vs.
Completed Journey Time of Test Vehicle

Validation Method

Estimated average
journey time at theroad segment• Floating car method

Road segments 1-4

Tate’s Cairn
Tunnel (TCT)

Individual car

Kowloon exit

• Observed average instantaneous journey times by test cars vs.
instantaneous journey time estimates on each selected pathfor each two‐minute interval during survey period.
• The targeted accuracy level of the computed journey time iswithin +/‐ 20% errors with a compliance of 95%.

12 min14 min21

Sample Sizes for Validation of the
Instantaneous Average JourneyTimes Provided by SMP

α = 0.05

21

SJ1‐LRT

7.53

9288180

2

SJ1‐TSCA

8.439583178

1303

SJ1‐SMT

7.68

7664

14056

4

SJ2‐TCT

5.435858116

54

5

SJ2‐LRT

7.06

94

87

181

45

6

SJ2‐TSCA

9.68

89

79

168

88

7

SJ3‐TCT

10.1763

72

13570

8

SJ3‐LRT

11.04

9284176

619

SJ3‐TSCA

11.94

5671127118

10S4‐TKTL

12.0256591156611S4‐TKTM

26.86

8867

155

53

12

S5‐TWTM

16.87

10074

174

56

13

S5‐TWCP

17.26

52

41

9356

‐Total

‐

1011

927

1938913

No.

Selected path

Pathdistance(km)

No. of samples used for validation
Weekday

Weekend

Overall two
survey days

22

Percentages of samples within  20% errors

Permitted error (20%)

Minimum samples
required within
two survey days(N)
60

completedjourney time

Instantaneous average journey time

Validation Results of the Instantaneous Average
Journey Times Provided by SMP

Standard deviation

z sN    /2  e 

SJ2 (Tate’sCairnHighway)

No.

Selected path
Weekday

Weekend

Overall two
survey days

1

SJ1‐LRT

100%100%100%

2

SJ1‐TSCA

96.8%100%98.3%

3

SJ1‐SMT100%100%100%

4

SJ2‐TCT

100%100%100%

5

SJ2‐LRT

100%100%100%

6

SJ2‐TSCA#

100%98.7%

99.4%

7

SJ3‐TCT

100%100%100%

8

SJ3‐LRT

100%100%100%

9

SJ3‐TSCA

96.4%

100%98.4%

10

S4‐TKTL

100%100%100%

11

S4‐TKTM

100%100%100%

12

S5‐TWTM

100%100%100%

13

S5‐TWCP#100%100%100%

The requirement ofthe targeted accuracy
level is achieved.

*The targeted accuracy level of the computed journey time is within+/‐ 20% errors with a compliance of 95%.
23

#The selected path with traffic signals and roundabouts.

24Validation Results of Traffic Speed Range (Color)
Provided by SMP

Recent ITS Research Works
Conducted byHong Kong Polytechnic University
Targetedaccuracy level**The targeted accuracy level of the computed speed range (color)
is to be fallen within +/‐ 20% with a compliance of 95%.
25

Sources of Network Uncertainty

26

Network Uncertainty under Adverse Weather

Supply and Demand Uncertainties(Non‐recurrent conditions)

Multiple sources, which can be broadly divided into two categories,
contribute to network uncertainty.
Classification
Specifiedform

Supply uncertainty

Demand uncertainty

Adverse weather Traffic accidents, Travel demand
fluctuations between
conditions, road vehicle
breakdown, etc. a specified origin ‐works, etc.
(Less predictable) destination pair
(Predictable)

Non-recurrent conditions

Recurrent conditions

1:04pm 26 May 2006 (Fri, no rain)

11:20 am 19 Nov 2008 (Wed)8:04 am 24 Feb 2006 (Fri)

1:04pm 29 May 2006 (Mon, raining)

Adverse weather in Hong Kong: Three levels of warning signals by the levels of
rainfall expected:‐ AMBER rainstorm signal (>30 mm/hour), RED rainstorm
signal (>50 mm/hour) and BLACK rainstorm signal (>70 mm/hour).

8:04 am 25 Feb 2006 (Sat)27

28

Traffic Dynamics – Stochastic Effects

Stochastic Journey Time Estimationin Road Network with Uncertainty

33

Within‐dayvs.
Day‐to‐day Dynamics

19• The second‐order statistical property(standard deviation) of the observed data isused to estimate the journey time of eachpath in the territory‐wide road network. It
has the potential to provide traffic
information for the whole network.

Within-day travel time variations• Different types of observed data (link flow
data and journey time data) are jointly usedin the proposed modeling approach.

The first‐order (Mean) and second‐order(Standard Deviation) statistical propertiesof the traffic data should be considered for
capturing the stochastic effects over time.

First-order (Mean)
Second-order (Standard Deviation)

Day-to-day travel time variations

Shao H., Lam W.H.K., Sumalee A. and Chen A. (2013) Journey Time Estimator for Assessment of Road Network
Performance under Demand Uncertainty. Transportation Research Part C, 35, 244‐262.
29

Travel Time Reliability
in Network with Uncertainties

30

New concept of Travel Time Reliability:
Probability of on‐time arrival
Path1 (40min, 24min)

 A new concept of travel time reliability is introduced for capturing
the stochastic effects over time, in which the first‐order andsecond‐order statistical properties of the traffic data areconsidered.

