25th ITS World Congress, Copenhagen, Denmark, 17-21 September 2018

Paper ID #A Study on the Effects of Green Wave System on CO2 Emissions
Hisatomo Hanabusa1*, Shinichiro Kai1*, Ryota Horiguchi1*, Shuichi Kanari2*
1*
2*

i-Transport Lab. Co., Ltd., Japan

Japan Automobile Research Institute, Japan

Abstract
This paper describes the result of a simulation study about the effects of green wave system on CO2emissions. Green wave system typically controls (or give the information of) the vehicle’s speed tosmooth traffic flow and to improve road traffic environment. The various green wave system has been
demonstrated through the field operation tests in various regions responding to the expectation of thesynergistic effect by the collaboration with automated driving and V2I communications. Therefore, wefocused on the situation when the automated vehicles would became widely used to the road traffic inreal world, and discussed the environmental impact of green wave system using our evaluation toolthat combined traffic simulation and CO2 emission model.
Keywords: Green Wave System, CO2 Emissions, Traffic Simulation
Introduction
The purpose of this study is to describe the environmental impact of green wave system to road traffic.
In recent years, green wave system is highly expected to smooth road traffic and improve theenvironment. Various types of the system have been demonstrated, for example, the field operation
tests in three regions (Kanagawa, Gunma and Aichi) were practiced in Japan from 2014 to 2015. Inaddition, green wave system is begging to be studied as one of key technologies to support automateddriving. In general, green wave system controls (or give the information of) the vehicle’s speed not tostop at the signal intersection. The target speed is estimated using the signal timing information andthe distance to the signal intersection in real time. On the other hand, automated driving system issimilarly needed to control the acceleration and the speed. Therefore, it is said that these two systems
have the complementary strength of the cooperation to improve the environmental of road traffic. In
this study, we discuss the effects of green wave system using the combination of traffic simulation
model and CO2 emission model, based on the assumption that automated driving is widely used in thefuture.
Evaluation Framework
To discuss the impact of green wave system on CO2 emissions, we applied an evaluation tool1 of thecombination of traffic simulation model (TS) and CO2 emission model (EM) developed in Energy ITSPaper title <for all but first pages>

Project (from 2008 to 2012). Figure 1 shows the framework of the evaluation tool. The model type ofTS is mesoscopic scale model to be able applied various types of TS. Therefore, the interface between
TS and EM is considered the data passing based on the simplified vehicle behaviour data consists on
the short trip or the short stop with the average speed profiles. This data is named Stepwise Speed
Function (SSF). On the other hand, EM is also modelled by the statistical approach based on the actual
vehicle’s behaviours and engine characteristics to calculate CO2 emissions using SSF from TS. This
framework is verified and validated using our benchmark data set according to “Guidelines forAssessing the Effects of ITS on CO2 Emissions - International Joint Report -” (IJR).

Figure1 – Evaluation Framework of CO2 Emissions

Modelling of Green Wave System
Reference Model of CO2 Impact
We propose some steps for the impact study of ITS system on CO2 emissions in JIR. Making the“Reference model” is the first step of implementation of the evaluation target system. The reference
model aims to describe and to be understood the impact mechanism from the evaluation target system
to CO2 emissions for everyone involved. Figure 2 shows the reference model of green wave systemwith automated vehicle. We assumed that green wave system consists on automated vehicle (AV) andsignal controller and V2I beacon and traffic sensor for two type of green wave system by signal
control optimization or maintaining of vehicle’s speed. In this study, we focused the service of
maintaining of vehicle’s speed, and implemented the green wave system in TS. The model type of TS
we applied is the micro scale model named MicroAVENUE2.

2

Paper title <for all but first pages>

Figure 2 – Reference Model of Green Wave System with Automated Vehicle

Implementation of Green Wave System
We implemented the green wave system (GW) to MicroAVENUE according to the reference model inFigure 2. Figure 3 shows the image of the implemented model of green wave system. The green wave
system gets the signal timing plan from each signal controller and send the signal timing information
to target vehicles through V2I beacon. The target vehicle with GW find the possible speed to passthough not to stop at signal intersection. Then the vehicle maintains the target speed based or
decelerate on the two assistance options below.
-Passing Assistance:
If the vehicle can pass though without stop at the intersection, the device requests the vehicle tokeep the target speed.
-Stopping Assistance:
If the vehicle cannot pass through, the device requests the vehicle to decelerate earlier (noacceleration). Figure 5 shows the demonstration.

3

Paper title <for all but first pages>

Figure 3 –Implemented model of green wave system

The behaviours of the vehicle with GW is verified in the test case of three intersection as shown inFigure 4. We confirmed that the vehicle trajectories with GW are smooth comparing to the vehicle
trajectories without GW.

Figure 4 – Demonstration of Green Wave System for Verification

4

Paper title <for all but first pages>

Evaluation of Green Wave System
Figure 5 shows the target area for the evaluation. We chose Route 16 in Kashiwa city for about 16 km.
In this study, the traffic census data of Ministry of Land, Infrastructure, Transport and Tourism wasused to make the input file and the validation data. Figure 6 and Figure 7 shows an example of vehicle
trajectories of base case (without GW). We calibrated the simulation parameters to fit the real traffic
situation and validated the reproducibility comparing to the census data such as traffic volume andaverage speed.

Figure 5 – Target Area(Route 16, Kashiwa)

Figure 6 – An Example of Vehicle Trajectories of Base Case (without GW), from North to South5Paper title <for all but first pages>

Figure 7 – An Example of Vehicle Trajectories of Base Case (without GW), from South to North

Future Works
We developed the evaluation model of green wave system and built the base case of Route 16 in
Kashiwa city. As the future works, we have a plan to simulate the future cases changing someparameters (such as the penetration of GW vehicles) on the assumption that the green wave system is
introduced. And we will analyse the condition that green wave system have a major impact on traffic
flow and road environment. On the other hand, we will also discuss the ineffective situation, andconclude and describe our result in the World Congress.
References
1. M. Kuwahara, R. Horiguchi, S. Hayashi and S. Tanaka:Development of an Assessment Tool of theEffects of ITS on CO2 Emissions, International Journal of Intelligent Transport Systems Research,
Vol. 11, No. 3, pp. 129-140, September 2013. (DOI)10.1007/s13177-013-0063-y.
2. R. Horiguchi and T. Oguchi:A Study on Car Following Models Simulating Various Adaptive
Cruise Control Behaviors, International Journal of Intelligent Transportation Systems Research,January 2014. (DOI) 10.1007/s13177-013-0077-5.
3. Füßler, H., J. Widmer, M. Käsemann, M. Mauve, H. Hartenstein (2003). Contention-based
forwarding for mobile ad hoc networks, Ad Hoc Networks, vol.1, pp.351-369.
4. Ohmori, S. T. Horimatsu, M. Fujise, K. Tokuda (2001). Radio communication technologies forvehicle information systems, in L. Vlacic, M. Parent and F. Harashima (eds), Intelligent vehicle
technologies, Butterworth.

6