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MaaS (Mobility as a Service) - ビジネスモデル、ベンダー戦略、市場予測:2020年~2027年

Mobility-as-a-Service (MaaS): Business Models, Vendor Strategies & Market Forecasts 2020-2027

発行 Juniper Research Ltd 商品コード 931174
出版日 ページ情報 英文
納期: 即日から翌営業日
価格
本日の銀行送金レート: 1GBP=138.99円で換算しております。
MaaS (Mobility as a Service) - ビジネスモデル、ベンダー戦略、市場予測:2020年~2027年 Mobility-as-a-Service (MaaS): Business Models, Vendor Strategies & Market Forecasts 2020-2027
出版日: 2020年04月07日 ページ情報: 英文
概要

MaaS (Mobility as a Service) における新興市場の詳細な分析と評価を提供し、ビジネスモデルとその将来の見通しに関する重要な考察を提供します。また、MaaSの導入に関する現在の市場の進捗状況の評価を提供し、MaaSエコシステムにおけるステークホルダーの将来の機会についても取り上げます。

主要部門、地域別の機会、市場の発展に関する分析を提供し、2027年末までの成長予測に対する3つのシナリオ (低・中・高のユーザー導入率) を提供し、広範なシナリオベースの市場規模予測と連携しています。

当レポートには、以下が含まれます。

提供内容

  • 詳細戦略・競合 (PDF)
  • 7年間の詳細データ・予測 (PDF・Excel)
  • エグゼクティブサマリー・主な調査結果 (PDF)
  • オンラインデータプラットフォームへの12ヶ月間のアクセス

主な特徴

  • MaaS市場分析
  • ビジネスモデル分析
  • MaaS導入・準備の地域別分析
  • Juniper Research Leaderboard
  • MaaS都市ランキング
  • インタビュー
  • ベンチマーク業界予測

主な質問

掲載企業

データ・インタラクティブ予測

  • 7年間のベンチマーク予測:8地域別、12ヶ国別
    • カナダ
    • 中国
    • デンマーク
    • ドイツ
    • 日本
    • ノルウェー
    • ポルトガル
    • スペイン
    • 韓国
    • スウェーデン
    • 英国
    • 米国
  • 3つのシナリオベースの予測セット (主な影響要因の変動に応じて、低・中・高の導入率をモデル化)
    • MaaSユーザーの数
    • サービスに加入するMaaSユーザーの数
    • MaaSサブスクリプションの総収益
    • アドホックベースでサービス料金を支払うMaaSユーザーの総数
    • MaaSのアドホック総収益
    • MaaSからの総収益
    • MaaSのトリップ総数
    • MaaSのトリップに取って代わられた自家用車トリップの総数
    • 自家用車の旅とMaaSを比較した、節約時間の合計
    • MaaS導入による燃料コスト節約の合計
    • MaaS導入によるCO2e節約の合計
  • 予測データセット (44の表、6,600以上のデータポイントを含む) へのアクセス
  • インタラクティブなExcelシナリオツールが利用可能
目次

Juniper Research's latest ‘Mobility-as-a-Service ’ research provides an in-depth analysis and evaluation of this emerging market; delivering critical insights on the business models being formulated and their future prospects. It also provides an evaluation of the current market progress on MaaS (Mobility-as-a-Service) implementation and highlights the future opportunities for stakeholders across the MaaS ecosystem.

The research provides astute analysis of the key sectors, regional opportunities and developments in the market, whilst providing insights into the impact expected on existing transport methods. This is aligned with an extensive scenario-based market sizing forecast suite, offering three scenarios (for low, medium and high user adoption) to forecast growth until the end of 2027.

