Java Arterial Road Network Study (JARNS) Technical ReportNo.10: Network Model Development and Application

Carl Bro International a|s and Associates i

TECHNICAL REPORT NO. 10:

NETWORK MODEL DEVELOPMENT AND APPLICATION

TABLE OF CONTENTS

1. INTRODUCTION 1

1.1 Objectives 1

1.2 Background 1

1.3 Scope of this Report 1

2. JARNS NETWORK MODEL OVERVIEW 2

2.1 Introduction 2

2.2 Study Area and Zone System 2

2.3 Vehicle Types Modelled 2

2.4 The Study Area Networks 4 2.4.1 Development of Base Year Network 4 2.4.2 Development of Future Networks 5 2.4.3 Other Model Parameters 6 2.4.4 Network Model Outputs and Evaluation 6 2.4.5 JARNS Modelling Software 7

3. TRAFFIC SURVEYS AND DATA COLLECTION 8

3.1 Introduction 8

3.2 Traffic Surveys 8 3.2.1 Other Traffic Data Sources 8 3.2.2 Other Surveys and Data 8

3.3 Scope of Traffic Surveys 9 3.3.1 Survey Site Selection 9 3.3.2 Survey Time Periods and Coverage 9

3.4 Traffic Count Data 9 3.4.1 JARNS Traffic Count Surveys 9 Source: JARNS Traffic Surveys 11 3.4.2 Traffic Count Data from Other Sources 11

3.5 Roadside Interview Surveys (RIS) 11 3.5.1 General Background 11 3.5.2 Car O-D Surveys 12


Carl Bro International a|s and Associates ii

3.5.3 Goods Vehicles O-D Surveys 12 3.5.4 Angkot O-D Surveys 13 3.5.5 Bus O-D surveys 13

4. DEVELOPMENT OF BASE YEAR VEHICLE TRIP MATRICES 15

4.1 Introduction 15

4.2 Development of Observed Trip Matrices 15

4.3 Synthesis of Base Year Trip Matrices 17 4.3.1 Build Prior Trip Matrices 17 4.3.2 Initial Matrix Estimation 18 4.3.3 Final Matrix Estimation 19

5. JARNS NETWORK MODEL DEVELOPMENT AND VALIDATION 21

5.1 Introduction 21

5.2 Network Data Sources and Coding 21 5.2.1 Network Model Data Sources 21 5.2.2 Coding of Study Area Network General 21 5.2.3 Coding of Strategic Network Links 23 5.2.4 Coding of Urban Area Links 26 5.2.5 Coding of Urban Area Corridors 27 5.2.6 Toll Roads and Toll Road Access Links 27 5.2.7 Rural Area Roads 27 5.2.8 Summary 27

5.3 Link Capacity and Speed Flow Relationships 28

5.4 Base Year Assignment and Model Validation 29 5.4.1 Assignment Time Period and PCE factors 30 5.4.2 Assignment Results Validation 31

6. FUTURE TRAVEL DEMAND 33

6.1 Introduction 33

6.2 Trip End Models 33

6.3 Future Travel Demand Matrices 35

6.4 Forecast Future Pre-Load Traffic Volumes 35

7. NETWORK MODEL APPLICATION 36

7.1 Introduction 36

7.2 Development of Future Year Networks 36 7.2.1 Base 2005 Network 36 7.2.2 Future Year 2010 Networks 37 7.2.3 Future Year 2020 Networks 37


Carl Bro International a|s and Associates iii

7.3 Future Year Network Model Parameters 38

7.4 Testing of Future Year Network Scenarios and Evaluation 38

7.5 Network Model Inputs to the Economic Evaluation Framework 38 APPENDICES: APPENDIX A: ZONE SYSTEM DEFINITION APPENDIX B: TRAFFIC SURVEY DATA APPENDIX C: MATRIX ESTIMATION APPENDIX D: NETWORK DATABASE AND CODING APPENDIX E: ROAD CAPACITY AND SPEED FLOW

RELATIONSHIPS APPENDIX F: ASSIGNMENT VALIDATION APPENDIX G: TRIP END MODELS APPENDIX H: TRAFFIC GROWTH FACTORS


Carl Bro International a|s and Associates 1

1. INTRODUCTION 1.1 Objectives A key component of the JARNS study was to develop a road network model capable of providing road traffic estimates for the strategic road network of Java for the next twenty years. The model needs to be responsive to development changes and robust enough to provide the testing and evaluation of alternative network development strategies, involving both toll and non-toll road capacity expansion programmes. This report provides a full documentation on the JARNS network model development and its application. A summary report on network model has also been included in the JARNS Final Report as Appendix C. 1.2 Background The JARNS study included the development of a network model based on the use of existing data where possible and by carrying out new traffic surveys where appropriate. As a first step, a review of the existing data sources was undertaken and reported in the Inception report. There exists a considerable amount of data on the Java road network within the Indonesian Road Management System (IRMS) and the Automated Road Monitoring Study (ARMS) database systems. For data on traffic itself, the data within the IRMS database and from other sources was limited, and lacking in detail and accuracy. These issues were noted earlier, and were fully addressed during the JARNS surveys and data collection program. A review was undertaken of road traffic demand forecast models developed for studies carried out in Java. This revealed that most of these models are limited in scope, and were largely developed for specific geographical areas within Java, or for checking the feasibility of particular road / toll road schemes. The major highway projects that covered a wider area of Java were the CAPEX and North Java Corridor study, but models and data from these are now of limited relevance. The most recent study that looked at the road traffic demand at a strategic level was the Transport Sector Strategy Study (TSSS). The traffic model developed for the TSSS was also reviewed. The study only modelled inter-kabupaten road traffic in Java, and at an aggregate level for all vehicle types. This is considered too coarse for the level of detail required for the JARNS scope of analysis. The reported results of growth in traffic demand were also of limited use, as the TSSS forecast time frame was limited to the next ten years. However, where appropriate information and data from the TSSS study was used in the development of the JARNS network model. 1.3 Scope of this Report This Section comprises of seven sections. In the next section an overview of the JARNS network model is presented. Section 3 gives a brief description and summary of survey results. Sections 4 and 5 present the development and the validation of the trip matrices and networks. Section 6 illustrates the forecast methodology and a summary of results, followed by descriptions of forecast year networks and model application in Section 7. A Full discussion of the output model results for alternative network development strategies and scenarios is given in Section 6 of the JARNS Final Report. There are eight Appendices to this report, which provide further details on the model development and application.



2. JARNS NETWORK MODEL OVERVIEW 2.1 Introduction The JARNS study developed a simple but robust model for forecasting of traffic on the strategic road network of Java. The traffic model is fully integrated with the JARNS Socio-economic model SPADEL (see Technical Appendix A, and Technical Report No. 9), from which it takes the alternative planning scenario inputs. The network model outputs are then used as inputs to the evaluation framework for testing / evaluation of alternative road network development strategies. The overall structure of the network model is shown in Figure 2.1. This Section describes the key elements of the model, which need to be specified at the outset, and these elements form the basis of the whole model. 2.2 Study Area and Zone System The study area comprises the whole of the islands of Java and Madura. The two neighbouring islands of Bali and Sumatra are represented as external zones. For road traffic studies, the study area is divided into smaller units called traffic zones. The traffic demand model then represents travel (number of trips) in or between these areas. The size and boundaries of the traffic zones are defined in such a way as to capture the level of detail required by the study objectives. For example, for an intra-urban travel study the urban area will be divided into smaller units to reflect travel within the urban area, and areas around will be coarsely aggregated to only represent travel to/from or through the urban area. In the case of an inter-urban study such as JARNS, the urban areas are best represented at a coarse level. As the study objective was to look at inter-urban travel only, the study area zone system was designed to capture the maximum amount of inter-urban traffic and to exclude intra-urban local traffic, where possible. The size of each zone was determined such that each contains about the same level of population, with the exception of major metropolitan and urban areas. These were represented at an aggregate level of Kotamadya. All zone boundaries conform to the administrative boundary of Kabupaten/ Kotamadya, and where a Kabupaten is divided into many zones, the zone boundaries conform to the smaller administrative unit of Kecamatan. The JARNS study area was divided into 264 such traffic zones, 262 for Java, and Bali and Sumatra represented by 2 external zones. The zones were mapped using the combination of Kecamatan boundary mapping and a full description is given in Appendix A. 2.3 Vehicle Types Modelled In Indonesia, 11 types of motorised road vehicles are usually defined for surveys. These are:

