GeNSIT: Generating Neural Spatial Interaction Tables

GeNSIT: Generating Neural Spatial Interaction Tables
Introduction
Installation
Inputs
- Data
  - Real-world
  - Synthetic
- Configs
Problem setup
Functionality
- Run
- Plot
- Summarise
- Reproduce
Conclusion
Acknowledgments

Quick Start: We recommended going through sections on Installation and Run if you wish to run GeNSIT using default settings.

Introduction

Tip Watch our video explainer here!.

Motivation

High-resolution complex simulators such as agent-based models (ABMs) are increasingly deployed to assist policymaking in transportation , social sciences, and epidemiology. They simulate individual agent interactions governed by stochastic dynamic systems, giving rise to an aggregate, in a mean field sense, continuous emergent structure. This is achieved by computationally expensive forward simulations, which hinders ABM parameter calibration and large-scale testing of multiple policy scenarios. Considering ABMs for the COVID-19 pandemic as an example, the continuous mean field process corresponds to the spatial intensity of the infections which is noisily observed at some spatial aggregation level, while the individual and discrete human contact interactions that give rise to that intensity are at best partially observed or fully latent. In transportation and mobility, running examples in this work, the continuous mean field process corresponds to the spatial intensity of trips arising from unobserved individual agent trips between discrete sets of origin and destination locations.

The formal object of interest that describes the discrete count of these spatial interactions, e.g. agent trips between locations, is the origin-destination matrix (ODM). It is an $I\times J$ (two-way) contingency table $\mathbf{T}$ with elements $T_{i,j} \in \mathbb{N}$ counting the interactions of two spatial categorical variables $i,j \in \mathbb{N}_{>0}$, see Figure above. It is typically sparse due to infeasible links between origin and destination locations, and partially observed through summary statistics -- such as table row and/or column marginals -- due to privacy concerns, data availability, and data collection costs. Operating at the discrete ODM level and learning this latent contingency table from summary statistics is vital for handling high-resolution spatial constraints and partial observations such as the total number of agents interacting between a pair of locations. It is also necessary for population synthesis in ABMs, which is performed prior to simulation in order to reduce the size of the ABM's parameter space. Moreover, it avoids errors and biases due to ad-hoc discretisation required when working with continuous approximations of the underlying discrete ODM $\mathbf{T}^*$.

Contribution

This repository introduces a computational framework named GeNSIT see for exploring the constrained discrete origin-destination matrices of agent trip location choices using closed-form or Gibbs Markov Basis sampling. The underlying continuous choice probability or intensity function (unnormalised probability function) is modelled by total and singly constrained spatial interaction models (SIMs) or gravity models embedded in the well-known Harris Wilson stochastic differential equations (SDEs). We employ Neural Networks to calibrate the SIM parameters. We include Markov Chain Monte Carlo (MCMC) schemes leveraged to learn the SIM parameters in previous works. For more details on the mathematical aspects of this repository please look at the Publications section.

Related publications

Zachos, Ioannis, Theodoros Damoulas, et al. ‘Table Inference for Combinatorial Origin-Destination Choices in Agent-Based Population Synthesis’. Stat, vol. 13, no. 1, 2024, p. e656, https://doi.org/10.1002/sta4.656.

Zachos, Ioannis, Mark Girolami, et al. Generating Origin-Destination Matrices in Neural Spatial Interaction Models. no. arXiv:2410.07352, arXiv, Oct. 2024, https://doi.org/10.48550/arXiv.2410.07352. arXiv.

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Installation

Assuming Python >=3.9.7 and git are installed, clone this repository by running

git clone [email protected]:[REPONAME]/GeNSIT.git

Once available locally, navigate to the main folder as follows:

cd GeNSIT

Tip: We recommended running GeNSIT on a Docker container if you do not plan to make any code changes.

Docker

This section assumes Docker has been installed on your machine. Please follow this guide if you wish to install Docker. Build the docker image image

docker build -t "gensit" .

