Course: Grand Paris Express Project (2023)

Overview

Lectures covering example sand methods in large-scale transport planning making use of open data sources
Project work on the assessment of the Grand Paris Express
- Work in groups of ~5 students
- Exchange necessary between individual groups
Assessment
- Project presentation at the end of the course
- Short report on the project

Timeline

Lectures
- 10/01 8h30: Examples of large-scale models in transportation
- 10/01 10h45: Modeling methods I
- 13/01 14h00: Modeling methods II
- 13/01 16h45: Introduction to the student project
Project work
- 27/01 14h00 + 16h45: Intermediate results
- 09/02 14h00 + 16h45: Exchange with other groups
- 23/02 14h00 + 16h45: Final presentation

Timeline

Lectures
- 10/01 8h30: Examples of large-scale models in transportation
- 10/01 10h45: Modeling methods I
- 13/01 14h00: Modeling methods II
- 13/01 16h45: Introduction to the student project
Project work
- 27/01 14h00 + 16h45: Intermediate results
- 09/02 14h00 + 16h45: Exchange with other groups
- 23/02 14h00 + 16h45: Final presentation

2 weeks

Timeline

Lectures
- 10/01 8h30: Examples of large-scale models in transportation
- 10/01 10h45: Modeling methods I
- 13/01 14h00: Modeling methods II
- 13/01 16h45: Introduction to the student project
Project work
- 27/01 14h00 + 16h45: Intermediate results
- 09/02 14h00 + 16h45: Exchange with other groups
- 23/02 14h00 + 16h45: Final presentation

IRT SystemX

Public/private research institute situated in Paris
Focus on fostering digital transition in a range of fields from transport, cybersecurity to circular economy
Transferring research results and tools into active application by development and provision of industry platforms
Various collaborative projects with multiple French companies (Renault, SNCF, ...) and academic partners (Université Paris Saclay, CentraleSupélec, Université Gustave Eiffel)
Participation in European projects

I. Transport simulation

Classic transport planning

Zones
Flows
Peak hours
User groups

Aggregated

Agent-based models

0:00 - 8:00

08:30 - 17:00

17:30 - 0:00

0:00 - 9:00

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

Discrete locations
Individual travelers
Individual behaviour
Whole day analysis

Disaggregated

Icons on this and following slides: https://fontawesome.com

MATSim

Flexible, extensible and well-tested open-source transport simulation framework
Used by many research groups and companies all over the world
Extensions for parking behaviour, signal control, location choice, freight, ...

matsim-org/matsim-libs

MATSim

Synthetic demand

MATSim

Mobility simulation

Synthetic demand

MATSim

Decision-making

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

Mobility simulation

Synthetic demand

MATSim

Decision-making

Mobility simulation

Synthetic demand

MATSim

Decision-making

Mobility simulation

Analysis

Synthetic demand

Timeline

Lectures
- 10/01 8h30: Examples of large-scale models in transportation
- 10/01 10h45: Modeling methods I
- 13/01 14h00: Modeling methods II
- 13/01 16h45: Introduction to the student project
Project work
- 27/01 14h00 + 16h45: Intermediate results
- 09/02 14h00 + 16h45: Exchange with other groups
- 23/02 14h00 + 16h45: Final presentation

Transport planning: Overview

Goals
- Cost-benefit analysis
- Assessment of alternatives
- Prediction

Sources: https://de.wikipedia.org/wiki/Westtangente_Z%C3%BCrich, openstreetmap.org

Transport planning: Overview

Goals
- Cost-benefit analysis
- Assessment of alternatives
- Prediction
Use cases
- Traditionally: Infrastructure
- Today: Increasingly digital technology

Sources:
https://en.wikipedia.org/wiki/File:Highway_8_widening.png

https://commons.wikimedia.org/wiki/File:Self_driving_Uber_prototype_in_San_Francisco.jpg

Transport planning: Overview

Traditional approaches
- High aggregation
- Movements are flows
- Locations are zones
- Specific times of the day
Today's approaches
- Individual entities
- Movements are detailed in time and space
- Whole daily analysis possible

Four-step models

Activity-based models

Four-step model: Overview

Generation

Distribution

Mode choice

Assignment

Four steps making use of different data sources
From static to more dynamic mobility decisions
Multiple possible feedback loops (most common example here)

Four-step model: Overview

Generation

Distribution

Mode choice

Assignment

Where do trips appear?

