Building Responsive Flexible
Integrated Transport Services
David Emele, Richard Mounce, Steve Wright,
Cheng Zeng, John Nelson, Tim Norman
Outline

Societal challenges

Research challenges

Coordination

Reasoning Support

Visualisation

Methodology

Technology

Future directions
Societal Challenges
 High demand for transport
to health
 Limited resources and
transport services
(especially in rural areas)
 Passenger distribution is
varied and diverse
 Operators and planners
need helpful statistics
 Many map-based solutions
do not provide that
“[P]atients who miss hospital
visits cost the NHS £700m [...]
Millions of appointments are
missed in each one year ” Excerpt from Daily Express (on
August 27, 2012)
Transport Planning
Passenger Transport in the Rural
1. Many shared flexible
transport services
2. They operate as standalone services
3. Cater for a specific group
or to fill a specific need
4. Eligibility criteria relating
to user types and
operating areas/times
5. These place constraints
on effective sharing
Research Challenges
How do we utilise
available resources
to meet demands
in rural and lowdemand areas?
What operating
constraints do
operators work
within?
Vis
uali
Coordination
Reasoning
sati
on
How can we optimise
transport options in
rural and low-demand
areas?
How can operators
be supported to
relax these hard
constraints?
Research Challenges
How do we utilise
available resources
to meet demands
in rural and lowdemand areas?
What operating
constraints do
operators work
within?
Vis
uali
Coordination
Reasoning
sati
on
How can we optimise
transport options in
rural and low-demand
areas?
How can operators
be supported to
relax these hard
constraints?
Methodology

A user-led participatory approach (surveys,
questionnaires, interviews, workshop, etc.)

Hybrid (3 methods to help focus our solution)

Qualitative analysis to inform travel
requirements of passengers and service
constraints of providers

Quantitative analysis to generate summaries
about travel demands as well as supply

Simulation to trial the solution before real-life
implementation
Sample Scenario
P1, P2, P3, P4, P5
operating in
Q1, Q2, Q3, Q4, Q5
respectively
Eligibility criteria:
Over 60’s
For social or health
69 Year-old
Door-to-door
Visit a clinic
Clinic in Q5
Technology

A virtual transport marketplace – multi-agent
setting

A passenger-centric system - preferences and
options

Gather information from sources (e.g. fixed route
timetable, Google travel planner)

Computational mechanisms / agreement
technologies Argumentation - computing fairness


Between passengers

Between operators

Between passengers and operators
Machine learning for predictive analysis
System Architecture
Sample Scenario …revisited
P1, P2, P3, P4, P5
operating in
Q1, Q2, Q3, Q4, Q5
respectively
69 Year-old
Door-to-door
Visit a clinic
Clinic in Q5
Eligibility criteria:
Over 60’s
For social or health
Our system will highlight
P4 & P5 as potential
transport providers and
allow passenger R to
negotiate with providers.
Real-life Example...
>Lady in a wheelchair from
Pitgaveny has an appointment at Dr
Gray’s Hospital on 23 Jan 2014.
>3.5 miles each way.
>Lady has severe mobility problems
and only gets out for appointments.
>SAS were unable to help and WRVS
could take folding wheelchairs but
could not lift the patient.
>Lady told only other option is to try
taxis but this is likely to result in
similar difficulties with wheelchair
and would be very costly.
>Local quote for taxi cost would be
£2.40 flag drop + £1.80 per mile =
£17.40 return.
Possible Solution...
>Applying FITS with constraint
relaxation would result in the
possibility of using the BABS
bus service.
>Relaxing the eligibility
preference for social and
shopping trips and slightly
relaxing the operating area
boundary by allowing an extra
deviation of about 2miles to
the North along the A96.
>The BABS service is fully
wheelchair accessible and
provides fully trained
passenger assistants.
>Also free to the user.
 Visualise the effect
of shifting operating
constraints
 Potential change in
cost incurred
 Level of demand
that could be
covered
 Associated revenue
(fares & subsidies)
Future Directions
 Opportunistic seat sharing (going
slightly out of ones way to pick up
an additional passenger)
 Explore efficient ways to encourage
operators to relax their constraints
 Further analyse travel
demand/supply data to inform
transport planning
Questions?
Thank you for listening
David Emele
c.emele@abdn.ac.uk
For more information about the FITS project
 Please visit www.dotrural.ac.uk/fits