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Published on December 20th, 2017 | by admin

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‘Data Dawn’: Making Up for Lost Time in Transport Planning

Getting Big Data and commuter usage to plan and help us build the transport
networks of the future will go some way to making good on the shortcomings of the
past. Linkline Journal reports.

Quality infrastructure underpins national prosperity. Let’s say
that again – quality infrastructure underpins national
prosperity.

The Cross City Luas project officially opened earlier in
December, another small piece in Ireland’s growing
commuter network and another step on the way to a more
prosperous future for us all. The long slow grind of getting
Ireland’s transport infrastructure up to speed has not been
without its challenges. One point often made here is our lack
of foresight and planning on infrastructure, and it hasn’t
been easy recently on an economic front either.

The launch of the new Luas route also heralds in a new era in
transportation behavior. Commuting and transportation
behaviour, both private and commercial, is changing rapidly.
The factors that ultimately influence all of this are shifting
dramatically and creating new challenges for planners.
Household composition characteristics are changing,
younger generations are more likely to live in high density
housing, buying less cars and homes than their parents, and
are delaying having children. Combine this with the current
difficulties of getting appropriate accommodation in Ireland
and it can all lead to a very complicated picture on which
transport operators need to project the future.

But Travel Demand Modelling, as it’s known, is getting easier
by the day as more and more commuters begin to digitally
interact with whatever transport service they use. Planners
have historically relied on old or remodeled data to design
their project. They never had real-time data to evaluate
performance after a project was completed. The use of
sensors or surveys were labour intensive and expensive. If
planners wanted to make performance-based improvements
as they implemented policy or infrastructure changes, they
often had to start from scratch with data collection. This
could create flaws if the targeted commuters or transport
network had seasonal variations, changing populations/
workforce or new technologies such as ride-sharing (e.g.
Uber) impacting on the infrastructure’s usage.

Our cars, our trains and trams, our bikes, buses and taxi apps
are not only beginning to sync to a diverse number of
networks. These are giving us vast quantities of data that can
help plan, interpret and visualise solutions. This is where Big
Data comes into its own. There is a massive volume of
geospatial information created by mobile phones, GPS
devices, connected cars and commercial vehicles, fitness
trackers, tolling usage, city bikes and more. All of these
devices ping mobile phone masts and satellites while on
their journeys, creating location and movement records as
well as time of travel and length of journeys. With the right
software, these records are then mapped into useful
information that can aid planners on building the smart
transport network of the future that responds to the
demands of the commuters’ usage.

Transport providers across the globe are witnessing growing
demand that is exceeding capacity but are often unable to
build additional infrastructure as a result of lack of space
and/or funding. Passenger information has significantly
improved in recent years, with announcements, websites,
emails and app notifications alerting people to hold-ups and
suggesting alternative routes. Congestion and delays annoy
customers, increases the wear on assets and slows down the
network. At peak times, traffic flows unevenly, and buses and
trains become increasingly delayed at each stop as the crush
of passengers trying to get on board stops swift arrivals and
departures. In other words, congestion creates more
congestion.

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But with everyone diverted, these messages can create new
bottlenecks elsewhere. Similarly, while motorists’ satnavs try
to divert them around traffic jams, the different providers
– lacking coordination or predictive capabilities – simply
divert everyone in the same direction thereby creating new
queues on minor roads that are even less able to cope with
demand.

Technology and data requirements
Technologies have made significant advances in recent years
but infrastructure capacity remains stretched. Focussed
investment in tools has significant potential to improve
journey times, travellers’ experiences, and investment returns
across all of our major cities and our national transport
networks.

One such technology has been developed by a company
called StreetLight Data. An unimaginable amount of data
exists on people’s online and e-commerce activity but there
is virtually no information about how people commute, shop
and travel in the real world. Given how important
transportation is for quality of life, pollution, the economy
and climate change. StreetLight Data was started to unlock
barriers to data-driven transportation, urban design, and
commercial planning. We already know much of the data
and the underlying capacity of the transport infrastructure
but the dots need to be joined to find the solution to
planning which will allow for the variables of future transport
planning. But important questions have to be asked of the
data. How much of this data for instance relates to people on
shopping trips/work commutes etc? What percentage of
trips are for ‘spur of the moment’ business, family or other
reasons? What impact will the use of electric and driverless
cars have in the future?

