urban freight data collection
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Urban Freight Data CollectionUrban Freight Data Collection
1
Jeffrey Wojtowiczwojtoj@rpi.edu
VREF Center of Excellence for Sustainable Urban Freight Systems
2Introduction
The development of freight demand models is difficult due to:Lack of proper balance: knowledge, models and dataPoorly understood system
Complexity of the freight system:Multiple interacting agents with partial viewsMultiple metrics to measure freightLinks between participantsFunctions performedModes/vehicles usedLevels of geography
3Partial view of the freight system
Notes: (1): Only of the cargo that they handle. (2): For all the cargo they receive.
Freight generation:Shippers / Producers
CarriersDistribution
centers / Warehouses
Consumers of cargo
(receivers)
Transportation agencies
Amount of cargo Yes (1)
Yes (1)
Yes (1)
Yes (2) No
Number of loaded vehicle-trips Yes
(1)Yes
(1)Yes
(1) Not always
Number of empty vehicle-trips
No Yes (1) No No
Number, frequency, of deliveries Yes
(1)Yes
(1)Yes
(1)Yes
(2) No
Commodity type Yes (1) Not always Yes
(1)Yes
(2) Only at some ports of entry
Shipment size Yes (1)
Yes (1)
Yes (1)
Yes (2) No
Cargo value Yes (1) Not always Not always Yes
(2) Only at some ports of entry
Land use patterns Yes (1)
Yes (1)
Yes (1)
Yes (1) All
At key links (no distinction
between loaded and empty)
No single agent can provide a complete picture of the system
4Multiplicity of metrics
Base
1
2 3
4
5
Loaded vehicle-trip
Commodity flow
Notation:
Consumer of cargo (receiver)
Empty vehicle-trip
Data Needs and Sources
6Data required by modeling techniques
Both
Joint Commodity &
Vehicle-trip based
Vehicle-trip based
Both (commodity
flow & vehicle -trip
based
Com
mod
ity
gene
ratio
n m
odel
s
Dis
trib
utio
n m
odel
s
Inpu
t-O
utpu
t m
odel
s
Fre
ight
mod
e ch
oice
Em
pty
trip
m
odel
s
Spa
tial p
rice
eq
uilib
rium
m
odel
s
Tri
p ge
nera
tion
mod
els
Dis
trib
utio
n m
odel
s
Mic
ro-s
imul
atio
n m
odel
s
Mic
ro-s
imul
atio
n-hy
brid
mod
els
Spa
tial p
rice
eq
uilib
rium
m
odel
s
Fre
ight
ori
gin-
dest
inat
ion
mod
els
Production C C, F C C, F C C C, F Consumption C C, F C C, F C C C, F
Sequence C, F C, FLocation C, F C, FOD flows C, F C, F C, F C, F C, F C, F C, F
Empty flows CShippers C, F C, F C, F C, F C, FCarriers C, F C, F C, F C, F C, F
Receivers C, F C, F C, F C, F C, FShippers C, F C, F C, F C, FCarriers C, F C, F C, F C, F
Receivers C, F C, F C, F C, FTravel times/costs C, F C, F C, F C, F C, F C, F C, F C, FUse restrictions C, F C, F C, F C, F C, F C, F C, F C, F
Capacity C, F C, F C, F C, F C, F C, F C, F C, FTraffic volumes CMode choice C N.A.Delivery time N.A.
Mode attributes C, F N.A.Production functions C, FDemand functions C, F
IO tech. coeffs. C, F
C: Calibration; F: Forecasting
MODELING TECHNIQUE
Other economic data
Delivery tours
Agent economic characteristics
Agent spatial distribution
Network characteristics
Special purpose models
Freight generation
Trip interchange models Tour based models
Commodity based Vehicle-trip basedJoint Commodity and Vehicle-trip
based
Data categories
7Data sources
Primary data sources (in the USA)Commodity flow survey (CFS) data Zip code business patterns (ZCBP)Surveys + interviews + travel diaries …
Secondary sourcesGPS dataExperts
Data and Freight Demand SynthesisFill in gaps, could provide good estimatesReduce data collection costs but may introduce an
error
8Data gaps identified (United States)
Production
Consumption
Sequence Only GPS data from private vendors can provide good data
Location Low level of detail about locations
OD flows Some sources identified but no complete information
Empty flows No sources identified
Shippers
Carriers
Receivers
Shippers
Carriers
Receivers
Travel times and costs
Use restrictions
Capacity
Traffic volumes
Mode choice No information about mode choice
Delivery time Low level of detail about delivery times
Mode attributes Some level of detail about mode attributes
Production functions No sources identified
Demand functions No sources identified
Input-Ouput technical coefficients
Good level of detail specifically from REIS and 2002 Benchmark I-O Accounts of the USA
Some sources identified that can provide this type of information, but no complete depiction. The data have no extra information about other categories
Only a low level of detail about these categories was identified from different sources
Special choice processes
**The Commodity Flow Survey microdata could provide this information. Access to the data is restricted
Other economic data
