Category: IoT

Smart Cities – Urban transportation at a crossroad

Is it taking longer to get around town?

  • San Francisco Bay Area traffic up 80% since 2010
  • Los Angeles drivers average an annual 104.1 hour commute, worst in the world
  • Moscow drivers, in second most congested city, average 91.4 hour commutes
  • New York and San Francisco are close behind, at 89.4 and 82.6 hours
  • San Paulo citizens spend 3 to 4 hours behind the wheel due to traffic congestion
  • US loses $67.5bn, or 0.7% GDP, in productivity annually due to traffic congestion
  • UK will lose £22bn annually by 2025 due to traffic congestion
  • Can’t find parking? 30% cars on city streets are looking for parking
  • 76% US commuters drive alone to work

And, it’s not getting better.

  • 66%, or 6.4bn people worldwide will live in urban areas by 2050, double since 1970
  • 85%, in US, expect to live in urban areas by 2020

Adding more roads, trains, and parking is unlikely to solve traffic congestion problems.  Urban planners need to get in front of this growing problem.

Ascent of Mobility as a Service (MaaS)

MaaS, sometimes called Transportation as a Service (TaaS) promotes a shift from privately owned transportation to a synergy between public and private transportation to get people around easier, and reduce congestion.

One current approach adds actionable, real-time transportation data from public and private transports into mobile apps – bus, taxi, ride hail, carshare, carpool, light rail, shuttles, bikeshare, helicopters, walking.  This actionable data includes pricing, travel times, availability, departure times, arrival times, convenience, transfers, carbon footprint, more – empowering people to choose a transport based on their personal needs.

Need to get there faster?  Choose ride hail or taxi.  Don’t need to get there fast?  Spend less money and choose train, subway, bus.  Or, take more time and spend less with carpool, bikeshare, walk.  MaaS helps you decide.

Can we really get drivers out of their cars?

In past years, other types of transportation could not offer the convenience, comfort, and safety of cars.  Today, with rising traffic congestion and new innovative transport modes, such as ride hail, rideshare, carshare, bikeshare, convenient multi-modal trips on public and private transportation, a car is often no longer the most convenient, or fastest, way to get somewhere.

Convenient cashless payments

Urban planners understand convenience is key to convincing people to take alternate types of transport.  Today, many MaaS solutions provide cashless and ticketless payments, enabling people to book and pay with a single cashless account, with options to pay by trip, monthly subscription, or both.

Some MaaS solutions enable payment for multi-mode trips in a single transaction.  MaaS automatically apportions payments to participating transportation providers.  Simple, convenient, transparent.

Dynamic pricing reduces congestion

Forward thinking urban planners use dynamic pricing to encourage ridership when occupancy is low.  Likewise, the same urban planners use smart tolling and dynamic pricing to increase the cost of driving a private car to and from work during rush hour to reduce congestion.  High occupancy vehicle lanes are effective at reducing the cost of driving with passengers while reducing congestion.

IoT sensor networks

Aggregating a city’s transport modes into smart dashboards is good first step.  However, redefining how people get around requires much more – IoT sensors and smart software to collect and analyze enormous amounts of transit data, crucial to being able to optimize transportation for billions of people.

Gartner forecasts 20.4bn connected Internet of Things (IoT) devices by 2020.  Many of these devices will find their way into networks of machine-to-machine (M2M) city-wide transit sensors, monitoring city traffic flow, parking, pedestrian traffic, vehicle queuing, accidents, potholes, pop-up road construction, weather, intersection efficacy, pollution, and more.  

Technology in these IoT sensors continues to get smarter.  Today, in-ground vehicle detectors, in use for decades, facilitate traffic flow by controlling stop lights.  Bluetooth Low Energy, WiFi, cellular, and LD street light sensors are being deployed to measure vehicle and pedestrian traffic, with embedded vehicle-to-vehicle (V2V) sensors facilitating real-time, inter-car traffic flow.  

Public buses, light rail, ferries, and trains incorporate GPS IoT devices in their vehicles, with many using a technology called GTFS to help people track vehicles in real-time.  With GTFS, for example, a commuter can determine whether a bus is arriving late to a bus stop, and if so, number of minutes late and number of passengers already on the bus.