1. Motorcycles, 2. Private Car/Jeep/Station Wagons or other such passenger vehicles 3. Small vans used as public passenger vehicles (Angkot) 4. Light Goods Vehicle (Pick-up trucks / Utility vehicles) 5. Small Bus 6. Large Bus 7. Trucks with 2-axles and 4-wheels 8. Trucks with 2-axles and 6-wheels 9. Rigid 3-axle Trucks 10. Truck Trailer 11. Truck and semi-trailer



Figure 2.1: Overview of JARNS Network Model

ARM S / IRM SDatabase

Other StudiesDatabases

Road O/D Surveys

Traffic CountDatabases

Develop Netw ork M odel &Speed Flow Relationsh ips

Partially O bserved Trip M atrces

JARNS NetworkM odel D atabase

JARNS M atrixEstim ation byVehicle T ype

Base Year M odel Validation

Base YearNetw ork

Base YearVehicle T rip

M atrices

JARNS SPADE LM odel Socio-

econom ic Database

Develop Base N etworkInclud ing O ngoing /

Com m itted RoadSchem es

Develop Travel D em andM odels and Calibra te

Param eters

Base Year Data

Forecast Future YearsVehicle T rip M atrices

Fu tu re Y ea rs D ata

Assign Future Dem and to Netw ork Stra tegies

Evaluation Fram ew orkEvaluate Test S trateg ies / Scenarios

Check Network Operational E fficiently andAssess Policy / Stra tegy Options

Evaluate NetworkStra tegies / Schem es

AvailableNetw ork Upgrade

Resources

Is S trategyAcceptab le

PrepareIm plem entation Plans

Yes

No



The traffic survey analysis showed that during the surveys it was difficult to distinguish between small and large buses, and in many instances there are buses of many different sizes. Most small buses also serve passengers for inter-urban travel. They were also observed to carry similar numbers of passengers to the large buses, and their trip-length frequency distribution was found to be similar to the large buses. Hence, the data for the two types of buses were combined to form a single vehicle category Bus for the public passenger vehicles. Angkots operate a frequent service over short distances on fixed routes; therefore, these were retained as a separate category. The trip length analysis of vehicle types 7 and 8 (2-Axle Trucks) revealed that the two types perform similar functions of distribution of goods over short to medium distances. The total numbers of each vehicle type observed were small. Therefore, for modelling purposes these two types of vehicles were combined to form a single category of Medium Goods Vehicles (MGV). Vehicle type 4 (Utility vehicles) were retained as separate category as Light Goods Vehicles (LGV). The numbers of multi-axle (3 or more) trucks (vehicle types 9, 10 & 11) were observed to be small, and therefore did not warrant to be modelled as separate categories. These three types of multi-axle trucks were modelled together, as a single category called Heavy Goods Vehicle (HGV). Travel Demand Matrices of zone-to-zone travel were developed for the following vehicles categories:

Car (vehicle type 2) Light Goods Vehicle LGV (vehicle type 4) Medium Goods Vehicles MGV (vehicle types 7 & 8) Heavy Goods Vehicles HGV (vehicle types 9, 10 & 11) Buses (vehicle types 5 & 6)

Motorcycles and Angkots No trip matrices were developed for travel by motorcycle or Angkots. As stated above, these vehicles usually make short distance intra-zonal trips along a route. Their effect on the road capacity was taken into account by including them as pre-loads on the network, in the assignment model. The JARNS traffic surveys recorded the numbers of non-motorised vehicles. The survey data showed that numbers of such vehicles were very low on the inter-urban roads. Hence, these vehicles were excluded from any further analysis in this study. 2.4 The Study Area Networks 2.4.1 Development of Base Year Network The roads to be included in the network model, was dictated by the strategic nature of the study. The base year strategic network has been described in detail in Section 2.5 of the main report. The strategic network includes:

All inter-urban toll roads, and urban toll roads that act as major bypasses for the main urban areas, such as those around Cirebon, Bandung and Semarang.

Urban toll roads of Jakarta and Surabaya were also included in the network to act as the main arteries /corridors serving these metropolis, provide a direct link to the centre, and these also serving as a through route for the non-local traffic. In total, there were some 439 km of toll roads in the JARNS network model.



Of the 2,760 km of arterial roads in the JARNS study area, 2,539 km were included in the strategic road traffic model network. The remainder of the arterial roads were either part of the urban areas, or were some isolated sections. These sections of the arterial roads were considered inappropriate to be part of an inter-urban strategic network. Similarly, some 1,337 km (96%) of primary collector (K1) roads were included in the strategic network. In order to provide direct routes from most of the main towns and urban areas of Java to the main section of the strategic network, 3,496 km of the remainder of the collector roads were included in the strategic network to provide collector/distribution functions.

In addition to the above strategic road network, the JARNS model network includes a number of urban areas roads and notional links. The main function of these roads/links in the study area network to be simulated, was to cater for the movement of strategic traffic in and out of the urban areas, and to allow for the passage of through inter-urban traffic. Another important function of these urban area roads/links was to adequately represent the effect of congestion on the inter-urban travel times. While JARNS cannot attempt to model urban traffic, it was nevertheless essential to adequately represent the urban area networks. The modelling of these links/roads was therefore not to the same level of detail as the strategic network. A brief description and their scope is outlined below, further details on their full role in the network model are given in Section 5 of this Appendix.

Urban / Kota links: These links are part of the national roads that extend into urban areas, and have been designated as urban links by the IRMS/ARMS database systems. These links lie within urban areas along the strategic routes, and serve both the local and through traffic. In the JARNS, network model there are some 414 km of such roads.

Urban Area Corridor Links: These are notional links, which represent several urban area streets and roads used by the local and through traffic. These links were necessary for the network model to complete the network. In the JARNS, network model there are 182 km of such links. The majority of these are in Jakarta, (153 km) and remainder in major towns of West and Central Java provinces.

Toll Road Access Links: These are sections of roads, which provide connections to the toll roads from the adjacent network. These links form an essential part of the network, and represent 67km of roads.

Other Rural Area Links: There is 602km of rural roads included in the JARNS network model. These roads provide north-south connections to the strategic network for the rural population of southern seaboard of west Java. These roads carry little or nor strategic traffic, and therefore were not considered as part JARNS strategic network development options. It was assumed that any upgrade of these roads would be the responsibility of the local area authorities, like the development and upgrade of other Kabupaten/Kecamatan roads.

2.4.2 Development of Future Networks The future year networks developed for testing alternative network scenarios / strategies were based on the 2005 network. This 2005 network was developed from the base year (2000) network by including the following new road schemes and upgrades:

All road schemes (bypass or upgrades) currently under construction; All road improvement schemes for which funds are committed, and to be built

within the next five years; and All planned and committed road schemes, construction of which would be likely

to be completed by the year 2005.



This provided the base network for the development and testing of the forecast year (2010 and 2020) strategies and scenarios. The development of future network strategies / Scenarios are discussed in Section 5 and 6 of the main report, and the testing process is described in Section 7 of this Appendix. 2.4.3 Other Model Parameters Time Periods Modelled All travel demand matrices were developed for 24-hour AADT. For the network assignment model, the 24-hour vehicle trip matrices were converted to a typical one-hour travel matrix, including a proportion of trips from each of the five vehicle category matrices. Motorcycle and Angkot pre-loads were also converted to one-hour volumes. The typical hour adopted for the study represented traffic observed during an average hour of the day light hours (06:00-18:00), the 12-hour survey duration. The analysis of the survey data showed that the distribution of traffic during the day varies a little for most vehicle categories, but that there is no pronounced peak. See Figure 5.7. The typical average hourly factors adopted for each vehicle category were derived from the survey data and these factors varied between 4.9% for the buses to 6.6% for the motorcycles. It should be noted that morning or evening peak period modelling is usually carried out for urban studies. For inter-urban travel, a distinct peak is not very relevant as most trips are over long distances, and may stretch over many peak periods. Passenger Car Equivalent Factors All trip matrices were converted to the common unit of Passenger Car Units using the PCE factors specified in the Indonesian Highway Capacity Manual (IHCM). Speed Flow Relationships and Assignment Parameters Speed flow relationships are crucial to any capacity restraint (congested network) assignment modelling. In Indonesia, the IHCM provided a sound database for the development of speed flow relationships. The main source of road network data for this exercise was obtained from the IRMS/ARMS databases and the ARMS digital images. The choice of path in the assignment process is based on the Generalised Cost (GC) of travel, which includes:

Value of travel time; Perceived cost of travel related to the distance travelled; Perceived value of tolls paid as out-of-pocket costs.