What is the destination of the trips?

What transport mode is used?

What congestion is caused?

Four-step model: Overview

Generation

Distribution

Mode choice

Assignment

Travel demand

Travel supply

Characterizes the need for mobility in the population for economic activity, leisure, ...

Means that allow for mobility
Road infrastructure, rail network, ...

Four-step model: Overview

Generation

Distribution

Mode choice

Assignment

Travel demand

Travel supply

Characterizes the need for mobility in the population for economic activity, leisure, ...

Means that allow for mobility
Road infrastructure, rail network, ...

Equilibrium

Given limited capacity,
how is the system used?

Four-step model: Trip generation

Goal: Define statistically where trips starts, i.e. where they are "generated" or produced
Focus on peak hours in 4S, e.g. morning peak and focus on commuting activity
Either making use of direct data (e.g. from surveys) or based on population data, land use information, ...

Number of inabitants in Île-de-France

Four-step model: Trip generation

May be per homogeneous user group, e.g.
- Single-person households
- Multi-persons households
  without children
- Multi-person households
  with children
- Households with retired persons
Possible to use statistical models

O_{s,g} = f(X_s)

O_{s,g} = f(X_s)

Generated trips

in zone s in group g

Trip generation model

Model inputs

Four-step model: Trip generation

Example: Linear model based on socioprofessional category (CS):
- Employee
- Worker
- Retired
- ...
Do we have reference number of trips for some of the zones? Then we can create a model from this formula! Example: Ordinary Least Squares (OLS)

f(X) = \sum_{CS} \lambda_{CS} \cdot x_{CS}

f(X) = \sum_{CS} \lambda_{CS} \cdot x_{CS}

Model parameters (linear factors)

Census data (inhabitants)

\text{min}_\Lambda \sum_s (f(X_s|\Lambda) - \hat O_s)^2

\text{min}_\Lambda \sum_s (f(X_s|\Lambda) - \hat O_s)^2

Reference data

Four-step model: Trip distribution

Goal: Given the produced trips with their origin zone, define where they end = distribute the trips over a set of potential destination

Number of daily commutes arriving from 13th arrondissement

Four-step model: Trip distribution

Based on trip generation, we want to predict the number of trips going from zone s to zone t
Various ways to model flows between zones
- Gravity model
- Discrete choice model
- Maximum entropy
Can be formulated per group g.

F_{s,t} = f(X_s, X_t)

F_{s,t} = f(X_s, X_t)

Flow between s and t

Model

	O1	...	...	On
D1
...		Fst
...
Dn

O_s

O_s

D_t

D_t

Four-step model: Trip distribution

Growth factor approach
- Useful if information on existing flows is available for many relations and we want to obtain an explicative model
Constrained approach
- We have little flow information, but know about the total in each zone (e.g. population and workplaces)

F_{s,t} = \lambda_{s,t} \cdot O_t

F_{s,t} = \lambda_{s,t} \cdot O_t

\lambda_{s,t} = f(X_s, X_t)

\lambda_{s,t} = f(X_s, X_t)

Trip generation

Growth factor modeled through zone attributes

D_t = \sum_s F_{s,t}

D_t = \sum_s F_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

F_{s,t} = f(X_s, X_t)

F_{s,t} = f(X_s, X_t)

Require

Require

with

with

Four-step model: Trip distribution

Common approach: Gravity model

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

P_s = P(X_s)

P_s = P(X_s)

A_t = A(X_t)

A_t = A(X_t)

Production model, e.g. P = Population or Population Density

Attraction model, e.g. A = Workplaces or Workplace Density

R_{s,t} = A(X_s, X_t)

R_{s,t} = A(X_s, X_t)

Resistance model, e.g. based on travel time or distance (also called impedance)

R_{s,t} = exp( -\beta \cdot \text{Distance}(s,t) )

R_{s,t} = exp( -\beta \cdot \text{Distance}(s,t) )

Four-step model: Trip distribution

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

Common approach: Gravity model
If we know F, we can calibrate all models, e.g. using OLS ...
... but we may better know required origins, i.e.