Once installed, make sure everything is working by running

docker run gensit --help

OSX

This section assumes anaconda or miniconda has been installed on your machine. Please follow this or this guide if you wish to install either of them. Then, run:

conda create -y -n gensit python=3.9.7
conda activate gensit
conda install -y -c conda-forge --file requirements.txt
conda install -y conda-build
python3 setup.py develop

Otherwise, make sure you install the gensit command line tool and its dependencies by running

pip3 install -e .

Validate installation

You can ensure that the dependencies have been successfully installed by running:

gensit --help

You should get a print statement like this:

Usage: gensit [OPTIONS] COMMAND [ARGS]...

  Command line tool for Generating Neural Spatial Interaction Tables (origin-
  destination matrices)

Options:
  --help  Show this message and exit.

Commands:
  create     Create synthetic data for spatial interaction table and...
  plot       Plot experimental outputs.
  reproduce  Reproduce figures in the paper.
  run        Sample discrete spatial interaction tables...
  summarise  Create tabular summary of metadata, metrics computed for...

Throughout the remainder of this readme we illustrate GeNSIT's command line tool capabilities assuming that a docker container has been installed.

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Inputs

Inputs to GeNSIT are data and configuration files.

Data

The minimum data requirements include:

A set of origin and destination locations between which agents travel.
A cost matrix $\mathbf{C}$ reflecting inconvenience of travel from any origin to any destination. This can be distance and/or time dependent (e.g. Euclidean distance and/or travel times).
A measure of destination attractiveness $\mathbf{z}$. This depends on the types of trips agents make e.g. for work trips this would be number of jobs available at each destination.
The total number of agents/trips $M$. Each agent performs exactly one trip.

Optional datasets may be:

Origin and/or destination demand.
Partially observed trips between selected origin-destination pairs.
Total distance and/or time agents have travelled by origin and/or destination location.
A transportation network/graph.
A ground truth agent trip table to validate your model.

Real-world

We consider agent trips from residence to workplace locations in Cambridge, UK. We use the following datasets from the Census 2011 data provided by the Office of National Statistics:

Lower super output areas (LSOAs), Middle super output areas (MSOAs) as origin, destination locations, respectively.
Average shortest path in a transportation network between a random sample of 20 residences inside each LSOA and 20 workplaces inside each MSOA as a cost matrix.
Number of jobs available at each MSOA as a destination attraction proxy used in the NN's loss function.
Total distance travelled to work from each LSOA as an input to the NN's loss function.
Ground truth agent trip table a validation dataset. Parts of this table such as origin/destination demand (row/colsums) and a random subset of trips (cells) are also conditioned upon acting as table constraint data.

We note the transportation network as well as the residence and workplace locations were extracted using Arup's genet and osmox, respectively. The geo-referenced map used as an input to these tools was downloaded from Open Street Maps.

Synthetic

Alternatively, synthetic data may be generated by running commands such as:

docker run gensit create ./data/inputs/configs/generic/synthetic_data_generation.toml \
-dim origin 100 -dim destination 30 -dim time 1 \
-sigma 0.0141421356 --synthesis_n_samples 1000 --synthesis_method sde_solver

The command above creates synthetic data based on the requirements in the section above using origin and destination aritificial locations (in our case 100 origins, 30 destinations). A cost matrix is randomly generated for every OD pair. Destination attraction data is generated by running the synthesis_method for synthesis_n_samples steps (in our case by running the Harris Wilson SDE solver for 1000 steps).

You noticed that we load a configuration file named synthetic_data_generation.toml to achieve all this. We elaborate on the use of configs in the next section.

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Configs

Configuration files contain all settings (key-value pairs) required to run NN-based or MCMC-based algorithms for learning the discrete origin-destination table and/or underlying continuous SIM parameters. They are stored in a toml format.

Each type of algorithm is associated with an experiment type . We hereby refer to the process of running one algorirthm for a given set of configuration parameters as running an experiment. Examples of experiments include SIM_NN, SIM_MCMC, JointTableSIM_MCMC, DisjointTableSIM_NN, and JointTableSIM_NN.

