This document discusses methods for calculating time-dependent origin-destination demand for large-scale dynamic transportation models. It presents three types of OD demand sources with varying levels of time-slicing and describes approaches for static and dynamic OD adjustment using traffic counts. The document also discusses combining static and dynamic OD adjustment methods and accounting for future demand growth through modeling changes in individual travel behavior and departure time choice in response to congestion.

1. The challenge of calculating a time-dependent
Origin Destination demand for large-scale dynamic models
Alex Torday
AITPM 2017
Melbourne

2. Demand vs. Throughput

3. Type of OD demand source (strategic model)
• Type 1
– Household survey over 24 hours with departure/arrival time
– Calibrated and time sliced static assignment
• Type 2
– Household survey over 24 hours with departure/arrival time
– Peak hours and flat demand assignment
• Type 3
– Household survey peak hour and aggregated
– Peak hours and flat demand assignment

4. Toronto case

5. Toronto case

6. Use traffic profiles

7. Static adjustment

8. Static OD Departure Adjustment
• Assumptions
– Fixed paths from calibrated static assignment/adjustment
– Travel times from Volume-Delay Functions
– No queueing and spillback
• Guidelines
– Define a travel time Function Component in the VDF
– Prevent unrealistically high
travel times
• Output
– Profiled demand (based on
approximate travel time)

9. Static OD Departure Adjustment
• Algorithm
– 1000 trips from a to b through detector da
– Count data in 4 intervals of 15 minutes (900 s)
– Travel time from a to da is 600 s
– Least square solution using a gradient descent method
𝑑 𝑎1
= 0.33 1000 α1
𝑑 𝑎2
= 0.67 1000 α1 + 0.33 1000 α2
𝑑 𝑎3
= 0.67 1000 α2 + 0.33 1000 α3
𝑑 𝑎4
= 0.67 1000 α3 + 0.33 1000 α4
α1+α2 + α3+α4 = 1

10. Dynamic OD Adjustment
• Assumptions
– Full dynamic simulation with congestion effects
– Mesoscopic one-shot simulation with 100% path file
• Guidelines
– Check the mesoscopic one-shot simulation for behavioral issues
and gridlocks
– Limit the maximum deviation of the initial demand
• Output
– Profiled demand

11. Dynamic OD Adjustment
• Algorithm
– Rolling horizon
– Gradient descent solution method
• Counts are weighted with the inverse of their value to
prevent higher counts to determine all the changes
Iteration 1
Iteration 2
Iteration 3
Interval 1 Interval 2 Interval 3 Interval 4 Interval 5 Interval 6
Iteration 1
Iteration 2
Iteration 3
Interval 1 Interval 2 Interval 3 Interval 4 Interval 5 Interval 6

12. Dynamic OD Adjustment
• Workflow
Initial profiled
Demand
Meso DUE
Dynamic OD
Adjustment
Validation

13. Combined approach
Initial Matrix
7:00 – 9:00
Extended
Matrix 6 – 10
Static OD Departure Adjustment
Dynamic OD Adjustment
Real Data
6:00 – 10:00
Multi-
resolution
network
Static OD Adjustment

14. Future demand
• Unconstrained trip growth leads to peak hour traffic
demand well above network capacity
• This can lead to difficulties such as network lock-up or
unfeasible levels of queuing/delay
• Network may even be at capacity in base year and
unable to process additional peak traffic
• In reality, whilst congestion does increase, some
travellers change their travel behaviour in order to
avoid the worst of the congestion

15. Individuals change travel behaviour in response to
increasing congestion
Travellers can change:
 Frequency of travel: i.e. making less trips of this type
 Destination: such as a different superstore for shopping
 Mode: switching between private car and rail or bus
 Time of travel: leaving earlier for a less congested commute
 Route taken: avoiding congested areas, increased rat-running
Increasingsensitivitytocostoftravel

16. New Dynamic Departure OD Adjustment
Drivers change their departure time in response to congestion, allowing:
 More realisable future demand: forecast growth during the
absolute peak reduced; more feasible simulations
 Modelling impact on peak shoulders: some demand will shift
as drivers travel earlier or later to avoid the worst congestion
 Reduction in model run/convergence issues due to over-
saturation in peak hour, reduced potential for network lock-up
 Impact on scheme benefits could be considered
capacity

17. • Equilibrium scheduling theory applied
• Individuals balance travel time improvements with
scheduling costs for early/late arrival, example:
• Optimal arrival time 8:00, traveller accepts scheduling
cost of arriving 15 minutes early in exchange for travel
time saving
Trading-off travel time and scheduling costs
Late arrival
attracts higher
scheduling cost
Preferred
arrival
time

18. • Can supply base year path assignment file to determine
preferred arrival times (PATs)
• Assumes that the PATs in future are distributed in the
same way as actual arrival times in the base
• Increased congestion in forecast demand will then
provoke changes to trip departure times
• The algorithm seeks to minimise
𝐺𝐶 = 𝑇𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 + 𝛼 𝑒 𝑒𝑎𝑟𝑙𝑦 + 𝛼𝑙late
where 𝛼e, 𝛼l = weight of 1 minute of early, late arrival outside the
preferred arrival time ‘PAT’ window, relative to 1 minute of travel
time
Process requires PATs, and forecast demand and
network

19. Q & A