COLLECTING DATA to UNDERSTAND MOBILITY PATTERNS Beyond That, Automated Means of Counting Have Been Developed

LEPT POLICY BRIEFS - 4 J u n e 2019

CITIZENS AND NEIGHBOURHOOD imbalance in cycle usage as well as rebrand cycling infrastructure. COLLECTING DATA TO UNDERSTAND MOBILITY PATTERNS Beyond that, automated means of counting have been developed. Various tools exist, EVIDENCE AND KEY ISSUES with onboard technologies such as vehicle load counting or Electronic Registering Device-tracking using WLAN enables high Fareboxes (ERF) (more simply put ticket spatial accuracy (Ahlers & al., 2016). counting). TfL’s Oyster Card is such a system. It maps all public transport trips but Mobile phone data tracing is another does not have a tap-out option on buses. solution (Calabrese & al., 2013). Part of the journeys are therefore not being counted. In order to access that missing Travel behaviour varies according to data, TfL developed a model, the ‘Origin- characteristics of users: it has been Destination Interchange Tool’ that documented for example that across the integrates bus entry tapping with other taps European Union caregivers (often women) using the same card as well as bus location tend to undertake multiple small trips in a data to act as a proxy for end of bus journey day using public transport when working calculation. In 2018, TfL conducted a trial men will tend to use a motorised vehicle on automated passenger counting for and travel mostly to and from work in a buses. It ran on seven lines, using cameras day (URBACT, 2019). for tracking footprint, sensors at doors, weight analysis and depersonalised Wi-Fi Data sharing and newly available forms data. Both methods allowed for increased of tracking can allow planners to understanding of mobility patterns. respond to specific mobility needs by shaping appropriate and inclusive Another issue is the lack of information of infrastructure and policies. Although in-tube journeys, as only origin and understanding mobility forms in a city destination are known. For a month in has always been a key issue for 2016, TfL conducted a pilot using transport planners, new forms of depersonalised Wi-Fi data from mobile mobility like ride-hailing or dockless connections in 54 of its stations. It collected bike schemes pose a new challenge to over 500 million connection requests to public authorities’ understanding of understand mobility paths inside the movement. network. The pilot was implemented for less than £100,000 and allowed for a better LONDON CONTEX T understanding of mobility in busy stations, trains and times as well as preferred route In London, Transport for London is the main options. TfL’s EDMOND (Estimating transport operator. It handles most of the Demand from Mobile Network Data) project public transport (underground, bus, tram, is more widely looking at transport demand overground) and is responsible for the across London. majority of the main traffic arteries. London boroughs are responsible of most of the TfL shares data through a unified API that road network (95%). is accessible to all and that powers over 650 apps. The operator also provides The traditional option to understand monthly statistics on public transport use on mobility choices is surveying. It is widely London’s open data portal. UK-wide, the used in London as elsewhere, for example, Department for Transport published in 2018 to understand cycling choices (2016). guidance on local transport data usage. Surveys may help inform policy like the Cycling Action Plan (2018), where user Understanding mobility is also demographics were examined to try and understanding modes such as cycling. In overcome the gender or ethnicity 2017, following an initiative by Transport for

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Greater Manchester, TfL installed cycle Similarly, the TRACE project developed counters on the border of its Cycle Super tracking tools in eight pilot areas to better Highways. Boroughs have similar promote active mobility and plan for initiatives. Camden publishes regular sustainable urban mobility in general. updates of its own cycle counters’ results, Regarding cycling data, many EU cities also using open data. Camden Cyclists re- have put in place counting methods, such use that data to provide useful and as Utrecht in the Netherlands. readable graphs on cycling data in the borough. Boroughs also use resources The question of who maps the city has provided by private companies. Lewisham become a major governance stake, notably for example has in the past used data from in the US, where new mobility services fitness social network Strava to understand have been implemented for longer than in cycling patterns. Europe and public transport is not as attractive. There, new actors have seized With regards to private operators, TfL the mapping of mobility, be it private require private hire vehicle (PHV) operators transport operators which often have their to share weekly data on drivers and own agenda, companies, major vehicles used the previous week as well as consultancies or mixed initiatives like any changes to their operating systems. Shared Streets. However, traditional actors New mobility services that have been such as academics and transport operators trialling their services on an independent still act as entry points when it comes to level with boroughs have various data collecting, as they hold most of the arrangements according to boroughs’ mobility data. For example, Madrid’s requirements. The services are usually operator, CRTM, recently launched a provided with a dashboard allowing them to multimodal mobility platform. To operate in plot the journeys of all vehicles in operation, the city, providers must provide usage data be it Lime e-bikes in Islington or Mobikes in to the platform. At the national level, as part Ealing. That data is sometimes too limited of the Single European Transport Area for boroughs to develop a strategy. agenda, members states have been encouraged to put in place National Access INSPIRATION FROM ELSEWHERE Points that act as portals with standardised formats. Some European projects led by cities also have a focus on mobility patterns: MoTiV, a As private mobility providers launch their Horizon 2020 funded project, looks at Value services across cities, one of the stakes of Time Travel. The idea is to understand becomes the unification of data mobility choices through an app that acts as requirements: can there be a standardised a multi-modal journey planner and a means of collection? The Los Angeles mobility diary. Authorities can participate in Department of Transport requires micro- data collecting campaigns and request the mobility managers to share data in order to app. The project has also experimented operate in their city and has done so with other means of counting trips: around through the creation of an open source the Sagrada Familia in Barcelona MoTiV mobility data specification that can be taken deployed a mix of Wifi sensors, GSM and up by any city. Our enhanced ability over 3D cameras to obtain real-time data of the last years to gather more and more data mobility flows to understand tourist is providing a clearer picture of how our behaviour. The Los Angeles Transportation citizens interact with the city and transport authority recently used data from 5 million network. What is important is that the city cell phones to understand mobility patterns has free and open access to it to in its urban area, using data provided by a successfully deliver clean and sustainable transportation consultancy. By combining streets of the future. this data with their own, they were able to determine the ‘transit mode share’.

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