The concept of bikesharing has been around since the 1960s, but early iterations struggled from problems including theft, vandalism, and clunky payment. The recent rise of the Internet of Things (IOT) and advances in smartphone usage, telecommunications networks, and GPS systems have improved the logistics of bike sharing programs. This has made these programs more compelling for both municipalities and potential bike share riders, which has resulted in substantial growth in adoption and usage.

The Internet of Things (IOT) has far reaching implications for our cities. One of which is reducing traffic congestion and gridlock by reducing barriers to cycling. Bike sharing offers a low-cost option for last-mile transit – the term that transportation experts use to describe travelling to/from a train or bus station in addition to short end-to-end commutes within urban areas. The concept of bikesharing has been around since the 1960s, but early iterations struggled from problems including theft, vandalism, and clunky payment. The recent rise of the Internet of Things (IOT) and advances in smartphone usage, telecommunications networks, and GPS systems have improved the logistics of bike sharing programs. This has made these programs more compelling for both municipalities and potential bike share riders, which has resulted in substantial growth in adoption and usage.

Rise of IOT

The rise of the IOT has enabled several things: 1) seamless user experience where cyclists can use a mobile app to rent a bike, plan their route, and view public transit connections, 2) intelligent fleet management by operators via the use of tracking and analytics to adjust the number of bikes at a given station or area based on availability and expected demand, and 3) smart urban transit planning by integrating a variety of options to reduce pollution and gridlock. 

Bike Sharing and the Internet of Things IoT.jpg

1) Seamless User Experience

The IOT enables simpler registration and payment via mobile devices, smart keys or credit cards, which encourages greater use of urban bikesharing systems. Furthermore, a variety mobile apps such as Spotcycle or CycleFinder provide users an array of features that include station information, available bikes, rental timers, bike paths, and mobile payment. Now, rather than hoping a station has available bikes and paying with clunky coin-based deposits, cyclists can check if a station has available bikes, plan their route, and pay for their ride with a few clicks.

2) Intelligent Fleet Management

Superior fleet tracking enabled operators to overcome the issues of theft and vandalism by: 1) simplifying registration so operators know which users are riding which bikes, and 2) GPS tracking to monitor the location of bikes when they aren’t docked at a station. Additionally, the IOT allows operators to optimize service through better station monitoring. Operators know which stations receive the most usage and at which time of day. Most residents of cities with a sizable bikesharing system probably see trucks moving bikes from station to station during the evening in preparation of the early morning rush.

3) Smart Urban Transit Planning

Data generated by bikesharing systems provides urban planners a comprehensive understanding of where and how people get to and from work every day, particularly when integrated with smart-ticketing on transit systems (e.g. bus and metro), so that the end-to-end journey and user preferences can be tracked. Anonymized usage data gives planners an extraordinary amount of intelligence including areas lacking transportation options, congested areas, or areas to direct traffic towards. Armed with new intelligence, planners can introduce a variety of measures to reduce transit times and gridlock such as selecting areas for new bike lanes, identifying train or bus stations for expansion, or identifying roadways that could use an additional lane.

A Look at Some of the Data: Bay Area Bike Share (BABS)

The IOT enabled advances in bikesharing systems have created a plethora of publicly available usage data. There are about 1000 cities worldwide, and 65 cities in the US, with active bikeshare programs. Many make usage data available to the public, and we decided to look at Bay Area Bike Share (i.e., BABS) data because there are near-term expansion plans that should be informed by the program’s usage and performance to date.

To provide some context, Bay Area Bike Share was introduced as a pilot program in August 2013 as a last-mile transit solution for Caltrain commuters living in Mountain View, Redwood City, Palo Alto, and San Jose. Bay Area Bike Share has relatively low usage levels compared to other US bikesharing systems, but this is partially due to the pilot nature of the program and distribution of stations across the San Francisco Peninsula, rather than a single city.

We downloaded all usage data (August ’13 – August ’16) from Bay Area Bike Share’s website to conduct our analysis. In the below interactive dashboard "Bay Area Bike Share Usage", you can visualize some of the trends

> Click to interact on a mobile device: Bay Area Bike Share Usage

 

In the above data visualization analysis, you can clearly see these findings: 

  • 91% of All Trips are in San Francisco: It is expected that most trips happen in San Francisco as this is the destination of Caltrain commuters, but the extremely high share indicates commuters don’t use the system as much as expected. Better integration between BABS and San Francisco’s other public transportation options (e.g. BART, Muni) could drive usage rates even higher.
  • Subscribers Represent Significant Share of Riders: Riders with a monthly or annual subscription accounted for 90% of trips in August 2016. This stat speaks to the increasing use of BABS as an integral part of the public transit system rather than a cool way for tourists to explore San Francisco. One can also see the greatest share of subscribers on weekdays during rush hour.
  • Riders are Sensitive to Adverse Weather Conditions: Examining temperature and precipitation against average daily rides shows a sharp decline during the winter, which is also San Francisco’s rainy season. Although there is no way to control weather, urban planners can prepare alternative transit options for the influx of would-be cyclists during the winter.

Findings like the ones above are critical to making informed operational decisions:

  • Knowing that BABS is mostly used by commuters could inform marketing and sign-up approaches
  • The fact that commuters are less-likely to cycle when it’s cold and wet could reduce operational costs by reducing the need for bike movements based current weather
  • Detailed station-level demand can be used to support planning for new station locations

Bikesharing is just one cog in urban transportation systems. Increasingly, the IOT and communications networks are being leveraged to improve transit services from ridesharing to subway services, increasing the amount of data at urban planners’ fingertips. As more data is collected, urban planners will understand how to integrate all transportation options and create a better transit experience for all.


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