Representation of mobility demand in agent-based transport simulation

Sebastian Hörl

15 April 2022

at ENPC

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

The street in 1900

The street today

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

• Autonomous Mobility
  
  
• Mobility as a Service
  
  
• Mobility on Demand
  
  
• Electrification
  
  
• Aerial Mobility

Julius Bär / Farner

The street of tomorrow?

Julius Bär / Farner

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

https://pixabay.com/en/zurich-historic-center-churches-933732/

II. AMoD in Zurich

Cost structures?

User preferences?

System impact?

Cost Calculator for automated mobility

Stated preference survey

MATSim simulation

1

2

3

What do we know about automated taxis?

What do we know about automated taxis?

Bösch, P.M., F. Becker, H. Becker and K.W. Axhausen (2018) Cost-based analysis of autonomous mobility services, Transport Policy, 64, 76-91

What do we know about automated taxis?

Felix Becker, Institute for Transport Planning and Systems, ETH Zurich.

VTTS

AMoD

Car by Adrien Coquet from the Noun Project

Bus by Simon Farkas from the Noun Project

Wait by ibrandify from the Noun Project

VTTS

AMoD

Car by Adrien Coquet from the Noun Project

Bus by Simon Farkas from the Noun Project

Wait by ibrandify from the Noun Project

Model structure

Cost calculator

Plan modification

Discrete Mode Choice Extension

Mobility simulation

Prediction

Price

Trips

- Utilization

- Empty distance, ...

- Travel times

- Wait times, ...

Fleet sizing with dynamic demand

Fleet sizing with dynamic demand

Fleet sizing with dynamic demand

Visualisation

Automated taxi

Pickup

Dropoff

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

13% reduction in vehicles

100% increase in VKT

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

100% increase in VKT

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

Other aspects

Hörl, S., C. Ruch, F. Becker, E. Frazzoli and K.W. Axhausen (2019) Fleet operational policies for automated mobility: a simulation assessment for Zurich, Transportation Research: Part C, 102, 20-32.

Fleet control

Operational constraints

Spatial constraints

Intermodality

Pooling

III. Demand data

https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/

Macroscopic four-step model

Generation

Distribution

Mode choice

Assignment

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

Macroscopic four-step model

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?

Macroscopic four-step model

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, ...

Macroscopic four-step model

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?

I) 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

I) 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

Generated trips

in zone s in group g

Trip generation model

Model inputs

I) 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)

Model parameters (linear factors)

Census data (inhabitants)

Reference data

II) 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

II) 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.

Flow between s and t

Model

II) 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

III) 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?

III) Mode choice

• 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

• Example: Choice between two public transport connections

Connection A

Connection B

III) Mode choice

III) Mode choice

• Example: Mode choice model

III) Mode choice

• Example: Mode choice model

How to disaggregate the demand?

Synthetic travel demand

Synthetic travel demand

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

Synthetic travel demand

Person ID    

Age

Gender

Home (X,Y)

1

43

male

(65345, ...)

2

24

female

(65345, ...)

3

9

female

(65345, ...)

Synthetic travel demand

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

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

+

=

Synthetic travel demand

+

=

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

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

Emissions in Paris

Grand Paris Express

Automated taxis in Paris

Automated taxis in Paris

IV. Logistics

Photo by Tom Fisk from Pexels

LEAD Project

• H2020 Project from 2020 to 2023
   
• Six living labs with different innovative logistics concepts
  • Lyon, The Hague, Madrid, Budapest, Porto, Oslo
  • One partner for implementation and one for research each
     
• Development of a generic modeling library for last-mile logistics scenario simulation and analysis

Lyon Living Lab

• Peninsula Confluence between Saône and Rhône
   
• Interesting use case as there are limited access points
   
• Implementation of an urban consolidation center (UCC) to collect the flow of goods and organize last-mile distribution
  • using cargo-bikes
  • using electric robots
  • and others
     
• Analysis and modeling through
  • Flow estimation through cameras
  • Simulation of future scenarios
     
• Focus on parcel deliveries due to data availability

Modeling methodology

Agent-based simulation of Lyon

Demand in parcel deliveries

Distribution by operators

KPI Calculation

• Main question: What is the impact on traffic and population of implementing an Urban Consolidation Center in Confluence?
   
• Focus on B2C parcel deliveries due to data availability

Methodology: Parcel demand

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

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

Methodology: Parcel demand

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

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

Methodology: Parcel demand

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

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

Methodology: Parcel demand

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

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

Methodology: Parcel demand

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

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

• 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.

Methodology: Parcel demand

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

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

• 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.

Methodology: Parcel demand

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

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Parcel demand

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

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Parcel demand

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

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Parcel demand

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

+

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Route optimization

Using a heursitic routing solver, an optimal distribution scheme per distribution center is obtained to arrive at a lower bound estimate for the distance covered for the deliveries.

JSprit

First case study

Hörl, S. and J. Puchinger (2021) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, Working paper.

Solve VRP-TW based on generated parcels and household presence

JSprit

First case study

Hörl, S. and J. Puchinger (2021) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, Working paper.

Solve VRP-TW based on generated parcels and household presence

JSprit

Scaling up: City-wide baseline

Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.

Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.

Scaling up: City-wide baseline

Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.

Scaling up: City-wide baseline

Scaling up: City-wide baseline

• Allows us to cut out a better baseline scenario for Confluence

First main result: Estimate of the total distance driven for parcel deliveries in Lyon (by the most frequent operators).

KPI Calculation

GHG/PM Emissions: Simulation based on Hbefa database with detailed driving conditions (cold start, current speed, ...)

(Alternative in LEAD: COPERT)

Noise Emissions: Simulation simulates noise emissions and emissions to arrive at exposure values

Aggregation

• Global emissions are summed up over the territory (CO2)
• Local emissions need to be looked at in terms of exposure (PM)

• Noise is local by definition, impact is calculated in terms of exposure (of the population) and expected social cost (in EUR)

Questions?

https://slides.com/sebastianhorl/enpc-15apr22