Route Reconstruction from Traffic Flow via Representative Trajectories

TL;DR

This paper explores combining loop-detector traffic flow data with representative trajectories to reconstruct realistic vehicle routes, proposing heuristics and evaluating their effectiveness on synthetic and real data.

Contribution

It introduces novel heuristics for route reconstruction that balance realism and flow explanation, addressing NP-hardness of the problem.

Findings

GMCF explains flow best but produces many nonsensical routes.

MCMCF yields realistic routes that reasonably explain flow.

FR generates smaller, realistic route sets with higher computational cost.

Abstract

Understanding human mobility is an important aspect of traffic analysis and urban planning. Trajectories provide detailed views on specific routes, but typically do not capture all traffic. Loop detectors capture all traffic flow at specific locations instead, but provide no information on individual routes. Given a set of loop-detector measurements and a set of representative trajectories, our goal is to investigate how one can effectively combine these two partial data sources to create a more complete picture of the underlying mobility. Specifically, we want to reconstruct a realistic set of routes from the loop-detector data, using the given trajectories as representatives of typical behavior. We model loop-detector data as a network flow that needs to be covered by the reconstructed routes and we capture realism of the routes via the Fr\'echet distance to the representatives. We prove that several forms of the resulting problem are NP-hard. Hence we explore heuristics that decompose the flow well while following the representatives to varying degrees. First we propose the Fr\'echet Routes (FR) heuristic which generates candidates routes with bounded Fr\'echet distance. Second we describe a variant of multi-commodity min-cost flow (MCMCF) which is loosely coupled to the trajectories. Lastly we consider global min-cost flow (GMCF) which is essentially agnostic to the representatives. We evaluate these approaches on synthetic and real-world trajectory data with a map-matched ground truth. We find that GMCF explains the flow best, but produces a large number of routes (significantly more than the ground truth); these routes are often nonsensical. MCMCF produces a large number of mostly realistic routes which explain the flow reasonably well. In contrast, FR produces significantly smaller sets of realistic routes that still explain the flow well, albeit with a higher running time.