Time Sensitive Multiple POIs Route Planning on Bus Networks

2025-12-28

AI Summary

This paper addresses the multi-point-of-interest (POI) routing problem with timetable constraints in public transit networks: given a designated origin bus stop, the objective is to visit all target POIs while minimizing total travel time—including both in-vehicle and waiting time. To overcome limitations of classical Traveling Salesman Problem (TSP) formulations—which fail to capture dynamic, time-varying bus delays and stochastic waiting times—we propose a dynamic time-dependent transit graph model. We further design EA-Star, a novel heuristic algorithm that integrates A* search with intelligent pruning and early termination mechanisms, thereby avoiding exhaustive enumeration of POI visit sequences. Evaluated on a real-world New York City transit dataset, EA-Star significantly reduces computational overhead compared to baseline methods while rigorously preserving solution optimality. Our approach establishes an efficient and reliable paradigm for large-scale, real-time transit-aware POI routing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

This work addresses a route planning problem constrained by a bus road network that includes the schedules of all buses. Given a query with a starting bus stop and a set of Points of Interest (POIs) to visit, our goal is to find an optimal route on the bus network that allows the user to visit all specified POIs from the starting stop with minimal travel time, which includes both bus travel time and waiting time at bus stops. Although this problem resembles a variant of the Traveling Salesman Problem, it cannot be effectively solved using existing solutions due to the complex nature of bus networks, particularly the constantly changing bus travel times and user waiting times. In this paper, we first propose a modified graph structure to represent the bus network, accommodating the varying bus travel times and their arrival schedules at each stop. Initially, we suggest a brute-force exploration algorithm based on the Dijkstra principle to evaluate all potential routes and determine the best one; however, this approach is too costly for large bus networks. To address this, we introduce the EA-Star algorithm, which focuses on computing the shortest route for promising POI visit sequences. The algorithm includes a terminal condition that halts evaluation once the optimal route is identified, avoiding the need to evaluate all possible POI sequences. During the computation of the shortest route for each POI visiting sequence, it employs the A* algorithm on the modified graph structure, narrowing the search space toward the destination and improving search efficiency. Experiments using New York bus network datasets demonstrate the effectiveness of our approach.

Problem

• Optimizing bus route planning for visiting multiple POIs efficiently
• Addressing dynamic bus travel and waiting times in route optimization
• Developing algorithms to find minimal travel time routes on bus networks

Innovation

• Modified graph structure for bus network representation
• EA-Star algorithm for optimal POI sequence evaluation
• A* algorithm on modified graph for efficient route search