Second Phase of Cross-Ministerial Strategic Innovation
Promotion Program— Innovation of Automated Driving for Universal Services(System and Service Expansion)

Study of the Impact of AutomatedDriving on Reducing Traffic Accidents
and on Others
Report (Summery)
March 2019
The University of Tokyo
Doshisha University

Objective of study
Research and development plan for Second Phase of Cross-Ministerial Strategic
Innovation Promotion Program — Innovation of Automated Driving for UniversalServices (System and Service Expansion)
Commercial development and increased diffusion of automated driving (AD) vehicles will
help to reduce traffic accidents, alleviate traffic congestion, ensure mobility forvulnerable road users, resolve the driver shortage and reduce costs in logistics andtransport services, and resolve other social problems. The aim is to achieve a society inwhich everyone is able to enjoy a high-quality life.

Quantification and monetaryvaluation of impact (benefitsand potential risks)

Focus on differences in
impact caused bygovernmental policies and/or
manufactures’ launching
methods

Basic references forfostering social acceptanceUse in corporate
management andpolicymaking
2(3) Effect on road transport(4) Effect on traffic services
sector(5) Effect on industry andsociety

Advice(6) Internationalcooperation(1) Contribution to UN SDGs

(2) Diffusion simulation

Impact assessment(7) Advisory Committee

Overall configuration of study3

Study items(★ indicates items conducted in FY 2018)(1) Relevance of AD to SDGs ★
(2) Simulation of AD vehicle diffusion ★
(3) Effect on road transport ★
i. Estimation of effectiveness in reducing traffic accidents
ii. Estimation of reduction of traffic congestion and reduction of CO2 emissions(4) Effect on traffic services sector
i. Ensuring mobility for vulnerable road users and in depopulated areas and other locations withpoor access to transport
ii. Reduction of costs and resolution of driver shortage in logistics and transport services
iii. Change in ownership and usage of vehicle, and the structure of consumers’ choice(5) Effect on industry and society
i. Effect on whole automobile industry due to change in vehicle ownership structure and othereffects
ii. Contribution to growth of the total factor productivity of the Japanese economy(6) Formation of organization for international cooperation ★
(7) Convening of Advisory Committee ★

4Relevance of AD to SDGs
The method of organization was studied. The relevance of the following fivesocial impacts of AD to 169 Targets is going to be organized:
1) Reduction of traffic accidents,
2) Reduction of traffic congestion and CO2 emissions,3) Effect on city structure,
Changes in land use (e.g. conversion of parking lots to the other uses) , urban
compactization or expansion into outlying areas etc.

4) Effect on economy and industries,
Changes in the industrial organization of automobile industry, changes in thenational industrial structure in relation to the changed inputs and final demand in theautomobile industry, improvement in the productivity in a wide range of industrial
sectors including transportation and agriculture, creation of new business etc.

5) Effect on lifestyles and educational opportunity.
Changes in people’s lifestyles, securing a means of transport for vulnerable road
users, improved accessibility to facilities such as schools thanks to increased
mobility and expansion of travel area etc.
On April 5, 2019, a “Advisory Committee on the Relevance of AD to SDGs” is scheduled to be held at
Doshisha University to study the impacts from AD and their relevance to SDGs.

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Simulation of AD vehicle diffusion (1)1) Survey of existing research on factors affecting the diffusion of ADvehicles
A survey of existing research in Japan and other countries is ongoing. The factors affecting the diffusion of AD vehicles will be
divided into(a) Supply side factors including “technical development”, and “cost ofautomated driving”,(b) Demand side factors including “conditions for establishment of car
sharing and ride sharing”, “city structure” , “trip demand” , “consumermode selection” , and “lifespan of a vehicle ”.
Points to be considered:• Use of self-driving vehicles by adults not having a driver’s license(Sivak and Schoettle, 2015） .
• Use of self-driving vehicles by persons of ten years and older (oneof simulation scenarios in Trommer et al.（2016）)
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Simulation of AD vehicle diffusion (2)2) Development of a prototype simulation model for discarding owned
vehicles and new vehicle diffusion
A simulation was conducted for passenger vehicles (standard-sized+ small-sized, not including light motor vehicles) based on thefollowing assumptions:
1. The number of owned vehicles is assumed to be constant at the2017 level,
2. The same number of the vehicles discarded each year isassumed to be replaced with new vehicles,
3. SAE Level 4 passenger vehicles appear on the market in 2025
and consist 90% of annual new vehicle sales by 2040 and 100%by 2050. (The proportion of new vehicle sales increases linearlyin 2025 - 2040 and 2040 - 2050.)
The result of the simulations is that the proportion of total passengervehicles accounted for by SAE Level 4 passenger vehicles would bearound 50 % in 2040 and more than 80 % in 2050.