Metrics featured in each scenario include:

  • Total MaaS Users
  • MaaS Revenue
  • Time Savings from MaaS Deployment Compared with Private Car Use
  • Fuel Cost Savings from MaaS Introduction Compared with Private Car Use

This research suite includes:

  • Deep Dive Strategy & Competition (PDF)
  • 7-Year Deep Dive Data & Forecasting (PDF & Excel)
  • Executive Summary & Core Findings (PDF)
  • 12 months' access to harvest online data platform

Key Features

  • MaaS Market Analysis: Analysis of the potential impact of MaaS and what constraints are in place. The analysis is conducted utilising an impact assessment, and covers the following areas:
    • AVs (Autonomous Vehicles)
    • EVs (Electric Vehicles)
    • On-demand transport services
    • Ridesharing
    • Shared micromobility
    • Ticketing
  • Business Model Analysis: Detailed insight into the business models emerging in MaaS, including subscription services, and what incentives will be required.
  • Regional Analysis of MaaS Adoption & Readiness: Includes analysis of adoption and barriers to growth across 8 key global regions according to an evaluation of:
    • The supporting ecosystem
    • Existing MaaS efforts
    • MaaS growth prospects
  • Juniper Research Leaderboard: 15 leading MaaS vendors compared, scored and positioned on the Juniper Research Leaderboard matrix, including:
    • Citymapper
    • CTS
    • Fluidtime
    • GoTo
    • HERE Mobility
    • Karhoo
    • MaaS Global
    • Masabi
    • Moovit
    • Moovel
    • Ridecell
    • Siemens Mobility
    • Skedgo
    • Trafi
    • Ubigo
  • MaaS City Ranking: Evaluation of the top 10 pioneering cities experiments with MaaS deployment and their progress to date.
  • Interviews: Leading MaaS stakeholders interviewed, including:
    • Fluidtime
    • GoTo Global
    • HERE Mobility
    • Moovel
    • Masabi
    • Moovit
    • Nextbike
    • Trafi
  • Benchmark Industry Forecasts: MaaS adoption forecasts in terms of:
    • Users
    • Service revenue
    • End-user time and cost savings
    • Splits according to three user adoption scenarios and 8 key global regions

Key Questions

  • 1. What is the revenue opportunity for MaaS deployment in the next 7 years?
  • 2. What business models are emerging to support MaaS rollouts?
  • 3. How will the deployment of MaaS differ around the globe?
  • 4. Which cities are best placed for MaaS disruption?
  • 5. What are the biggest challenges to MaaS deployment and how are vendors approaching these?