Four-step model: Trip distribution

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t} = \rho \cdot P_s \cdot \sum_t A_t \cdot R_{s,t}

O_s = \sum_t F_{s,t} = \rho \cdot P_s \cdot \sum_t A_t \cdot R_{s,t}

Common approach: Gravity model
If we know F, we can calibrate all models, e.g. using OLS ...
... but we may better know required origins, i.e.

Four-step model: Trip distribution

Common approach: Gravity model
If we know F, we can calibrate all models, e.g. using OLS ...
... but we may better know required origins, i.e.

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

O_s = \sum_t F_{s,t}

P_s = \frac{O_s}{\rho \sum_t A_t R_{s,t}}

P_s = \frac{O_s}{\rho \sum_t A_t R_{s,t}}

Four-step model: Trip distribution

... which can be inserted into the model ...
... leading to

with
If R is given or estimated independently (usually the case), the flows F are determined!

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

F_{s,t} = f(X_s, X_t) = \rho \cdot P_s \cdot A_t \cdot R_{s,t}

\alpha_s = \frac{1}{\sum_t A_t R_{s,t}}

\alpha_s = \frac{1}{\sum_t A_t R_{s,t}}

F_{s,t} = f(X_s, X_t) = \alpha_s \cdot A_t \cdot R_{s,t} \cdot O_s

F_{s,t} = f(X_s, X_t) = \alpha_s \cdot A_t \cdot R_{s,t} \cdot O_s

P_s = \frac{O_s}{\rho \sum_t A_t R_{s,t}}

P_s = \frac{O_s}{\rho \sum_t A_t R_{s,t}}

Four-step model: Trip distribution

Including both origin and destination constraints gives

with

F_{s,t} = f(X_s, X_t) = \alpha_s \cdot \beta_t \cdot O_s \cdot D_t \cdot R_{s,t}

F_{s,t} = f(X_s, X_t) = \alpha_s \cdot \beta_t \cdot O_s \cdot D_t \cdot R_{s,t}

P_s = \frac{O_s}{\rho \sum_t A_t R_{s,t}}

P_s = \frac{O_s}{\rho \sum_t A_t R_{s,t}}

\alpha_s = \frac{1}{\sum_t A_t R_{s,t}}

\alpha_s = \frac{1}{\sum_t A_t R_{s,t}}

\beta_t = \frac{1}{\sum_s P_s R_{s,t}}

\beta_t = \frac{1}{\sum_s P_s R_{s,t}}

A_t = \frac{D_t}{\rho \sum_s P_s R_{s,t}}

A_t = \frac{D_t}{\rho \sum_s P_s R_{s,t}}

By construction, the origin and destination counts are replicated when iteratively solving

Timeline

Lectures
- 10/01 8h30: Examples of large-scale models in transportation
- 10/01 10h45: Modeling methods I
- 13/01 14h00: Modeling methods II
- 13/01 16h45: Introduction to the student project
Project work
- 27/01 14h00 + 16h45: Intermediate results
- 09/02 14h00 + 16h45: Exchange with other groups
- 23/02 14h00 + 16h45: Final presentation

Four-step model: Mode choice

Goal: Given the infrastructure and the generated OD trips, which mode of transport should be used for them.
Expressed as mode share: How many percent of the trips on a OD relation are performed with mode m?