These parameters differ for different types of vehicles. Average values were estimated reflecting the vehicle mix in the average peak hour traffic stream. Details of these parameters are given in Section 5. 2.4.4 Network Model Outputs and Evaluation The JARNS network model provides a variety of results. The main model output is the network assignment results, which estimate link traffic volumes for the typical hour modelled, and in terms of AADT. The model assignment results used for the operational evaluation of the network in terms of Volume Capacity Ratio (VCR). The VCR could be viewed graphically or output as link based tables to be used for further analysis. Network

MikeFigure 5.7.



travel summaries in terms of vehicle-kilometres and vehicle-hours can be calculated. These statistics were direct input to the network evaluation framework that computes the benefits (as difference between a base case and scheme test) of a scenario / scheme accrued over the whole network. An accident cost estimation module was used, which requires network statistics of amount of travel by road, by a separate classification of roads. Network travel statistics of vehicle-hours travelled by motorcycles and Angkot were also estimated and input to the overall evaluation framework. The model was also used to produce network wide zone-to-zone travel time, distance and Vehicle Operating Cost (VOC) skim matrices for the base case, and test scenario. These skim matrices were based on paths built using the converged assignment network and generalised cost of travel as the route choice parameter. These matrices were then directly used to calculate the economic benefits or dis-benefits weighted by the travel demand (number of trips between each zone pair). The sum total of all these trip-weighted time-savings, distance related and VOC savings represented the total benefits for the scheme, which were then used by the economic evaluation framework. 2.4.5 JARNS Modelling Software The JARNS network model is simple in nature, but it is robust and detailed enough to fully achieve the objectives of the study. The model is developed within the MVAs own transport planning suite of programs, the TRIPS package. For survey data analysis and other similar tasks Microsoft Access and worksheet package EXCEL has been used. SPSS for Windows was used for statistical analysis, and trip-end model development. The Map-Info package was used for plotting of networks and study area maps and zonal information.



3. TRAFFIC SURVEYS AND DATA COLLECTION 3.1 Introduction The main data required for modelling was the necessary Origin-Destination (O-D) patterns of traffic in Java. It is these O-D patterns that provide the information on the amount and distribution of travel, and hence the cost of travel on the network. These Roadside Interview Surveys (RIS) require considerable resources, and are complex to analyse. Therefore could only be conducted at a limited number of sites. At any site, a sample of the total traffic passing is interviewed to obtain the O-D and other travel related information. In order to expand the sample data to represent total traffic, it is also necessary to count total traffic passing that site. These total traffic count surveys, usually called Manual Classified Counts (MCC) are conducted simultaneously at the RIS sites. These provide the data needed to gross-up the sample O-D survey results. Readers interested in further details are referred to the Technical Report 2, Traffic Surveys and Data Collection. However, some summary results are given in Appendix B. 3.2 Traffic Surveys The RIS survey provided some of the O-D travel patterns throughout Java giving partially observed trip matrices. The modelling approach adopted is reliant on the method of Matrix Estimation, which estimates the remainder of the unobserved O-D movements. This process requires collection of MCC data at numerous other key locations across the network. The MCC data was also used to update the previously-collected count data, available from numerous other sources. The JARNS study carried out a comprehensive set of RIS and MCC surveys, in total at one hundred sites in Java. The remainder of this section briefly describes the conduct, analyses, and summary results obtained from these surveys. 3.2.1 Other Traffic Data Sources Traffic data was available from a number of other sources. It was checked for consistency, and where appropriate was used in model development, validation and calibration. The major sources of traffic data were:

IRMS database ARMS survey data including moving observer count data Toll roads and other parallel roads traffic count data from Jasa Marga Automatic Traffic Count (ATC) data from West, Central and East Java sources TSSS study total vehicle trip matrices and summary of count data Traffic Count data from the on-going SURIP studies Road Capacity Expansion Project Phase II (CAPEX-2) data Madura-Surabaya, Java-Sumatra, and Java-Bali vehicular ferry traffic count data

were obtained from the appropriate authorities.

3.2.2 Other Surveys and Data In addition to the traffic data collection exercise, Value of Time (VOT) and a limited number of informal journey time surveys were also conducted. The work on value of time surveys has been reported in Technical Report 7.

MikeTechnical Report 2,

MikeAppendix B.

MikeTechnical Report 7.



3.3 Scope of Traffic Surveys 3.3.1 Survey Site Selection The Traffic survey programme had two main components: RIS and MCC data collection. The selection of RIS (O-D) sites was based on the review of major road transport corridors in Java. This ensured that all long distance trips are covered, and the short distance local trips are not over represented. MCC sites were selected in such a way that all major east-west and north south routes, and links into and out of major cites were fully covered. All survey sites were located away from the main towns, to capture the maximum amount of inter-urban traffic, and to minimise the inclusion of local intra-urban traffic. Neither RIS nor MCC surveys were conducted on toll roads, because this was operationally difficult, and toll road traffic count data was available for most mid-link and on/off ramps. Traffic surveys were conducted at 100 stations across Java. The exact RIS and MCC survey locations and the full survey programme are detailed in Technical Report 2, Traffic Surveys and Data Collection. However, some summary results are presented in Appendix B. 3.3.2 Survey Time Periods and Coverage Traffic surveys (RIS/MCC) were conducted either for 12-hour or 24-hour periods. The 12-hour period stretched over the day light hours between 06:00 to 18:00. The 24-hour period stretched from 06:00 hour to 06:00 the following day. Both 12-hour and 24-hour surveys were conducted from Monday to Fridays. No surveys were carried out during Saturdays and Sundays. Public Holidays were also avoided. The Table 3.1 below gives the extent of the traffic survey coverage over the study area. Table 3.1: Number of Traffic Sites in Java by Type and Duration of Survey Period

Survey Type Survey Period West Java Central Java East Java Total

12-Hours 9 12 9 30 RIS Surveys

24-Hours 2 2 3 7

Sub-total RIS Sites 12/24 Hours 11 14 12 37

12-Hours 20 25 21 66 MCC Surveys

24-Hours 12 10 12 34

Total Sites 12/24 Hours 32 35 33 100 Note: MCC Sites Include the RIS Survey Sites 3.4 Traffic Count Data 3.4.1 JARNS Traffic Count Surveys Traffic count surveys involved continuously counting all traffic by predefined types of vehicles passing the survey locations. The vehicle classification was based on the IRMS classification of vehicles, to ensure compatibility with the other count data sources. The number of vehicles observed (2-way) at each site varied from less than 5,000 vehicles per day to just over 100,000 vehicles at a site (Waru - Sidoarjo) just outside Surabaya. However, the survey sites represented a broad range of traffic volumes observed at locations in all three provinces. The Table 3.2 below shows the range of 2-way traffic volumes observed in each province.

MikeTechnical Report 2,



Table 3.2: Number of Sites by Range of Traffic Volumes Observed, by Province Range of 2-Way Daily

Traffic (AADT) West Java Central Java East Java Total

All Sites Less than 10,000 5 10 4 19

10,000 15,000 6 7 8 21

15,000 20,000 7 3 8 18

20,000 25,000 8 4 4 16

25,000 30,000 4 4 5 13

30,000 40,000 2 4 0 6

Over 40,000 0 3 4 7

Total 32 35 33 100 Source: JARNS Surveys (AADT Includes Motorcycles) It can be seen that on inter-urban roads the traffic volumes are generally low, only at 13 locations the 2-way observed volumes were in excess of 30,000 vehicles per day. At nineteen locations, the total daily traffic did not exceed 10,000 vehicles. Only at seven sites the traffic volumes were over 40,000 vehicles per day, and at four of these locations the numbers of vehicles were less than 50,000. Analysis of traffic composition showed that by far the majority of vehicles are motorcycles, accounting for over 35% of all vehicles observed. The proportion of motorcycles in East Java was highest, close to 45% of all traffic. Distribution of cars was higher in West Java than Central and East Java. Angkots accounted for over 21% all traffic in West Java compared to just over 7% in Central and East Java provinces. This shows that the choice of mode is somewhat similar in Central and East Java, i.e. majority of the short distance travel is by motorcycles. The proportion of cars observed at sites in West Java was close to a quarter of all traffic and slightly lower (21-22%) in Central and East Java. The proportion of buses and goods vehicles was almost similar across three provinces. This variation in the distribution of traffic across provinces is illustrated in Figure 3.1 below. Whereas summary traffic counts are given in Table B.2 in Appendix B.