Most configuration keys can be sweeped for each type of experiment being run. This means that a range of values over which the experiment will be run can be provided. For example, the sigma parameter below

[harris_wilson_model.parameters.sigma.sweep]
  default = 0.0141421356
  range = [0.0141421356, 0.1414213562, nan]

means that each experiment in the experiments section will run with sigma = 0.0141421356 and sigma = 0.141421356. A sweep is therefore one run of an experiment over a unique set of config values. Sweeps can be either isolated or coupled. The above example constitutes an isolated sweep. A coupled sweep is shown below:

[harris_wilson_model.parameters.sigma.sweep]
  default = 0.0141421356
  range = [0.0141421356, 0.1414213562, nan]
[training.to_learn.sweep]
  default = ['alpha', 'beta']
  range = [['alpha', 'beta'],['alpha', 'beta'],['alpha', 'beta', 'sigma']]
  coupled = true
  target_name = 'sigma'

Here sigma is coupled with the to_learn parameter, meaning the vary together. In this case each experiment will be run for three different sweep settings: (sigma = 0.0141421356, to_learn = ['alpha','beta']), (sigma = 0.1414213562, to_learn = ['alpha','beta']), and (sigma = nan, to_learn = ['alpha','beta','sigma']). We note that more than one sweep keys can be coupled.

Note: More information on each key-value pair found in Configs can be found here.

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Problem setup

Consider $M$ agents that travel from $I$ origins to $J$ destinations to work. Let the expected number of trips (intensity) of agents between origin $i$ and destination $j$ be denoted by $\Lambda_{ij}$. The residential population in each origin (row sums) is equal to

$$ \Lambda_{i+} = \sum_{j=1}^{J} \Lambda_{ij}, i=1,\dots,I, $$

while the working population at each destination (column sums) is

$$ \Lambda_{+j} = \sum_{i=1}^{I} \Lambda_{ij}, j=1,\dots,J. $$

We assume that the total origin and destination demand are both conserved:

$$ M = \Lambda_{++} = \sum_{i=1}^{I} \Lambda_{i+} = \sum_{j=1}^{J} \Lambda_{+j}. $$

The demand for destination zones depends on the destination's attractiveness denoted by $\mathbf{z} = (z_1,\dots, z_J) \in R_{>0}^{J}$. Let the log-attraction be $\mathbf{x} = \text{log}(\mathbf{z})$. Between two destinations of similar attractiveness, agents are assumed to prefer nearby zones. Therefore, a cost matrix $\mathbf{C} = (c_{i,j})_{i,j=1}^{I,J}$ is introduced to reflect travel impedance. The maximum entropy distribution of agent trips subject to the total number of agents being conserved is derived by maximising the Lagrangian

$$ \mathcal{E}(\boldsymbol{\Lambda}) = \sum_{i=1}^{I}\sum_{j=1}^J -\Lambda_{ij}\log(\Lambda_{ij}) - \zeta \sum_{i,j}^{I,J} \Lambda_{ij} + \alpha \sum_{j}^{J} x_j\Lambda_{ij} - \beta \sum_{i,j}^{I,J} c_{ij}\Lambda_{ij}, $$

where $\zeta,\alpha$ and $\beta$ are the Lagrange multipliers. This yields a closed-form expression for the expected flows (intensity) of agents from $i$ to $j$ for the total constrained SIM:

$$ \Lambda_{ij} = \frac{\Lambda_{++}\exp(\alpha x_j -\beta c_{ij})}{\sum_{k,m} \exp(\alpha x_m-\beta c_{km})}, $$

where the multipliers $\alpha,\beta$ control the two competing forces of attractiveness and deterrence, respectively, while $\zeta$ bears no physical interpretation. A higher $\alpha$ relative to $\beta$ characterises a preference over destinations with higher job availability, while the contrary indicates a predilection for closer workplaces. If we further assume that origin demand (${\boldsymbol{\Lambda}}_{.+}$) is also fixed then we get the singly constrained SIM:

$$ \Lambda_{ij} = \frac{\Lambda_{i+}\exp(\alpha x_j -\beta c_{ij})}{\sum_{k,m} \exp(\alpha x_m-\beta c_{km})}. $$

