A method for estimating traffic flow fuel consumption — Using traffic simulations

ELSEVIER

JSAE Review 17 (1996) 307-311

A method for estimating traffic flow fuel consumption - Using traffic simulations Yasuhisa Kishi

a, Shinichi Katsuki a, Yasuo Yoshikawa b, Ikuhiro Morita b

a Social and Advanced Research Laboratory, Transportation Reserch Laboratory, Nissan Motor Co., Ltd., 17-1, Ginza 6-choume, Chuo-ku, Tokyo, 104-23 Japan Engeneering Systems Department, Nissan Motor Co., Ltd., 1, Natsusima-cho, Yokosuka-City, Kanagawa, 237 Japan

Received 9 October 1995

Abstract A method was developed to estimate the potential reduction in fuel consumption that might be obtained by improving traffic flows. This method is accomplished by calculating the speed and acceleration of every vehicle per second, and matching the results with a table showing the fuel consumption for every vehicle model based on chassis dynamometer tests. The method makes it possible to show quantitatively the effect of improving traffic flows on reducing fuel consumption. It is expected to be useful in stepping up improvement of the road environment and traffic flows.

1. Introduction As the number of motor vehicles on the roads tends to increase, the impact on the global environment becomes more serious. This has made it necessary to reduce fuel consumption and purify motor emissions. These goals can be accomplished in two ways. One method involves manufacturing more efficient vehicles, while the other involves improving traffic flow. In the past, most efforts have been directed toward improving vehicle efficiency. However, vehicle improvement seems to be approaching its limit. Therefore, we anticipate that improving traffic flow will bring about greater reductions in fuel consumption and emissions than improving vehicle efficiency. Although this method should be used in the future, no method has yet been developed for quantitatively determining the effects of improved traffic flow on fuel consumption and emissions. This paper gives the results of a method for quantitative understanding of the effects of traffic flow improvement on fuel consumption in Japan. Traffic flow simulation was used to obtain these results.

2. Traffic flow simulation Various traffic flow simulation models are currently available, depending on the user's purpose. These simula-

tion models can be divided into two types, micro models and macro models. A micro model deals with the behavior of independent vehicles, while a macro model is for the behavior of a group of vehicles. The model used in this paper for estimating the effects of traffic flow improvement on fuel consumption is a micro model for an ordinary road network. This model was developed based on the U.S.A. simulation model " T R A F - N E T S I M " . Figure 1 shows how this model simulates traffic flow. Vehicles are first generated on a road network. The vehicles on the network are then moved according to the car-following theory, signal control and sign. After moving the vehicles, traffic condition data (for example, traffic signal status, location of parked vehicles) is updated. Thus, traffic flow can be reproduced by repeating this process every second.

3. Fuel consumption calculation method In this model, the individual movement or status of all the vehicles in the traffic network is calculated every second. Based on the results, the fuel consumption per second for each vehicle is also calculated. Then, the total is obtained for determining the general fuel consumption. Figure 2 shows how fuel consumption is calculated when simulating traffic flow. Consumption is calculated every

038%4304/96/$15.00 © 1996 Society of Automotive Engineers of Japan, Inc. and Elsevier Science B.V. All rights reserved PH S0389-4304(96)0001 3-6

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Y. Kishi et al./JSAE Review 17 (1996) 307-311

Driving conditions are calculated based on movement logic that varies with the driving pattern (whether standalone travel or following travel) and the vehicle driving position (for example, close to an intersection where the vehicle would be influenced by signals, or whether other vehicles are in the adjacent lanes). Acceleration and deceleration for driving on an intersection-free road for most of a trip can be calculated with the following equations: • No leading vehicle (free-flow)

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Fig. 1. Traffic simulation flow-chart (outline).

second by using fuel consumption data that matches the required conditions from the Fuel Consumption Unit Table. This table is based on vehicle type (such as passenger car, truck or bus), and on driving conditions (such as speed, acceleration, and deceleration). Each process is described below in detail.

