MODELING TRANSPORTATION DEMAND USING EMME/2 A CASE STUDY OF HIROSHIMA URBAN AREA

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1 MODELING TRANSPORTATION DEMAND USING EMME/2 A CASE STUDY OF HIROSHIMA URBAN AREA By Masazumi Ono Overseas Services Division, FUKKEN Co., Ltd. Hiroshima Japan Hikari-machi Higasi-ku Hiroshima, Japan 732 Phone: , Fax: , ono@fukken.co.jp Hoong Chor Chin Department of Civil Engineering, The National University of Singapore 10 Kent Rigde Crescent, Singapore, Phone: , Fax: Seng Lip Lee Department of Civil Engineering, The National University of Singapore ABSTRACT In the last 30 years, there have been three transportation master plan studies for the Hiroshima urban area, Japan. The planning software used in these studies have been developed by the consultants engaged in the studies. Generally, the transportation models developed for the Hiroshima projects have not been used elsewhere. There is also a lack of information regarding the uses and application of these software. The objective of this study is to develop a transportation model for the Hiroshima urban area using EMME/2 and to evaluate and explore the usefulness of EMME/2. This involved modeling EMME/2 with the 1987 urban activities and transportation data in Hiroshima urban area. The results from the calibrated EMME/2 model were checked against the observed traffic and demand data for the year Suitability of EMME/2 was judged by the ease of coding and modeling as well as the flexibility of usage and the level of errors between outputs and observed values. It was found that EMME/2 produced the result compatible with those of Japanese planning models. Several features of EMME/2, such as the unified database, the matrix editor and the powerful macro system, proved to make transportation modeling more flexible than the Japanese models. INTRODUCTION Since 1970 s, many of the urbanized areas in Japan having a population of more than 1 million have conducted transportation master plan studies every 10 years. Hiroshima urban area located at western Japan with a population of about 1.5 million has three transportation master plan studies on the based of the person trip survey in the last 30 years [1]. 1

2 The planning software used in these studies have been developed by the consultants engaged in the studies. More than 85% of software used in the transportation projects are in-house software [2]. The contents of the software were generally not transparent, which resulted in a lack of information regarding the usage and application of these software and models. The transportation models developed for the specific projects have not been allowed to apply to other projects because there was no transparency in the model. The preparation of transportation simulation accounts for about 40 % of all works [2]. Most of the projects have not been able to use the previous transportation database effectively. The building database and models have had much time, effort and money invested in them. It will be important to unify the past traffic survey data and revise the transportation models developed from the past surveys. The objective of this study is to develop a transportation model for the Hiroshima urban area using EMME/2 given the limitation of survey data, and to evaluate and explore the usefulness of EMME/2. STUDY AREA Hiroshima urban area had three transportation master plan studies in 1967, 1977 and The last study was based on the person trip survey with 7.5 % sample size. The study area in this paper was as those in 1987 involving five cities and five towns [See Figure 1]. The land size was about 1436 km 2. The population size was about 1.58 million. The study area was divided into 198 analysis zones aggregated into 16 district zones [1]. BUILDING DATA BANK Generally, transportation database for long-term transportation planning consists of transportation network data and urban activity. It needs much time and money to collect the data and build database. In Japan, the private consultants engaged in the project have developed the database software. These systems were developed for only the target project and were not flexible. Previous databases were also not easily used in later studies. Software copyright also limited the development of software. The software used in Hiroshima Urban Area Transportation Master Plan Study 1987 (HATS) was known as FTFS (Fukuyama Transportation Forecasting System), which was developed by FUKUYAMA Consultant Co., Ltd.. FTFS is a demand modeling system, but had limited capability in manipulating the database system. The parameters of the model were fixed and were applicable to HATS. Consequently, the model and the data were practically not used for later projects. These transferability problems are common in Japan. In Japan, a Digital Road Map composed by links, nodes and coordinates has been developed nationwide. This has been used in many fields, such as road traffic surveys, traffic control system and road accident database system [3]. These database will be 2