P[m]

P[m]

m \in \{ car, pt, ... \}

m \in \{ car, pt, ... \}

\sum_m P[m] = 1

\sum_m P[m] = 1

P[m] \geq 0

P[m] \geq 0

Utility maximization

Utility maximization is important concept in transport planning: Given a set of choices (modes, destinations, ...) we can assign a utility v to each alternative k.
We assume rational decision makers that always want to choose the alternative with the highest utility.
Homo oeconomicus

v_k = \beta^T X

v_k = \beta^T X

k^* = \text{arg max}_k \{ v_1, ..., v_k, ... v_K \}

k^* = \text{arg max}_k \{ v_1, ..., v_k, ... v_K \}

Utility maximization

Example: Choice between two public transport connections

v_k = \beta_\text{travelTime} \cdot x_{k, \text{travelTime}} + \beta_\text{interchanges} \cdot x_{k, \text{interchanges}}

v_k = \beta_\text{travelTime} \cdot x_{k, \text{travelTime}} + \beta_\text{interchanges} \cdot x_{k, \text{interchanges}}

Connection A

x_{A, \text{travelTime}} = 20\ \text{min}

x_{A, \text{travelTime}} = 20\ \text{min}

Connection B

x_{B, \text{travelTime}} = 30\ \text{min}

x_{B, \text{travelTime}} = 30\ \text{min}

x_{B, \text{interchanges}} = 0

x_{B, \text{interchanges}} = 0

x_{A, \text{interchanges}} = 1

x_{A, \text{interchanges}} = 1

Utility maximization

Example: Choice between two public transport connections

v_k = \beta_\text{travelTime} \cdot x_{k, \text{travelTime}} + \beta_\text{interchanges} \cdot x_{k, \text{interchanges}}

v_k = \beta_\text{travelTime} \cdot x_{k, \text{travelTime}} + \beta_\text{interchanges} \cdot x_{k, \text{interchanges}}

Connection A

x_{A, \text{travelTime}} = 20\ \text{min}

x_{A, \text{travelTime}} = 20\ \text{min}

Connection B

x_{B, \text{travelTime}} = 30\ \text{min}

x_{B, \text{travelTime}} = 30\ \text{min}

x_{B, \text{interchanges}} = 0

x_{B, \text{interchanges}} = 0

x_{A, \text{interchanges}} = 1

x_{A, \text{interchanges}} = 1

-0.6

-1.0

Utility maximization

Example: Choice between two public transport connections

v_k = \beta_\text{travelTime} \cdot x_{k, \text{travelTime}} + \beta_\text{interchanges} \cdot x_{k, \text{interchanges}}

v_k = \beta_\text{travelTime} \cdot x_{k, \text{travelTime}} + \beta_\text{interchanges} \cdot x_{k, \text{interchanges}}

Connection A

x_{A, \text{travelTime}} = 20\ \text{min}

x_{A, \text{travelTime}} = 20\ \text{min}

Connection B

x_{B, \text{travelTime}} = 30\ \text{min}

x_{B, \text{travelTime}} = 30\ \text{min}

x_{B, \text{interchanges}} = 0

x_{B, \text{interchanges}} = 0

x_{A, \text{interchanges}} = 1

x_{A, \text{interchanges}} = 1

-0.6

-1.0

-0.6 * 20 - 1.0 * 1 = -13

-0.6 * 30 - 1.0 * 0 = -19

Connection A is better alternative.

Utility maximization

Example: Mode choice model

Random Utility Models

How to estimate such models?
Assume K alternatives and N observed choices y described by index i.
We want to find the set of parameters such that all choices i are replicated correctly:

v_k(X_i) = \beta^T \cdot X_k

v_k(X_i) = \beta^T \cdot X_k

y_i \in \{ 1, ..., K \}

y_i \in \{ 1, ..., K \}

\beta

\beta

y_i = \text{arg max}_k \{ v_k(X_i|\beta) \} \ \ \forall i

y_i = \text{arg max}_k \{ v_k(X_i|\beta) \} \ \ \forall i

Find such that

Random Utility Models

Not easily possible: We cannot model reality sufficiently well, people have own tastes.
Hence, we introduce an error term.
This leads to a random utility maximization (RUM) model

u_{k,i} = v_{k,i} + \sigma \epsilon_{k,i}

u_{k,i} = v_{k,i} + \sigma \epsilon_{k,i}

E[\epsilon_{k,i}] = 0

E[\epsilon_{k,i}] = 0

\sigma \geq 0

\sigma \geq 0

k^*_i = \text{arg max}_k \{ u_{k,i} \}

k^*_i = \text{arg max}_k \{ u_{k,i} \}

k^*_i = \{ k \ | \ (u_{k,i} \geq u_{1,i}) \land \ (u_{k,i} \geq u_{2,i}) \land ... \}

k^*_i = \{ k \ | \ (u_{k,i} \geq u_{1,i}) \land \ (u_{k,i} \geq u_{2,i}) \land ... \}

