mobility-mode-sequence-search 0.1.1

Rust mode-sequence search kernel for mobility

mobility-mode-sequence-search

Rust package for mode-sequence search in mobility.

Scope

This package owns:

• compact indexing of mode and cost data
• top-k mode-sequence search
• chain-level parallelism
• a Python extension API that accepts and returns columnar data

The main mobility repository remains responsible for orchestration, validation, and any fallback Python implementation.

How It Works

This package searches feasible mode sequences for an ordered chain of places.

For example, if someone goes from home to a shop, feasible answers might include:

• walk, walk
• car outbound, car return
• bike outbound, bike return

Feasibility is constrained by a few simple rules:

• a vehicle can only be used if it is currently at the traveler's location
• if a vehicle leaves home, it must end back at home
• some outbound choices come in matched pairs, requiring a linked return mode later in the same subtour

Conceptually, the algorithm has four stages:

1. Split long chains into smaller home-to-home segments when possible.
2. Search each segment incrementally, always expanding the cheapest partial answer first.
3. Merge the best segment-level answers into full-chain answers.
4. Keep only the leading answers needed to reach the requested cumulative probability threshold.

Before search, each input chain is closed internally by appending the starting location to the end. This matches the legacy Python backend, which searches a closed tour rather than the raw caller-provided chain.

Pseudocode

for each chain:
  split the chain into home-to-home segments

  for each segment:
    start with one empty partial answer

    while there are still partial answers to explore:
      take the cheapest partial answer so far

      if it already covers every leg:
        if all vehicles are back home:
          save it as a feasible full answer
        continue

      look up the next leg's available modes

      for each allowed mode:
        update vehicle locations
        update any forced future return-mode rule

        if the partial answer is still feasible:
          put the extended partial answer back into the queue

  merge the best segment answers into full-chain answers
  prune the low-probability tail
  write the retained answers as output rows

Implementation details are in rust/search.rs and rust/input.rs.

Python API

import polars as pl
from mobility_mode_sequence_search import search_mode_sequences

result = search_mode_sequences(
    location_chain_steps=pl.DataFrame(...),
    leg_mode_costs=pl.DataFrame(...),
    mode_metadata=pl.DataFrame(...),
    k_sequences=20,
    cumulative_prob_threshold=0.98,
    n_threads=None,
)

Input Schemas

location_chain_steps

Grouped form:

• utility_profile_id: UInt32 (optional, defaults to zero)
• dest_seq_id: UInt64
• locations: List[UInt32]

Long-form:

• utility_profile_id: UInt32 (optional, defaults to zero)
• dest_seq_id: UInt64
• seq_step_index: UInt32
• location: UInt32

leg_mode_costs

• utility_profile_id: UInt32 (optional, defaults to zero)
• origin: UInt32
• destination: UInt32
• mode_id: UInt16
• cost: Float64

mode_metadata

• mode_id: UInt16
• needs_vehicle: Boolean
• vehicle_id: UInt8 | Utf8 | null
• multimodal: Boolean
• is_return_mode: Boolean
• return_mode_id: UInt16 | null

At the package boundary, vehicle_id may be integer/null or string/null. String labels are normalized internally into numeric ids before search. Mixed integer and string representations within one call are rejected.

Several utility profiles can be searched in one call. Chains read the costs with the same utility_profile_id, while mode metadata and the Rayon thread pool are shared across all profiles. Each referenced profile must provide the mode costs needed by its chains. Calls without the column keep the original single-profile behavior.

Output Schema

• utility_profile_id: UInt32 (when supplied on the chains)
• dest_seq_id: UInt64
• mode_seq_index: UInt32
• seq_step_index: UInt32
• location: UInt32
• mode_index: UInt16

Development

mamba run -n mobility python -m pip install -e .[dev]
mamba run -n mobility python -m pytest
mamba run -n mobility python -m maturin build --release

Performance Fixture

A standalone synthetic performance case is available at tests/perf_synthetic_case.py.

Example:

mamba run -n mobility python tests/perf_synthetic_case.py --n-chains 2000 --chain-len 18 --n-locations 128 --k-sequences 20