Companies Referenced

  • Interviewed: Fluidtime, GoTo Global, HERE Mobility, Masabi, moovel, Moovit, Nextbike, Trafi.
  • Profiled: Citymapper, CTS, Fluidtime, GoTo Global, HERE Mobility, Karhoo, MaaS Global, Masabi, moovel, Moovit, Ridecell, Siemens Mobility, SkedGo, Trafi, UbiGo.
  • Mentioned: 2GetThere, AAA, Access IS, Adform, Aimsun, ALD Automotive, Alibaba, Anchorage People Mover, Apple, AppSolu, ARC, Arup, AS Roma, Atlanta Streetcar, Audi, Autocab, Autotel, Axygest, Bay Area Rapid Transit, BerlKönig, Bird, BlaBlaCar, blueSG, BMW, Bosch, BP, Breeze Bikeshare, Bytemark, Cab Guru, Cabify, Cabonline, Call-n-Ride, Caltrain, Car Next Door, Carey International, Chalo, Chicago Transit Authority, Citi Bike, Citybikes, CityFleet Networks, Civil Maps, ComfortDelGro Corporation, Conduent, Contact Light, Continental, Cox Automotive, Cubic Corporation, Daimler, Dataspeed, Debono Group, DENSO, Denver RTD, Deutsche Bahn, Deutsche Bank, Dial-a-Ride, Didi Chuxing, Dojo, Dot Transfers, DOVU, DriveNow, East Japan Railway Company, EkoRent, emmy sharing, Enterprise Car Club, eos.uptrade, ESP Group, Experian, FDM (the Danish Touring Club), FEDA (Forces Elèctriques d'Andorra), Ferrovial, Fever, Fleetondemand , Flinkster, Flit Technologies, Flitways, Ford, Foursquare, FREE NOW, Galois, Gett, Go Get, GoCatch, Gojek, Goldbeck Parking, Goldman Sachs, Google, Grab, HaCon, Haulmont, Hertz, Highways England, Hitachi, HRT (Helsinki Regional Transport Authority), Hubway, Hyundai, iCabbi, InComm, Ingogo, INIT, InMobi, Insert Effect, Intel, Inter American Development Bank, Iomob, IQ Payments, ITO World, ITT Inc, iVcardo, Jaguar Land Rover, Jayride, JLM, Kadient, Kapsch Group, Kapten, Keolis, Kisio, LA Metrolink, Las Vegas RTC, Legatum, Liftshare, Lime, Los Angeles DOT, Los Angeles Metro, Lyft, MaaS Alliance, MaaS Australia, MaaS Scotland, Magenta Technology, Manly Ferries, Mapstr, MARii (the Malaysia Automotive, Robotics and IoT Institute), Mastercard, MBTA, Mercedes-Benz, Metro Transit (Minneapolis), MG, MILES Mobility GmbH, Mitsubishi, Mitsui Fudosan, Mobileeee, Mobileye, Mobility Lab UK, MobilityMixx, MobilityX, Montargis, Moses, Move About, Mudlark, Munich Airport, Musement, MVG (Münchner Verkehrsgesellschaft), National Entitlement Card, National Express, NaviGoGo, Netflix, New York MTA, Nirenberg Neuroscience, Nissan, NorisBike, Nuro, Ofo, Ola, Optus, Orange County Transportation Authority, oxipit, Pace, Padam, Parkopedia, PayPal, Penske Automotive Group, Pioneer, PTV, Rakuten, RCI Bank, Renault, Renfe, respire, Road and Transport Authority Dubai, Roll Scooters, Route Monkey, SAIPS, San Antonio VIA, San Diego MTS & North County Transit District, San Francisco Municipal Transportation Agency, Santa Clara VTA, Santander, Saudi Aramco, Schaeffler AG, Scheidt & Bachmann, SEAT, SHARE NOW, Share Transport, Shell, Sixt, Skip, Skoda, SL (Storstockholms Lokaltrafik), smart, Smove, SMRT Corporation, SNCF, Société Générale, SoftBank, Sony, SouthWest Transit, SPARK, SpotHero, SPTrans, SRTA (Shasta Regional Transportation Agency), Stadtmobil, Südostbahn, SUMA (Shared Use Mobility Agency), Swift Fleet, SYSTRA, Taksee, TAO, Taxi Berlin, Telebus, Tesla, Tessa, TfL (Transport for London), TfWM (Transport for West Midlands), Thames Clippers, The Hague HTM, Ticketer, TIER, TomTom, Toronto Transit Commission, Toyota, TransitLink Anywheel, Transport for Greater Manchester, Transport for New South Wales, Transport Scotland, Transport Systems Catapult, Travelport, TriMet, Uber, UK Research and Innovation, UN, Urbano, Velodyne, Vesta, Virginia Railway Express, Visa, Voi Technology, Volkswagen, Volvo, Walmart, Waymo, Waze, WeWork, What3Words, Wiman, Wirecard, XXImo, Yamaha, Young Scot, Yuso, ZF, Zipcar, ZITY.

Data & Interactive Forecast

Juniper Research's ‘Mobility-as-a-Service ’ forecast suite includes:

  • 7-year benchmark forecasts for key metrics by 8 key regions and 12 country-level splits including:
    • Canada
    • China
    • Denmark
    • Germany
    • Japan
    • Norway
    • Portugal
    • Spain
    • South Korea
    • Sweden
    • UK
    • US
  • Three scenario-based forecast sets modelling low, medium and high adoption rates according to variations in key influencing factors. Outcomes cover the following metrics:
    • Number of MaaS Users
    • Number of MaaS Users who Subscribe to the Service
    • Total MaaS Subscription Revenue
    • Total Number of MaaS Users who Pay for the Service on an Ad Hoc Basis
    • Total MaaS Ad Hoc Revenue
    • Total Revenue from MaaS
    • Total Number of MaaS Trips
    • Total Number of Private Car Trips Replaced with MaaS Trips
    • Total Time Saved via MaaS Compared with Private Car Journeys
    • Total Fuel Cost Savings from MaaS Implementation
    • Total CO2e Savings from MaaS Implementation
  • Access to the full set of forecast data of 44 tables and over 6,600 datapoints.
  • Interactive Excel Scenario tool allowing the user the ability to manipulate Juniper Research's data for 12 different metrics.

Juniper Research's highly granular IFxls (Interactive Excels) enable clients to manipulate Juniper Research's forecast data and charts to test their own assumptions using the Interactive Scenario Tool, and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.