Project Management & Communication

Sebastian Hörl

14 January 2021

H2020 Projet LEAD

This part of the course

  • Today: Lecture
    • Basic concepts in transport planing
    • Four-step models vs. activity-based models
       
  • Coming sessions: Project work
    • 21 January, 14:00 - 16:00
    • 11 February, 14:00 - 17:00
    • 25 February, 14:00 - 16:00
       
  • Assessment
    • Final presentation (25 February)
    • Short report (until 15 March)

Today

  1. Introduction to transport planning
    1. Four-step models
    2. Activity-base models
    3. Agent-based models
       
  2. Details on the course project
    1. Four different groups
    2. Interaction and assessment criteria
       
  3. Time plan and definition of groups

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)

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}

Model parameters (linear factors)

Census data (inhabitants)

\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)

Flow between s and t

Model

O1 ... ... On
D1
... Fst
...
Dn
O_s
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
\lambda_{s,t} = f(X_s, X_t)

Trip generation

Growth factor modeled through zone attributes

D_t = \sum_s F_{s,t}
O_s = \sum_t F_{s,t}
F_{s,t} = f(X_s, X_t)
Require
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}
P_s = P(X_s)
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)

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

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}
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}
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}
  • 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}
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}}

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}
\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
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}
P_s = \frac{O_s}{\rho \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}}
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

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]
m \in \{ car, pt, ... \}
\sum_m P[m] = 1
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
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}}

Connection A

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

Connection B

x_{B, \text{travelTime}} = 30\ \text{min}
x_{B, \text{interchanges}} = 0
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
y_i \in \{ 1, ..., K \}
\beta
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}
E[\epsilon_{k,i}] = 0
\sigma \geq 0
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 ... \}

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

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
\epsilon_{k,i} \sim \text{IV}
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'})}
v_k(X_i) = \beta^T \cdot X_k
y_i \in \{ 1, ..., K \}
\mathcal{L}(\beta) = \prod_i 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]

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_{B}
N = n_{A} + n_{B}
t_A(n_A) = 500 + n_A \cdot 2
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 = n_{A} + n_{B}
t_A(n_A) = 500 + n_A \cdot 2
t_B(n_B) = 1000 + n_B \cdot 1
n_{A}
t_A(n_A) = t_B(n_B)
500 + 2 \cdot n_A = 1000 + 1 \cdot n_B
n_A = N - n_B

(*)

from (**)

(**)

N = 1000

n_A = 433.33
n_B = 566.66
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
\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
f_k^{rs} \geq 0
x_a \geq 0
s.t.

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, ...

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).

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

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

  • Last semester
    • Example with eqasim (for demand synthesis)
    • and MATSim (traffic simulation)

Activity-based models: Example

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

Course project

Demand

GTFS

Routing

Mode choice

  • Estimate commuting
    demand based on census
  • Look into weighting and down-scaling
  • Visualize commuting
    demand
  • Understand GTFS format
  • Integrate Grand Paris Express in digital transit schedule
  • Visualize network
  • Use routing software to calculate commuting trip characteristics today and with Grand Paris Express
  • Understand delays in car traffic
  • Visualize high-level routing metrics
  • Use multinomial logit model to analyze mode choice for commuting trips
  • Visualize difference from reference data and mode use on a map
  • Coordination within each group
  • Coordination across groups

Course project

Demand

GTFS

Routing

Mode choice

  • Estimate commuting
    demand based on census
  • Look into weighting and down-scaling
  • Visualize commuting
    demand
  • Understand GTFS format
  • Integrate Grand Paris Express in digital transit schedule
  • Visualize network
  • Use routing software to calculate commuting trip characteristics today and with Grand Paris Express
  • Understand delays in car traffic
  • Visualize high-level routing metrics
  • Use multinomial logit model to analyze mode choice for commuting trips
  • Visualize difference from reference data and mode use on a map
  • Demand analysis: Where does GPE have the highest impact (in travel time improvements and in actual mode-choice-based use)?
     
  • Supply analysis: Which existing lines loose how many users? How many users do the new lines have compared to existing lines?

Course project (Information flow)

Demand

GTFS

Routing

Mode choice

[ ID,

  Origin XY,

  Destination XY,

  Reference Mode ]

[ GTFS ]

[ ID,

  Car travel time,

  PT travel time,

  PT line switches,

  [ IDs of used PT lines ]

]

[ ID,

  Chosen transport mode

]

For instance, count how many commutes make use of RER A before and after including the Grand Paris Express.

Group: Demand

  • Technical task: Produce a commuting data set for Île-de-France as a list of trips with origin coordinate, destination coordinate, and usual mode of transport. It should be possible to scale down the data set to make it easier to use for the other groups.
     
  • Analysis task:
    • Visualize the number of trips in each municipality on a map
    • Visualize the number of people using car vs. public transport in those municipalities. Which patterns do you observe in Ile-de-France?

Group: Demand

Group: Demand

  • Recommendations
    • Origin and destination in the data set are given in terms of municipality identifiers (INSEE number). They are formatted as
      [2x Department code][3x Municipality code]. You can use the field "REG" or this code to filter for all the trips starting and ending in Ile-de-France.
       
    • You can sample trips at random from the data set, based on the weight of each trip. This way, you can assume a number N of trips that you want to sample and this way you can provide data sets to the other group that have a size they can handle.

