Innovations in Freight Demand Modeling and Data Sep.14-15, 2010

Modeling Truck Route Choice Behavior by Modeling Truck Route Choice Behavior by Traffic Electronic Route Information Data Traffic Electronic Route Information Data

for Oversize / Overmass Vehiclesfor Oversize / Overmass Vehicles

Innovations in Freight Demand Modeling and DataInnovations in Freight Demand Modeling and DataSep.14-15, 2010Sep.14-15, 2010

0. Background1. Generating information of truck route choice with electronic route

information data for oversize/overmass vehicles2. Truck route assignment model using the electronic route data3. Proposals for analysis of route choice by sea container trailers　　　　　　　　　　　　　　　　　　

Tetsuro HYODOTetsuro HYODO

Tokyo University of MarineTokyo University of MarineScience and Technology Science and Technology

Yasukatsu HAGINOYasukatsu HAGINO

Institute of BehavioralInstitute of BehavioralSciences, JAPANSciences, JAPAN

2

OUTLINE

Oversize/Overmass vehicles (including 20 ft, 40ftsea containers) must get permission

From 2004, online application system started.It is simple GIS based internet system

The huge application data (route of each vehicle)are collected automatically (over 1 million per year)

How to use them for effective policy measuresor understanding of truck behavior

3

0. Background

Needs of road network for increasing sea containers and guiding container traffic

・ In 2006, Ministry of Land, Infrastructure, Transport (MLIT) announced “International Freight Arterial Network” to smooth international freight container trailers, and tries to remove traffic barriers.

“International Freight Arterial Network”

　　　高速道路

　　　一般道路

Expressway

Ordinary road

Vehicle weight, axle weight, wheel lord, etc.

vehicle weight

20t

4

1. Generating information of truck route choice with electronic route information data

General limit of vehicles

- Roads are built according to structural standards. Road Act specifies maximum size and weight of vehicle called “general limit.”

- Vehicles exceed general limit in length, height, or weight are called “oversize/overmass vehicles.” They require permit to drive on roads.

(1) Electronic route information data for oversize/overmass vehicles

FeatureGeneral limit (maximum)

Width 2.5m

Length 12.0m

Height 3.8m

Weight

Total weight 20.0t

Axle weight 10.0t

Wheel lord 5.0t

Minimum turning radius 12.0m

Turning circle

Vehicle width, length, height

5

Designated heavy truck network

in TMR (20 – 25 ton)

6

Designated high cube container network

in TMR (3.8 – 4.3 m)

7

- Oversize/overmass vehicles are required to apply for a permit to road operators to drive on roads.

- “Online application system” was introduced in Regional Development Bureaus of the Ministry in March, 2004.

Permit for oversize/overmass vehicles


■ Number of issued Permissions■ Online Application System

2004 2005 2006 2007 2008

Number of IssuanceNumber of vehicles

Applicant (home, office)

1. Apply

2. Fee payment

BankOffice of Permit

4. Permit issuance

3. Confirm payment

8

Sample image of online application system

9

Layout of electronic route information data for oversize/overmass vehicles

Following tables are the layouts of electronic data, route data, vehicle type, and freight category of permit application

Electronic route information data for oversize/overmass vehicles

Layout of route information data corresponding to intersection codes


Applicationno.

Origin address Destination addressOriginintersectionno.

Destinationintersectionno.

Vehicletype

Freightcategorycode

Freightitemcode

Freightitem

Routeno.

No. ofroutes

round (1)/one way (2)

#: nodataavailable

Double the value ofone way for roundtrip

1100427744 ○ ○愛知県師勝町大字鹿田字天井田－ ○-○-○名古屋市中村区稲上町 5236675660 5236563728 12010 12 0 # 1 2 11100441319 ○○春日井市下条町陸運（株） ○-○-○大阪市港区石田 5236673659 5135734188 21031 # # # 1 56 11100441319 ○○春日井市下条町陸運（株） ○○米子市吉岡 5236673659 5333120038 21031 # # # 2 56 11100441319 ○○春日井市下条町陸運（株） ○○愛知県海部郡飛島村 5236673659 5236460993 21031 # # # 3 56 11100441319 ○○春日井市下条町陸運（株） ○○小牧市三ッ渕 5236673659 5236770371 21031 # # # 4 56 11100441319 ○○春日井市下条町陸運（株） ○○戸田市美女木向田 5236673659 5339552633 21031 # # # 5 56 11100446454 ○-○ ○○愛知県半田市川崎町工場 ○-○愛知県弥富市富浜 5236270106 5236461106 22000 4 5 # 1 2 1

Applicationno.