Freight generation data
Delivery tours
Economic characteristics of
participating agents
Spatial distribution / Location of participating
agents
Notes: * ITE Trip Generation Manual contains trip rates but no cargo attracted or produced information
Network characteristics
No sources were identified that could provide information
about Production and Consumption*, **
Some sources identified that can provide this type of information, but no complete depiction. The data have no extra information about other categories
Most data needed must be collected from scratch
Data collectionTypes of data collection techniques or surveys
depend on how the sampling frame is defined: Establishments at origin or destination of the
shipmentTruck trafficDelivery tourShipment
This leads to data collection methods that focus on:Origin or destination of the cargoEn-route, as in a truck intercept surveyAlong the supply chain
9
Surveys
Data collection methodologies vs. sampling frame:Establishment-based surveys
Shipper, receiver, and carrier basedTrip intercept based surveys
Roadside interviewsVehicle based surveys
Travel diaries, and surveys assisted by GPSTour based surveys
Longitudinal surveysFreight volumes data collection techniques
10
GPS and freight data collection
Global Positioning Systems track routing patternsSpatial and temporalCannot provide data collected by traditional surveys
e.g., commodity type, shipment size, trip purposeNeed other data sources/methodsGood complement to more traditional freight
data collection proceduresCommercially available GPS data are likely to
be biased and difficult convert into a representative sample
11
Advantage: Engine status (Ignition off, Ignition On) and travel status (start, stop) Assumption: Apart from warehouse and truck centers, a vehicle will only turn the
engine off for deliveries at stores. This helps identify delivery stops.
Event Based GPS Data12
Label Date / Time Address Latitude Longitude Event
928 4/3/2012 21:50 521 Park Ave, New York, NY, 10065 40.763525 -73.9692138 Travel Stop
928 4/3/2012 21:50 1 Central Park S, New York, NY, 10019 40.76478 -73.9737944 Travel Start
928 4/3/2012 21:55 937 7th Ave, New York, NY, 10019 40.76668 -73.9790527 Drive
928 4/3/2012 22:00 98 W 53rd St, New York, NY, 10019 40.761666 -73.9790111 Drive
928 4/3/2012 22:03 65 W 56th St, New York, NY, 10019 40.763447 -73.9769638 Travel Stop
928 4/3/2012 22:04 65 W 56th St, New York, NY, 10019 40.763447 -73.9769638 Ignition Off
928 4/3/2012 22:04 70 W 57th St, New York, NY, 10019 40.763825 -73.9768972 Ignition On
928 4/3/2012 22:06 68 W 55th St, New York, NY, 10019 40.762497 -73.9772 Travel Start
928 4/3/2012 22:08 62 W 57th St, New York, NY, 10019 40.763788 -73.9768055 Travel Stop
928 4/3/2012 22:08 47 W 56th St, New York, NY, 10019 40.763569 -73.9765194 Ignition Off
928 4/3/2012 22:34 42 W 56th St, New York, NY, 10019 40.762877 -73.9767305 Ignition On
Sample GPS route data13
Sample GPS data14
Sample analysis from GPS data15
5 15 25 35 45 55Mor
e0
20406080
100
# of stops per tour
Frequency
1 2 3 4 5 6 7 8 9 10Mor
e0
20406080 # of stops per tour (1-10)
Frequency
012
0024
0036
0048
0060
0072
0084
0096
000
20
40
60Service time distribution
Frequency
Sampling frames and data16
Prod
ucti
onC
onsu
mpt
ion
Sequ
ence
Loc
atio
nO
D fl
ows
Em
pty
flow
sSh
ippe
rsC
arri
ers
Rec
eive
rsSh
ippe
rsC
arri
ers
Rec
eive
rsT
rave
l tim
es, c
osts
Use
rest
rict
ions
Cap
acit
y T
raff
ic v
olum
esM
ode
choi
ceD
eliv
ery
tim
eM
ode
attr
ibut
esPr
oduc
tion
func
tion
sD
eman
d fu
ncti
ons
IO te
ch. c
oeff
s.
Shipper
Carrier
Receiver
Unit/ Sampling Frame
Trip intercepts
Vehicle
Tour
Excellent level of detail Good level of detail Some level of detail Low level of detail Only general information
Establishment
Oth
er e
cono
mic
dat
a
Fre
ight
gen
erat
ion
data
Del
iver
y to
urs
Eco
nom
ic
char
acte
rist
ics
of
part
icip
atin
g ag
ents
Spat
ial d
istr
ibut
ion
/ L
ocat
ion
of
part
icip
atin
g ag
ents
Net
wor
k
Spec
ial c
hoic
e pr
oces
ses
Summary
There is no magic answer for getting freight dataRelationships must be cultivatedPatience must be practiced
Asking for too much data can be a disadvantageRequest needs to be defensibleGenerally willing to collaborate if requests are within
reason
17
Thank you!Questions?
18
Jeffrey WojtowiczSr. Research Engineer
Assistant Director of Administration VREF CoE-SUFS
Rensselaer Polytechnic Institute Troy, NY 12180wojtoj@rpi.edu
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