Mike3.1

MikeFigure 3.1

MikeTable B.2



Figure C3.1: Distribution of Vehicle Types Observed in Three Provinces

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

M/C Car Angkot Buses LGV MGV HGV

% o

f All

Vehi

cles

W-Java

C-Java

E-Java Total

Source: JARNS Traffic Surveys 3.4.2 Traffic Count Data from Other Sources Traffic data were also obtained from a number of other sources. Where the traffic count location was common with the JARNS traffic surveys, the JARNS data was used. The data for other locations was selected form different sources in the following order of preference.

1. JARNS (2000) 2. Jasa Marga (2000) 3. ATC (1997 & 1998) 4. Pekalongan / Probolinggo SURIP Studies (2000) 5. TSSS (1998) 6. CAPEX (1997) 7. JARNS Estimates (2000)

In all cases, data was converted to the base year of 2000, using the average growth rates experienced in each province. Also not in all cases, the available data was grouped into classes of vehicles used by JARNS. These data were disaggregated to the same vehicle categories as used in JARNS modelling work. A synthesis of all data sources was used to update IRMS link traffic data. Traffic counts for all strategic links in the network are reported in Technical Report 2. 3.5 Roadside Interview Surveys (RIS) 3.5.1 General Background Roadside Interview surveys were conducted at 37 sites through out Java for the following seven private vehicle types; listed below as four combined categories:

Private Car/ Jeep etc (Vehicle Type 2) Light Goods Vehicles LGV (Utility Vehicle, Type 4)




Medium Goods Vehicles MGV (Goods Vehicle Types 7 & 8) Heavy Goods Vehicles HGV (Goods Vehicle Types 9, 10 & 11)

The survey method entailed stopping a representative random sample of these vehicle types, and interviewing the driver, regarding the origin and destination (O-D) of the trip. For private vehicles trip purpose and number of vehicle occupants were also recorded. For goods vehicles, commodity types and quantities carried were also recorded. For Angkots and Buses the origin and destination (O-D) as specified by the route displayed at the front of the vehicles was recorded. Observations of the number of occupants in the vehicle were made, albeit in crude categories: empty, , or , or , or completely full. These vehicle occupancy observations were then converted to average vehicle occupancy depending upon the size and type of vehicle. The O-D survey data recorded at sites was coded and entered into a computer database using Microsoft EXCEL and Access software packages. Trip origin and destination addresses were converted to JARNS study area zone system. All survey data was checked and validated through a series of range, logic and consistency checks. In most cases, correction of coding/ data entry errors was possible. A small proportion of data found to be erroneous, was rejected. Over all, the sample size achieved was statistically significant for the O-D data for each vehicle category. The final sample size of the accepted data set, varied from site to sites, and between categories of vehicles. 3.5.2 Car O-D Surveys Table 3.3 below summarises the sample size after all the editing and validation of the interview data. The overall sample size of 17% is considered very good. Even at the lower end, in West Java the sample size was 9%, which is considered significant for such surveys. Table 3.3: Car RIS O-D Survey Sample Size

Survey West Java Central Java East Java Total Java

RIS Final Sample 5,047 16,102 11,990 33,139

Total MCC Counts 56,806 86,277 53,768 196,851

% Sampled 9% 19% 22% 17% Car occupancies were also recorded during the O-D surveys. Car occupancies varied from site to site, and were observed to be as high as 12-persons per vehicle, with an average occupancy of 2.7. Other attributes of car trips were also analysed, but were found to be not of use to the modelling process. 3.5.3 Goods Vehicles O-D Surveys The O-D surveys were conducted for all goods vehicles at the same sites as for Cars. The sample size achieved is summarised below for the three aggregate goods vehicle categories. The overall sample size achieved was considered adequate for this study. No particular reason could be found why the sample sizes are low for sites in West Java. The final sample sizes are summarised in Table 3.4 below. The data on commodity types and quantities carried was also analysed. It was found that in most cases it was not possible to make effective use of this data, as the drivers/conductors of the vehicles were not willing to provide adequate/accurate information.

MikeTable 3.4



Table 3.4: Goods Vehicle O-D Survey Sample Sizes Goods Vehicle Category Survey West Java

Central Java

East Java

Total Java

RIS Final Sample 1,723 8,823 5,091 15,637

Total MCC Count 19,557 31,810 19,113 70,480 Light Goods Vehicles (Vehicle Type 4) % Sampled 9% 28% 27% 22%

RIS Final Sample 3,576 9,016 7,453 20,045

Total MCC Count 36,617 46,682 27,727 111,026 Medium Goods Vehicles (Vehicle Types 7&8) % Sampled 10% 19% 27% 18%

RIS Final Sample 663 1,999 2,624 5,286

Total MCC Count 11,399 17,315 14,068 42,782 Heavy Goods Vehicles (Vehicle Types 9,10&11) % Sampled 6% 12% 19% 12%

3.5.4 Angkot O-D Surveys Angkot O-D surveys were conducted by recording the origin and destination of the vehicles. Analysis of the O-D data revealed that most of the Angkots travel over short distances (average trip length was observed to be less than 40 km), normally between two or three adjacent small towns. O-D data when converted to zone-to-zone movements showed that high proportions of the trips are either intra-zonal (40%) or between adjacent zones. This reflects that the passenger O-Ds were of even shorter distances; i.e. passengers get on and off Angkots all along the route. Therefore, it was decided that it would not be appropriate to model the O-D patterns of Angkot in terms of a matrix of movements, as there would not be any route choice applicable to the travel of these vehicles. Instead, these vehicles were modelled as fix loadings on the sections of the network where they operate. This is a better way to reflect their use of the road space in the modelling process. Vehicle occupancies of Angkots were also recorded. The analysis showed that some 15% of these vehicles were empty. It was implied that these vehicles were travelling empty back to get a new load of passengers. The Angkot occupancy varied between few passengers to maximum of 15 passengers per vehicle over the survey sites. It should be noted that not all Angkots are of the same design capacity. Some vehicles have seating capacity of more than twelve passengers whereas other vehicles have seats just for 6-8 passengers. The occupancy survey showed that close to 34% of vehicles were almost full carrying 12 or more passengers, and about the same percentage carrying between 6 and 11 passengers. Average occupancy was computed to be 7.6 Pax per vehicle. No noticeable differences in vehicle occupancy were observed across survey sites or the regions. 3.5.5 Bus O-D surveys Bus origin and destination surveys were conducted at all RIS locations. At four locations, it was found that the number of buses is small therefore, no bus O-D were recorded. Separate analyses of RIS data for the medium and large buses (vehicle types 5&6) were carried out. The analysis showed that there are only a small number of medium size buses in operation on inter-urban routes. It was also observed that, during the surveys the distinction between the medium and the large bus was not clear at all times. Therefore, for further analysis and the matrix building process the data for the medium and large buses were combined. In all subsequent analyses, the category bus implies medium and large buses. The O-D sample size achieved was high (60%). This is detailed for each province in Table 3.5 below.

MikeTable 3.5



Table 3.5: Bus O-D Survey Sample Size Survey West Java Central Java East Java Total Java

Final O-D Sample 9,146 16,665 5,695 31,506

Total MCC Count 11,298 29,697 11,215 52,210

% Sampled 81% 56% 51% 60% Bus occupancies were observed during the surveys by either counting the total number of passengers in a bus or by recording the overall occupancy of the bus under one of the five occupancy status, (Empty, , or , or , or full). The average occupancy was observed to be 33 Pax per bus, and was recorded to be as high as 70 Pax.



4. DEVELOPMENT OF BASE YEAR VEHICLE TRIP MATRICES 4.1 Introduction The base year trip matrices were developed from a number of data sources, and through a complex series of processes. The starting point was the building of the observed trip matrices from the RIS surveys. These observed matrices accounted for only a small proportion of the trips that take place over the strategic network. The remainder of trips were, either imported from other sources, or estimated using well-known Matrix Estimation techniques. This Section briefly describes the complete process involving the development of the base year trip matrices. In total five trip matrices are estimated, one for each of the five categories of vehicles for a 24-hour period. Detailed tables and figures are also presented in Appendix C. 4.2 Development of Observed Trip Matrices Roadside Interview Surveys recorded the origin-destination of vehicle trips as reported by the drivers. Each cell in the trip matrix represents a combination of origin zone and destination zone, i.e. O-D pair. The content of the cell is the number of trips for that O-D pair. The observed matrix development process calculates the number of trips from the data collected from the RIS survey stations. The series of steps that were followed in building the observed O-D matrices are outlined below.