Routeno.

Intersectionorder

Firstintersection

EndingIntersection

1100427744 1 1 5236675660 52366622791100427744 1 2 5236662279 5236675952 ○ ○ ○ ○1100427744 1 3 5236675952 5236675937 5236675660 5236662279 5236675952 52366759371100427744 1 4 5236675937 52365630291100427744 1 5 5236563029 52365637281100441319 1 1 5236673659 5236675800 Order of intersections1100441319 1 2 5236675800 52367713361100441319 1 3 5236771336 52367704461100441319 1 4 5236770446 52350324591100441319 1 5 5235032459 52350327131100441319 1 6 5235032713 52350349721100441319 1 7 5235034972 52350347381100441319 1 8 5235034738 52350304931100441319 1 9 5235030493 51357313981100441319 1 10 5135731398 51357313631100441319 1 11 5135731363 51357342331100441319 1 12 5135734233 5135734188

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Types of oversize/overmass vehicles by vehicle category


Category Description

Construction crane, other than crane

Semi-trailer van, tank, framed awning, auto carrier, others

Sea container high-cube, non high-cube, none of the above

Full trailer van, tank, framed awning, auto carrier, others

Others heavy-goods semi-trailer, pole trailer, others

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Intersection codes of electronic route information were matched to DRM node data.

Next, truck route data were reproduced on DRM by searching routes on DRM and connecting intersections of electronic route information.

1. Generating information of truck route choice with electronic route information dataGenerating information of truck route choice with electronic route information data

Route A

Route CRoad network on electronic route information system

Network on DRM

Intersection on electronic route information data

Node on DRM

Intersection of route A with B

Intersection of route A with D

There are more than one routes between two points on DRM. We assumed the applicant selected route A since either point is on route A.

Route B

Route D

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Converting number of applications to road traffic volume (ton-km)

Location of ports covered for international sea containers


Annual ton-km for sea container trailers

凡例

利用なし2.5万未満ﾄﾝ /年2.5万ﾄﾝ /年～ 7.0万ﾄﾝ /年7.0万ﾄﾝ /年～ 20.0万ﾄﾝ /年20.0万以上ﾄﾝ /年

Estimated international sea

containers

20 mill. ton/year

10 mill. ton/year

5 mill. ton/year

Legend

no traffic

less than 25,000 ton/year

25,000 – 70,000 ton/year

70,000 – 200,000 ton/year

Over 200,000 ton/year

-Number of electronic applications was converted to ton-km using statistics of Surface Import/Export Freight Survey.

-Conversion factor for “tonnage per application” was calculated using int’l container traffic data.

0

5

10

15

20

25

30

35

-99 100- 200- 300- 400- 500- 600- 700- 800- 900- 1000+

13

Share of application for international sea containers by travel distance (2005-2007)


Analysis of freight flow using electronic route information data

distance (km: origin to destination)

Approximately 20% of container trailers drive "500km and over."

(international sea container case)

%

1414


Analysis of freight flow using electronic route information data

Share of international sea containers by travel distance and road type (2005)

13

21

26

31

26

31

35

54

54

54

54

59

56

45

12

13

10

7

7

5

10

21

12

10

8

8

8

11

0% 20% 40% 60% 80% 100%

-99

100-

200-

300-

400-

500-

600-

Expressway Trunc national road Other national road Local

Distance (km)

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International freight arterial network is consisted of expressways and trunk national roads. Roads in center of metropolitan are not designated for preserving living environment.

However, according to the data generated in this study, it was clear that container trailers drive ordinary roads in Tokyo city center given that Tokyo Port is in the vicinity.

Tonnage of international sea containers (in central Tokyo)

International freight network


Route4

Pref. roads and higher standard roads

Tonnage of international sea containers (Int’l freight arterial and other roads, annual )

凡例

利用なし2.5万未満ﾄﾝ /年2.5万ﾄﾝ /年～ 7.0万ﾄﾝ /年7.0万ﾄﾝ /年～ 20.0万ﾄﾝ /年20.0万以上ﾄﾝ /年

Legend

no traffic

less than 25,000 ton/year

25,000 – 70,000 ton/year

70,000 – 200,000 ton/year

Over 200,000 ton/year

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Route choice models for sea container trailers using traffic flow of those container trailers generated with application data.

Two different techniques were examined: Maximum Overlapping Ratio (MOR) model, and modified Dial technique (“Path Size Dial Logit: PSDL model) which is one of multiple-route assignment techniques.