3.1. Determination of vehicle type and driving conditions The vehicle type is determined randomly for each vehicle, based on conditions (percentage of large vehicles, percentage of car-pools) set manually at the time of vehicle generation. This data is referenced when obtaining the vehicle capabilities and characteristics, such as maximum speed, maximum acceleration and effective vehicle length. These conditions are given for each vehicle type. The vehicle type is also used to select data from the fuel consumption unit table established for each vehicle type.

• Subject vehicle has leading vehicle (car-following)

{a(XAcceleration =

{a(X-

2Y) - ( y 2 _ Z2)}(b + 2 Y ) 2Y) - ( y 2 _ Z2)} + (b + 2 Y ) z

(2) where: - 12 < Acceleration < free-flow speed - present speed, a, b: Constants, X: Front-to-rear separation distance to lead vehicle, Y: Subject vehicle speed at start of time-step, Z: Lead vehicle speed at end of time-step. Using this acceleration/deceleration, the speed at the end of a time-step can be expressed as: S p e e d a t end of time-step = Speedat start of time-step + Acceleration

(3) where: 0 ~< Subject vehicle speed ~< 127.

3.2. Fuel consumption unit table This table is used based on the vehicle type, speed and acceleration to obtain the fuel consumption per vehicle per second. The speed axis ranges from 0 to 76 k m / h , while the acceleration and deceleration axis ranges from - 0 . 4 to 0.4 G. Both ranges fully cover conditions that may occur on ordinary roads in normal driving conditions. The data in this table must be very accurate, because it is used to estimate the fuel consumption. Figure 3 shows the fuel consumption unit table. In addition, since the creation of this table required a huge amount of data (as many as 19 x 71 = 1349 items for

Acceleration range

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uel consumptio

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Fig. 2. Calculation flow-chart of fuel consumption.

( 1)

(1 9 step) Speed range (7 1 step) Table unit

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Fig. 3. Summary of unit-table.

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Y. Kishi et a l . / J S A E Review 17 (1996) 307-31 l

Fuel consumption

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senger car, truck, or bus). Then, this data can be used as indexes for evaluating fuel consumption. We also graphed this data and generated an output file of text form to make it easier to evaluate the results.

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each vehicle type, for example) acquisition of such data itself was not easy. We obtained this data from the Japan Automobile Research Institute. Since the data is the result of fuel consumption measurements at various speeds and acceleration/deceleration on a dynamometer, we determined that it is accurate enough for our use. In actual traffic flow, however, the ratio of one type of vehicle to another vehicle type varies with vehicle location and time. Hence, we created ten different tables, as shown in Table 1. Based on these tables, we graphed fuel consumption (Fig. 4). These graphs clearly indicate that fuel consumption at acceleration is critical.

3.3. Determining of results Using the simulation model it is possible to add up, for both individual links and for the entire network, the total fuel consumption (in liters) and the fuel consumption rate (in km/liter) per time unit for each vehicle type (pas-

Estimation of fuel consumption has long been studied, and various estimation methods have been proposed. There are two methods that are conventionally used for estimating fuel consumption: measurement by actually driving the vehicle and regression analysis using the mean travel speed and fuel consumption. Measurement by actually driving allows fuel consumption to be gauged for only a limited number of vehicles. Hence, it cannot measure the fuel consumption of the traffic flow as a whole. In addition it cannot estimate the fuel consumption in hypothetical situations, such as after traffic flow has been improved. For these reasons, this method is not suitable for estimating how improved traffic flow affects fuel consumption. Hence, we used regression analysis and emphasized two points: adaptability of fuel consumption estimation method to arbitrary locations and accuracy indicating the reproducibility of estimated values about regression analysis and simulation method. Table 2 compares the results of regression analysis with the results from our traffic flow simulation estimation method. In actual traffic situations, driving conditions such as speed and acceleration vary with the driver and the road conditions. This results in a variation in the relation between the mean travel speed and fuel consumption. Accordingly, if conventional regression analysis is applied to various locations, reproducing the estimated values becomes difficult. Instead, with simulation, speed and acceleration are used to calculate fuel consumption. Consequently, differences in

Table I Vehicle specification for unit-table Item

Gasoline auto

Displacement (cc) Weight (kg) Engine type Turbo Max power (ps/rpm) Transmission

1272 780 4cycle EFI x 100/6500 5MT

Diesel truck 1838 1485 4cycle EFI X 115/5600 4AT

Notes: Gasoline auto is full; Diesel truck has full load and no load.