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Simulation of AD vehicle diffusion (3)Provisional estimate based on the conditions noted in the previous slide
100%

80%

Newly registered vehicles
in 2017 and previous years
Newly registered vehicles
in 2018 and thereafter(other than SAE Level 4)

60%

40%

Newly registered vehicles
in 2018 and thereafter(SAE Level 4)

20%

0%
2017

2020

2025

2030

2035

2040

2045

2050
Year

Note: This Figure depicts one of the simulation results by the prototype model8

Effect on road transport (1)1) Estimation of effectiveness in reducing traffic accidents• The logic behind the method of estimation was clarified .
• In this study, the monetary value for the reduction in traffic
accidents due to the diffusion of AD vehicles is going to beestimated. In this process, the monetary value for the reduction in“psychological burden of perpetrator” due to avoidance of traffic
accidents, for which no monetary valuation had been conductedpreviously, will be estimated by means of economic experiments.
• In fiscal year of 2018, the method of using the software used for
economic experiments, was shared among the members of theresearch team. The use of web-based experiments in addition tolaboratory experiments was also studied.

9Effect on road transport (2)2) Estimation of reduction of traffic congestion and reductionof CO2 emissionsa) Estimation of reduction of traffic congestion• Data on the effect of adaptive cruise control (ACC) and cooperative
adaptive cruise control (CACC) in reducing traffic congestion will beorganized, based primarily on Japanese research papers.
• Research papers published in Japan have reported that studies
have not always found that existing ACC performance, whichstresses riding comfort at high speed, is sufficiently effective inalleviating traffic congestion.
• Among the research papers published in other countries,
Hoongendoorn et al. (2014, 2017) and Milakis et al. (2017) foundthat ACC changed traffic capacity -5% to +10%, and CACC further
improved traffic capacity at diffusion rates of 40% or greater.
10Effect on road transport (3)b) Estimation of reduction of CO2 emissions (CO2 emission
factor for EVs)
• The CO2 emission factor for EVs in 2015 was less than half that of gasoline
vehicles (59 g-CO2/km). However, it was worse than 2010 (47 g-CO2/km)
due to a change in the power supply configuration.*1 In 2030, the power
supply configuration is expected to be changed to a low carbon supply, andthe emission factor is expected to improve to 41 g-CO2/km.
• In Germany, which uses large
Gasoline
vehiclesquantities of coal-fired thermalpower, the CO2 emission factor
was 49 g-CO2/km in 2015. InFrance, which uses large
quantities of nuclear power, it is 5g-CO2/km. In the EU overall, itwas 34 g-CO2/km in 2015. This is
expected to improve to 23gCO2/km by 2030 through areduction in the use of coal-fired
thermal power.
Figure CO emissions by power train configuration 11
EV (CO2 emission * [g-CO2/km])

CO2 emission*[g-CO2/km]

Japan

EU

Chinaa: 132
Note: aReduction

Germany

FranceSource: 2nd Roundtable Discussion on the Energy Situation, Agency for Natural Resources
and Energy, Ministry of Economy, Trade and Industry, September 2017
2*1: 2nd Roundtable Discussion on the Energy Situation, Agency for Natural Resources and Energy, Ministry of Economy, Trade and Industry, September 2017Formation of organization for international
cooperation• Japanese-German
cooperation• Based on the Joint Declaration ofIntent signed on January 12, 2017by the Cabinet Office of theJapanese government and theGerman Federal Ministry of
Education and Research (BMBF)• Japan and Germany will
cooperate in the area of“socioeconomic impactassessment.”(See joint press release issued onFebruary 4, 2019 by the Cabinet
Office and BMBF)
February 4, 2019 Joint press release by 12Japanese Cabinet Office and German BMBF