Because of time limitation, only MOR model will be explained.

Examined route choice models

2. Truck route choice model using electronic route information data

17

highwayRecognized distance

Concept of “Maximum Overlapping Ratio (MOR) model”

Route Overlapping Ratio:

Actual route

Estimated Path

Shortest Path

If the each link length = 1 …

[Overlapping length]

[Actual Length]＝

3

5＝　 0.60

Objective Function=How to maximize the “Overlapping Ratio”

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Formulation of MOR model

Definition of “Cognitive Distance” Assumption: “Recognized link distance is varied by the conditions of link”

2121

* ZZaa ll β・β・β

kZ

*alalkβ

： Recognized distance： Actual distance

： Parameter

： Dummy

variable (0 or 1)

Actual distance： 100mRecognized distance： 80m

1

× 0.81100[m]

0

80[m]　＝ × 0.60

0.60.8

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Applying “Recognized Generalized Link Cost”instead of “Recognized link distance”

2121

* )(, ZZaaa TimeCostGC β・β・ω・βω

*aGC

aTimekβaCostkZ

： Rec. G. Cost

： Driving Cost ： Parameter

： Dummy variable： Driving Time

ω　： Value of Time

Generalized Cost

β*al al

Actual Dist.Rec. Dist.


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Definition of “Overlapping Ratio” (Objective Func.)

n

n

a

anana

n

X

l

D

・βωδ・δ

βω

,

,

*

βω,D anana l・βωδ・δ ,*

nX

： Overlapping Ratio

： Overlapped distance between

estimated & actual path： Actual distance


21

Objective function of MOR model can not be differentiated by parameters Genetic Algorithm is applied (20 genes, 7 bits, 50 generations)

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Min=0, Max=1, (20+25)*(1/27)=0.25

212223242526

20

Min=10, Max=100, (21+23)*(90/27)+10=17.0

212223242526

How to evaluate the dispersion of estimated parameters ? “Bootstrap method” is applied (100 sets are examined)

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Result of Parameter Estimation

* Link is designated for heavy truck (over 20t)=1, otherwise=0** Link is designated for height container (over 3.8m)=1, otherwise=0

Explanatory variablesModel 1

(High cube containers)Model 2

(All sea containers)

Value of time (yen/min.) 67.68 115.39

Multiple lanes (dummy)(dual 2 or more lanes)

0.4445 0.4954

Weight designated road (dummy)*

0.3671 0.4198

Height designated road (dummy)**

0.6106 0.6399

Overlapping ratio (%) 36.3 33.3

Number of samples 5,820 24,497

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Contour of objective function by100 Bootstrap sets (VOT & “Heavy” case)

Weight designated road dummy

Value of Time

Metro. Inter-

city Exp.

Central Circular Route

Inner Circular Route

Narita Airport

Haneda Airport

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Change in traffic flow was estimated using route choice model for sea container trailers established in this study, assuming that roads are to be developed in the future.

2. Truck route assignment model using electronic route information data

Three belt ways in Tokyo Region

Policy simulation using truck route assignment model

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Change in traffic by completing three belt ways (annual) ［ with three ring roads – without three ring roads］

By completing three belt ways, sea container trailer flow would decrease significantly in central Tokyo, as well as CO2 emission.

2. Truck route assignment model using electronic route information data

- 10,000 台未満- 10,000 以上 - 100 台未満

- 100 以上 100 台未満100 以上 10,000 台未満

10,000 台以上

凡例差分（三環状道路整備あり－三環状道路整備なし）

With 3 belt

waysWithout

Change(with-

without)

CO2 in 5 prefectures

98,161 100,774 -2,613

Change in CO2 emission (t-CO2 / year)

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3. Proposals for analysis of route choice by maritime container trailers

(1) Achievement to date

1) Developed a method to generate truck route choice data using electronic route information data for oversize/overmass vehicles

2) Learned actual freight flow such as “flow on international freight arterial network”

3) Reproduced truck route data on network for assignment, and generated basic data for quantitative analysis such as establishing truck route choice model.

(2) Future possibilities

1) Building time-series truck route data by continuous data generation Period of traffic permit is one year. By generating continuous data, flexible

route analysis will be possible with fuel cost, economy, toll discount, etc.

2) Apply data to further truck route choice model Utilizing extensive data enables highly accurate validation of model

reproduction which could be applied to further multiple-route choice model. 3) Utilize on policy evaluation

Documents

Innovations in Freight Demand Modeling and Data Sep.14-15, 2010