1. Coding of Addresses: Code the location to a JARNS study area traffic zone; if it was not possible to directly code the zone number due to incomplete address or recorded information then enter the an area-code (i.e. Kecamatan / Kabupaten / Kotamadya specific code). If an address or location could not be mapped, the interview was rejected. Up to this stage due to incomplete information on O-D addresses 2 to 3 percent of the interviews were rejected.

2. Validation of Address Codes: Check if the coded O-D area code or zone number

is valid or not? If incorrect then go back to step-1, and check the O-D coding. Repeat steps 1&2 until all O-D codes are acceptable, and then proceed to Step-3.

3. Conversion to Zone System: Convert the interviews with an area code to the

zone numbers:

o Change area codes to zone number for areas such as Kecamatan/ or other locations, which lie wholly within a zone;

o Observed trips with O-D recorded as area code(s), that represented a group

of zones (such as a Kabupaten comprising of a number of zones) were disaggregated to represent zonal trips in proportion to the population in each zone.

4. Validation of an O-D Pair at A Survey Station: Check the validity of the O-D

zone pair, by checking if the trip passes through the survey station or not? If O=D, i.e. the trip is intra-zonal then the O-D path will not exist. These trips were included in the observed matrix. If the trip is inter-zonal (i.e. OD) and the O-D path does not pass through the survey station, the observation was rejected. These errors are quite common for such surveys. Because of the repeated editing and updating the interview data, the number of interviews that were rejected was quite low. The number of interviews that were rejected varied between survey sites and was 5 to 10 percent of the sampled interviews.

MikeAppendix C.



5. Calculate Gross-up Factors: The accepted interviews represented only a sample

of total traffic passing a survey station. To represent all traffic, the sampled interviews are grossed up to the total traffic. The gross-up factors were calculated for each survey station, for each direction of travel, for specific time bands over the survey duration. Usually the time band is in or one-hour duration. In instances when the observed sample was too small, longer time bands of up to 3-hour duration were used.

6. Adjustment for Multiple Station Observations: A path between an O-D pair may

pass through more than one survey station, so it may be sampled at more than one station. In principle, this was allowed for by allocating a multi-path factor to each interview. The factor was computed to be equal to 1/(the number of survey stations on a path).

7. Adjustment Factor for Survey Period: At most locations the RIS were carried

out for a period of 12-hours. In order to obtain the full 24-hour trips, the 24-hour/12-hour factor was calculated from the MCC surveys.

8. Calculation of Total Observed Daily Trips: The total number of daily trips

observed between an O-D pair were then computed by multiplying the number of valid interviews per O-D pair by the grossing-up factor, multiple station observation factor, and the survey period factor.

The observed trip totals for each vehicle category are summarised in Table 4.1. Car trips accounted for 43% of all observed trips. The second largest category was medium goods vehicles. All goods vehicles accounted for 46% of the observed total trips, whereas the bus volumes on inter-urban roads were low and accounted for only 11% of the observed trips. Intra-zonal trips were observed at a few survey sites that were located inside the zone boundary due to safety reasons. These trips account for 5% of the total observed volumes. The proportion of intra-zonal trips is low for vehicle categories that make shorter journey than those which travel over longer distances, such as large buses or heavy goods vehicles.

Table 4.1: Summary of 2000 Observed Total Trips by Vehicle Category (AADT)

The total trips represented only a fraction of the zone-to-zone (264x264) O-D pairs, which the matrix represents. In order to intercept all the O-D pairs, a large number of surveys would be required and this was beyond the resources of the study. However, these observed O-D pairs do provide a sound basis to estimate the trips between the remaining unobserved O-D pairs. This process is described next.

Vehicle Category

Total Inter-Zonal Trips Observed

Total Intra-Zonal Trips Observed

% Intra-Zonal

Total Trips

% of Total Trips

Car 166,023 9,407 5% 175,430 43%

LGV 60,399 4,511 7% 64,910 16%

MGV 87,736 4,716 5% 92,452 23%

HGV 28,891 462 2% 29,353 7%

Bus 42,443 896 2% 43,339 11%

All Vehicles 385,492 19,992 5% 405,484 100%



4.3 Synthesis of Base Year Trip Matrices There are a number of analytical techniques, which could be adopted for the synthesis of the unobserved O-D pairs. Some of these techniques rely wholly on the observed matrix and the associated network, but require an estimate of the total number of trips in the whole of the study area, which is usually difficult to provide in the absence of any household or other survey data. Other techniques rely on an initial matrix, and a series of traffic counts at key screenlines and cordons in the study area. From the outset, the JARNS study approach was based on the later technique of estimating the trip matrices from the road traffic interview and count surveys. The full matrix estimation process as developed for the JARNS study was based on a series of well thought-out process, which made extensive use of not only the traffic survey data but also of O-D and traffic data available from other studies and databases. The JARNS matrix estimation methodology comprised three main stages. The same methodology was adopted for the estimation trip matrices for all five categories of vehicles. 4.3.1 Build Prior Trip Matrices This stage involved updating the five (Car/ LGV/ MGV/ HGV & Bus) observed AADT trip matrices to include trips for as many O-D pairs as possible from a number of available sources. This required that the trips to be included into the JARNS observed trips matrices must be compatible in terms vehicle category and the zone system. The data sources available were:

1. JARNS Observed O-D trip matrices, 2. TSSS 1998 total vehicle Inter-Kabupaten trip matrix, 3. TSSS 1998 Inter-provincial matrix of ferry traffic (for external trips to/from Bali

and Sumatra) 4. SURIP studies trip matrices, and 5. 1996 NODS includes three basic trip matrices of annual inter-Kabupaten travel:

o Total private road traffic O-D, vehicles and passengers (excluding motorcycles);

o Total public road traffic (i.e. buses and Angkots) O-D, vehicles and passengers; and

o Total goods traffic O-D vehicles and tonnage. The observed O-Ds were given the highest level of confidence, and all trips were included in the prior matrix. The data from other sources was reviewed and analysed. The conclusion was that only the TSSS data is of suitable standard and level of detail, and could be incorporated into the prior trip matrices, albeit after some manipulations. The data from SURIP studies was either too detailed or the O-D pairs had already been observed by in the JARNS survey. Therefore, no O-D trip data from SURIP studies was included. It is clear from a number sources and analyses that the 1996 NODS data is not reliable, and to some extent is outdated. Therefore, no further use of the 1996 NODS trip matrices was made. The key steps involved in building the prior matrices are outlined below.

1. Build symmetrical RIS matrices for each vehicle category, and all vehicles combined.

2. Sector RIS matrices (22 Sectors) and calculate percentage of trips by Car, LGV, MGV, HGV and Bus as percentage of the total trips for each sector-pair.

3. Convert TSSS 1998 all vehicle trip matrix to 2000, by applying an average growth factor of 1.088, and expand the inter-Kabupaten trips to JARNS 264 zone system. The expansion factors were based on the distribution/ proportion of population in each zone within the Kabupaten.



4. Disaggregate the TSSS total vehicle trip matrix into five vehicle categories, based on sector-to-sector percentages derived in Step-2.

5. RIS captured a reasonable number of Java-Bali trips; but a few Sumatra- Java trips. Vehicle trips for each category of vehicle, from both external zones (Bali & Sumatra) were estimated from the TSSS ferry trip matrices and dis-aggregated to zones using proportions based on population in each zone.

6. Prior matrix was then formed by selecting all trips from RIS matrix, and including trips from TSSS matrix if an O-D pair was not observed. Similarly, unobserved O-D pairs between internal and external zones were added from the dis-aggregated TSSS ferry traffic matrices.

7. O-D pairs with zero values were given a seed value of 0.1 trips.

The Table 4.2 compares the trip totals in RIS, TSSS and prior matrices for each of the five vehicle categories. The increase in trips from RIS to the prior matrix indicates how the observed matrices missed more that half of the total trips, which were taken from the TSSS trip matrices.

