1. Modeling Truck Route Choice Behavior by Traffic Electronic Route Information Data for Oversize / Overmass Vehicles Innovations in Freight Demand Modeling and Data Sep.14-15, 2010 0. Background 1.Generating information of truck route choice with electronic route information data for oversize/overmass vehicles 2.Truck route assignment model using the electronic route data 3.Proposals for analysis of route choice by sea container trailers Tetsuro HYODO Tokyo University of Marine Science and Technology Yasukatsu HAGINO Institute of Behavioral Sciences, JAPAN

2. 1 OUTLINE Oversize/Overmass vehicles (including 20 ft, 40ft sea 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 measures or understanding of truck behavior

3. 2 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

4. Vehicle weight, axle weight, wheel lord, etc. vehicle weight 20t 3 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 Feature General limit (maximum) Width2.5m Length12.0m Height3.8m Weight Total weight20.0t Axle weight10.0t Wheel lord5.0t Minimum turning radius12.0m Turning circle Vehicle width, length, height

5. 4 Designated heavy truck network in TMR (20 – 25 ton)

6. 5 Designated high cube container network in TMR (3.8 – 4.3 m)

7. 6 - 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 1. Generating information of truck route choice with electronic route information data ■ Number of issued Permissions ■ Online Application System 2004 2005 2006 2007 2008 Number of Issuance Number of vehicles Applicant (home, office) 1. Apply 2. Fee payment BankOffice of Permit 4. Permit issuance 3. Confirm payment

8. 7 Sample image of online application system

9. 8 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 1. Generating information of truck route choice with electronic route information data

10. 9 Types of oversize/overmass vehicles by vehicle category 1. Generating information of truck route choice with electronic route information data CategoryDescription Constructioncrane, other than crane Semi-trailervan, tank, framed awning, auto carrier, others Sea containerhigh-cube, non high-cube, none of the above Full trailervan, tank, framed awning, auto carrier, others Othersheavy-goods semi-trailer, pole trailer, others

11. 10 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 data Generating information of truck route choice with electronic route information data Route A Route C Road 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

12. 11 Converting number of applications to road traffic volume (ton-km) Location of ports covered for international sea containers 1. Generating information of truck route choice with electronic route information data Annual ton-km for sea container trailers 凡例 利用なし 2.5 万ﾄﾝ未満 / 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 万ﾄﾝ以上 / 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.

13. 12 Share of application for international sea containers by travel distance (2005-2007) 1. Generating information of truck route choice with electronic route information data 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) %

14. 13 1. Generating information of truck route choice with electronic route information data Analysis of freight flow using electronic route information data Share of international sea containers by travel distance and road type (2005) Distance (km)

15. 14 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 1. Generating information of truck route choice with electronic route information data 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 万ﾄﾝ未満 / 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 万ﾄﾝ以上 / 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

16. 15 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. 16 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] ＝ 3535 ＝ 0.60 Objective Function=How to maximize the “Overlapping Ratio”

18. 17 Formulation of MOR model Definition of “Cognitive Distance” Assumption: “Recognized link distance is varied by the conditions of link” ： Recognized distance ： Actual distance ： Parameter ： Dummy variable (0 or 1) Actual distance ： 100mRecognized distance ： 80m 1 × 0.8 1 100[m] 0 80[m] ＝ × 0.6 0 0.60.8

19. 18 Applying “Recognized Generalized Link Cost” instead of “Recognized link distance” ： Rec. G. Cost ： Driving Cost ： Parameter ： Dummy variable ： Driving Time ω ： Value of Time Generalized Cost Actual Dist. Rec. Dist. Formulation of MOR model

20. 19 Definition of “Overlapping Ratio” (Objective Func.) ： Overlapping Ratio ： Overlapped distance between estimated & actual path ： Actual distance Formulation of MOR model

21. 20 Objective function of MOR model can not be differentiated by parameters  Genetic Algorithm is applied (20 genes, 7 bits, 50 generations) 2020 Min=0, Max=1, (2 0 +2 5 )*(1/2 7 )=0.25 21212 2323 2424 2525 2626 2020 Min=10, Max=100, (2 1 +2 3 )*(90/2 7 )+10=17.0 21212 2323 2424 2525 2626 How to evaluate the dispersion of estimated parameters ?  “Bootstrap method” is applied (100 sets are examined)

22. 21 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 variables Model 1 (High cube containers) Model 2 (All sea containers) Value of time (yen/min.)67.68115.39 Multiple lanes (dummy) (dual 2 or more lanes) 0.44450.4954 Weight designated road (dummy)* 0.36710.4198 Height designated road (dummy)** 0.61060.6399 Overlapping ratio (%)36.333.3 Number of samples5,82024,497

23. 22 Contour of objective function by 100 Bootstrap sets (VOT & “Heavy” case) Weight designated road dummy Value of Time

24. Metro. Inter- city Exp. Central Circular Route Inner Circular Route Narita Airport Haned a Airport 23 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

25. 24 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 CO 2 emission. 2. Truck route assignment model using electronic route information data With 3 belt ways Without Change (with- without) CO 2 in 5 prefectures 98,161100,774-2,613 Change in CO 2 emission (t-CO 2 / year)

26. 25 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