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

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D fl

ows

Em

pty

flow

sSh

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rsC

arri

ers

Rec

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rsSh

ippe

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arri

ers

Rec

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rsT

rave

l tim

es, c

osts

Use

rest

rict

ions

Cap

acit

y T

raff

ic v

olum

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ode

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ceD

eliv

ery

tim

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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