Computer vision algorithms, with the help of deep learning neural networks, are beginning to reliably manage vehicle and pedestrian traffic in good weather with 90% to 95% accuracy.  Future computer vision advances promise increased efficacy at night and poor weather.

With mobile phone penetration exceeding 80%, crowdsource transit data from millions of users is helping commuters find best routes, receive accident alerts, avoid road hazards, and reduce transit times.   Waze, Moovit, and Transit are examples of effective crowdsource GPS apps.

Today, with billions of daily crowdsource transit data points, real-time GTFS updates, and burgeoning networks of transit sensors, it’s a good start.  Sensor coverage remains inadequate and unreliable.  Urban planners do not yet have good visibility into their transport networks, nor the tools to optimize city-wide real-time traffic flow.  

Smart city APIs (for software developers)

Open public and private APIs are crucial to collecting and analyzing telematics data from disparate transport sources to provide a modern, urban transport experience.  

As networks of IoT transport sensors are deployed, most government agencies will mandate real-time transit data be made available to the public.  During planning for a MaaS project, for example, a project manager will plan for a separate “deliverable” that includes APIs and SDK documentation.

Likewise, private companies will begin providing APIs and data to augment and complement community-data.  Moovit, for example, recently licensed crowdsource data to government agencies.

When APIs are done well, developers will mashup disparate public and private transit data in innovative ways we cannot imagine today.

MaaS pilots

MaaS is in its infancy, with many projects labelled as pilots or proof of concepts.

  • City planners in Helsinki, Finland, aim to eliminate the need for a resident to own a car by 2025.  Since 2016, residents use an app called Whim to find, book, and pay for trips by train, bus, taxi, carshare, and bikeshare.  When a trip entails multiple transportation modes, Whim app handles all cashless payments and transfers.
  • Queenstown, New Zealand, is a vibrant city with 28,200 residents, and an internationally acclaimed four season resort city with 2m visitors annually.  In 2017, New Zealand Transport Agency developed a mobile app and high performance MaaS server platform for tourists to find and book trips by bus, taxi, shuttle bus, water taxi, carshare, and helicopter.  Mobile app dashboard provides an easy-to-use journey planner, enabling users to select the type of transportation that fits their needs – by travel time, price, and convenience.  Mobile app displays nearby transit vehicles moving in real-time on a local map, providing situational awareness for first-time tourists.
  • Qixxit project in Germany provides journey planning for more than 21 service providers, including carsharing, ridesharing, bikesharing, trains, bus, taxi, car rental.
  • Moovel project in Stuttgart, Hamburg, Boston, Portland, and Helsinki enables users to search, book, and pay for public and private transportation in a single app.  Scope is similar to a pilot in Vienna called SMILE.
  • Innovative Beeline project in Singapore crowdsources bus services, enabling passengers to suggest new on-demand bus routes.  Similar to a project called Bridj in Boston, Kansas City, and Washington DC, which provides on-demand commuter shuttle service; Bridj found its on-demand model is 40% to 60% more efficient than traditional bus transit.
  • Ridership on commuter rail systems is growing. Though, often, the nearest train station is too far without requiring a car, causing congestion in train station parking lots. To fill this need, Lyft and Uber partner with city planners and transit agencies to provide, sometimes subsidized, first- and last-mile segments, reducing commute times and train station congestion.
  • In Portland, Lyft integrated vehicle availability and pricing into TriMet Tickets app, providing additional multi-segment trip choices for public bus, light rail and commuter rail customers.
  • In Florida, Uber partnered with Florida’s Pinellas Transit Authority to provide service to areas in which bus services were curtailed due to budget cuts.
  • San Francisco installed sensors under thousands of parking spaces, enabling drivers to find open spaces in real-time.  Dynamic pricing software enables parking managers to adjust prices based on occupancy.  Parking managers found that keeping one or two spaces free on each block led to significant improvements in cars looking for parking.
  • Moovit, a private company, provides MaaS services in more than 1,400 cities in 77 countries, with more than 80m users.  
  • Citymapper uses open public data and analytics to optimize commuter transit.  Company also used analytics to find coverage gaps at night in London’s public transport network, and runs new bus routes to cover these gaps.  Company analytics also spotted congested public transport routes, and began running buses to augment these routes.
  • US Department of Transportation (DOT) launched a Smart Cities Challenge to encourage cities to think creatively and experiment with new mobility alternatives.  78 cities submitted proposals.  As a result, states such as Nevada, Michigan, Pennsylvania, and Florida are developing MaaS pilots.
  • Airbus is developing piloted air taxis carrying four passengers, due for 2018 launch, and a self-piloting air taxi that can carry two, due for 2020 launch.  Other firms developing air taxis include Volocopter, Vilium, and Uber.  Imagine using your MaaS mobile app to hail an air taxi for a 15 minute, $40 ride from San Jose to San Francisco (normally takes one hour with no traffic).
  • EU members created the MaaS Alliance, a public-private partnership to create the foundation and common approach to MaaS.
  • Future MaaS projects will incorporate autonomous vehicles, which inject new cost and pricing dynamics into public and private transportation.  Singapore is testing limited driverless taxis, with Tokyo aiming to provide fleets of robot shuttles and driverless vehicles for 2020 Olympics.  Volocopter is testing air taxis in Dubai, while Airbus is looking to put its flying taxis in service in 2018.