Spatial interaction models are connected to physics models through the destination attractiveness term $\mathbf{z}$, which is governed by the Harris-Wilson system of $J$ coupled ordinary differential equations (ODEs):

$$ \frac{dz_j}{dt} = \epsilon z_j \left( \Lambda_{+j} - \kappa z_j + \delta \right) + \sigma z_j \circ B_{j,t}, ; \mathbf{z}(0) = \mathbf{z}'. $$

where $\epsilon$ is a responsiveness parameter, $\kappa>0$ is the number of agents competing for one job, $\delta>0$ is the smallest number of jobs a destination can have and $\Lambda_{+j}(t) - \kappa z_j(t)$ is the net job capacity in destination $j$. A positive net job capacity translates to a higher economic activity (more travellers than jobs) and a boost in local employment, and vice versa. A stochastic version of the Harris Wilson model is the following:

We recommend you look at relevant publications for more information on the Harris Wilson model. Our first goal is to learn the parameters $\alpha,\beta$ using either sampling (MCMC) or optimisation (NN) algorithms. To achieve this goal we leverage data $\mathcal{D}$ about either the observed destination attraction (e.g. the number of jobs available at each destination) and/or the total distance/time traveled by agents in their work trips. In general, we can say that our first goal is to learn the distribution of the agent trip intensity $p(\boldsymbol{\Lambda}\vert \mathcal{C},\mathcal{D})$.

We note that the discrete number of agents traveling to work is represented by

$$ T_{ij} \sim \text{Poisson}(\Lambda_{ij}). $$

Although $\mathbf{T}$ and $\boldsymbol{\Lambda}$ look like similar quantities we emphasize that they are distinct. The former is a discrete quantity while the latter is a continuous quantity and many $T_{ij}$ may be ''plausible'' under a single $\Lambda_{ij}$. The SIM intensity $\boldsymbol{\Lambda}$ is a mean-field approximation and can be thought of as the expectation (average) of $\mathbf{T}$ across time for all work trips. We can also reason at a probability level by thinking of $0 \leq \Lambda_{ij}/\Lambda_{++}\leq 1$ as transition probabilities from an origin $i$ to a destination $j$. Depending on the available summary data (e.g. ${\mathbf{T}}_{.+}, {\mathbf{T}}_{+.}$) we define a set of constraints $\mathcal{C}$. Our second goal is to sample $\mathbf{T}$ subject to these constraints, i.e. sample from $p(\mathbf{T}\vert \mathcal{C},\mathcal{D})$. More information is provided in the papers provided in the publications section.

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Functionality

The GeNSIT package provides functionality for five different operations: create, run, plot, reproduce, summarise.

⚠️ WARNING: Python tests have not been updated yet!

Run

This command runs experiments using Markov Chain Monte Carlo and/or Neural Networks based on a Config file. For example, we can run joint table and intensity inference using the following command

docker run gensit run ./data/inputs/configs/generic/joint_table_sim_inference.toml \
-et JointTableSIM_NN -nw 6 -nt 3

This config runs a JointTableSIM_NN experiment using 6 number of workers and 3 number of threads per worker. A list of experiments and the types of algorithms they use to learn $\boldsymbol{\Lambda}$, $\mathbf{T}$ is provided below. We note that experiments that use NNs to learn the intensity function parameters tend to be computationally much faster.

Experiment	$\mathbf{T}$	$\boldsymbol{\Lambda}$
`SIM_MCMC`	-	MCMC
`JointTableSIM_MCMC`	MCMC	MCMC
`SIM_NN`	-	NN
`DisjointTableSIM_NN`	MCMC	NN
`JointTableSIM_NN`	MCMC	NN

The run command can also be programmatically executed using the notebook Example 1 - Running experiments.

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Plot

Once an experiment has been completed, we can use the following command to plot its data:

docker run gensit plot [PLOT_VIEW] [PLOT_TYPE] -x [X_DATA] -y [Y_DATA]

where PLOT_VIEW defines the type of view the data should be shown. Views can be simple, tabular or spatial. PLOT_TYPE can be either line or scatter. The X_DATA or Y_DATA are provided as names of experiment outputs or their evaluated expressions (see Config settings).

