Smart Urban Mobility | Visions of the Future
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Connected Intelligence
- Sustainable Growth and Tech Trends
Publication | Update: Sep 2020
According to Jon Glasco, a consultant and writer focused on innovation in smart cities and smart urban mobility, in an article for Bee Smart City, “Smart Mobility: Challenges and Solutions in Smart Cities,” judging from innovation trends and disruptive forces in urban mobility, it is realistic to envision a future scenario when smart city residents and visitors enjoy a wider range of affordable, multimodal, on-demand mobility options; and conventional cars and ownership practices are replaced by shared electric and autonomous vehicles.
The Boston Consulting Group believes widespread adoption of autonomous technologies could yield substantial benefits by eliminating road fatalities, improving travel times by up to 40%, recovering billions of hours lost to commuting and congestion, and generating total benefits to society worth $ 1.3 trillion.
Lukas Neckermann, strategist and transport visionary and author of "Smart Cities, Smart Mobility,2019,” predicts the rapid adoption and positive impact of electric vehicles: "Close to 100% of new vehicles sold in 2025 in the developed world will be electrified (including hybrids)" […] and "however transformational electric vehicles are to cities, their lasting legacy will be the reduction of deadly air pollution."
According to Akhil Chauhan, Arcadis National Director, Smart Mobility and CAV, the mobility landscape is shifting to make journeys safer, cheaper and more efficient. Vehicles are evolving to sense and share journey data that can increase reliability and limit delays.
Connected vehicles (CV) - By transcending traditional communication barriers such as bandwidth and distance, CV technology facilitates the exchange of data via vehicle-to-everything (V2X) communications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communication. The connected aspect of CAV will put real-time data to work to enhance safety and combat travel delays. Vehicles will form a roving network of traffic data sensors using V2V, V2I and V2P solutions to map timely routes for travelers and help them avoid hazards. Another benefit of CV is the potential to shrink transportation’s carbon footprint. According to the Environmental Protection Agency, transportation accounted for the largest portion (29%) of total U.S. Greenhouse Gas Emissions. CVs using V2X applications can curtail congestion, reducing gasoline consumption and emissions
Autonomous vehicles (AV) - According to National Highway Traffic Safety Administration, 94% of serious crashes can be attributed to human e r ro r.8 Imagine the potential for fewer collisions, injuries and deaths by taking human error out of the equation via automated passenger vehicles and freight – research suggests it could potentially cut the number of car crash fatalities in half.
Connected and autonomous vehicles - CAV incorporates CV and AV benefits at once. For example, an AV that’s forced to slam on its brakes can share that information through V2V applications with other vehicles outside its AV sensor range immediately, limiting not only the potential for a collision but also the ripple effect of unnecessary stops and starts that can cause traffic backups. Right now, the U.S. Department of Transportation (USDOT) plans to reserve the 5.9 GHz part of the spectrum for dedicated short-range communications for CV applications, but other technologies that use cellular technology, such as 4G or 5G, could be incorporated to maximize the benefits of V2X communications.
A number of cities are already testing autonomous cars. The city of Singapore is testing autonomous bus and taxi trials in a part of its city; in Gothenburg, Volvo is testing 100 of its autonomous cars, while in Boston NuTonomy has tested a number of vehicles on the roads and is now testing with passengers — it is also now collaborating with Lyft to optimize the automated vehicle user experience.
Shared mobility - Emerging technologies and business models. Vehicle ownership is trending downward. Shared mobility, from ridesharing and automated shuttles to e-scooters and bike-sharing services, will expedite journeys and help reverse the impact of climate change by reducing the number of gas-powered cars on the road.
New Vehicle Share Mobility | Autonomous Driving - Currently there is an enormous amount of research being done on the technology of driverless/autonomous cars. The Citi GPS, “Car of the Future” series, highlights these technologies, but also discuss the different business models in which driverless cars could operate. It estimates that on-demand driverless rideshare networks in urban environments — a so-called Robotaxi model situated in highly dense cities where dedicated tax-fleets could be used 70% of the time — could charge $ 0.25-$ 0.50 per mile per person and still earn strong gross margins. This cost is less than for a personal car, which is estimated at $ 0.76 per mile in the 2020-25 timeframe.
