Open data travel demand synthesis

Sebastian Hörl

9 June 2021

Seminar Series Chaire Anthropolis / Future Cities Lab

The street in 1900

http://www.loc.gov/pictures/item/2016800172/

The street today

https://commons.wikimedia.org/wiki/File:Atlanta_75.85.jpg

The street of tomorrow?

Julius Bär / Farner

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

Synthetic travel demand

Population census (RP)

Population census (RP)

Income data (FiLoSoFi)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

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

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Person ID    

Age

Gender

Home (X,Y)

1

43

male

(65345, ...)

2

24

female

(65345, ...)

3

9

female

(65345, ...)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Person ID    

Activity

Start

End

Loc.

523

home

08:00

(x,y)

523

work

08:55

18:12

(x,y)

523

shop

19:10

19:25

(x,y)

523

home

19:40

(x,y)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

National HTS (ENTD)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

EDGT

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Open
Data

Open
Software

+

=

Reproducible research

Integrated testing

National HTS (ENTD)

EDGT

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Open
Data

Open
Software

+

=

Reproducible research

Integrated testing

National HTS (ENTD)

EDGT

Current use cases

Nantes

  • Noise modeling

Current use cases

Lille

  • Park & ride applications
  • Road pricing

Current use cases

Toulouse

  • Placement and use of shared offices

Current use cases

Rennes

  • Micromobility simulation

Current use cases

Paris / Île-de-France

  • Scenario development for sustainable urban transformation
     
  • New mobility services

    Mahdi Zargayouna (GRETTIA / Univ. Gustave Eiffel)
    Nicolas Coulombel (LVMT / ENPC)

Current use cases

Paris / Île-de-France

  • Cycling simulation

Current use cases

Paris / Île-de-France

  • Simulation of dynamic mobility services
     
  • Fleet control through reinforcement learning

Current use cases

Lyon (IRT SystemX)

  • Low-emission first/last mile logistics

Current use cases

eqasim-org/eqasim-java

eqasim-org/ile-de-france

Current use cases

eqasim-org/sao_paulo

eqasim-org/california

Balac, M., Hörl, S. (2021) Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County, presented at 100th Annual Meeting of the Transportation Research Board, Washington, D.C.

Sallard, A., Balac, M., Hörl, S. (2021) Synthetic travel demand for the Greater São Paulo Metropolitan Region, based on open data, Under Review

Sao Paulo, San Francisco Bay area, Los Angeles five-county area, Switzerland, Montreal, Quebec City, Jakarta, Casablanca, ...

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

Use cases: Emissions in Île-de-France

Use cases: Automated taxis in Paris

Use cases: Automated taxis in Paris

Use cases: Grand Paris Express

  • Sustainable first-/last-mile delivery services in the Confluence quarter of Lyon
     
  • Focus on household parcel delivery
     
  • Need for demand estimation
     
  • Detailed understanding of operational constraints for a delivery service

LEAD: Study idea

Parcel demand

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Iterative proportional fitting (IFP)

  • Based on synthetic population, find average number of purchases delivered to a household defined by socioprofesional class, age of the reference person and household size per day.
\mu_{a,h,s} = d_{s} \cdot \frac{w_{a,h,s}}{365}

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Maximum entropy approach

  • We now the average number of parcels, but we do not now the distribution of the number of parcels for a household on an average day.
     
  • We know it must be non-negative, and we know the mean.
     
  • Without additional data, we assume maximum entropy distribution, which is Exponential in this case.
F(N \leq n) = 1 - \exp(-\mu \cdot n)

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Routing problem

Vehicle routing problem with time windows (VRPTW)

  • We have a fleet of N vehicles (capacity, speed).
     
  • They need to delivery the parcels when somebody is home (i.e. they need to respect time windows).
     
  • Problem: Find optimal route that minimizes the distance covered while fulfilling the criteria.

Routing problem

Vehicle routing problem with time windows (VRPTW)

  • We have a fleet of N vehicles (capacity, speed).
     
  • They need to delivery the parcels when somebody is home (i.e. they need to respect time windows).
     
  • Problem: Find optimal route that minimizes the distance covered while fulfilling the criteria.

Routing problem

Vehicle routing problem with time windows (VRPTW)

  • We have a fleet of N vehicles (capacity, speed).
     
  • They need to delivery the parcels when somebody is home (i.e. they need to respect time windows).
     
  • Problem: Find optimal route that minimizes the distance covered while fulfilling the criteria.

First case study

  • Synthetic population parcel
    demand (101 parcels)
     
  • Parcel size: 1
     
  • Available vehicles: 10
  • Vehicle capacity: 5
  • Vehicle speed: 10 km/h
     
  • Pickup time (at depot):
    60s
     
  • Delivery time (at customer):
    5min
     
  • Minimize driven distance

First case study

  • Synthetic population parcel
    demand (101 parcels)
     
  • Parcel size: 1
     
  • Available vehicles: 10
  • Vehicle capacity: 5
  • Vehicle speed: 10 km/h
     
  • Pickup time (at depot):
    60s
     
  • Delivery time (at customer):
    5min
     
  • Minimize driven distance
  • Three vehicles used
  • Total distance 41.35 km

Scaling up to the digital twin

Synthetic popualtion

  • Simulation of Lyon (and Rhônes-Alpes) with influence of traffic, including freight traffic

 

MATSim

MASS-GT

  • Simulation of shipper behaviour (B2C and B2B)

 

  • For instance: What will be the effect of a zero-emission zone in the city on traffic?

 

Scaling up to the digital twin

  • Simulation of Lyon (and Rhônes-Alpes) with influence of traffic, including freight traffic

 

  • Simulation of shipper behaviour (B2C and B2B)

 

  • For instance: What will be the effect of a zero-emission zone in the city on traffic?

 

Questions?

https://slides.com/sebastianhorl/anthropolis-fcl-8june21