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Contents
� Demand models for new stations
� Defining station catchments
� Catchments in reality
� Probabilistic station choice – discrete choice models
� Next steps
Simple demand models
Used to forecast the number of entries and exits (Vi) at a new station:
� Trip rate model - function of population of catchment:
� Trip end model - function of population plus other factors:
( )i iV f population=
( , , , )i i i i iV f population frequency parking jobs=
Spatial interaction (flow) models
Used to forecast the number of trips (T) from each origin (i) station to each
destination (j) station:
� Oi – attributes of origin (e.g. population, parking, frequency)
� Dj – attributes of destination (e.g. number of workplaces)
� Sij – separation between origin and destination (e.g. journey time)
( )ij i j ijT f O D S=
Defining station catchments
� Calibrate models using observed entries/exits or flows at existing stations.
� But must define a catchment first.
� Circular (buffer) around station:
1 2i i iV Pop Popα β γ= + +iV Popα β= +
Defining station catchments
� Nearest station – zone based:
� Choice of station is deterministic.
� Catchments are discrete, non overlapping.
Catchments in reality
� Use origin-destination surveys.
� 2km circular catchments account on average for 57%
of observed trips – between 0-20% for some stations
(Blainey and Evens, 2011).
� Only 53% of trip ends located within zone-based
catchments (Blainey and Preston, 2010).
� 47% of passengers in the Netherlands do not use their
nearest station (Debrezion et al., 2007).
Catchments in reality
� Catchments are not discrete, they overlap,
and stations compete.
� Station choice is not homogenous within
zones.
� Catchments vary by access mode and station
type.
� Station choice more complex than models
allow – need an alternative.
Mahmoud et al., 2014
Improving demand forecasting models
� Include a probability-based station choice
element.
� Should produce more accurate and
transferable models.
� For each catchment zone calculate the
probability of each competing station being
chosen.
� Allocate zonal population to each station based
on the probabilities.
Discrete choice models
� Individual chooses from a finite
number of mutually exclusive
alternatives.
� Individual chooses the alternative
that maximises their utility
(satisfaction).
Factor Change Expected affect on utility
Frequency of
service� �
Car parking spaces � �
Fare � �
Access distance � �
Interchanges � �
Journey time � �
Discrete choice models
Station Access Distance (km)
Direct destinations
Off-peak fare to London (£)
Journeytime to London (mins)
Transfers (to London)
Frequency per day (to London)
Parking Spaces
Pen Mill 0.5 Cardiff-
Weymouth
86.00 206 1 8 25
Yeovil Junction
2.1 Waterloo-
Exeter
52.00 140 0 19 199
Castle Cary
24.1 Paddington-
Penzance
86.00 100 0 8 120
Discrete choice models
� Actual utility an individual gains from an alternative is not
known.
� Researcher tries to measure utility by identifying
attributes of the alternatives and/or the individual:
Utility = Measured utility + Unobserved utility
Measured utility = αFreq + βFare + γPkg + δDis
� If we assume that the unobserved utility of the
alternatives is independent of each other and identically
distributed (extreme value) then can use logit models.
Logit models
� Binary logit (choice of two alternatives, i and j):
� Multinomial logit (e.g. three alternatives, i,j and k):
Pr( )ni
njni
MeasuredUtility
MeasuredUtilityMeasuredUtility
eni
e e=
+
Pr( )ni
njni nk
MeasuredUtility
MeasuredUtilityMeasuredUtility MeasuredUtility
eni
e e e=
+ +
Estimating logit models
� Need to estimate the parameters in the utility function:
Measured utility = αFreq + βFare + γPkg + δDis
� Collect individual-level data – usually from in-train passenger surveys.
� Dependent variable is the observed choice (the station each participant
actually chose).
� Parameters are estimated using maximum likelihood estimation - R, STATA,
LIMDEP.
Logit models - substitution behaviour
� Independence from irrelevant alternatives (IIA).
� For each pair of alternatives, the ratio of their probabilities is not affected by adding or
removing another alternative, or changing the attributes of another alternative.
� Consequence – proportional substitution pattern.
� Stations are located in space.
� Are a-spatial choice models appropriate?
( ) 0.42
( ) 0.2
P A
P C= =
( ) 0.662
( ) 0.33
P A
P C= =
Next steps
� Obtain and prepare data:
� Transport Scotland ≈ 23,000 responses
� London Travel Demand Survey 2005/06 to 2012/13 –
but rail trips a minor component.
� Carry out on-train survey?
� Big-data: transport timetables
� Descriptive analysis – observed catchments.
� Develop and validate choice models.
� Incorporate choice models into trip-end, flow models.
References
Debrezion, G., Pels, E. and Rietveld, P. (2007) “Choice of Departure Station by Railway Users,” European Transport, 37, 78–92.
Blainey, S. P. and Preston, J. M. (2010) “Modelling Local Rail Demand in South Wales,” Transportation Planning and Technology, 33, 55–73.
Blainey, S. and Evens, S. (2011) “Local Station Catchments: Reconciling Theory with Reality.” In European Transport Conference.
Mahmoud, M. S., Eng, P. and Shalaby, A. (2014) “Park-and-Ride Access Station Choice Model for Cross-Regional Commuter Trips in the Greater Toronto and Hamilton Area (GTHA).” In Transportation Research Board 93rd Annual Meeting.
50K Raster [TIFF geospatial data], Ordnance Survey (GB), Using: EDINA DigimapOrdnance Survey Service, <http://edina.ac.uk/digimap>, Downloaded: April 2015.
250K Raster [TIFF geospatial data], Ordnance Survey (GB), Using: EDINA DigimapOrdnance Survey Service, <http://edina.ac.uk/digimap>, Downloaded: April 2015.
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