21 APRIL Open data. Powering London through free open transport data

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1 21 APRIL 2016 Open data Powering London through free open transport data

2 Our purpose Keep London moving, working and growing and make life in the city better Plan ahead to meet the challenges of a growing population Unlock economic development and growth Meet the rising expectations of our customers and users

3 Breadth of TfL 30 million journeys daily In addition to all road and rail transport, we look after rivers, assisted travel, taxi and private hire regulation We do much more also from education to running the world s largest out of home advertising estate of its type We re 150 years old and chock full of heritage and design assets

4 Why open data? Public data Reach Optimal use of transport network Economic benefit Innovation

5 OPEN DATA Benefits Deloitte study estimated 15m- 58m per annum benefits from customer time saved in apps powered by TfL open data Usage has since doubled bringing the estimate to 30m- 116m per annum. Significant investment from app development firms has attracted hundreds of millions of pounds in technology investment in London and elsewhere off the back of our data Over 1,000 jobs estimated to be enabled by our open data ecosystem London is now a leading technology centre with around 175,000 people are now employed in the industry in London, in 45,000 companies with 30bn annual turnover

6 21 ST APRIL 2016 The Unified API api.tfl.gov.uk Dan Mewett TfL Online

7 Some source data

8 Formats and style

9 Timings

10 Join it all up

11 The Unified API api.tfl.gov.uk

12 The Unified API

13 The Unified API

14 Unified API Summary Free Simplified access to TfL s Transport Data Standardised Consistent Realtime Designed for customer facing application developers Web scale low latency, high availability New data being added and connected as it becomes available

15 21 ST APRIL 2016 Opening up TfL s ticketing data Ryan Sweeney Customer Experience Analytics

16 Background 72% NOV 14

17 What are we looking to release? Transport Node Volumes Entry Exit

18 What are we looking to release? Journey time flows and metrics

19 Applications -Analysis -Developers -Transparency and Accountability

20 Contact Ryan Sweeney

21 CROWDING DATA Lucy Fish, Travel Demand Management (TDM) April 2016

22 What do our Tube customers want? 57% of Tube users would change their journey to avoid crowds A large majority of our customers are infrequent and irregular. 71% of visitors want to know when it will be crowded, 65% would consider changing to avoid it. Customers are more likely to change the time and route of their journey, compared to the type of transport The prospect of a comfortable journey, holds equal importance to the cost and duration of a journey Customers want new, granular and personalised information to make their own informed travel decisions and put them in control Customers want to know what the journey experience is likely to be, so they can make decisions before leaving Customers want to know the levels of crowding they will experience for all stages of the journey Customers want information and data they can trust Sources: TfL Travel Behaviour Segmentation 2015 & Visitor s Survey 2015

23 What are we providing via the TfL API? Data set 1 All London Underground (LU) stations Customer counts of entries / exits / interchanges Ticket data enriched with survey data updated annually Historic based on a typical weekday in Autumn Start to close of service, 15 minute intervals Data set 2 All London Underground (LU) lines Train loading (percentage full) on departure Between each station, in each direction Ticket data enriched with survey data updated annually Historic based on a typical weekday in Autumn Start to close of service, 15 minute intervals

24 What are the potential uses? 1. Suggested departure times or best time to travel alerts to avoid travelling during the acute peak 2. Tube map data visualisations of crowding levels by station, line, direction and time of day 3. Ability to plan alternative routes that are predicted to be less crowded than the typical fastest journey plan a less crowded route

25 What are the future developments for crowding data? Now Future Frequency Historic Seasonal / specific periods Real-time and predicted Mode LU DLR, LO Buses Journey stage Station Line Platform Trains arriving

26 What is the potential of real-time crowding information? Crowding levels of trains as they arrive at the platform Crowding levels on the platform Journey times from origin to destination taking crowding levels and service status into account

27 Duncan Elder, Improving our traffic data, part 2 - Planned Road Closures trialling a standardised way to share data Travel Demand Management (TDM) April 2016

28 Overview The complex environment of roads information Acquisition, collation and provision of road information is complex more so than rail. Third parties provide a significant amount of information to road users/customers in London Information includes real time conditions (e.g. road network conditions) and changes to the operation of the road network (e.g. road closures, banned turns). Data often collected from a variety of sources Importance then of data and spatial data accuracy, data exchange standards and completeness Road owners play a significant role

29 Our current roads data release - summary We currently provide a number of (digital) road information including: TIMS - Live traffic information and planned works/events disruption. Includes planned closures. Road status tool Web interface VMS (including some journey times), JamCams, Post Code impact areas and Other Camera locations. Many users, applications and value added providers

30 Is there room for improvement? And where? Some potential shortcomings of current approach (Planned closures) Publication dates of known road closures. Some cases planned road closures are only published a few weeks before event. This can lead to information providers not picking up in advance. Some inaccuracy of exactly where closures start and end. In some cases the current data is not explicit, precise or accurate enough to be used. Spatial/Network referencing system used is not ideal. Can our data be more interoperable?

31 Project to explore and trial a better approach: London Marathon Small project to explore whether a different approach could rectify some of these shortcomings London Marathon: significant road closures on and around the route of the Marathon. Annual event: Good proportion of closures (both on route and side roads) known well in advance. Trial a standardised way of publishing road information: DATEX II Release data earlier, provide an updated and more accurate network and geographic description of the road closures. All planned road closures in London, including closures on both the TfL and borough road networks. Include geographic descriptions of road closures, time and dates. Work with directly with a select number of would be consumers of data

32 DATEX overview DATEX is a standardised way of communicating and exchanging traffic information. Used across Europe and in UK. A multi-part Standard, maintained by CEN Technical Committee. Accommodates a range of data including road closures, journey time, parking information etc. As such the standard can be fiddly and is quite verbose. Requirement on third parties to write receivers. Published as XML (Not JSON as yet) See for more information. Maintained and updated

33 What we have done, will be doing Capture of all closures in data. Significant business process Liaison and review with a select number of would be consumers: review and update Publication of data in new format on TfL web site: Post event review and impact assessment

34 Future and next steps Post trial assessment. Did this make a difference compared to the do nothing scenario? Were third parties more likely to consume? Did it assist them? Did it make a difference to the customer? Can we prove this? Assuming a positive response to the above, further road closures and related data releases. E.g. PRL, planned closures Incorporation of diversion routes in data (?)

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36 Data from the Road Space Roland Major Enterprise Architect Information Management

37 London is growing Every Londoner, business and visitor is affected by what happens on London s streets and roads The predicted growth and travel demand creates a significant challenge, to which a business as usual response will not be enough

38 Growing brings challenges

39 What is needed The ability to understand the real time demand for time and space The intelligence to make sure the needs of all customers drives what happens on our roads New thinking and better information, enabled by technology

40 Opening up our Data There is a task force working on sharing more of the data sets The Urban Traffic Control (UTC) system is a relatively untapped data source, as a system it: Monitors junctions on a 1 second basis Measures traffic occupancy (loops) at 250mS intervals On optimised junctions it updates the timing of stages We are already learning a lot from this data

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42 What have we done? A proof of concept to show we can use new approaches to process the data and gain insight Held a Data Science Hackathon Nov15 Held an AWS Hack Day Accelerated sharing of roads data

43 What is next? Understanding better the needs of the open data community What is useful to share? Raw Processed Insight

44 tfl.gov.uk