urban-dollop 0.3.2

Urban freight simulation based on the MASS-GT multi-agent model

urban-dollop

urban-dollop is a Python library that extracts and packages the mathematical models from MASS-GT — a multi-agent urban freight simulation system originally developed at TU Delft for the Dutch Randstad region. The models are reimplemented as a transparent, installable Python pipeline intended for academic research and reproducible urban logistics studies.

The primary novel contribution is the introduction of road grade as a dimension in emission accounting, enabling topographically accurate estimates in hilly cities.

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Usage

Parcel demand generation

Estimate the daily parcel delivery demand for a study area from zonal population and employment data.

from urban_dollop import Zone, Depot, Carrier, SkimMatrix, generate_parcel_demand

zones    = Zone.from_file("zones.gpkg")
depots   = Depot.from_file("depots.gpkg")
carriers = Carrier.from_file("carrier_shares.csv")
skim     = SkimMatrix.from_file("skim_time.mtx", zones)

demands  = generate_parcel_demand(zones, depots, carriers, skim)

demands is a list[ParcelDemand] — one record per unique (destination zone, depot, vehicle type) combination:

field	type	description
destination_zone_id	int	zone ID of the delivery address
depot_id	int	depot that handles this flow
vehicle_type	int	vehicle type code (default 7 = van)
n_parcels	int	number of parcels in this flow

The origin zone of each flow is implicit — it is always the zone of the depot.

Save to CSV (join to your zones layer in QGIS or ArcGIS on destination_zone_id to map parcels delivered per zone):

ParcelDemand.to_file(demands, "parcel_demand.csv")

Calibration — via urban-dollop.toml:

Place an urban-dollop.toml in your current working directory to set calibration parameters without touching code:

[parcel_demand]
parcels_per_household = 0.2054  # B2C daily deliveries per household
parcels_per_employee  = 0.0     # B2B daily deliveries per employee
delivery_success_b2c  = 0.75    # first-attempt success rate, residential
delivery_success_b2b  = 0.95    # first-attempt success rate, commercial
default_vehicle_type  = 7       # 7 = van
random_seed           = 42

Calibration — programmatic override:

Pass a ParcelDemandConfig to override TOML values in code:

from urban_dollop import ParcelDemandConfig

demands = generate_parcel_demand(
    zones, depots, carriers, skim,
    config=ParcelDemandConfig(
        parcels_per_household = 0.178,
        parcels_per_employee  = 0.029,
        delivery_success_b2c  = 0.80,
        delivery_success_b2b  = 0.95,
        default_vehicle_type  = 7,
    ),
)

Programmatic values take precedence over TOML. Missing fields fall back to TOML. Pydantic raises if a required field is absent from both sources.

Column mapping — when your files use different column names:

zones = Zone.from_file("zones.gpkg", columns={
    "zone_id":    "id",
    "households": "hh_count",
    "employment": "jobs",
})

carriers = Carrier.from_file("shares.csv", columns={
    "name":  "courier",
    "share": "market_share",
})

Canonical column names:

model	field	description
Zone	zone_id	unique integer zone identifier
Zone	households	household count
Zone	employment	employee count
Depot	depot_id	unique integer depot identifier
Depot	zone_id	zone the depot is located in
Depot	carrier	carrier name (must match Carrier.name)
Carrier	name	carrier name
Carrier	share	market share fraction (all carriers must sum to 1)

CLI

Run the parcel demand module from canonical files:

urban-dollop generate-demand data/

This command:

• reads urban-dollop.toml from the current working directory
• reads zones.gpkg, depots.gpkg, carrier_shares.csv, and skim_time.mtx from data/
• writes parcel_demand.csv to the current working directory by default

To write the CSV somewhere else, pass --outdir with either an existing directory or a full .csv path:

urban-dollop generate-demand --outdir results/ data/
urban-dollop generate-demand --outdir results/joinville_parcel_demand.csv data/

Example outputs

[!NOTE] Example choropleth of simulated parcel deliveries aggregated by destination_zone_id for the Joinville fixture scenario. Zone geometry is based on IBGE territorial and census meshes, household counts on IBGE Census / SIDRA, employment on RAIS microdata, and travel times on OpenStreetMap routed with OSRM. Depot locations are scenario inputs compiled from public carrier and agency sources including Correios, Mercado Envios, Loggi, and Amazon. Carrier shares are scenario estimates synthesized from public market and company sources, including Correios and ABComm.

[!NOTE] Example choropleth of simulated parcel deliveries aggregated by destination_zone_id for the Delft fixture scenario. Study area covers five municipalities: Den Haag, Delft, Rijswijk, Leidschendam-Voorburg, and Midden-Delfland. Zone geometry, household counts, and employment are based on AHN and CBS data via the LEAD project (2020). Travel times are derived from the MASS-GT Netherlands skim matrix. Depot locations and carrier shares are sourced from the MASS-GT LEADVersion scenario.

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Mathematical models

The following models are extracted from MASS-GT's parcel_dmnd module and implemented in generate_parcel_demand().

Parcel demand generation

Zonal demand

The number of parcel flows destined for zone $z$ on an average weekday:

$$D_z = \left\lfloor \frac{H_z \cdot r_{HH}}{s_{B2C}} + \frac{E_z \cdot r_E}{s_{B2B}} \right\rceil$$

symbol	ParcelDemandConfig field	description
$H_z$	—	households in zone $z$
$E_z$	—	employees in zone $z$
$r_{HH}$	parcels_per_household	B2C parcels per household per day
$r_E$	parcels_per_employee	B2B parcels per employee per day
$s_{B2C}$	delivery_success_b2c	first-attempt delivery success rate, residential
$s_{B2B}$	delivery_success_b2b	first-attempt delivery success rate, commercial

The success rates correct for failed first-attempt deliveries: the generated volume reflects shipments sent, not deliveries completed.

Carrier split

Total zonal demand is distributed across carriers by market share:

$$D_{z,k} = \left\lfloor \sigma_k \cdot D_z \right\rceil$$

where $\sigma_k$ is the market share of carrier $k$ (Carrier.share), and $\sum_k \sigma_k = 1$. This normalization is required by the implementation and validated before demand is allocated across carriers.

Depot assignment

Each (zone, carrier) flow is assigned to the nearest depot of that carrier by travel time from the depot zone to the destination zone:

$$\delta(z, k) = \underset{n \in \mathcal{N}k}{\arg\min}\ t(n{\text{zone}}, z)$$

where $\mathcal{N}k$ is the set of depots operated by carrier $k$ and $t(n{\text{zone}}, z)$ is the travel time in seconds from the depot's zone to zone $z$, read from the pre-computed skim matrix.

Aggregation

Flows that share the same destination zone, depot, and vehicle type are summed:

$$F_{z,n,v} = \sum_{k,:,\delta(z,k) = n} D_{z,k} \quad \text{for fixed vehicle type } v$$

Each resulting $(z, n, v)$ tuple with $F_{z,n,v} > 0$ becomes one ParcelDemand record.