Route Optimization Using Q-Learning for On-Demand Bus Systems

Mukai, Naoto; Watanabe, Toshio; Feng, Jun

doi:10.1007/978-3-540-85565-1_70

Cited by 14 publications

(14 citation statements)

References 3 publications

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“…An algorithm, i.e., a location recommendation system that targets the potential passengers during the reservation when buses are idle is introduced by [97]. With the use of machine learning knowledge, Q-learning [98] has found an effective pick-up point selection process.…”

Section: Shared Mobilitymentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Section: Shared Mobilitymentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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“…To the best of our knowledge, the use of the reinforcement learning-based approach to tackle the vehicle routing problem (VRP) in the context of ITT is still limited. Mukai et al [ 24 ] adopted and improved the native Q-learning, one of the reinforcement learning (RL) algorithms, to optimize the route of on-demand bus systems. In the on-demand bus system, the travel routes for the buses were not determined in advance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By improving the updated process of the Q values, the results showed the effectiveness of the proposed method in addressing the problem. Similar to Mukai et al [ 24 ], Jeon et al [ 25 ] also implemented the Q-learning algorithm to identify routes with the shortest travel time for AGVs in port terminals. They determined the shortest-time routes inclusive of the expected waiting times instead of the simple shortest distance routes, which are usually used in practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Interterminal Truck Routing Optimization Using Deep Reinforcement Learning

Adi

Iskandar

Bae

2020

Sensors

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The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms.

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“…Alternatively, some approaches consider dynamical corrections to maintain equal headway between buses. e popular method to stabilise the buses' headways is to implement a holding strategy [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] which slows down a fast bus if its headway with respect to the bus ahead of it is deemed to be too small. Other approaches include stop-skipping [11, [22][23][24][25][26] or deadheading (i.e., sending an empty bus directly to a set of designated bus stops) [23,[26][27][28][29], limiting boarding [30][31][32][33][34][35], as well as despatching buses with wide doors [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…Literature Review. Many studies in the literature have focused on the holding strategy [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]: if a bus is too fast, it would exercise an extended stoppage duration to correct for the headway from the bus in front of it-otherwise, it would bunch with it. Holding back buses, however, may tend to slow down the system and would require that some slack in the schedule has been allocated beforehand.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies

2020

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We study how N intelligent buses serving a loop of M bus stops learn a no-boarding strategy and a holding strategy by reinforcement learning. The no-boarding and holding strategies emerge from the actions of stay or leave when a bus is at a bus stop and everyone who wishes to alight has done so. A reward that encourages the buses to strive towards a staggered phase difference amongst them whilst picking up passengers allows the reinforcement learning process to converge to an optimal Q-table within a reasonable amount of simulation time. It is remarkable that this emergent behaviour of intelligent buses turns out to minimise the average waiting time of commuters, in various setups where buses move with the same speed or different speeds, during busy as well as lull periods. Cooperative actions are also observed, e.g., the buses learn to unbunch.

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Route Optimization Using Q-Learning for On-Demand Bus Systems

Cited by 14 publications

References 3 publications

Applications of Artificial Intelligence in Transport: An Overview

Applications of Artificial Intelligence in Transport: An Overview

Interterminal Truck Routing Optimization Using Deep Reinforcement Learning

Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies

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