Convening of Advisory Committee• The advisory committee will discuss simulation methods and results, the implications of thesimulation results, ways of using the results to foster social acceptance and other issues.
• The first meeting of the advisory committee will be held on March 7, 2019.
Members of the Advisory Committee on the Social Impact of Automated Driving Systems
Name
Masato Itohisa
Takeyoshi Imai
Keisuke Uehara

Shusuke Kakiuchi

Affiliation
Associate Professor, Faculty of Social Science, Hosei University
Professor, Graduate School of Law, Hosei University
Associate Professor, Faculty Environment and Information Studies, Keio UniversityProfessor and Director, Advanced Mobility Research Center, Institute of Industrial Science,
The University of Tokyo
Professor, Faculty of Law, Graduate Schools for Law and Politics, The University of Tokyo

Masanobu Kii

Professor, Faculty of Engineering and Design, Kagawa University

Yuto Kitamura

Associate Professor, Graduate School of Education, The University of Tokyo
Specially Appointed Associate Professor, Center for Embedded Computing Systems,
Graduate School of Informatics, Nagoya University
Associate Professor, Department of Civil and Environmental Engineering, College ofScience and Engineering, Ritsumeikan University
Associate Professor, Automated Vehicle Research Unit, Future Society Creation ResearchCore, Institute for Frontier Science Initiative, Kanazawa University
Professor, Faculty of Commerce and Director, Institute for Technology, Enterprise andCompetitiveness, Doshisha University
Professor, Graduate School of International Management, Yokohama City University
Professor, Department of Mechanical Systems Engineering, Tokyo University ofAgriculture and Technology
Professor, Faculty of Policy Studies and Director-General, Institute for Technology,
Enterprise and Competitiveness, Doshisha University
Professor, Department of Civil and Environmental Engineering, Faculty of Science andEngineering, Waseda University
Associate Professor, Center for the Study of Co Design, Osaka University○ Takashi Oguchi

Ryo Kurachi
Yasuhiro Shiomi
Naoki Suganuma
Satoshi Taguchi☆ Akihiro Nakamura
Pongsathorn
Raksincharoensak
Hiroaki Miyoshi
Akinori Morimoto
Goro Yamazaki○: Chair ☆: Observer

Speciality
Technology management
Criminal law
Iinformation technology
Traffic control engineering
Civil procedure law
Urban and transportation
planning
Education
Cybersecurity
Traffic engineering
Robotics engineering
Behavioral economics
Public economics
Machine dynamics andcontrol
Technology and publicpolicy
Urban planning

13
Cultural anthropology

References
Hoogendoorn, R.G., Van Arem, B., Hoogendoorn, S.：Policy and Society Related
Implications of Automated Driving: A Review of Literature and Directions for Future
Research, Journal of Intelligent Transportation Systems Technology Planning andOperations 21(4):324-348, 2017
Hoogendoorn, R.G., Van Arem, B., Hoogendoorn, S.：Automated Driving, Traffic Flow
Efficiency, and Human Factors, Transportation Research Record Journal of theTransportation Research Board 2422:113-120, 2014
Milakis, D. , Snelder, M., Van Arem, B., Van Wee, B., Correia, G.H.D.A.：Development andTransport Implications of Automated Vehicles in the Netherlands: Scenarios for 2030 and2050，European Journal of Transport and Infrastructure Research vol.17 no.1, 2017
Sivak, M., Schoettle, B.：Influence of Current Nondrivers on the Amount of Travel and Trip
Patterns with Self-Driving Vehicles, Report No. UMTRI-2015-39, The University of Michigan
Transportation Research Institute, 2015, http://umich.edu/~umtriswt/PDF/UMTRI-201539.pdf （accessed March 3, 2019）
Trommer, S., Kolarova, V., Fraedrich, E., Kröger, L., Kickhöfer, B., Kuhnimhof, T., Lenz, B.,
Phleps, P.： Autonomous Driving: The Impact of Vehicle Automation on Mobility Behavior,Institute of Mobility Research, 2016,
https://www.ifmo.de/files/publications_content/2016/ifmo_2016_Autonomous_Driving_2035_14
en.pdf（accessed January 27, 2019）