Random Utility Models

McFadden has shown in the seventies that if error follows Extreme Value distribution ...
... there is a closed form expression for the choice probability:
Two alternatives k:
Binomial logit model
Multiple alternatives k:
Multinomial logit model

u_k = v_k + \sigma \epsilon_k

u_k = v_k + \sigma \epsilon_k

\epsilon_{k,i} \sim \text{EV}

\epsilon_{k,i} \sim \text{EV}

P[k] = \frac{\exp(\sigma^{-1} v_k)}{\sum_{k'}\exp(\sigma^{-1} v_{k'})}

P[k] = \frac{\exp(\sigma^{-1} v_k)}{\sum_{k'}\exp(\sigma^{-1} v_{k'})}

Random Utility Models

Coming back to estimation:
Under multinomial logit assumption, we can write a (log-)likelihood function ...
... which is proven convex and can be maximized using any optimization method!

P[k] = \frac{\exp(\sigma^{-1} v_k)}{\sum_{k'}\exp(\sigma^{-1} v_{k'})}

P[k] = \frac{\exp(\sigma^{-1} v_k)}{\sum_{k'}\exp(\sigma^{-1} v_{k'})}

v_k(X_i) = \beta^T \cdot X_k

v_k(X_i) = \beta^T \cdot X_k

y_i \in \{ 1, ..., K \}

y_i \in \{ 1, ..., K \}

\mathcal{L}(\beta) = \prod_i P[y_i|X_i,\beta]

\mathcal{L}(\beta) = \prod_i P[y_i|X_i,\beta]

l(\beta) = \sum_i \log P[y_i|X_i,\beta]

l(\beta) = \sum_i \log P[y_i|X_i,\beta]

\beta^* = \text{arg max}_{\beta} \sum_i \log P[y_i|X_i,\beta]

\beta^* = \text{arg max}_{\beta} \sum_i \log P[y_i|X_i,\beta]

Random Utility Models

Multinomial logit is commonly used and part of larger class of discrete choice models.
Can also be used in distribution step.

Last step of 4S model
What are resulting flows on the roads?
What are the resulting travel times?

Four-step model: Assignment

Again, based on rational decision maker
Game theoretic perspective: Travellers compete about using the roads, each maximizing the utility they gain
Many ways of solving assignment problem

Four-step model: Assignment

Example: Two routes
- Travellers on Route A:
- Travellers on Route B:
- Total travellers from S to E:
- Travel time route A (normal road):
- Travel time route B (highway):
- Wardrop principle: Travellers tend to minimize their travel time.

Four-step model: Assignment

S

E

Route A

Route B

n_{A}

n_{A}

n_{B}

n_{B}

N = n_{A} + n_{B}

N = n_{A} + n_{B}

t_A(n_A) = 500 + n_A \cdot 2

t_A(n_A) = 500 + n_A \cdot 2

t_B(n_B) = 1000 + n_B \cdot 1

t_B(n_B) = 1000 + n_B \cdot 1

How many people use each road? What are the travel times?