In Dublin alone we already have established databases
serving Leap card, tolling, tolling tags, data from car parks,
electronic street parking, city bikes cards, bus cards and rail
cards, taxi hailing apps etc. These are all powerful data assets:
contact details for many commuters, plus an insight of their
typical travel patterns. Now is the time to invest heavily in
tools and resources to analyse data and tailor customer
communications. The growth of data collection in respect of
individual customers together with multiple engagement
channels and the use of individual customer accounts allows
transport operators to segment travellers experiencing
congestion or delays and suggest to each group a different
way to reach their goal. Using emails, SMS and apps,
operators can offer passengers incentives if they take a
particular route, travel at a particular time, or use a particular
mode. And with real-time data coming in on recipients’
behaviour, operators can quickly adjust their messages to
focus on the most effective incentives and the most
responsive travellers.

Transport for London (TfL) is currently investigating the use
of mobile phone network data to track increases in road
traffic in real time. Along with the growth in ‘connected cars’
– which transmit data on their movements and satnav
destination – this will soon provide transport managers with
enhanced tools to predict and respond to the formation of
traffic jams in real-time.

abstract-1511734635675-7746

Knowing the purpose of the customer journey is a more
challenging issue but one that is of importance to address in
order to manage demand both optimally and equitably. A
family of four travelling on holiday with a car full of luggage
are unlikely to change mode of travel; however an individual
travelling for a business meeting is potentially more likely if
he/she has relative certainty of not missing that meeting. The
problem lies in collecting this data and, currently, most
passive collection solutions (e.g. Mobile Network Data and
GPS analysis) are able to determine “normal” routes –
identifying what is likely to be a home to work route and
what is a-typical, but not an individual’s propensity to change
or the purpose of an a-typical route. Short of asking all
travellers to register normal journeys and modal shift
preferences there is a clear requirement for more granular
but non-invasive mechanisms for determining journey
purpose and responding to this with tailored options to
deliver Mobility-as-a-Service (‘MaaS’).

The optimal solution would result in strategic management
of demand across public and private transport. Then, for
example, a Dublin-bound driver heading in the M7 into a
major traffic hold up on the Longmile Road could be told
how much time could be saved by stopping at the Red Cow
Park & Ride and taking the Luas; but only if this is appropriate
based on the purpose of the journey.

As these systems are developed, their implementation will
encounter many impediments around the technology, the
data-gathering, the analytics techniques and the
communications systems. The biggest challenges are likely to
lie in persuading and organising people. Travellers will only
listen to messages if they trust the source: if they trust their
personal data is being used in an ethical and transparent way
and that altering their route will produce the promised
benefits. Passenger instructions and communications need
to provide consistent messaging which, if acted on, delivers
beneficial outcomes – both on an individual basis and for the
transport system as a whole – without penalising users of the
transport network with seemingly little overall benefit (e.g.
camera-enforced average/temporary road speed
restrictions). Only in this way can sufficient trust be built in
the user base to provide confidence that instructions will be
adhered to in order to deliver the aforementioned outcomes.
This in turn requires good coordination between all of
Ireland’s transport operators and infrastructure managers to
manage the flows of data around the system, with the tools
and relationships to gather data from – and transmit
messages via – all the key actors guiding and carrying
travellers around our growing transport infrastructure.
As we move from connected to autonomous cars –
strengthening technology’s role in deciding vehicles’ routes
– we’ll increase our ability to manage traffic flows across the
whole network.

Integration and interaction should be broader than operators
in a single mode (e.g. road) and should bridge both public
and private transport such that, for example, passengers
delayed en route to an airport for a flight can be fast-tracked
through security scanning and an informed decision made
on delaying flight departures.

If we use this data and develop an understanding of its use
with the correct methodology, then it will go some way to
catching up on the decades of transport planning and
infrastructure that we have missed out on. Congestion and
delays and sub-standard service needs to be consigned to
history and the new ‘Data Dawn’ – helping citizens, transport
managers and infrastructure investors to get the best out of
our hard-pressed transport networks. At the same time we
will help operators and authorities to maximise capacity of
their transport networks and to realise cost efficiencies in
enhanced management.

Sources: KPMG UK, Digitalist.com, SAP, StreetLightData, The
Register, MIT Technology Review.
(Written by Universal Media Agency)

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