Data & The City - Guido Legemaate - Brandweer Amsterdam Amstelland
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Transcript of Data & The City - Guido Legemaate - Brandweer Amsterdam Amstelland
(Fire)Data & The CityAmsterdam, 3 oktober 2016
Data Driven Public Safety
Guido Legemaate
Brandweer Amsterdam-Amstelland
Centrum Wiskunde & Informatica
Data VaultThe backbone of our data
management solution.
Arjen ter Heide
Over the last 4 years we have build a
robust data warehouse, modelled
according to “data vault” techniques.
All core data sources are integrated
and can be used for e.g. dashboarding,
but also (as was my initial goal when
starting the data collection) to use as
input for models/algorithms.
Risk ProfilesThe first real usage of our data
was the creation of risk profiles.
Barry van ‘t Padje et al.
source: BBC @ http://www.bbc.com/news/business-21902070
1874
In 1874, the Dutch capital Amsterdam was the first city in
the Netherlands with a pro-fessional fire service.
With 144 people personnel and 9 fire stations covering 30 square
kilometers, it ensured fire protection safety for
approximately 285,000 inhabitants.
Location of fire stations
Optimizing fire station locations.
Pieter van den Berg
Guido Legemaate
Rob van der Mei
Location of fire stationsDecisions:
● How many fire stations do we need?
● Where to locate them?
● How to distribute the available fire trucks?
● How to distribute (types of) personnel?
Goal:
● Maximize coverage for different types of fire trucks
Constraints:
● Limit amount of fire stations and trucks
● Crew
Location of fire stations
Extensive analysis of a large dataset of historical incidents demonstrates:
● that, and how, response time can be improved by “simply” relocating only three
out of 19 base locations and redistribution of the different vehicle types over the
base locations
● that there is no need to add new base locations to improve performance:
optimization of the locations of the current base stations is just as effective
However…€€€
Relocating fire trucks during big
incidents.Maximize coverage / response
times in times of ‘shortage’.
Dimitrii Usanov
Peter van de Ven
Guido Legemaate
Moumna Rahou (student)
It is not uncommon that three or
more fire trucks/stations are
attending one big incident, like a fire.
Also, these incidents tend to last
longer than usual, potentially leaving
parts of the city with a less than
optimal coverage. In those cases fire
trucks are relocated, but there is no
clear method on how to do this. We
use mathematical programming to
propose a method.
GPS routesMapping and matching prognosed
routes with those routes that we
actually took.
Guido Legemaate
Arjen ter Heide
Anne-Frances Appelman (student)
We have extracted all GPS data from
all of our fire trucks from over the last
7 years. Datapoints are taken every 10
to 30 seconds and are comprised of
date/time, licenseplate no., lat/long
coordinates, speed.
Goal is to extend knowledge about
driving speed and vital infrastructure.
Vital LogisticsEmergency service logistics:
network design and dynamic
dispatching.
NWO granted project.
Work in progress.
Emergencies such as the breakdown of an
MRI-scanner or a domestic fire demand a timely
response. This means that the resources required
for addressing such incidents (spare parts and
fire trucks, respectively) need to be stored in
relative proximity of potential incidents and
dispatched on short notice. This requires a
network of resources in several storage locations.
Owners of such Emergency Resource Networks
(ERNs) face three issues: (i) Where should
resources be stored, and how many resources
need to be available at each location? (ii) How
should resources be dispatched in response to an
emergency? (iii) Can the performance of the
system be improved by proactive relocation of
resources?
Guido Legemaate [email protected]@gaglegemaate [email protected]