Example: Two routes
- If travel time on A is shorter than B, people would switch to B, and vice versa. Hence, travel times, must be in equilibirum!
- This gives

Four-step model: Assignment

S

E

Route A

Route B

n_{B}

n_{B}

N = n_{A} + n_{B}

N = n_{A} + n_{B}

t_A(n_A) = 500 + n_A \cdot 2

t_A(n_A) = 500 + n_A \cdot 2

t_B(n_B) = 1000 + n_B \cdot 1

t_B(n_B) = 1000 + n_B \cdot 1

n_{A}

n_{A}

t_A(n_A) = t_B(n_B)

t_A(n_A) = t_B(n_B)

500 + 2 \cdot n_A = 1000 + 1 \cdot n_B

500 + 2 \cdot n_A = 1000 + 1 \cdot n_B

n_A = N - n_B

n_A = N - n_B

(*)

from (**)

(**)

Example: Two routes
- If travel time on A is shorter than B, people would switch to B, and vice versa. Hence, travel times, must be in equilibirum!
- This gives

Four-step model: Assignment

S

E

Route A

Route B

n_{B}

n_{B}

N = n_{A} + n_{B}

N = n_{A} + n_{B}

t_A(n_A) = 500 + n_A \cdot 2

t_A(n_A) = 500 + n_A \cdot 2

t_B(n_B) = 1000 + n_B \cdot 1

t_B(n_B) = 1000 + n_B \cdot 1

n_{A}

n_{A}

t_A(n_A) = t_B(n_B)

t_A(n_A) = t_B(n_B)

500 + 2 \cdot n_A = 1000 + 1 \cdot n_B

500 + 2 \cdot n_A = 1000 + 1 \cdot n_B

n_A = N - n_B

n_A = N - n_B

(*)

from (**)

(**)

N = 1000

n_A = 433.33

n_A = 433.33

n_B = 566.66

n_B = 566.66

t_A = t_B = 1366.66

t_A = t_B = 1366.66

What about many different routes between two points?
What about many different start/end points in a network?
Wardrop defined general formulation, leading to a game-theoretic Nash equilibrium

Four-step model: Assignment

\text{min}_x \sum_a \int_0^{x_a} t_a(x_a) \text{d}x

\text{min}_x \sum_a \int_0^{x_a} t_a(x_a) \text{d}x

\sum_k f_k^{rs} = q_{rs} \ \ \ \forall r, s

\sum_k f_k^{rs} = q_{rs} \ \ \ \forall r, s

x_a = \sum_r \sum_s \sum_k \delta^{rs}_{k,a} f_k^{rs} \ \ \ \forall a

x_a = \sum_r \sum_s \sum_k \delta^{rs}_{k,a} f_k^{rs} \ \ \ \forall a

f_k^{rs} \geq 0

f_k^{rs} \geq 0

x_a \geq 0

x_a \geq 0

s.t.

s.t.

Four-step model: Feedback

Generation

Distribution

Mode choice

Assignment

Now travel times in the network are known. They can be fed back to the distribution stage (which travel times did we assume back then)?
4S model can be run iteratively to bring stages in equilibrium.
Many caveats in terms of consistency and convergence (beyond the scope in this lecture).

Four-step model: Assignment

So far, Static Traffic Assignment (STA): Departure time of vehicles does not matter (we look at peak our time slice).
Methods for Dynamic Traffic Assignment (DTA) exist, which consider detailed movements of vehicles, traffic lights, ...

Activity-based models

Non-equilibrium models
- Sequential decisions during simulation for each agent
- Dynamic reaction to environment

Activity-based models

Activity 1

Activity 2

Activity 3

Decision

Analysis

Person 1

Activity 1

Activity 2

Person 1

Decision

Non-equilibrium models
- Sequential decisions during simulation for each agent
- Dynamic reaction to environment
Equilibrium models
- Decision made for a whole day
- All decisions of persons go into equilibrium after multiple iterations

Activity-based models

Activity 1

Activity 2

Activity 3

Person 1

Activity 1

Activity 2

Person 1

Analysis

Decision making

Iterations

Non-equilibrium models
- Sequential decisions during simulation for each agent
- Dynamic reaction to environment
Equilibrium models
- Decision made for a whole day
- All decisions of persons go into equilibrium after multiple iterations
Hybrid models

Activity-based models

Generally, detailed input data is needed to reach at detailed output
Data processing similar to 4S models, but much more complex and some components are dynamically treated in the simulation

Activity-based models

Population synthesis
The task is to create a synthetic representation of the population with socio-demographical households and person attributes
Multitude of approaches
- Iterative Proportional Fitting (IPF)
- Iterative Proportional Updating (IPU)
- Gibbs Sampling