Driverless carpools working in the same capacity as public transport networks where multiple people are picked up and dropped off at their destination could create low-cost commuting alternatives with the luxury of being picked up at home.
Other models include car peer-to-peer timeshare models where people with comparable schedules could partially own a vehicle and automaker subscription models where buying a driverless car could become a subscription to a whole entire fleet. All these models could ultimately reduce the number of cars in a city — reducing the amount of space needed for parking lots and garages and improving congestion and air quality in a city.
For example Spieser estimates that shared vehicle mobility, in particular shared-driverless cars can meet the personal mobility of the entire population of Singapore with one-third of the total number of passenger vehicles in operation.
A BCG study estimated that under there could be an 11% reduction in the number of vehicles on the road in Boston if there is a gradual shift from private to shared driverless cars in Boston reducing emissions to 42% from current and reducing parking space by 16%.
First- and last-mile mobility - First- and last-mile mobility (journeys from origins/destinations to the main transportation network) are the least efficient piece of the puzzle, comprising up to 28% of the total cost of moving goods and taking an inordinate amount of time and effort for individuals on the move. CAV – and especially shared mobility – could change that. They could allow citizens to use more energy efficient modes to bookend their trips and lessen the need for parking, saving citizens time and money while also reducing the need for parking structures. Automation can revolutionize goods delivery as well.
By 2030, the global market for delivery bots is expected to reach $ 17 billion. These automated freight and delivery fleets could change workforce demand, and replacing trucks and vans with drones and bots could free up space on congested streets and curbsides.
Intelligent transportation systems - High-speed communication networks and advanced traffic management systems are required for making CAV a core piece of a transportation network. Agencies must equip their streets and freeways with ITS tech before citizens can realize the full benefits of CAV. Cameras, radars and other roadside sensing equipment are crucial for capturing real-time data. But a robust communication infrastructure, from sophisticated fiber optic networks to backend systems that store, analyze and share data, must be in place to turn the data into actionable insights.
Electric vehicle infrastructure - There will be 559 million EVs on the road globally by 2040. Cities and states will have to install many more charging stations and account for the impact on the power grid. To allow easy access to power, four types of charging infrastructure points will be needed: Residential, workplace, destination, and in-transit charging points.
A future-proof plan
According to Akhil Chauhan, Arcadis National Director, Smart Mobility and CAV, transportation systems are advancing at an unprecedented pace. Autonomous driving, connectivity, electrification, and shared mobility offer exciting advantages for the future.
He provides a process for laying out and achieving a strategic vision for connected and autonomous vehicles (CAV). It’s all about installing a sustainable user experience using a future-proof transportation system.
To ensure your plan is future proof, your organization must:
1.Create a strong communications backbone. CAV will bring monumental levels of data exchange into the transportation sphere. Take advantage of other initiatives to install a strong communications backbone, preferably using fiber optics (although fiber-wireless systems could work). For example, if you widen a roadway, include a fiber installation as part of the project. Public-private partnerships could offer ways to lower costs on these implementations, as companies are looking to tap into robust communication networks for the coming 5G revolution. Companies will “pay” public agencies in fiber strands in exchange for right-of-way.
2.Be technology agnostic. Although public-private partnerships can be very useful, don’t get locked into one specific vendor or technology. Just because something is fancy and exciting doesn’t mean it’s right for your system, any advances you deploy should be interoperable and scalable over the long-term.
3.Look to your peers for great ideas. Identify specific use cases that address problems similar to yours. Organizations in the U.S. are experimenting with ideas like smart work zones, integrated data portals and truck platooning. See how others use emerging technologies and consider how they might enhance transportation in your area. Keeping these principles in mind will help you develop a roadmap of policy, planning, and infrastructure milestones that your organization can strive for in a systematic and safe manner.
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Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.