A Goal-Prioritized Algorithm for Additional Route Deployment on Existing Mass Transportation System

Lin, Fandel; Hsieh, Hsun-Ping

doi:10.1109/icdm50108.2020.00137

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…(b) Greedy: Greedy search iteratively explores the neighbor grid that holds the highest precalculated PF of the grid itself and thereafter constructs trajectories from a station to another iteratively until all stations are connected once but not repeated. (c) BiasSpan [52]: BiasSpan is performed for each station in parallel and is modified to allow the formation of a route that connects all stations once but not repeated. (d) M-Dijkstra: Dijkstra's algorithm [25] is performed in parallel at each station and searches for another station as a destination.…”

Section: Comparative Methodsmentioning

confidence: 99%

“…Therefore, most of the algorithms are unable to solve the problem with these characteristics [6]. To overcome this challenge, Multi-Source Bidirectional Gaussian-Prioritized Spanning Tree (BiasSpan) [52] is proposed for solving this non-monotonic multi-criteria constrained route planning problem. It can also be applied to deploy additional transportation routes in existing public transit systems.…”

Section: Constrained Multi-criteria Route Planningmentioning

confidence: 99%

“…We adopt the idea of the Gaussian Mixture Model (GMM) along with negative feedback in BiasSpan [52], then combine it with Poisson Mixture Model, a state-of-the-art approach in spatiotemporal modeling. The Gaussian-Poisson Mixture Model (G-PMM) is used for modeling the spatial influence of stations and each grid itself.…”

Section: Station Recommendationmentioning

confidence: 99%

See 2 more Smart Citations

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

Lin

Hsieh

2022

ACM Trans. Intell. Syst. Technol.

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Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as heuristics based on neural networks, and thereafter formulate the traveling transporter problem (TTP). TTP is a multi-criteria optimization problem and may be applied to the circular route deployment in public transportation. In particular, TTP aims to find an optimized route that maximizes passenger flow according to a neural-network-based inference model and minimizes the length of the route given several constraints, including must-visit stations and the requirement for additional ones. As a variation of the traveling salesman problem (TSP), we propose a framework that first recommends new stations’ location while considering the herding effect between stations, and thereafter combines state-of-the-art TSP solvers and a metaheuristic named Trembling Hand , which is inspired by self-efficacy for solving TTP. Precisely, the proposed Trembling Hand enhances the spatial exploration considering the structural patterns, previous actions, and aging factors. Evaluation conducted on two real-world mass transit systems, Tainan and Chicago, shows that the proposed framework can outperform other state-of-the-art methods by securing the Pareto-optimal toward the objectives of TTP among comparative methods under various constrained settings.

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Section: Comparative Methodsmentioning

confidence: 99%

Section: Constrained Multi-criteria Route Planningmentioning

confidence: 99%

See 1 more Smart Citation

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

Lin

Hsieh

2022

ACM Trans. Intell. Syst. Technol.

Self Cite

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A survey of route recommendations: Methods, applications, and opportunities

Zhang,

Luo,

Yang

et al. 2024

Information Fusion

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A Decision Framework to Recommend Cruising Locations for Taxi Drivers under the Constraint of Booking Information

Hsieh

Lin

Chen

et al. 2022

ACM Trans. Manage. Inf. Syst.

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As the demand for taxi reservation services has increased, increasing the income of taxi drivers with advanced services has attracted attention. In this article, we propose a path decision framework that considers real-time spatial-temporal predictions and traffic network information. The goal is to optimize a taxi driver's profit when considering a reservation. Our framework contains four components. First, we build a grid-based road network graph for modeling traffic network information for speeding up the search process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide proper search directions for recommending cruising locations. One module of the taxi demand prediction is used to estimate the pick-up probabilities of passengers in the city. Another one is destination prediction, which can predict the distribution of drop-off probabilities and capture the flow of potential passengers. Finally, we propose the H* (Heuristic-star) algorithm, which jointly considers pick-up probabilities, drop-off distribution, road network, distance, and time factors based on the attentive heuristic function to dynamically recommend next cruising locations. Compared with existing route planning methods, the experimental results on a real-world dataset have shown that our proposed approach is more effective and robust. Moreover, our designed search scheme in H* can decrease the computing time and allow the search process to be more efficient. To the best of our knowledge, this is the first work that focuses on guiding a route, which can increase the income of taxi drivers under the constraint of booking information.

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A Goal-Prioritized Algorithm for Additional Route Deployment on Existing Mass Transportation System

Cited by 4 publications

References 27 publications

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

A survey of route recommendations: Methods, applications, and opportunities

A Decision Framework to Recommend Cruising Locations for Taxi Drivers under the Constraint of Booking Information

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