Cycling is perceived by many cities as an alternative form of first- and last-mile transportation. To achieve mass adoption, city planners need to go beyond providing hard infrastructure for bikes, and address remaining physical and psychological barriers to commuting by bike, such as the perceived danger of cycling.
Studies have shown that the perceived lack of safety is the main psychological barrier to cycling uptake in cities. According to a report published in the journal Urban, Planning and Transport Research, additional factors such as comfort, continuity and speed are also major barriers affecting uptake and could lead to new cyclists if these barriers were reduced1.
These barriers can be overcome through leveraging adaptive technology and big data from multiple stakeholders such as bike sharing operators, to improve the urban cycling experience and nudge behavioural changes towards cycling as a commute.
As part of its broader strategy to recognise cycling as an official mode of transportation, the city of Copenhagen in Denmark has factored cyclists into traffic management systems2. Consequently, the number of bicycle commuter trips has risen by 68 per cent in the past two decades, and bicycles have recently surpassed automobiles as the major mode of transport3.
In 2015, Copenhagen authorities improved cyclist safety and traffic flow issues by redesigning traffic intersections4 and implementing an Intelligent Transportation System (ITS) via the MobiMaestro traffic management platform. The platform executes cyclist-friendly measures such as 5,6:
Green Wave for Cyclists using Embedded Pavement LEDs. (Video credit: SWARCO AG)
Other cities are also addressing safety issues with adaptive technology, for example:
In Liverpool, UK, ThermiCam thermal detectors have been installed at certain road junctions. Communicating with traffic lights, the detectors are capable of identifying cyclists from a distance and distinguishing them from other vehicles. Cyclists get a five-second head start before other traffic users are given the full green light9.
In New York, a head-based wearable called MindRider has been trialled on cyclists in Manhattan9. The wearable incorporates an EEG brainwave sensor and is part of the Multimer analytics system that measures “the concentration and stress signals of riders as they ride through the city”. With the data, urban planners can identify relaxing (sweetspot) and attention-demanding (hotspot) routes and respond by either diverting cyclists to sweetspot routes via signage or introducing dedicated bike lanes to hotspots10.
In Eindhoven in the Netherlands, an intelligent warning system Bikescout is mounted near crossings. A sensor monitors the speed and distance of oncoming cyclists up to 50 metres in advance of the crossing, calculates their arrival time at the crossing and notifies motorists via LED road-surface indicators. Local traffic control can also remotely monitor traffic flow at Bikescout-enabled crossings11.
Research reveals that being familiar with a route also contributes to cyclists’ feelings of safety12. A Danish survey proved that smart wayfinding methods could encourage cycling uptake13. A safe means of receiving accurate directions on the go — using a cue-giving device such as BeeLine14 or SmartHalo15 — can reassure potential and existing cyclists who face navigation issues. To further improve uptake of shared bicycles, private sector operators could incorporate these unobtrusive wayfinding technologies onto their bikes.
Urban planners should also establish a clear navigational system — such as a network of adaptive LED displays communicating with the city’s ITS — for cyclists. This helps cyclists avoid annoyances such as backtracking when they encounter a dead end. The multifunctional displays can even warn cyclists about impending inclement weather.
For city planners to understand and shape urban cycling behaviour, collaboration is vital. They can leverage big data available from both municipal and private sector sources, while sharing their own data to develop effective plans for urban cycling. Already, Transport for London (TfL) has expanded the data it makes available to app designers to encourage the development of improved navigation services for riders16.
The granular user data collected by shared bicycle operators via mobile apps can also assist urban planners. In Singapore, these operators use location-based and Smart Bluetooth technology to manage and optimise dockless fleets. Bike usage data can be anonymised for privacy, aggregated to spot behavioural patterns, and shared with authorities to calibrate the urban cycling environment. For example, shared bicycle operator oBike has collaborated with Tampines Town Council to use data from residents to formulate future town plans17.
Citizen engagement platforms are also effective in alerting urban planners to acute issues affecting cyclists. An example is Ping, a wireless button device that lets cyclists in Brussels, Belgium record safety issues — such as poor visibility around corners — encountered during a trip. Users can then upload the recorded issues for city authorities to view and address. Providing a direct feedback channel cyclists gives cyclists a voice and helps prioritise their status as road users18.
Together, collaborating stakeholders can improve the urban cycling experience by nudging behavioural changes towards mass first-last mile adoption through improving safety and wayfinding, as well as creating favourable traffic flow for cyclists. In addition, urban planners can make use of big data and citizen engagement platforms to plan and execute effective, ITS-driven cycling infrastructure. Combined, the incremental effects of technology, backed by solid research and analytics, could enable any sizeable city to become a pedal-powered success story.
1. http://www.tandfonline.com/doi/full/10.1080/21650020.2014.955210 2. http://www.eltis.org/sites/eltis/files/case-studies/documents/copenhagens_cycling_strategy.pdf 3. https://www.theguardian.com/cities/2016/nov/30/cycling-revolution-bikes-outnumber-cars-first-time-copenhagen-denmark 4. http://www.statensnet.dk/pligtarkiv/fremvis.pl?vaerkid=12587&reprid=0&filid=2278&iarkiv=1 5. https://www.kk.dk/sites/default/files/uploaded-files/ITS%20-%20Action%20Plan%202015-2016.pdf 6. https://www.technolution.eu/en/mobility/137-smart-working-and-living-in-copenhagen.html 7. https://www.technolution.eu/en/mobility/112-copenhagen-the-tube.html 8. https://www.vox.com/2014/6/5/5782472/study-bike-lanes-really-do-increase-biking 9. http://www.flir.com/traffic/display/?id=7118710. http://mindriderdata.com 10. https://www.wired.com/2015/01/mindrider-manhattan-bike-map/ 11. https://www.heijmans.nl/en/bikescout/ 12. http://content.tfl.gov.uk/atc-online-autumn-2015-report.pdf 13. http://d27j0td1cyubi5.cloudfront.net/uploads/upload/asset/24/54087b3f2581702d139c0143b0511fd346894cee.pdf 14. http://www.bikeradar.com/gear/article/urban-routefinder-beeline-will-add-navigation-to-your-bike-for-peanuts-45545/ 15. https://www.dezeen.com/2015/08/25/smarthalo-cycling-bicycle-accessory-combines-navigation-activity-tracking-alarm-system-cyclelabs-crowdfunding-kickstarter 16. https://tfl.gov.uk/info-for/media/press-releases/2017/april/challenging-app-designers-to-utilise-cycling-data 17. Interview with Elgin Ee, general manager of oBike Singapore 18. http://pingifyoucare.brussels/en/how-it-works/