MATraM: A Multi-Activity Transport and Mobility Agent-Based Model for Activity Modifications

Alison Heppenstall; Esra Suel; Gary Polhill; Ricardo Colasanti; Tatsuya Mitomi; Yahya Gamal

arxiv: 2605.30547 · v1 · pith:5FKURICOnew · submitted 2026-05-28 · 💻 cs.MA

MATraM: A Multi-Activity Transport and Mobility Agent-Based Model for Activity Modifications

Yahya Gamal , Ricardo Colasanti , Gary Polhill , Tatsuya Mitomi , Esra Suel , Alison Heppenstall This is my paper

Pith reviewed 2026-06-28 23:39 UTC · model grok-4.3

classification 💻 cs.MA

keywords agent-based modeltransport modelingactivity-based modelsmobility simulationactivity schedulingadaptive behavioremergent patterns

0 comments

The pith

MATraM enables agents to flag requests to modify activities when travel conditions worsen and couples this with scheduling to generate adaptive daily plans.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the MATraM agent-based model to move beyond traditional transport simulations that rely on fixed daily activity schedules. It does so by letting individual agents detect sub-optimal travel times and issue modification requests that feed into an activity scheduling and modification framework. This integration is meant to produce emergent mobility and congestion patterns that reflect real behavioral flexibility under uncertainty. A reader would care if the approach yields system-level outcomes that fixed-schedule models cannot generate from the same network conditions.

Core claim

MATraM addresses the limitation of pre-defined trip patterns by enabling agents to flag activity modification requests in response to sub-optimal travel conditions such as increased travel times; by coupling with an activity scheduling and modification framework, the model integrates adaptive decision-making into the generation and execution of daily activity schedules, allowing a more realistic representation of how individuals adjust behaviour in response to transport system dynamics and leading to emergent mobility and congestion patterns.

What carries the argument

The activity modification request flagging mechanism, coupled to the activity scheduling and modification framework, which lets agents respond dynamically to transport conditions within their daily schedules.

If this is right

Models can now represent how individuals adjust activity timing or location when travel times rise.
Emergent system outcomes such as congestion arise directly from the interaction of individual adaptation decisions.
The framework bridges activity-based generation of schedules with interaction-based mobility simulation.
It supplies an extensible platform for studying transport dynamics when conditions are uncertain.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same request-flagging logic could be applied to evaluate how populations respond to new policies such as congestion charges.
Simulations might reveal critical thresholds at which many agents simultaneously request changes, amplifying network effects.
Calibration against household travel survey data on rescheduled activities would be a direct next test of the claimed gain in realism.

Load-bearing premise

The coupling of activity modification requests with scheduling and routing submodels will produce emergent mobility patterns that are meaningfully more realistic than those from fixed-schedule models.

What would settle it

A side-by-side comparison of activity modification rates and resulting congestion levels produced by MATraM against observed data from a documented real-world transport disruption such as a major road closure.

Figures

Figures reproduced from arXiv: 2605.30547 by Alison Heppenstall, Esra Suel, Gary Polhill, Ricardo Colasanti, Tatsuya Mitomi, Yahya Gamal.

**Figure 2.** Figure 2: cars-travel sub-submodel logic flowchart The sub-submodel then checks the condition as+1 ≤ ts+1 – where ts+1 is the target arrival time to the next stop point. If this condition is satisfied, the bus is expected to either arrive on-time as+1 = ts+1 or late as+1 > ts+1 to the next bus stop. In both cases, the buses-period of the next stop point is modified to reflect the expected arrival time. This assures … view at source ↗

**Figure 3.** Figure 3: buses-travel sub-submodel logic flowchart 8.4 Sub-submodel: buses-start-SPs The buses-start-SPs addresses the bus templates to create buses. The sub-submodel accesses the stop point nodes from which buses should start this time step (i.e., nodes with 0 ∈ buses-period ). It extracts a list of these starting bus service numbers and accesses their respective bus templates. Accordingly, it updates the buses-pe… view at source ↗

**Figure 4.** Figure 4: pedestrians-travel sub-submodel flowchart ties the movement of the pedestrian spatially with that bus. The passenger then updates its current-bus , flags that it is on a bus ( on-bus? = False ) and flags it is no longer waiting for a bus ( waiting? = False ). It updates its location as the current-bus , and it updates its destination as its destination stop point. It also updates the trip to only include t… view at source ↗

read the original abstract

This paper introduces the Multi-Activity Transport & Mobility (MATraM) Agent-Based Model (ABM), a novel framework designed to advance activity-based transport modelling by incorporating dynamic activity adaptation. Traditional transport models simulate system performance using varying levels of abstraction, including flow-based, queue-based, and interaction-based mobility representations. While these approaches differ in their treatment of movement and congestion, they typically rely on pre-defined trip patterns that limit responsiveness to changing conditions. In particular, conventional activity-based models generate trips from fixed daily schedules, constraining their ability to capture behavioural flexibility and uncertainty. MATraM addresses this limitation by enabling agents to flag activities modification requests in response to sub-optimal travel conditions, such as increased travel times. By coupling with an activity scheduling and modification framework, the model integrates adaptive decision-making into the generation and execution of daily activity schedules. This allows for a more realistic representation of how individuals adjust their behaviour in response to transport system dynamics, leading to emergent mobility and congestion patterns. The ABM is presented following the ODD protocol, outlining its purpose, structure, and implementation. MATraM includes detailed representations of agents, their activity schedules, and the transport network, alongside submodels governing routing, scheduling, and behavioural adaptation. By bridging activity-based modelling with interaction-based mobility simulation, MATraM provides a flexible and extensible platform for exploring transport dynamics under uncertainty. This work contributes to the development of next-generation transport models capable of capturing the complex interplay between individual behaviour and system-level outcomes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MATraM describes an ABM architecture for activity modification in transport models but supplies no validation, results, or comparisons to support its claims of improved realism.