3 downsized from nationwide to regional level and city level. Hence, it is necessary to build the transportation planning database using by transparent system in region level. It is thought that EMME/2 has a possibility for a common system in transportation planning if the national database can be used. Network and Urban Activity Date The EMME/2 data bank in this study was built based on HATS. The base network was an integrated auto and transit network. It contained 1000 nodes and 2700 directional links. There were 90 transit lines and 2500 transit line segments. The modes of transport modeled included walk, cycle, auto, bus, tram, walk access and feeder access. Available urban activity data were area, population, employee, school enrolment by zone and travel demand matrices by mode by district zone. The base year of the database was Volume Delay Function Data In this study, the daily base Volume-Speed (Q-V) table used in HATS was selected. In Japan, predicted daily link volume is required for road planning by The Road Structure Regulation. Thus, daily based assignment model and speed daily-flow relationship (Q- V table) is commonly used in network simulation. The Q-V table is empirically defined based on the relationship between design standard volume and speed [4]. The design standard volume and speed determines the road structure, such as, the number of lanes, radius of curve. These design standard speed and volume are different from free flow speed and possible capacity [4]. The Q-V table in this study allows over capacity and assumes that the speed is 0.1 times of design standard speed when the volume exceeds 1.2 times of design standard volume [Figure 2]. The Q-V table in this study applied ten sets of design standard speed and volume depending on the road types [1]. Verification of reproducing the network condition In order to verify the reproduction of the base year network condition, the travel time by mode simulated by EMME/2 was compared with the observed data. The travel time from the city center to district zone centers by car, bus and tram modes were computed using all-or-nothing assignment methods. The link speed used in the simulation was defined as 0.6 times of the design standard speed [1]. Several statistical values have been performed to confirm the difference between the simulated and observed travel time were not significant [Table 1]. This showed EMME/2 s capability of reflecting the network condition as close as possible. Table 1 Result of Travel Time Simulation Statistical Value Travel Mode R-squared RMS Slope of regression line Car % Bus % Tram % MODELING TRANSPORTATION DEMAND 3

4 In long-term planning projects in Japan, the method of demand modeling has been standardized. Only for the urbanized area with more than 1 million populations in last 30 years, 15 studies were done. The conventional four-step forecasting method was usually adopted. The unit of trip is daily based, because hourly based trip is not adopted in Japan. Modeling demand is by trip purposes, such as, work, school, business, private and home trips. Historically, new ideas or models have not been imported into practical transportation projects [5]. FTFS model covered all the modeling procedures of the HATS models, but was limited in model inputs only for HATS models. Most of software used in practical projects was in-house and modeling procedure was limited and not transparent. This is caused by a lack of coordinating organization and a lack of common software distribution [6]. The conventional four-step forecasting method adopted in HATS was chosen for this study. This consists of trip generation, trip distribution, modal split and trip assignment models. The trip adopted in this study was defined as a day trip as in HATS. Since the observed travel demand by trip purpose was not available, the trip by total purpose was discussed in this study. Following section discusses the manner of four-step modeling. Trip Generation Model The regression models were adopted for the trip production and attraction models and these are: where Oi = X1i X 2i X 3i X 4i X 5i Dj = X1 j X 2 j X 3 j X 4 j X 5 j Oi : trip production in zone i Dj : trip attraction in zone j X1i :population in zone i X 2i :employeeof agriculture in zone i X 3i :employeeof manifucturing in zone i X 4i :employeeof cpmmercein zone i X 5i :school enlorment in zone i The attributes of these models were the resident population, the size of employed populations in agriculture, manufacturing, commerce and school enrollment. The parameters of the attributes were calibrated from observed trip production and attraction by 198 analysis zones based on the person trip survey in HATS. Trip Distribution Model The doubly constrained gravity model was chosen as the trip distribution model in this study T ij = A i B j O i D j t ij where T ij : trip distribution from i to j O i : trip production of zone i D j : trip attraction of zone j 4

5 t ij : travel time from i to j A i, B j : balancing factor The travel time that was used in the gravity model was defined as the average of car mode and transit mode travel times. The travel time of intra-zonal trips was defined by the average intra-zonal travel length divided by the average trip speed. The coefficient of impedance was calibrated equal to 2.0. Modal Split Model Modal split model is divided into two stages. The first stage is a primary split into walk + cycle and car + transit using the diversion curve model by trip length adopted in HATS. The second stage consist of splitting car + transit into each mode, that is, car, bus and tram mode. This was done by using multi-modal logit model. The utility function of the multi-modal logit model included the usual EMME/2 time components (access time, waiting time, in-vehicle, transfer time and egress time), travel cost, car availability and parking availability. where U auto = t autoij C autoij Y 1i Y 2j U bus = t busij w busij C busij U tram = t tramij w tramij C tramij t ij : in-vehicle, access and egress time from i zone to j zone (min) w ij : waiting time and transfer time from i zone to j zone (min) C ij : travel cost from i zone to j zone (yen/km) Y 1i : the rate of vehicle-hold in origin zone i (veh/1000person) [car availability] Y 2j : employment density in destination zone j (per/ha) [parking availability] The parking cost by zone was not available so employment density was regarded as parking availability. Coefficients of the utility function were obtained from the observed trip matrix by mode by district zones in HATS. The calibration method was minimizing root-mean-squares-error (RMS) programmed by using the EMME/2 macro. Calibration results of multi-modal logit model obtained were as follows. External Demand Model The demand related to external zones was added based on the cordon line survey of HATS. This demand was modeled by mode since the travel pattern was different among modes. External zones were set up on the roads and the transit lines that cross the cordon line. Assignment Model For car assignment, capacity constrained equilibrium assignment model implemented in EMME/2 was applied. For transit assignment, the transit assignment model implemented in EMME/2 was applied to the study. 5