Table 4.2: Summary of RIS, TSS and Prior Matrices Trip Totals

4.3.2 Initial Matrix Estimation Matrix Estimation takes several inputs, which provide information on number of observed/ existing trips, trip patterns, traffic counts, a set of paths that define the likelihood of an O-D pair passing through count site(s). It also takes user-defined confidence levels associated with each element of the input data. The estimation process then computes a new trip matrix that fits the input count data best. The user can change the input confidence levels, to control the output. A number of criteria are employed to check the output results, and declare that the output matrix is the best estimate that could be achieved. These involve checking and comparing the observed, and output trip length frequency distributions, comparison of observed and assigned traffic volumes at input count stations and at locations not included in the estimation process, and check of reasonableness on certain known area-to-area movements. Matrix Estimation was carried out in two parts. The inputs to the initial matrix estimation process for each vehicle category were:

Prior trip matrix, as reported above; Trip ends taken as the average of the sum of row and column totals of the prior

matrix; Trip ends seeded with 50 trips if no observation, (10 for HGV) A set of five Generalised Cost (GC) paths built using a Burrell technique with

a spread of 100. The GC values used are summarised in Table 4.3 below.

Vehicle Category

RIS Total Trips

TSSS Total Trips

Prior Mx Trip Totals

% Increase from RIS

Car 175,430 370,297 397,030 126%

LGV 64,910 106,609 128,591 98%

MGV 92,452 183,258 207,110 124%

HGV 29,353 45,887 60,740 107%

Bus 43,339 66,183 91,638 111%

All Vehicles 405,484 772,234 885,109 118%



Table 4.3: GC Parameters for Building Path for Matrix Estimation

Source: Consultants Estimate, all values in 2000 prices

Traffic count data for a number of links in the network was input to the matrix estimation process. It was input for 16 screenlines defined to capture east-west movements and for 10 cordons around major urban areas (see Figure 5.9). Screenlines and cordons were designed to include as many links as possible with JARNS traffic counts, and avoid links for which count data was taken from other sources. The traffic counts were input to the matrix estimation as counts on individual links (not as aggregates for screenlines/ cordons) as in most cases parallel links served different pairs of O-D movements.

This initial matrix estimation stage yielded a trip matrix that provided a good estimate of inter-Kabupaten / inter-zonal trips. The process could not estimate intra-Kabupaten / inter-zonal trips because the prior matrix contained few observations for such O-D pairs. The TSSS matrix had no intra-Kabupaten trips, and the RIS matrix had a small number of intra-kabupaten trips. 4.3.3 Final Matrix Estimation Subsequent to the first stage estimation intra-Kabupaten / inter-zonal trips were extracted from the estimated matrices and tabulated vs. population and employment for the corresponding Kabupaten. Models were derived relating intra-Kabupaten / inter-zonal trips to population and employment. Regression models were examined but coefficients were not significant and R2 values were too low. Therefore, average trip rates vs. employment (population for bus) were calculated. The estimated matrices from the previous stage were in-filled with estimated intra-Kabupaten / inter-zonal trips. These in-filled matrices were now input with the same traffic counts and the other data as specified above to this second/ final stage of matrix estimation process. The process was repeated for each vehicle category separately. The output estimated matrices were then assigned to the network. The assigned volumes were compared against the traffic counts. Upon achieving a satisfactory comparison, the matrix estimation process was terminated. If the comparison of assigned volumes were significantly different, the whole process was repeated, with amended input data. The amendments amounted to either updating the network or changing the confidence levels associated with the input data, input to the estimation process. The Table 4.4 below compares the final estimated trip totals with the RIS, TSSS, prior and initially estimated trip totals. It can be seen that the number of trips did not change by much between the two stages of the estimations. However, the final matrices did provide a much better comparison of the observed and assigned traffic volumes on the network. These results are reported in detail for each vehicle category in Technical Report 10.

Vehicle Category

Value of Time (Rp/Hour)

Vehicle Operating Cost (Rp/Km)

Average Toll Payment (Rp/km)

Car 22,300 80 100

LGV 9,300 120 100

MGV 45,000 110 100

HGV 53,000 180 200

Bus 120,000 100 160

MikeFigure 5.9).

MikeThe Table 4.4




Table 4.4: RIS, TSSS, Prior and Estimated Matrices Trip Totals for 2000 (AADT)

It may be of some concern that the TSSS all-vehicle trips total is about 9% more than the JARNS 2000 final estimated trips. This was investigated further. The main reasons were found to be:

The TSSS estimated a single matrix of all vehicles based on the 1998 counts for some 75 sites grouped into 19 screenlines. The JARNS matrix estimation is based on over 150 count sites.

The TSSS estimate was for much coarser zone system of 98 zones, whereas as JARNS study area has a much more refined system comprising of 264 zones.

The TSSS used a much simplified highway network, treating many parallel roads as corridors. Whereas the JARNS highway network is more detailed, covering a much wider selection of route choices, and is based on a more detailed database.

In any case, the difference of 9% is well within the margin of error of data used for such strategic studies.

These final estimated trip matrices were used to form the basis of JARNS network model validation, and the development of forecast models. All trip matrices are summarised in Appendix C.

Vehicle Category

RIS Total Trips

TSSS Total Trips

Prior Mx Trip

Totals

Initial Estimation Trip Totals

Final Estimation Trip Totals

Car 175,430 370,297 397,030 354,233 350,448

LGV 64,910 106,609 128,591 109,360 114,363

MGV 92,452 183,258 207,110 151,901 157,396

HGV 29,353 45,887 60,740 35,451 38,208

Bus 43,339 66,183 91,638 44,623 45,457

All Vehicles 405,484 772,234 885,109 695,568 705,872

MikeAppendix C.



5. JARNS NETWORK MODEL DEVELOPMENT AND VALIDATION 5.1 Introduction A key component of the modelling process is the development of a road network model that would allow the forecasting of future traffic flows on the strategic network. The network model should be detailed enough so that it can adequately represent the strategic inter-urban traffic in Java. It should also be robust enough to test a wide range of different network development strategies. An accurate representation of the road network and its attributes is therefore essential. It is the network model from which travel time, cost, distance are taken for the development of the trip matrices, and are essential inputs to the economic evaluation framework for testing alternative network strategies/ scenarios. The scope of the study area network has been outline in section 2 of this Appendix and it is illustrated in Figure 5.1 by type of roads. This sections elaborates on the development of the road network and its elements, and its use in the over modelling process. 5.2 Network Data Sources and Coding 5.2.1 Network Model Data Sources The development of the study area network model relied heavily on the IRMS and ARMS databases. Data on a number of road characteristics were also obtained from the ARMS database of digital images of roads. Figure 5.2 shows the flow of data, the interaction and interfaces between the IRMS/ARMS and the TRIPS network model. How different elements of the network were developed from different data sources and combined to form a complete network model are described in the next section. Further details are given in Appendix D. 5.2.2 Coding of Study Area Network General The coding of the network model involves representation of the selected roads network into a form acceptable to the modelling process. A highway (road) network in the TRIPS model is represented by links (representing sections of roads) and nodes (represents junctions or the end of a road section). In a network model, every link must be connected to the adjacent links at both of its ends to form a continuous system of links called a network.

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LEGEND :Single-2 < 6.7 mSingle-2Single-4Dual-2Dual-3Toll Dual-2Toll Dual-3Toll Dual-4Toll Access LinksUrban/Kota LinksUrban CorridorsRural ( Non-Strategic Roads )

Province Boundary FIGURE 5.1: BASE YEAR (2000) NETWORKBY ROAD TYPES



Figure 5.2: IRMS/ARMS Interface with TRIPS Network Model

The only links that do not have both ends connected to other links are notional links called Centroid Connectors. These links are used to load traffic onto the network from the zone system, and the open end of these links is represented by that zone. A link in the TRIPS network model should be roughly homogeneous in terms of its physical and operational characteristics along its length. A contiguous road section (may be without junctions) with variable characteristics along its length may be split into two or more links in order to adequately represent the features that influence the flow of traffic. Each link in the TRIPS model requires the coding of the following fields:

1. Nodes: Each link is represented by two nodes, usually called as Anode and Bnode. All nodes are numbered using a 5-digit code representing its location in the study area.

2. Distance: Represents the link length in 10m units between Anode and Bnode. 3. Link type: A 2-digit code representing the road type, used for tabulations. 4. Jurisdiction Code: A 2-digit code that may be used to represent different aspects

a link, e.g. its location. In JARNS model, it is used to represent road classification specifically used for the calculation of accidents for the evaluation framework.

Travel DemandM atrices

AveragePCE

Factors

Travle DemandM atrix of Trips

in PCU/hour

Road TypeTerrain

Pavem ent &Shoulder W idths

Side Friction

Road TypeTerrain

Pavem ent &Shoulder W idths

Side FrictionRoadside Land Use

LinkCapacity

Free Flow Speed,and Speed Flow

Curve No.Congested

Speeds

Assignm entVolum e/Capacity

IH CM

Exogenous Estimatesfrom IHCM,

IRM S/ARMS

TRIPS



5. Capacity Index: A 2-digit code used to represent the number of the Speed/Flow curve for that link. Details in Section C5.3 on Link Capacities and Speed/Flow Relationships.

