Urban Mobility Solutions in Smart Cities

Urban Mobility Solutions in Smart Cities

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Connected Intelligence
  • Sustainable Growth and Tech Trends

Publication | Update: Sep 2020

Planning a smart city that delivers effective and equitable urban mobility solutions is one of the most pressing problems for cities.

“Smart cities must deliver effective mobility solutions while encouraging innovation, facilitating a collaborative ecosystem, and meeting sustainability goals. These challenges are part of the rapidly changing landscape of urban mobility as seen through the lens of a smart city planner", states Thomas Müller, Co-Founder and Managing Partner at Bee Smart City.

McKinsey claims there are few places where the reality of urban mobility "matches the public's aspirations for safe, clean, reliable, and affordable ways to get from A to B — and back again." Finding ways to improve urban mobility while "reducing congestion, accidents and pollution is a common challenge to all major cities in Europe."

Taking action to reduce and control pollution levels is a major priority. "Urban mobility accounts for 40% of all CO2 emissions of road transport and up to 70% of other pollutants from transport" in the EU. A highly critical report from the EU Court of Auditors indicates most European countries do not meet air quality standards. Air pollution is now the 'biggest environmental risk to public health in Europe, causing an estimated 400,000 premature deaths a year […] but the huge impact of toxic air has not been reflected in action to reduce emissions."

Urbanization and continuing car dependence lead to inevitable traffic congestion, emissions and mobility safety problems for transport policymakers and urban planners. According to the European Joint Research Center, "the cost of road congestion in Europe is equivalent to an estimated 1% of GDP, and its mitigation is the main priority of most infrastructure, traffic management and road charging measures. […] The reason for congestion in many cases is not a lack in capacity of road infrastructure, but rather an issue of demand management."

Although cities recognize the benefits of public transport in reducing pollution and congestion, local government efforts to deliver the benefits may collide with disruptive business models such as Uber and other ride-hailing services. The popularity of ride-hailing has "transformed the transportation marketplace in over six hundred cities," and in some cases Uber is seen as a threat to cities by increasing pollution and congestion while reducing public transport ridership. For many citizens (especially those in suburban areas), public transport is an uninviting option if transit stops are too far from their home or place of work. This is public transport's first-mile / last-mile problem. Ride-hailing and other sharing options provide opportunities to complement public transport. The challenge is how to overcome the first-mile / last-mile problem and enable citizen-centric journeys by integrating public transport with ride-hailing, ride-sharing, vehicle-sharing, and smart ticketing services.

In spite of measures to improve urban mobility safety, "road fatalities are increasing in many cities" and comprised 37% of Europe's total traffic fatalities. Urban population density, combined with cars, trucks and public transport vehicles "sharing crowded streets with vulnerable road users (pedestrians, cyclists and motorcyclists), makes the task of providing safe mobility a complex challenge (read more on mobility safety)." The safety challenge is further complicated by unsafe driving habits and inadequate infrastructure for cycling and micro-mobility users.

With greater reliance on digital technologies, the transport sector faces increased cyber-security risks. "Cybercriminals are increasingly able to attack not only the information technology, but also the operational technology that runs a city’s signalling and control systems." Cyber attacks could disrupt urban transport networks and trigger outages in public transport services.

In developing innovative smart city transportation solutions, cities face the challenge of how to ensure usability and continuity of services for citizens who have limited mobility options and those in "transport poverty".

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,” contests that cities must deliver effective smart mobility solutions while encouraging innovation, facilitating a collaborative ecosystem, and meeting sustainability goals. These challenges are part of the rapidly changing landscape of urban mobility as seen through the lens of a smart city planner. Strategies to meet city mobility challenges and solve urban mobility problems are unique to each city and involve:

  • Designing effective, equitable, safe and secure public transport systems, integrated with mobility-as-a-service (MaaS) and other platforms
  • Adapting to vehicle innovation and adoption (autonomous, connected, electric, shared, dockless)
  • Crafting policies and strategies to promote adherence to air quality standards and other quality-of-life measures
  • Developing public-private partnerships (PPPs) and collaborating with knowledge institutions to address air quality, traffic congestion, and sustainability issues
  • Building sustainable infrastructure — physical and digital — to support innovative mobility solutions from public and private sectors

Digital innovation is transforming the transport sector. Connected transport systems are already being deployed in a number of cities — these systems connect residential, employment, and innovation clusters in an organized way. Technology is also allowing public networks to become: (1) personal, based on user choices and data flows, (2) integrated and intelligent, (3) digitized (i.e., digitization of tickets and payless systems) and (4) automated and safer.

Examples of Smart Urban Mobility Solutions

Mobility as a Service (MaaS)

Moovel, an innovative MaaS platform combines and facilitates the use of multimode transport and shared mobility services and enables payments via a single interface. This smart urban mobility solution offers a multimodal capability which bundles transport options such as public transport, on-demand services, vehicle sharing, bike sharing and ride hailing. With access to the Moovel app, customers can book and pay for mobility services through an integrated account. For more information on MaaS, read our article "Mobility as a Service: A Blueprint for Disruption?".