Consumers are increasingly embracing new mobility choices, and local government agencies will increasingly partner with private transport providers.  Global carsharing is expected to grow from $1.1bn in 2015 to $6.5bn in 2024.  On-demand bus routes continue to make inroads in Singapore, Boston, Kansas City, London, and elsewhere.  In 2004, 11 cities provided bikeshare services; today, more than 1,000 companies in 50 countries provide bikeshare services; Beijing, for example, has 1 shared bicycle for every 10 residents, while Washington DC has 1 shared bicycle for every 175 residents. Popular ride hailing services, such as Uber and Lyft, are closing on 1,000 cities and 80+ countries.

The number of MaaS pilots is expected to dramatically increase worldwide.  Stay tuned as urban planners continue innovating and experimenting – the table stakes are enormous.

Tags: MaaS, TaaS, mobility, smart city, sensor, IoT, V2V, API, SDK

Crowdsource GPS With Bluetooth LE

Tile, TrackR, Duet, iHere3, and other tracking device vendors use Bluetooth LE (BLE) to locate lost items with companion iPhone and Android smartphone applications.  Since BLE connectivity is limited to 30 feet, these companies use crowdsource GPS techniques to help locate items.

If an item is lost, and is detected by another user’s TrackR smartphone application, perhaps hundreds of miles away, the location of the item is forwarded to the cloud and then to the first user’s TrackR smartphone application, dramatically extending the range of TrackR.

Wonderful idea.  However, these crowdsource GPS techniques rely on a strong community of BLE products from a single vendor, and are not interoperable with tracking devices from other vendors.  For example, if your item is tracked by a Tile and is lost, TrackR users will not detect the presence of your item.

This technical paper, written for AmbyGear, and distributed to multiple BLE vendors for comment,  documents a proposed amendment to Bluetooth LE advertisement packet to extend crowdsource GPS to include interoperability with multiple vendors.   Adoption of this amendment provides a significant multiplier effect.

Rather than rely on a community of tens or hundreds of thousands of BLE devices from a single vendor, imagine having access to millions of BLE devices worldwide from many vendors to help track your items.

Multi-Vendor Crowdsource BLE GPS Cloud Service

Sequence of operation follows:

  • Smartphone application from vendor A detects crowdsource GPS packet from BLE tracking device designed by vendor B
  • Smartphone application from vendor A forwards crowdsource GPS packet to a vendor-agnostic cloud service (perhaps operated by a Bluetooth SIG), which forwards the packet to vendor B cloud (company ID assigned by Bluetooth SIG resides in the crowdsource GPS packet field)
  • Vendor B receives the crowdsource GPS package from the cloud, and provides the location of the item to the user in the user’ smartphone application

Ambit BLE Advertisement Pkt Format 0.6

This paper uses proprietary value 0xFF in the AD Type field to indicate a Vendor ID advertising packet containing data, and a Crowdsource flag to indicate the packet should be forwarded to the cloud service.  Variations obviously exist in how this feature can be implemented by a Bluetooth SIG.   For example, a crowdsource GPS feature can be implemented as an encrypted GATT service to secure the device serial number for increased security.