Real-Time Approximate Routing for Smart Transit Systems

TL;DR

This paper introduces a real-time approximation algorithm for optimizing bus routes and frequencies in smart transit systems, improving efficiency over existing heuristics and exact methods under certain assumptions.

Contribution

It develops a $1-rac{1}{e}- ext{epsilon}$ approximation algorithm for real-time line planning, extending transportation optimization with performance guarantees.

Findings

The algorithm achieves near-optimal social welfare in real-time scenarios.

It outperforms exact ILP methods within fixed time budgets.

Assumptions are critical; relaxing them negates constant-factor approximations.

Abstract

We study real-time routing policies in smart transit systems, where the platform has a combination of cars and high-capacity vehicles (e.g., buses or shuttles) and seeks to serve a set of incoming trip requests. The platform can use its fleet of cars as a feeder to connect passengers to its high-capacity fleet, which operates on fixed routes. Our goal is to find the optimal set of (bus) routes and corresponding frequencies to maximize the social welfare of the system in a given time window. This generalizes the Line Planning Problem, a widely studied topic in the transportation literature, for which existing solutions are either heuristic (with no performance guarantees), or require extensive computation time (and hence are impractical for real-time use). To this end, we develop a $1-\frac{1}{e}-\varepsilon$ approximation algorithm for the Real-Time Line Planning Problem, using ideas from randomized rounding and the Generalized Assignment Problem. Our guarantee holds under two assumptions: $(i)$ no inter-bus transfers and $(ii)$ access to a pre-specified set of feasible bus lines. We moreover show that these two assumptions are crucial by proving that, if either assumption is relaxed, the Real-Time Line Planning Problem does not admit any constant-factor approximation. Finally, we demonstrate the practicality of our algorithm via numerical experiments on real-world and synthetic datasets, in which we show that, given a fixed time budget, our algorithm outperforms Integer Linear Programming-based exact methods.