Activity-based models

Population synthesis
The task is to create a synthetic representation of the population with socio-demographical households and person attributes
Multitude of approaches
- Iterative Proportional Fitting (IPF)
- Iterative Proportional Updating (IPU)
- Gibbs Sampling
- Bayesian Networks

Activity-based models

Sun, L., Erath, A., 2015. A Bayesian network approach for population synthesis. Transportation Research Part C: Emerging Technologies 61, 49–62. https://doi.org/10.1016/j.trc.2015.10.010

Population synthesis
The task is to create a synthetic representation of the population with socio-demographical households and person attributes
Multitude of approaches
- Iterative Proportional Fitting (IPF)
- Iterative Proportional Updating (IPU)
- Gibbs Sampling
- Bayesian Networks
- Hidden Markov Models

Activity-based models

Saadi, I., Mustafa, A., Teller, J., Farooq, B., Cools, M., 2016. Hidden Markov Model-based population synthesis. Transportation Research Part B: Methodological 90, 1–21. https://doi.org/10.1016/j.trb.2016.04.007

Population synthesis
The task is to create a synthetic representation of the population with socio-demographical households and person attributes
Multitude of approaches
- Iterative Proportional Fitting (IPF)
- Iterative Proportional Updating (IPU)
- Gibbs Sampling
- Bayesian Networks
- Hidden Markov Models
- Deep Generative Modeling
  Variational Auto Encoders

Activity-based models

Borysov, S.S., Rich, J., Pereira, F.C., 2019. How to generate micro-agents? A deep generative modeling approach to population synthesis. Transportation Research Part C: Emerging Technologies 106, 73–97. https://doi.org/10.1016/j.trc.2019.07.006

Synthesis of activity chains
Given sociodemographic attributes, generate daily activity chains for the agents.
Today increasingly use of GPS / GSM trace data sets to train models
Some examples
- Statistical Matching Approaches
- Skeleton-based approaches
- Sequential choice models (classic approach)
- Sequential Alignment Methods (SAM)
- Bayesian Networks
- ...

Activity-based models

Joubert, J.W., de Waal, A., 2020. Activity-based travel demand generation using Bayesian networks. Transportation Research Part C: Emerging Technologies 120, 102804. https://doi.org/10.1016/j.trc.2020.102804

Further aspects:
- Departure times from activities
- Locations of activities
  - Potential Path Areas (PPA)
  - Space-time prisms
- ...

Activity-based models

Yoon, S.Y., Deutsch, K., Chen, Y., Goulias, K.G., 2012. Feasibility of using time–space prism to represent available opportunities and choice sets for destination choice models in the context of dynamic urban environments. Transportation 39, 807–823. https://doi.org/10.1007/s11116-012-9407-8

As is demand generation, mobility simulation is often also more complex
Usually agent-based
Microscopic simulation
- VISSIM, SUMO, ...
Mesoscopic simulation
- MATSim, SimMobility,
  POLARIS, ...

Activity-based models

Convergence
- Usually based on utility maximization principle
- Detection of Nash equilibria
- Output metric convergence
Calibration
- Complex simulator (no closed form expression)
- Noisy output (needs stochastic optimization)
- Large run-times

Activity-based models: Challenges

Activity-based models: Examples

Timeline

Lectures
- 10/01 8h30: Examples of large-scale models in transportation
- 10/01 10h45: Modeling methods I
- 13/01 14h00: Modeling methods II
- 13/01 16h45: Introduction to the student project
Project work
- 27/01 14h00 + 16h45: Intermediate results
- 09/02 14h00 + 16h45: Exchange with other groups
- 23/02 14h00 + 16h45: Final presentation

Grand Paris Express

One of largest infrastructure projects in Europe
Affecting mobility behaviour of large share of population in Île-de-France

Course project

Learn more about Grand Paris Express
Simple demand estimate for Grand Paris Express and its impact on existing public transport
Four groups:
- Demand
- Routing
- GTFS
- Mode choice

https://vu.fr/WLqY

Please register for the groups:

Course project

Project description