read the letter

The main thing to know is that this paper lays out MATraM, an agent-based model that lets agents request activity changes when travel conditions worsen and couples those requests to scheduling and routing submodels. It positions this as a way to move past fixed daily schedules in conventional activity-based transport models.

The paper does a clear job on the structural side. It follows the ODD protocol and spells out the agents, activity schedules, network, and the submodels for routing, scheduling, and adaptation. The coupling mechanism is described in enough detail to show how the pieces are meant to interact and allow behavioral flexibility under uncertainty. That architectural outline is the actual new element here.

The soft spot is the complete absence of testing. The manuscript has no validation runs, no benchmark comparisons against fixed-schedule baselines, no calibration to observed data, and no sensitivity checks on the modification rules. The claim that the coupling will produce meaningfully more realistic emergent mobility and congestion patterns is left as an assertion. Without those checks, it is impossible to tell whether the added mechanisms deliver what is promised or simply add complexity.

This is for transport modelers who work with activity-based ABMs and want a blueprint for adding adaptation. A reader looking for an idea of how to structure such a model might find the ODD description useful as a starting point. Anyone expecting evidence that the approach improves outcomes will not get it.

I would not bring this to a reading group. I would not cite it. It does not deserve peer review until there is at least some implementation and basic validation to show the mechanisms behave as described.

Referee Report

1 major / 1 minor

Summary. The paper introduces the MATraM agent-based model for multi-activity transport and mobility. It enables agents to flag activity modification requests in response to sub-optimal travel conditions such as increased travel times, couples this capability to activity scheduling and modification frameworks, and integrates adaptive decision-making into daily activity schedules. The model is presented following the ODD protocol, with detailed representations of agents, activity schedules, the transport network, and submodels for routing, scheduling, and behavioral adaptation. The central assertion is that this produces more realistic emergent mobility and congestion patterns than conventional fixed-schedule activity-based models.

Significance. If the adaptive mechanisms function as described and are validated, MATraM could advance activity-based transport modeling by adding behavioral flexibility and uncertainty handling, providing an extensible platform that bridges activity-based and interaction-based mobility representations. The explicit use of the ODD protocol for documentation is a positive contribution to model transparency and reproducibility in the ABM literature.

major comments (1)

[Abstract] Abstract: The claim that MATraM 'leads to emergent mobility and congestion patterns' that are more realistic than those from fixed-schedule models is load-bearing for the paper's contribution yet is unsupported by any validation experiments, benchmark comparisons against non-adaptive baselines, sensitivity tests on modification rules, or calibration against observed activity-travel data.

minor comments (1)

The ODD-protocol description would benefit from explicit pseudocode or parameter tables for the routing, scheduling, and adaptation submodels to support independent implementation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and recommendation. The single major comment identifies a mismatch between the abstract's claims and the manuscript's content. We address it directly below.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that MATraM 'leads to emergent mobility and congestion patterns' that are more realistic than those from fixed-schedule models is load-bearing for the paper's contribution yet is unsupported by any validation experiments, benchmark comparisons against non-adaptive baselines, sensitivity tests on modification rules, or calibration against observed activity-travel data.

Authors: We agree that the abstract currently asserts greater realism in emergent patterns without supporting validation, benchmarks, sensitivity analysis, or calibration. The manuscript presents the model design, ODD documentation, and mechanisms for adaptive activity modification, but does not contain empirical validation against observed data or non-adaptive baselines. We will revise the abstract to remove the comparative claim of superior realism. The revised wording will describe the model's capacity to generate emergent patterns via dynamic activity adaptation while clearly stating that empirical validation of realism relative to fixed-schedule models remains future work. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural model description with no derivations, equations, or fitted predictions

full rationale

The manuscript is a descriptive introduction of the MATraM ABM following the ODD protocol. It outlines agents, networks, submodels for routing/scheduling/adaptation, and the design choice to allow activity modification requests. No equations, parameter fits, predictions, or first-principles derivations are present. Claims about emergent realism are stated as intended outcomes of the architecture rather than results computed from prior quantities. No self-citations, uniqueness theorems, or reductions of outputs to inputs by construction appear in the provided text. This is a standard non-circular model-description paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are specified in the abstract; the model description does not detail any fitted values or new postulated components.

pith-pipeline@v0.9.1-grok · 5823 in / 1069 out tokens · 21550 ms · 2026-06-28T23:39:03.662334+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

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