| paper | d4-conf-ecai-2014-0164 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
The paper contains no numbered theorem, lemma, corollary, or proposition asserting an algorithmic or complexity result. The proposed mass route assignment is a statable batch-routing extension, but it is unanchored and leaves each type’s route choice independent; it cannot supply the missing computational result of this paper.
fails bit a — no named computational result to mirror
The proposed assignment extends the paper’s fixed-cost, single-request route model; it does not turn the reported experiments or runtime measurements into a named computational result.
The paper has no numbered Theorem, Lemma, Corollary, or Proposition asserting a complexity or algorithmic result. Section 3.3 defines the Cycle Planning Problem; Section 4 describes an A* solution; Section 6 reports experimental runtimes. Those are not qualifying anchors, and the reported sub-250 ms performance is not a worst-case complexity result. So I can’t make the requested result-anchored case from this paper.
The strongest plausible mirror I can offer is an unanchored follow-up: *Mass Route Assignment for Bicycle Requests*. Use the paper’s directed cycleway graph and cost functions. An instance has a finite support \(S\) of journey types \(t=(o_t,d_t,s_t,\vec w_t)\), each with rational mass \(\mu_t>0\) and \(\sum_{t\in S}\mu_t=1\). A type specifies the complete request: origin, destination, average speed, and criteria profile. Choose a finite route \(\pi_t\) from \(o_t\) to \(d_t\) for each type, minimizing
\[
\sum_{t\in S}\mu_t\sum_{e\in\pi_t}\vec w_t\cdot\vec c_{s_t}(e).
\]
The solution is the route assignment \((\pi_t)_{t\in S}\).
A plausible regime is a city service routing many commuters along a relatively small set of zone-to-zone trips, with a small set of speed and profile categories: many cyclists, but fewer distinct complete requests. This extends the paper’s route-planning problem to a population of requests. With nonnegative rational edge costs and no shared capacity constraints, it separates into one shortest-path problem per supported type, so I would expect this mirror to be tractable in \(m\), \(\tau=|S|\), and the input bit length. The paper’s results cover the route-cost model and single-request planning; they do not establish this population version’s complexity.
The soft spot is substantial: the mass only weights independent requests, so it does not change which route is best for any type. This is a credible high-volume batch-routing scenario, but a referee could reasonably view it as aggregating many queries rather than continuizing the paper’s central computational problem. That is why it is only a candidate mirror, not a case supported by a named anchor.
There is no named anchor to defeat. The paper defines a single-request Cycle Planning Problem, describes an A* solution, and reports experimental runtimes. It states no numbered complexity result or worst-case bound. Under the requested standard, it offers no computational result for a continuous mirror to generalize.
The proposed mass route assignment has a plausible population story: many commuters could share a small set of complete journey types, with rational masses representing their proportions. But with the paper’s fixed edge costs, each type’s route choice is independent. The masses weight the total reported cost; they do not affect the routes or couple the decisions. That makes the proposed object a batch of journey requests rather than a population-level planning question. This is a concern about what the model represents, rather than about what answer its algorithm would produce.
A stronger proposal would make edge costs depend on aggregate cyclist flow, for example \(x_e=\sum_t\mu_t\mathbf{1}[e\in\pi_t]\), so crowding or safety effects couple route choices. That would make population mass consequential and yield a substantive computational problem. But the paper fixes costs from map features and cyclist preferences; it neither models cyclist-generated congestion nor studies a joint assignment objective. Adding those elements changes the problem’s defining model, and the paper supplies no complexity result that such a model would extend.
So the strongest negative case is narrow: this paper has no result-anchored continuous mirror, and the proposed independent-request version has little population-level content. The broader claim—that no plausible population routing problem inspired by the paper could be worthwhile—is weak. A coupled traffic model could be a worthwhile adjacent project, even though this paper does not establish it as a mirror of one of its computational results.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.