Urban Delivery Disruption-Driven Short-Horizon Replanning Trajectory Dataset

#short-horizon task planning #plan replanning #agent trajectory learning #plan executability evaluation #urban freight delivery #temporary road closures #blocked delivery entrances #delivery task scheduling
Records
4250
Size
0.5GB
Format
JSON
Provider
MOBIUSI INCMOBIUSI INC
Updated
v1.02026-11-04
AI Analysis & Value Prop

This dataset contains agent state-decision trajectories following disruptions in urban delivery, including temporary road closures, impassable street segments, and blocked delivery entrances. Records cover disruption observations, the current delivery state, affected tasks, and plans before and after replanning. The data supports short-horizon task replanning and evaluation of whether revised delivery plans are executable under updated conditions.

Overview

Dataset Insights

Preview

Sample Examples

sample.json
{}

Schema

Technical Specifications

file_namestringFile name
revised_planarrayNew ordered sequence of delivery actions produced in response to the updated state.
affected_tasksarrayTask references whose route, order, or execution status changed because of the disruption.
plan_executablebooleanIndicates whether the revised plan can be carried out under the observed state and constraints.
decision_summarystringConcise explanation of how the agent adapted the plan to the disruption and current delivery state.
replanning_metricsobjectComputed summary measures describing the revised plan.
pre_disruption_planarrayOrdered plan active immediately before the disruption was processed.
current_delivery_stateobjectRaw vehicle and task state available to the agent when replanning begins.
disruption_observationobjectRaw report describing the temporary road closure, blocked street segment, or obstructed delivery entrance.
replanning_trigger_classificationstringNormalized category of the disruption that triggered replanning.

Trust

Compliance Statement

Authorization Type

Proprietary - Commercial AI Training License (No Redistribution)

Commercial Use

Requires exclusive subscription or authorization contract (monthly or per-invocation charging)

Privacy and Anonymization

No PII, no real company names, simulated scenarios follow industry standards

Compliance System

Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

FAQ

Frequently Asked Questions

What does the dataset contain?
It covers the replanning process after disruptions to urban delivery, with fields for event descriptions, the agent's current delivery state, the pre-disruption plan, trigger categories, revised plans, affected tasks, plan executability, replanning metrics, and decision summaries.
Which training and evaluation tasks does it support?
It can support short-horizon task planning, replanning after unexpected events, agent state-decision trajectory learning, and evaluation of whether revised plans are executable.
What are the dataset size and quality specifications?
The available information does not specify sample counts, file formats, or quality-control metrics. Before acquisition, review the product listing for specifications, field definitions, and sample records to confirm compatibility with your data interface and evaluation requirements.
How can I obtain the data, and what usage rights apply?
Use the delivery process provided on the product page or platform, and follow the license terms for the specific offering. Confirm permitted uses, redistribution restrictions, and commercial-use conditions before deployment.

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Cite this Work

@dataset{Mobiusi2026,
  title={Urban Delivery Disruption-Driven Short-Horizon Replanning Trajectory Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/ea2fb2d9a1b8e6cc03c6b39d447afaf3?dataset_scene_id=13},
  urldate={2026-10-26},
  keywords={urban delivery dataset,delivery route replanning,road disruption response,short-horizon task planning,agent trajectory data,urban freight scheduling,delivery plan optimization,plan executability evaluation},
  version={1.0}
}

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