Group: Demand

  • Recommendations
    • To add coordinates to your data set, you can make use of the geographical reference file for all IRIS in France. IRIS are one level of aggregation lower than a municipality. You can use this data set, for instance, to find the centroid of each municipality, and then you can use the centroids as start and end coordinates of your trips.
      https://www.data.gouv.fr/fr/datasets/decoupage-administratif-communal-francais-issu-d-openstreetmap/
       
    • Bonus: You can also sample for each trip coordinates at random from the municipality. Even further, you can use aggregate statistics provided by INSEE on the number of inhabitants in each IRIS to perform a weighted sampling of IRIS and thus make your trips resemble better the population density of each municipality.

Group: Routing

  • Technical task: Set up a system that allows you to route trips in Ile-de-France with a start coordinate and an end coordinate such that you obtain the travel time by car, travel time by public transport and potentially other attributes that are of interest. Take into account that during the morning peak hour, roads are congested!
     
  • Analysis task:
    • Given a set of trips (provided by "demand" group), show the distribution travel times by car and public transport in Ile-de-France. Where are regions where travelling by car is generally quicker or slower?

Group: Routing

  • Recommendations
    • Usually, we would use API services like HERE, Bing or Google. There, we can send (start, end) coordinates and receive travel times, number of transfers, etc. for all modes of transport
       
    • Here, we also want to look into the future transport system.
    • You can use OpenTripPlanner (or other routing software such as R5 to set up a routing service locally).

Group: Routing

Group: Routing

Group: Routing

  • Recommendations
    • You can then send requests to your OpenTripPlanner server via its web API, for instance:







       
    • This allows you to obtain travel times by car and public transport

Group: Routing

  • Recommendations
    • Note that OpenTripPlanner has no information about congestion in the morning. How can you solve this problem?
       
    • One option maybe to look into Uber Movements data for Paris and get a rough idea by how much travel times are reduced on average in the morning compared to other times of the day with low congestion (night). You may then obtain a global factor that you can apply to all the produced travel times.

Group: GTFS

  • Technical task: Get familiar with the structure of a digital transit schedule in GTFS format. Group "Routing" will be able to use this data to find routes of passengers with this data. Integrate the new Metro lines of the Grand Paris Express into the GTFS file of Ile-de-France.
     
  • Analysis task:
    • Visualize the Metro network (lines and stations) of Paris before and after including the Grand Paris Express on a map.

Group: GTFS

Group: GTFS

  • Recommendations
    • Attention! Is there any data missing? Are there any errors in the provided data sets? Inconsistencies? Do not stick to long to those problems, but try to find an easy and pragmatic solution. It is fine to report your findings in the presentations, no need to fix everything in detail!

Group: Mode choice

  • Technical task: Apply a discrete mode choice model on commuting data for Ile-de-France. Propose a model that makes it possible to perform a prediction based on car travel time and public transport connection characteristics.
     
  • Analysis task:
    • Visualize the use of car and public transport as the commuting mode in Ile-de-France on a map or as aggregate statistics.
    • Visualize the differences you see with different modelling assumptions.

Group: Mode choice

Group: Mode choice

  • Recommendations
    • Your task is merely to decide for a model and check how well it replicates the reference data. Bonus: Try to improve the model or come up with one that increase the fit of the calculated mode choice and the mode share from the data.
       
    • To start quickly, you may be able to create a couple of trips at random from Google Maps / Bing / HERE (remember we are looking at morning commute trips!), or you may use the data of the national household travel survey (K_deploc)

      At the bottom (Table de données)
      https://www.statistiques.developpement-durable.gouv.fr/enquete-nationale-transports-et-deplacements-entd-2008

Final analysis

  • Group "Routing" performs a routing of the trips provided by Group "Demand" given the original Ile-de-France transit schedule, and the updated transit schedule provided by group "GTFS". After, group "Mode choice" performs a mode choice experiment for the provided trips, obtaining share before and after including the Grand Paris Express.
     
  • Anaysis task:
    • Without any mode choice effects, which OD relations decrease most in travel time when the Grand Paris Express is implemented? How to visualize this?
    • Considering mode choice effects, which regions in Ile-de-France show the strongest changes in mode usage?
    • In both cases: Assess and visualize the daily number of users of the new and existing Metro lines, RER lines and Transilien in a before-after comparison.
  • Final presentation including all groups. During the presentation, each group presents their results. At the end, the final demand analysis combining all partial results is presented. The presentation date is 25 February.
     
  • Grading:
    • Results: It is not expected to achieve perfect results. Focus should be put on which components worked well, and where you have encountered difficulties. Grading is based on the quality of presenting those reflections.
       
    • Visual consistency: Make sure to coordinate on the visual appearance of the whole presentation. Grading will be based on how well the groups manage to stick to an overall scheme.
       
    • Each group receives one grade for their presentation.

Deliverables & Grading

  • Individual report of 2-3 pages written individually by each student. To be submitted until 15 March.
     
  • Grading:
    • Focus is on reflection.
      • (1) On teamwork: Write about how you were working together within your group and across the groups. Reflect on what went well, what went not ideally. What did you learn in terms of working together in a project? How did you organize your work given the circumstances with Covid?
      • (2) On your group task: Which elements were difficult? Where can the analysis be improved and how? What data did you exchange with other groups, how did you define the formats? Describe the methodology you have used. What approaches did you assess? Why did you choose a specific one? What were your selection criteria?

Deliverables & Grading

Timeline

  • Today: Defining groups
     
  • 21 January:
    • Intermediate presentation of initial analyses (no need to prepare slides)
    • Q&A, exchange among the groups
       
  • 11 February:
    • Presentation of developments in each group (no slides needed)
    • Coordination across groups for final project
       
  • 25 February
    • Final presentation

Please register for the groups:

Grand Paris Express Project

By Sebastian Hörl

Grand Paris Express Project

  • 395
Loading comments...

More from Sebastian Hörl