For example, the code below plots the log destination attraction predictions (x-axis) against the observed data (y-axis) for experiments JointTableSIM_MCMC,JointTableSIM_NN,NonJointTableSIM_NN.

docker run gensit plot simple scatter \
-y log_destination_attraction_data -x mean_log_destination_attraction_predictions \
-dn cambridge_work_commuter_lsoas_to_msoas/exp1 \
-et JointTableSIM_MCMC -et JointTableSIM_NN -et NonJointTableSIM_NN \
-el np -el xr -el MathUtils \
-e mean_log_destination_attraction_predictions "signed_mean_func(log_destination_attraction,sign,dim=['id']).squeeze('time')" \
-e mean_log_destination_attraction_predictions "log_destination_attraction.mean('id').squeeze('time')" \
-e log_destination_attraction_data "np.log(destination_attraction_ts).squeeze('time')" \
-ea log_destination_attraction -ea sign \
-ea "destination_attraction_ts=outputs.inputs.data.destination_attraction_ts" \
-ea "signed_mean_func=MathUtils.signed_mean" \
-k sigma -k title \
-cs "da.loss_name.isin([str(['dest_attraction_ts_likelihood_loss']),str(['dest_attraction_ts_likelihood_loss', 'table_likelihood_loss'])])" \
-cs "~da.title.isin(['_unconstrained','_total_constrained','_total_intensity_row_table_constrained'])" \
-c title -op 1.0 -mrkr sigma -l title -l sigma -msz 20 \
-ft 'predictions_figure/destination_attraction_predictions_vs_observations' \
-xlab '$\mathbb{E}\left[\mathbf{x}^{(1:N)}\right]$' \
-ylab '$\mathbf{y}$'

The -e,-ea,-el arguments define the evaluated expressions, the keyword arguments used as input to these expressions (also evaluated) and the necessary libraries that are used to perform the operations, respectively. The evaluation is performed using Python's eval function. The first two types of argument allow for reading input and/or output data directly. For example, -ea "destination_attraction_ts=outputs.inputs.data.destination_attraction_ts" loads the input (observed) destination attraction time series data while -ea log_destination_attraction -ea sign loads log_destination_attraction and sign output datasets.

The output data is sliced using the coordinate values specified by the -cs arguments. For instance, -cs "~da.title.isin(['_unconstrained','_total_constrained','_total_intensity_row_table_constrained'])" only keeps the datasets whose title variable is equal to any of the specified values. The sweep data are gathered either from the output dataset itself or from the output config file (in this case we elicit sigma,title sweep variables).

The scatter plot is colored by the title variable and its markers are determined by the sigma variable. Both of these variables are contained in each sweep that was run. The exact mappings from say sigma values to marker types are contained in this file. Each point is labeled by both the title and sigma values. The resulting figure is shown below.

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Summarise

This command summarised the output data and creates a csv file with each data summary from every sweep. For example, if we wish to compute the Standardised Root Mean Square Error (SRMSE) for JointTableSIM_NN we run

docker run gensit summarise \
-dn cambridge_work_commuter_lsoas_to_msoas/exp1 \
-et JointTableSIM_NN \
-el np -el MathUtils -el xr \
-e table_srmse "srmse_func(prediction=mean_table,ground_truth=ground_truth)" \
-e intensity_srmse "srmse_func(prediction=mean_intensity,ground_truth=ground_truth)" \
-ea table -ea intensity -ea sign \
-ea "srmse_func=MathUtils.srmse" \
-ea "signed_mean_func=MathUtils.signed_mean" \
-ea "ground_truth=outputs.inputs.data.ground_truth_table" \
-ea "mean_table=table.mean(['id'])" \
-ea "mean_intensity=signed_mean_func(intensity,'intensity','signedmean',dim=['id'])" \
-ea "mean_intensity=intensity.mean(['id'])" \
-cs "da.loss_name.isin([str(['dest_attraction_ts_likelihood_loss']),str(['dest_attraction_ts_likelihood_loss', 'table_likelihood_loss']),str(['table_likelihood_loss'])])" \
-btt 'iter' 10000 90 1000 \
-k sigma -k type -k name -k title -fe SRMSEs -nw 20