6 Verification of Reproducing Transportation Demand Even though the available input data were limited, this effort to match simulated traffic patterns to observed traffic patterns proved to the same level of accuracy as previous efforts with HATS [Table 2]. This showed EMME/2 has flexibility in modeling the demand by EMME/2 matrix calculator and EMME/2 is useful software instead of the previous system in the earlier project. Table 2 Simulation Result in Each Modeling Stage Model Type Stage of modeling Developed models in the study Trip generation R-squared = (Trip production) RMS Rate = 14% Trip generation R-squared = (Trip attraction) RMS Rate = 14% Trip distribution R-squared = RMS Rate = 16% Modal split 1 R-squared = (Diversion curve) RMS Rate = 49% Modal split 2 R-squared = (Multi-modal logit) RMS Rate = 18% Car assignment R-squared = RMS Rate = 34% Transit assignment R-squared = RMS Rate = 38% Hiroshima project models [HATS 1989] R-squared = R-squared = R-squared = R-squared = R-squared = R-squared = unknown R-squared = unknown EVALUATION OF CAR ASSIGNMENT MODEL BETWEEN EMME/2 AND THE JAPANESE MODEL For a car assignment model, the incremental method has been still used for 30 years in practical projects in Japan. This model is not rational because the number of OD matrix division and the rate of each division are set up by trial and error. Several kinds of new assignment models, such as equilibrium and dynamic models, have been introduced by an academic research, but there is still a considerable gap between the academic work and practical usage. This is caused by the shortage of new model feasibility study in real network size. For applying to practical projects, the possibility of the input data acquisition and the flexible operation of the model should be established [6]. In this study, the reproducibility of assignment model has been tested in comparison with several models. The equilibrium model implemented EMME/2, the standard of the Japanese model (Incremental model) and All-or-Nothing model were adopted for testing. To confirm the reproducibility of the car assignment models, several statistical tests between estimated and observed road link volume have been performed [Table 3]. R-squared and RMS error of the All-or-Nothing model is lower than other models because of much error at high link volume [Figure 3]. Based on R-squared values and RMS rates, EMME/2 and the incremental model is the same level of accuracy. This 6

7 showed EMME/2 model has enough reproducibility compared to the incremental model on a daily trip based simulation. Table 3 Simulation Result of Road Link Volume Statistical value Model type R-squared RMS Rate Slope of regression line EMME/2 model % Incremental model % All-or Nothing model % CONCLUSIONS This paper found that EMME/2 produced results compatible with those of HATS models. Further, Several features of EMME/2 proved to make transportation modeling more flexible than HATS software. All the data related to urban activities, network data, model definitions and travel demand can be unified as the EMME/2 database. They are easy to modify by means of the EMME/2 batch file in/out system. EMME/2 system permits flexible variables in model formulations by means of the powerful matrix calculator. The model calibration and future forecasting are repeatable by means of the EMME/2 powerful macro system. The EMME/2 system permits rapid testing of different options by means of EMME/2 powerful macro system. The foregoing discussions have shown that the developed models using EMME/2 could be effectively used for future traffic studies including street and highway corridor analysis, traffic impacts of new land developments, and long term transportation plans. REFERENCE 1. Hiroshima Urban Area Transportation Planning Study Committee, Hiroshima Urban Area Transportation Master Plan Study Report, Japan, March (in Japanese) 2. S. Mizokami, Feature of Transportation Network Projects in Practical Business in Japan, Analysis Method of Transportation Network, Japan Society of Civil Engineers, Japan, December (in Japanese) 3. M. Kuwahara, Wide Area Network Simulation, Traffic Engineering, Japan, Vol.32 No.5, (in Japanese) 4. Y. Asakura, Link Cost Functions (Speed-Flow Relationships) for Network Traffic Assignment and its Effects on Prediction Error, Analysis Method of Transportation Network, Japan Society of Civil Engineers, Japan, December (in Japanese) 5. Y. Asakura, S. Mizokami, Can Transport Network Researches Strike a Vein of Ore?, Civil Planning Research, No.16 (2), Japan, December (in Japanese) 6. S. Mizokami, What Can Be Done for an Improvement of Traffic Network Researches?, Traffic Engineering, Japan, Vol.30 No.4, (in Japanese) Figure 1 Study Area 7

8 Figure 2 Q-V Table (adopted in HATS) V V 0 8 V 0 : design standard speed Q 0 : design standard volume

9 Figure 3 Validation of EMME/2 Car Assignment Model Equilibrium Incremental All or Nothing Predicted Link Volume (v/d) Observed Link Volume (v/d) 9