2779 3245 4cycle IDI x 91/4000 5MT

7545 7765 4cycle DI X 185/2800 6MT

13741 19830 4cycle DI 0 340/2000 7MT

16991 19835 4cycle DI X 340/2200 6MT

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Y, Kishi et al./JSAE Review 17 (1996) 307-311 :30

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driving conditions can be represented more accurately. Thus, simulation is better than regression analysis for estimating the effects of improved traffic flow. To compare the accuracy of these two methods, we calculated fuel consumption using both methods with data from actual driving situations. This was more convenient because it is difficult to measure fuel consumption of all vehicles in actual driving situations (Fig. 5). Conventional regression analysis was not able to determine the variation in fuel consumption caused by differences in driving conditions (speed, acceleration), resulting in an RMS error of 2.59. However, our traffic flow simulation was able to detect variations in fuel consumption at medium and high-speeds, resulting in an RMS error of 2.27. These results show that the simulation method is more accurate than the regression method. To evaluate the above discussion as a whole, it can be said that the estimation method using traffic flow simulation is superior to the conventional method employing a regression analysis.

5. Trial calculation of the effect of traffic flow improvement on fuel consumption

Figure 6 shows the two routes both located near Oppama, Yokosuka City, Kanagawa Prefecture, where Nissan Motor is working to alleviate traffic congestion. Route 1 is a 1.0 km section between Hirakata Bridge and Uchikawa Bridge on Route 264 of the Yokohama city road system. The time of the evaluation was from 17:00 to 20:00 (three hours). Route 2 is a 2.4 km section between Funakoshi and Natsushima on the Yokosuka city road system. The time of the evaluation was 16:00 to 20:00 (four hours). On both routes, traffic congestion occurs in the evening with the congestion starting on Route 16. The reproducibility of the present situation for the two simulation models was checked before the trial consumption calculation (Fig. 7).

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~>10We applied our simulation model to an actual study of traffic flow improvement. We carried out trial calculations using two models to examine how improved traffic flow affects fuel consumption, as shown in the following example.

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Figure 8 shows the results of the trial calculation for the fuel consumption of passenger cars, which comprise more than 90 percent of the total traffic volume. Fuel consumption of around 5 km/liter at Uchikawa Bridge changed to around 13 kin/liter after improvement, increasing approximately 2.6 times. Also, at Funakoshi, an improvement of approximately 2 times (from 5 kin/liter to 11 kin/liter) was observed at peak time. In this way, it is possible to represent the effects of traffic flow improvement on fuel consumption by using the traffic flow simulation for the estimation of fuel consumption.

20:00

Fig. 8. Simulation results of effect of traffic flow improvement.

The reproducibility was measured using the time required to travel through a section as the evaluation index. The time required at peak traffic times and the tendency to change were almost precisely reproduced. These results indicate that these two models can be used without any problems. For the trial calculation of the effect of traffic flow improvement on fuel consumption, we compared data before and after improvements were made by modifying traffic signals near the Uchikawa Bridge intersection. In

6. Conclusion and future work

The outcome of our study can be summarized as follows: By estimating fuel consumption through traffic flow simulation, it has become possible to quantitatively and easily estimate the effects of better traffic flow on fuel consumption improvement. Meanwhile, as mentioned earlier, actual measurement of fuel consumption data of traffic flow as a whole is quite difficult. Hence, exact verification of the accuracy of this estimation method remains to be studied in the future.