The arguments are similar to the plot command. Here we also use -btt refered to as burning, thinning and trimming to slice the iter coordinate values based on their index. In this occasion, we discard the first 10000 samples and then only keep every 90th sample. Finally, we trim this data array to 1000 elements. A small part of the summarised table is shown below.

type	sigma	title	name	proposal	intensity_srmse	table_srmse
JointTableSIM_NN	0.141	doubly_20%_cell_constrained	TotallyConstrained	degree_higher	[1.98]	[0.38]
JointTableSIM_NN	0.0141	unconstrained	TotallyConstrained	direct_sampling	[29.51]	[1.73]
JointTableSIM_NN	0.141	unconstrained	TotallyConstrained	direct_sampling	[29.51]	[1.73]
JointTableSIM_NN	0.141	doubly_constrained	TotallyConstrained	degree_higher	[2.02]	[0.46]
JointTableSIM_NN	0.0141	doubly_10%_cell_constrained	TotallyConstrained	degree_higher	[0.89]	[0.42]
JointTableSIM_NN	0.141	total_intensity_row_table_constrained	TotallyConstrained	direct_sampling	[5.93]	[2.16]
JointTableSIM_NN	0.0141	doubly_20%_cell_constrained	TotallyConstrained	degree_higher	[0.94]	[0.38]
JointTableSIM_NN	0.141	doubly_constrained	TotallyConstrained	degree_higher	[0.68]	[0.55]
JointTableSIM_NN	0.141	unconstrained	TotallyConstrained	direct_sampling	[29.51]	[1.73]

Processing experimental outputs for uses similar to the ones provided by plot and summarise commands can also be achieved by following the steps of notebook Example 2 - Reading outputs.

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Reproduce

Finally, this command is run to reproduce the figures appearing in the papers. The commands are self-explanatory:

docker run gensit reproduce figure1;
docker run gensit reproduce figure2;
docker run gensit reproduce figure3;
docker run gensit reproduce figure4;

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Conclusion

We have introduced GeNSIT, an efficient framework for sampling jointly the discrete combinatorial space of agent trips ($\mathbf{T}$) subject to summary statistic data $\mathcal{C}$ and its mean-field $\boldsymbol{\Lambda}$ limit. Therefore, users of this package can perform agent location choice synthesis based on the available data $\mathcal{C},\mathcal{D}$. Although our discussion has been limited to residence to work trips, other types of trips could be modelled too, such as residence to shopping center. The main limitations of this package are the inability to model activity chains as opposed to trips and the fact that only static (time-independent) origin destination matrices are considered.

Acknowledgments

Thank you for visiting our GitHub repository! We're thrilled to have you here. If you find our project useful or interesting, please consider showing your support by starring the repository and forking it to explore its features and contribute to its development. Your support means a lot to us and helps us grow the community around this project. If you have any questions or feedback, feel free to open an issue or reach out to us.

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Name		Name	Last commit message	Last commit date
Latest commit History 878 Commits
data		data
docs		docs
gensit		gensit
notebooks		notebooks
scripts		scripts
tests		tests
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
requirements.txt		requirements.txt
setup.py		setup.py

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GeNSIT: Generating Neural Spatial Interaction Tables

Introduction

Motivation

Contribution

Related publications

Installation

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Inputs

Data

Real-world

Synthetic

Configs

Problem setup

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Plot

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Reproduce

Conclusion

Acknowledgments

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GeNSIT: Generating Neural Spatial Interaction Tables

Introduction

Motivation

Contribution

Related publications

Installation

Docker

OSX

Validate installation

Inputs

Data

Real-world

Synthetic

Configs

Problem setup

Functionality

Run

Plot

Summarise

Reproduce

Conclusion

Acknowledgments

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