Sustainable Travel Behavior

Innovactory is committed to making the travel behavior of its users more sustainable through development of TimesUpp, a smart travel assistant. Used by more than 150,000 people, TimesUpp "transforms a user’s calendar into the perfect travel assistant, advising on the best time and method of transport to get to their destination, with real-time updates on traffic jams and other unexpected delays."

In 2017, Innovactory introduced TimesUpp incentive programs with the goal of reducing transport-induced emissions and "prevented more than 250,000 car trips from being executed. This resulted in a CO2 saving of almost 650 tons." In 2018, TimesUpp launched the Smart Traveling! Campaign — an initiative of SmartwayZ.NL with stakeholders from public and private sectors — to reward commuters when they reduce usage of their car by switching to cycling, public transport or working from home.

Intelligent Traffic Management Solution (ITS)

PSIRoads is an intelligent traffic management solution that provides decision support enabled by artificial intelligence. This smart city mobility solution offers intelligent traffic management services such as change of traffic light phases, road user information, and dynamic changes in traffic capacity. This mobility solution is designed to help transport authorities meet strategic goals by minimizing vehicle emission levels and reducing traffic congestion in residential areas.

Traffic Congestion Service

An estimated 30% of traffic congestion in urban areas is caused by drivers looking for a parking space. Parquery — a cloud-based smart parking solution implemented in more than 15 cities worldwide — provides parking managers with accurate data on parking space usage and "also supports adaptive street light management, intelligent traffic management, and retail services for easy navigation in a smart city."

Micromobility Management

Micromobility — including systems and fleets of shared bikes and electric scooters — "is the hottest tech in transportation," according to CityLab. "The appeal of cycling and scooters to cities and startups alike is obvious: Micromobility systems complement each other while stealing trips from other modes." Read more about e-scooter sharing and e-scooter solution providers in our special market insight report E-Scooters: A Passing Fad or Smart Mobility?.

eCooltra is a European innovator in scooter sharing with a fleet of more than 3,000 electric scooters deployed in five cities. By using the eCooltra app, customers can book and unlock a free-floating scooter and pay only for minutes of usage. This e-mobility solution aims to improve the customer's quality of life, contribute to urban sustainability, and reduce CO2 emissions.

Public Transport Innovation

In Poland, an innovative passenger information system was designed and implemented in the City of Lublin. This project included modernization of urban transport infrastructure and the city's fleet of bus vehicles. The project involved installation of GSM and GPRS equipment in the vehicles; electronic displays at bus stops; dispatch center software; and a website offering dynamic information to passengers. For its innovation in traffic management and transport solutions, Lublin was named “Smart City of the Year" among cities with population between 100,000 and 350,000”. By modernizing transport infrastructure and improving communications with passengers, Lublin shows that mid-size cities can achieve far-reaching upgrades in the user experience and quality of urban mobility. 

Transport Poverty Reduction

The HiReach project, a research and innovation action funded under Europe's Horizon 2020 program, has the mission of finding solutions to improve accessibility, inclusion and equity of mobility by:

  • Exploring viable business models for affordable, modular and replicable mobility services (community transport, ridesharing, minibus)
  • Generating and testing mobility solutions created by startups and entrepreneurs
  • Enabling the viability and scaling-up of new mobility business models

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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:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

  • EMIS: EMIS Insight and EMIS Dealwatch

  • Enerdata: Energy Data Set, Energy Market Report, Energy Prices, LNG Trade Data and World Refineries Data

  • Euromoney: China Law and Practice, Emerging Markets, International Tax Review, Latin Finance, Managing Intellectual Property, Petroleum Economist, Project Finance, and Euromoney Magazine

  • Euromonitor International: Industry Capsules, Local Company Profiles, Sector Capsules

  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

  • FocusEconomics: Consensus Forecast Country Reports

  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

  • MarketLine: Company Profiles and Industry Profiles

  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

  • The Wall Street Journal

  • The Wall Street Transcript

  • Bloomberg

  • Standard & Poor’s Industry Surveys

  • Thomson Research

  • Thomson Street Events

  • Reuter 3000 Xtra

  • OneSource Business

  • Hoover’s

  • MGI

  • LSE

  • MIT

  • ERA

  • BBVA

  • IDC

  • IdExec

  • Moody’s

  • Factiva

  • Forrester Research

  • Computer Economics

  • Voice and Data

  • SIA / SSIR

  • Kiplinger Forecasts

  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

  • Deloitte

  • Oliver Wyman

  • Faulkner Information Services

  • Accenture

  • Ipsos

  • Mintel

  • Statista

  • Bureau van Dijk’s Amadeus

  • EY

  • PwC

  • Berg Insight

  • ABI research

  • Pyramid Research

  • Gartner Group

  • Juniper Research

  • MarketsandMarkets

  • GSA

  • Frost and Sullivan Analysis

  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

  • Open Europe

M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

  • MergerStat

  • Profound

  • DDAR

  • ISS Corporate Governance

  • BoardEx

  • Board Analyst

  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

Forecast methodology

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.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

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:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.


Market Quantification
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:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


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.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

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.