6. Link Speed: Base year link speed, computed from the base year traffic volume and the speed flow relationship.

7. Link Capacity: 1-way link capacity. Details in the Section C5.3 on Link Capacities and Speed/Flow Relationships.

The coding of these seven items for over 900 links of the JARNS network model required considerable planning and data manipulation. The sources of data for each of these seven items differ, depending upon the level of detail and function/importance of these links in the network model. The coding of these values is described in the following sub-sections. 5.2.3 Coding of Strategic Network Links Network coding requires representing the features of each link in the network. The level of detail required to code these features depends upon the function and importance of these links in the overall network model. For this study, the most important part of the network coding was the adequately detailed and accurate representation of the roads defined as strategic. The JARNS strategic network covered 7372 km inter-urban roads and includes 406 IRMS links. The majority of the junctions between arterial and collector roads are in cities. The IRMS database defines the sections of road between the formal administrative boundary of large city and the junction with another road within the city as urban links. This is illustrated below in Figure 5.3. The coding of these IRMS urban links is defined in a later section. This section deals with the coding of the inter-urban links.

MikeFigure 5.3.



Figure 5.3: IRMS Inter-urban and Urban Link Illustration

It was possible to code a number of these inter-urban links directly in to the TRIPS network However, in instances of small towns/ urban areas without formal administrative boundary, this division had not been carried out and the IRMS links continues right through the urban/developed areas. For the modelling of these roads, it was necessary to reflect the changes in the physical and traffic characteristics that occur on the sections within these urban/developed areas. This is further illustrated in Figure 5.4 below. Therefore, the sections of the IRMS links which continue into the urban/ developed areas (shaded) would have different characteristics, and need to be represented separately.

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IRMS Urban Links

IRMS Inter-urban Links

Formal AdministrativeBoundary of Urban Area

MikeFigure 5.4



Figure 5.4: Illustration of IRMS Inter-urban Link Subdivision

This subdivision of these links and their representation was a major exercise, and required detailed information about the start/ finish points of the urban area. This task was carried out using the ARMS digital image data. This data provide a digital image of all road links at every 10m intervals. The data was scanned and information on roadside landuse (in %), side friction, and road type was recorded for every 100m section, or wherever there was any other reason to subdivide the IRMS link. For each of the 406 IRMS links data was recorded on forms. The data was then collated, to subdivide the IRMS links into sections equivalent to TRIPS links at the boundaries of towns/ developed and undeveloped areas. The data was coded for each section of the IRMS link. The Table below summarises the data coded for each of the strategic TRIPS link. The JARNS network retained the unique IRMS Link-ID code with additional section numbers through out its own network database. This would maintain the much needed compatibility between the JARNS and the main IRMS/ARMS databases. The data items extracted from the IRMS/ARMS databases, and their use in the JARNS network model is given in Table 5.1.

IRM S Inter-urban Links

Subdivide IRM S Inter-urban Links

TRIPS Urban Links

IRM S Inter-urban Links

Urban Area W ithout Formal Administrative Boundary

TRIPS Inter-urban Links

MikeTable 5.1.



Table 5.1: Link Attributes Taken from the IRMS and ARMS Databases IRMS/ARMS

Database Field JARNS

Database TRIPS

Model Field

Used for Speed Flow Relationship

Comments

Link ID Link-ID - No A code created by concatenating the Province, Ruas and Suffix Numbers

- Section - No A 2-digit section number, where IRMS/ARMS Links has been Subdivided.

Province Code Prov-ID Jurisdiction Code No 22/24/26/28

Link Length (in 1m units) Length Distance No

Rounded and Coded in 10m units

ROAD_TYPE Link-Type Link Type Yes 2/3/4/5 for undivided IRMS Links

FUNCT Function - - A, K1, K2, K3 (Source IRMS) TERRAIN Terrain - Yes Flat, Rolling, or Hilly

WCARR Carr-Width - Yes 2-Way Width in Decimetre (xx.x m) SH_WIDTHL & SH-WIDTHR Shld-Width - Yes

Averaged of both Sides Decimetre (xx.x m)

ARMS Digital Images (Roadside Landuse) LandUse - Yes Roadside Landuse (%)

ARMS Digital Images (Side Friction) Rd-Friction - Yes

Side Friction (1-very low, to 5 very high)

ARMS Digital Images (Road Type) Link-Type Link Type Yes

2/3/4/5 as above for Sections of IRMS Links

ARMS Digital Images Int-Dev - - Code I for Inter-urban, or D for Developed Sections The main items coded directly into the JARNS database and TRIPS network model were:

Distance, Road Type, and Jurisdiction code, road classification based on road type and carriageway width

used for accident calculations.

The other data items were used to calculate the following attributes, and used in the JARNS database and TRIPS network model. These were:

Road Capacity Free Flow Speed, Capacity Index based on the Free Flow speed, and Link speed based on the base year capacity traffic volumes.

This effectively was the complete coding of the strategic network. In total, the network comprised of 808 TRIPS model links, representing the 406 IRMS links covering 7372 km of strategic road network of Java. 5.2.4 Coding of Urban Area Links These are sections of national road network, which extend into the urban areas (see Figure 5.3). These links are treated by the IRMS/ARMS database as urban/ Kota links, and are

MikeFigure

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classified as such. These links are identified in the IRMS database by the use of special 2-digit suffix and by a character, K in the special filed called Kota in the IRMS database. These links form an integral part of the JARNS/ TRIPS model network. Their function in the network model is to facilitate the movement of inter-urban traffic into the urban areas, and allow the through traffic to pass. These links carry both the local and strategic (i.e. Inter-urban) traffic. It was not within the scope of this study to fully model the local traffic. However, the account of the local traffic was taken by coding these links under a special category, with reduced capacity equal to 50% of a divided 4-lane urban road, and base year speed of 30 km/h. This process was validated by analysing the data of SURIP studies, which have modelled both the local and through (inter-urban) traffic explicitly. Under this category of Urban area roads, 123 IRMS links representing close to 414 km of urban roads of Java were represented in the network model by 142 TRIPS links. 5.2.5 Coding of Urban Area Corridors The network coding described so far covered majority of the inter-urban network and substantial part of the urban links. However, it did not include links through the major metropolitan areas of Jakarta, Bandung and Semarang, because the urban streets of these cities are not included in the IRMS database. The major urban area roads of these cities are represented by notional links in the TRIPS network model, and are called urban area corridors. These corridors provided the same function i.e. passage of inter-urban traffic into and through these cities, as did the urban links. The coding (Capacity, Speed and Speed/ Flow relationships) of these links was therefore similar to that of the urban area links as described above in Section C5.2.4. Urban corridors incorporated in the network had a total length of 182 km. 5.2.6 Toll Roads and Toll Road Access Links All urban and inter-urban toll roads of Java are included in the JARNS network. Wherever the toll roads have intersection with the strategic network, the access/ egress to the toll roads is via toll access links. The toll access links are also notional links of 0.5 km length, or longer depending upon the distance of the toll road from the nearest strategic inter-urban link. The inter-urban toll roads were coded with full details, including number of lanes etc. The out of pocket cost of toll charges paid by the users are also model fully by representing the toll roads under special category link types. The coding of toll access is also similar to the coding of the toll roads, but these links do not have the same capacity as the toll road. The JARNS base year model network included 439 km of toll roads and 67 km of access toll road access links. 5.2.7 Rural Area Roads JARNS network model also included 602 km of rural (non-strategic) roads from the southern part of West Java province. These roads are not treated as strategic roads, but do perform vital function of providing accessibility to these remote areas. These links represented 572 km of K2, and 30 km of K3 road. 5.2.8 Summary The complete representation of the JARNS model network is given in Table 5.2 below, and it is illustrated in Figure 5.1 representing each category of roads in the network.

MikeFigure 5.1



Table 5.2: JARNS Model Network Description by Province and Length of Roads (km) Road Function in the JARNS

Model Network Jakarta West Java

Cent. Java & Yogya.

