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David Watling, Richard Connors, Agachai Sumalee
ITS, University of Leeds
Acknowledgement: DfT “New Horizons”Dynamic Traffic Assignment Workshop, Queen’s University, Belfast
15th September 2004
Encapsulating between day variability in demand in analytical, within-day dynamic, link travel time functions
Aims
Dynamic modelling of network links subject to variable in-flows comprising:
Within-day variation described by inflow, outflow and travel time profiles
Between-day variation = random variation in these profiles
Thus identify mean travel times under doubly dynamic variation in flows
UK’s Department for Transport Work
Reliability impacts on travel decisions through generalised cost
Dynamic Models
Cellular Automata Microsimulation Analytical ‘whole-link’ models
Many shown to fail plausibility tests (FIFO) e.g. = f [x(t)], with x(t) = number cars on link
Carey et al. “improved” whole-link models guarantee FIFO and agree with LWR behaviour.
Modelling Within-Day Variation:Whole-link model (Carey et al,
2003)))(),(()( tvtuht
travel time for vehicle entering at time t
))(()1()()()( ttvtuhtwht
in-flow at entry time out-flow at exit time
)(τ1
)())(τ(
t
tuttv
Flow conservation (Astarita, 1995)
ttt
dssvdssu
00
Whole-link Model
)()(
)()1(1)(
1 tuh
tut
Combining gives a first-order differential equation:
1)('FIFO t
No analytic solution for most functions h(.), u(.). Can solve using backward differencing, applied in
forward time (to avoid FIFO violations).
Flow Capacity Should the link travel-time function h(w) inherently
define max (valid) w and hence capacity, c?
Out-flow can exceed capacity in computation so long as inflow ‘compensates’ such that w=βu(t)+(1-β)v(t+τ(t))< c
Can ensure outflows respect flow capacity by adapting the numerical scheme.
τ0
τ
wc
Scenarios for h(w) with finite capacity c
Desired meaning of capacity requires careful definition of h(w)
Day-to-day variation
Introduce day-to-day variation of inflow Derive expected travel time profile in terms of mean,
variances, co-variances of day-to-day varying in-flows
Mean travel time under between-day varying inflows
Travel time at mean inflow
Day-to-day variation
)(,2
1)]([)]([ tHtuEtE
Inflation term for between-day variation. Comprising: Variance-Covariance matrix of inflow variability and Hessian matrix“sensitivity of travel timeto inflows”Not a constant!
Day-to-day parameterisation
Practically unrestrictive: discretised case with N time slices
Univariate Case
General Case
Vart
ttE2
2 ,
2
1,,
CovtHttE ,
2
1,,
u(t) = u(t, )
each day has different value of (vector)
u(t) = = [θ1, θ2,…, θN]
Methodology
Monte Carlo simulations of day-to-day inflows drawn from a normal distribution gives many u(t, i)
Whole-link model gives travel time i(t)=(u(t, i)) Calculate mean of all the Monte Carlo days travel
times. This is the experienced mean travel time. Calculate travel time at mean inflow, using whole-link
model with inflow E[u(t,)] Calculate the “Inflation” Term: combination of the
Hessian and Covariance matrix Compare inflation term with ,, ttE
Numerical Example
BPR-type link travel time function
4
1c
wffwh
ff = 10mins
c = 2000 pcus/hour (‘capacity’)
In-flow profile with random day-to-day peak
240120
12060
600
240
πsin)ε4000(
)ε4000(120
πsin)ε4000(
)(
5
t
t
t
t
t
tU )1000,0(ε 2N
Solving Carey’s model with = 1, so that = h[u(t)]
No dependence on outflows.
Std dev of inflows
Travel time calculated for the mean inflow ][uE
Mean travel time over the days (with c.i.s)
Mean inflow over the days uE
)(uE
Numerical difference from plot above
Inflation term by calculation
Example: =0.1
Asymptotic link travel time function
cw
ffwh
1ff = 10mins
c = 7000 pcus/hour (‘capacity’)
In-flow profile with random day-to-day peak
)20,80( 2N
2
2
2exp
740000),,(
t
tu
Compare Two Link Travel Time Functions
0 1000 2000 3000 4000 5000 6000 700010
15
20
25
30
35
40
AsympBPR
w
τ=h(w)
7000
1
10w
wh
4
2000110
wwh
Example: =0.5
Asymptotic link travel time function
cw
ffwh
1ff = 10mins
c = 7000 pcus/hour (‘capacity’)
In-flow profile with random day-to-day peak
240120
12060
600
240sin)4000(
)4000(120
sin)4000(
500)(
5
t
t
t
t
t
tU
)1000,0(ε 2N
Example: =varying
Asymptotic link travel time function
cw
ffwh
1ff = 10mins
c = 7000 pcus/hour (‘capacity’)
In-flow profile with random day-to-day peak
240120
12060
600
240sin)4000(
)4000(120
sin)4000(
500)(
5
t
t
t
t
t
tU
)1000,0(ε 2N
Further Work
Analytic derivation of the correction term?
Modify whole-link model to limit outflows Augment with dynamic queuing model?
Conditions for FIFO?
Follow this approach on the links of a network to investigate its reliability under day-to-day varying demand.
Questions/Comments?