8AM
Path 2 (45min, 12min)

 Travel time reliability is defined as the probability that a traveler
can arrive at a destination within a given travel time threshold (i.e.
on‐time arrival probabilities).
Path 1 (40min, 24min)

8AM

mean

standard deviation
Path 2

CDF of arrival time

Deterministicvs.
Stochastic

9AM

9AM

0.9

Path 1

0.8~ts  9 AM

Path 2 (45min, 12min)mean

standard deviation

31

Chen B.Y., Lam W.H.K., Sumalee A., Li Q.Q., Shao H. and Fang Z.X. (2013) Finding Reliable Shortest Paths in Road 32
Networks under Uncertainty. Networks & Spatial Economics, 13(2), 123‐148.

Reliable Shortest Path Problem (RSPP) (1)Reliable Shortest Path Problem (RSPP) (2)Given departure time tr, to find earliest arrival time and the optimal
path required to satisfy pre‐specified probability of on‐time arrival Given preferred arrival time ~t s , to find latest departure time and thereliable shortest path required to satisfy pre‐specified probability ofon‐time arrival 8AM?
Path 2 (45min, 12min)

CDF of arrival time

Path 1 (40min, 24min)

=0.9
9AM
8:40AM

Path 1 (40min, 24min)

Path2 Path1
9:11AM?

8:45AM

9AM
Path 2 (45min, 12min)

A New Concept ofTravel Time Reliability
is introduced, in which
the first‐order andsecond‐order statisticalproperties of the traffic
data are considered.

33

Reliable Route Searching System

34

Route Guidance• Reliable routing service with on‐time arrival probability:

35

Chen B.Y., Lam W.H.K., Sumalee A. and Li Z.L. (2012) Reliable Shortest Path Findings in Stochastic Networks
with Spatial Correlated Link Travel Times. International Journal of Geographical Information Science, 26(2),365‐386.

36

Impact of Traffic Accident on Network Reliability

Technologies for Incident Management
Feasibility Study on
Deploying Advanced
Technologies in IncidentManagement‐ Executive Summary
February 2010

http://www.td.gov.hk/filemanager/en/
publication/executive%20summary_english.pdf
11:20 am on 19 November 200837

Dynamic Impact of Traffic Incident on
Network Reliability (On‐time Arrival Probability)

38

Impact of Traffic Incident on Network Performance

The capacity drop ratio is defined as
the ratio of the dropped capacity tothe original capacity.
Dropped Capacity
Capacity
Drop Ratio = Original Capacity

Incident
duration

On‐timearrival prob.

Capacitydegradation

Incident
occurrence time

Trafficdelay
On‐timearrival prob.

Capacity DropRatio

Incident Duration

15

Incident
duration

On‐timearrival prob.

Capacitydegradation(2‐lanesblocked)

Incident
occurrence time

1539

Traffic
delay
On‐timearrival prob.
40

Network with Day‐to‐day Recurrent& Non‐recurrent Uncertainties

Network with Non‐recurrent Uncertainty

Within‐day traffic congestion due toincidents and adverse weather in Kowloon,
Hong Kong on 9 May 2005 (Mon).

Before 28 September 2014 (Sun)

After 28 September 2014 (Sun)due to Occupy Central
Day‐to‐day Traffic Conditions at Causeway Bay, Hong Kong urban area

There is a need to examine the Travel Time Reliability (in term of ontime arrival probabilities) posed by non-recurrent uncertainties (due torainfall and traffic accidents) to understand their impacts on travel
choice behaviors and network performance context.

The Hong Kong Polytechnic University

There is a need to explore new avenues of research for development of
reliability-based ITS applications in road-based multi-modal transportation
networks with taking account network uncertainties under recurrent and nonrecurrent uncertainties due to demand fluctuations.

4142

The Hong Kong Polytechnic University

Need for Further Study
Based on the rainfall data (annual averages for 30‐year period) from the World
Weather Information Services (http://www.worldweather.org/), that Hong Kong
has the highest average annual rainfall of all the major Pacific Rim cities.
Further study is required to explore new avenues of research for developmentof Intelligent Transportation Systems (ITS) in Asian cities with relatively highannual rainfall intensities and/or high traffic accident rates.

Further StudyJanFebMar
AprMayJunJulAug
SepOctNovDec

Hong Kong in Year 2014
Total Rainfall
Number of Rain Days(mm)(Daily rainfall  0.1mm)
0.0039.5
7207.612132.412687.3
23436.6
19260.520548.2
17140.6
13109.8
731.1
744.7
15Total2638.3 mm

MonthNote: Rainfall intensity is based on annual averages for the 30-year period(as at September 2011). (http://www.worldweather.org)

43

152 days44

william.lam@polyu.edu.hk (Ir Prof. William H.K. Lam)

Future Works

http://www.cee.polyu.edu.hk/~cehklam/-THE END-• To examine the Travel Time Reliability (in term of on‐timearrival probabilities) posed by recurrent and non‐recurrent
uncertainties (due to rainfall and traffic accidents) tounderstand their impacts on travel choice behaviors andnetwork performance context.
• To explore new avenues of research for development ofreliability‐based ITS applications in road‐based multi‐modaltransportation networks with taking account network
uncertainties due to recurrent and non‐recurrent congestion.
• To develop reliability‐based Dynamic Traffic Assignment (DTA)
model for assessing network degradability due to adverse
weather and incidents.

Thank You‐THE END‐

Q and AACKNOWLEDGEMENTS
This work was jointly supported by research grants from theResearch Grant Council of the Hong Kong Special AdministrativeRegion (Project No. PolyU 5242/12E) and from RISUD of PolyU(Project Nos. 1‐ZVBZ).

The 20th HKSTS International Conference
12‐14 December 2015, Hong Kong
http://www.hksts.org
4546