East Java Total Network

Inter-urban Sections - 1720 2,532 2,052 6,304 Strategic Network

Links Urban-area Sections - 344 387 337 1,068

Sub-total Strategic Links - 2,064 2,919 2,389 7,372

IRMS Urban - 83 135 122 340 Urban Area Links IRMS Kota - - 22 52 74

Toll Roads 88 269 20 62 439 Toll Roads

Access Links 7 53 2 5 67

Urban Area Corridor Links 153 27 2 - 182

Rural Roads & Ferry Link - 602 - 7 609

Total Network 248 3,098 3,100 2,637 9,083

5.3 Link Capacity and Speed Flow Relationships The calculation of link capacity and the application of speed/flow relationships are crucial in any network modelling, as these relationships dictate the vital statistic relating to network costs and its operation. These statistics are also important for the economic and operational evaluation of network upgrade scenarios. Link capacities and speeds were available from the IRMS/ARMS database. A detailed review showed that these values are outdated, and need to be revised. The IRMS values were based on the IHCM relationships. For the JARNS network model, these relationships were reviewed and simplified to suit the modelling requirements, and the available data. The link-based attributes used for the calculation of capacity and speed/flow relationships are identified in Table 5.1 above, and a brief description is given below, and full details are given in Appendix E. Link Capacity Function of (Road-type, Carriageway and Shoulder widths, Terrain Type, and Side-friction) Link Free Flow Speed Function of (Road-type, Carriageway and Shoulder widths, Terrain Type, Side-friction, and Adjacent Land-use) Inspection of the computed Free-Flow speeds of inter-urban links shows the following ranges:

2 Lane roads: 30 75 km/h Multi-lane roads: 50 90 km/h

Using increments of 5 km/h, speed/flow relationships were derived using the IHCM model of three points per curve, these are:

1. Volume/Capacity = 0.0; Speed = Free Flow speed; 2. Volume/Capacity = 0.85; Speed = As per IHCM for 2 or Multi-lane roads; 3. Minimum speed set to 10 km/h; and V/C at 10 km/h was calculated from the

relationship given in the IHCM. In total 19 Seed/Flow, relationships covering all road types were defined. Figure 5.5 and Figure 5.6 show these Speed/Flow relationships for 2-lane, and multi-lane roads respectively.

MikeTable 5.1

MikeAppendix E.

MikeFigure 5.5

MikeFigure 5.6



Figure 5.5: Speed Flow Curves for 2-Lane Roads

0

10

20

30

40

50

60

70

80

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Volume Capacity Ratio

Spee

d (k

m/h

)

Figure 5.6: Speed Flow Curves for Multi-Lane Roads

0

10

20

30

40

50

60

70

80

90

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6

Volume Capacity Ratio

Spee

d (k

m/h

)

5.4 Base Year Assignment and Model Validation This stage of the modelling process brings together the travel demand (specified in terms of a trip matrices) and the network. The assignment process then determines the route choices people make on the network, and how the choice of route changes in a congested network environment. The JARNS model used the well-known technique called Equilibrium assignment. The key feature of this method is that it is a network cost optimisation process, and it continues to iterate until there is no route of lower cost of travel between an O-D pair. This technique is available as a standard program in the TRIPS package, and was adopted for the JARNS network assignment model.



5.4.1 Assignment Time Period and PCE factors The assignment process is carried out for a one-hour period for all vehicle types combined, as the link capacities are usually specified on a per-hour basis in a common unit for total traffic (i.e. Passenger Car Units PCUs). The (Passenger Car Equivalent) factors used to convert the vehicle trips to the common unit of PCUs, were taken from the IHCM. The factors adopted are reported below in Table 5.3. Table 5.3: Average Hourly and PCE Factors, and Assigned Traffic Volumes

Vehicle Category

24-Hours Total Trips

(AADT)

24-Hours Inter-Zonal

Trips (AADT)

Average Hourly

Factor (%)

PCE Factor

Average Hourly PCUs Inter-Zonal

Assigned Motorcycles Observed Counts 6.57 0.5 Link Pre-loads

Angkots Observed Counts 5.85 1.0 Link Pre-loads

Car 350,448 342,668 5.62 1.0 19,258

LGV 114,363 110,921 6.01 1.0 6,666

MGV 157,396 153,739 5.02 1.65* 12,734

HGV 38,208 36,774 4.30 3.8 6,004

Buses 45,457 44,781 4.85 1.67* 3,628

All Vehicles 705,872 688,853 5.38* 1.29* 48,290 * Weighted Average Values The JARNS traffic count data was analysed to determine what time period should be modelled. Figure 5.7 below shows the changes in total traffic volumes on the inter-urban roads by time of day. It is evident that apart from some minor peaks in the traffic volumes in the morning (06:00-08:00) and to a lesser extent in the late afternoon period the overall inter-urban traffic volumes are very similar through out the day. Therefore, it was decided to assign the average hourly traffic volumes, rather than a particular peak period. However, the proportions of vehicle types in the traffic stream during the day were observed to have some variations. The proportions of vehicles in the average hour of the day were therefore estimated separately for each vehicle category from the survey data. These average hourly proportions are given in Table 5.3 above.

MikeFigure 5.7



Figure 5.7: Distribution of Vehicle Volumes During the Day at 100 Survey Sites

4.0%

4.5%

5.0%

5.5%

6.0%

6.5%

6 7 8 9 10 11 12 13 14 15 16 17Time of Day (Hours)

% i

n 1-

Hou

r

Tot-PCU All Vehs

5.4.2 Assignment Results Validation The assignment of five vehicle categories combined was carried out by assigning the total average hourly PCU trip matrix using the equilibrium assignment technique. The route choice parameters were based on Generalised Cost (GC) of travel incorporating the value of time, vehicle operating cost and the toll charges. These parameters were estimated for all vehicles combined and weighted by vehicle mix. The values used in the assignment process are per PCU in Rp, at year 2000prices, and are listed below:

Value of Time (VOT) = Rp 450/minute, (Rp 27,000/hr) Vehicle Operating Cost (VOC) = Rp 70/km, and Toll Charges = Rp 100/km of travel on the Toll roads and Toll Access Links.

The equilibrium assignment process converged after 10 iterations. The assignment results were then compared with the observed traffic volumes. The assigned traffic volumes on the inter-urban toll road links were found to be consistently below the observed volumes. This aspect of under-assigning of traffic to toll roads is quite common when using a single route choice GC for all road users. In order to improve the assignment results, different values of VOT for the toll road users and other links were tested. It was found that the assignment when the VOT for toll road users is at 90% of the value used for other roads gives the best results. Therefore, for the base year and for the subsequent future year testing the VOT for toll road users was taken as 90% of the VOT for the use of other roads. The final base year assignment results are presented in detail in Appendix F. Table 5.4 compares the assigned and observed traffic volumes for a set of cordons and screenlines. This comparison is also illustrated graphically in Figure 5.8. Both these comparisons show that the assignment results show a close comparison between the observed and the assigned traffic volumes. This shows that the modelling methodology is robust enough to be used for future year testing of network scenarios.

MikeTable 5.4

MikeFigure 5.8.



Figure C5.8 Comaprison of Observed and Assigned Traffic Volumes (PCU/hr)

-

2,000

4,000

6,000

8,000

10,000

12,000

- 2,000 4,000 6,000 8,000 10,000 12,000

Observed Counts (PCU/hr)

Ass

igne

d V

olum

es (P

CU

/hr)

Table 5.4: Comparison of Observed and Assigned Traffic Volumes (PCU/hr)

Traffic Volumes (PCU/hr) Difference Percent Screenline/ Cordon* Observed Assigned (A - O) % (A/O)

1 2,837 2,760 -77 -3% 2 6,823 6,935 112 2% 3 4,314 4,615 301 7% 4 2,967 3,116 149 5% 5 2,786 2,939 153 5% 7 1,984 2,108 124 6% 8 2,913 2,907 -6 0% 9 2,463 2,717 254 10% 10 5,364 5,404 40 1% 11 2,420 3,030 610 25% 12 2,256 2,512 256 11% 13 3,695 3,852 157 4% 14 5,114 5,022 -92 -2% 15 1,926 1,856 -70 -4% 16 1,118 1,251 133 12% 17 9,989 10,127 138 1% 18 3,872 4,043 171 4% 19 5,424 6,464 1,040 19% 20 3,027 3,411 384 13% 21 6,282 6,246 -36 -1% 22 4,015 4,018 3 0% 23 4,134 4,274 140 3% 24 7,324 7,430 106 1% 25 7,191 7,064 -127 -2% 26 3,102 3,092 -10 0%

Sumatra Ferry 466 486 20 4% Madur

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