Performance of Multiagent Taxi Dispatch on Extended-Runtime Taxi Availability: A Simulation Study

Seow, Kiam Tian; Lee, Der-Horng

doi:10.1109/tits.2009.2033128

Cited by 22 publications

(9 citation statements)

References 20 publications

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“…Two main solution approaches have been proposed to solve the taxi problem. Customer requests can be managed by using various techniques, such as first come, first served, which has been widely used to dispatch taxis to customers [2][3][4], as well as "choose the closest taxi that is free," "choose the closest taxi that is occupied," and "choose the taxi that has been free for the longest" [5][6][7][8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equations (5) and (6) address the idea that the truck can pick up the product directly from the producer or can go via the supplier, while Equation (7) shows that the product can be shifted only when the route is formed. Equation (8) shows that the depot can get the product only from the truck that picks up the product directly from the producer or goes via the supplier location; there is no other way to pick up the product. Finally, Equation (9) shows that the product delivered from producer k must not exceed its capacity.…”

Section: Indicesmentioning

confidence: 99%

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Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Indicesmentioning

confidence: 99%

Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

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“…Specifically, collaborative multi-agent taxi dispatch systems that automate taxi dispatch in a distributed fashion have been studied extensively in a large body of previous work [4], [5], [6], [7], [8]. These proposals attempt to increase customer satisfaction concurrently dispatching multiple taxis to the same number of customers in the same geographical region.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Taxi Dispatching System for Improved User Satisfaction and Taxi Utilization

Verma¹,

Vo²

2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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Even though using taxi booking services is one of the major method for users to take a taxi, we argue that the customers may not satisfy with these services especially during high demand periods. Current taxi dispatching systems employed by these booking services mainly perform user-taxi matching based on real-time demand and available taxis. Hence, there are high chances that users fail to book for a taxi during periods of high taxi demand and have to repeat the requests multiple times without any guarantee of waiting time. In this paper, we propose data-driven techniques to effectively identify the taxi that the system should dispatch to a pick up location as requested by a user. Specifically, we build and leverage predictive models of taxi travel time and incorporate them into the dispatching system for realizing a much better user-taxi matching strategy which matches users with taxis that will be available in the near future near the users' locations. The benefits of such a predictive taxi dispatching system includes (1) less irritation for users due to uncertain waiting time and having to make repeated requests, (2) higher taxi utilization and revenue for taxi companies, and (3) last but not least better income for taxi drivers. We evaluate our proposed predictive user-taxi matching algorithm with real taxi data. Experimental results confirm that the predictive electronic dispatching system can efficiently handle high demand periods while reducing repeated requests from customers.

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“…The information enables the real-time monitoring and management of their vehicle fleet. In commercial applications, vehicle location information also helps in overall material planning and logistics by automatically taking into account traffic conditions [6], [7] and recommending optimum routes [8], [9] and dispatching [10]. See [11] for a great survey on in-vehicle positioning and navigation technologies.…”

Section: Related Workmentioning

confidence: 99%

“…Otherwise, a random number between 1 and N was chosen as the number of child nodes. From the aforementioned settings, the maximum depth was set to be bounded from the top by 10. The third parameter in the simulation was the probability P for sibling nodes to be in an AND relation.…”

Section: B Performance Of Optimization Rulesmentioning

confidence: 99%

Data Modeling and Optimization for Wireless Drive-Through Applications

Xü

Zhu

Guo

et al. 2011

IEEE Trans. Intell. Transport. Syst.

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The increasing capabilities and decreasing cost of information and communication technologies (ICTs) are enabling new categories of vehicular telematics applications, such as wireless drive-through (WDT) services, in which an external service provider provides services to in-vehicle occupants via a wireless connection. Most telematics systems rely on proprietary protocols for data exchange between an in-vehicle client and its external counterpart in individual applications, making it difficult to share data-exchange methods among applications. Sharing dataexchange methods is important because it allows a rapid return on ICT investments. In this paper, we study the data-modeling issues for data exchange between an in-vehicle client and its service provider for WDT services. We present a novel data model that captures the main characteristics of data exchange for the WDT application family, using a formal mathematical representation and an intuitive graphical representation. We show that the proposed data model provides generality for such data exchange. In addition, we propose several optimization techniques based on the data model to minimize user interaction so that a good usability can be achieved. Our simulations and analysis demonstrate that the proposed data model and relevant optimization techniques are promising in supporting efficient WDT applications.

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Performance of Multiagent Taxi Dispatch on Extended-Runtime Taxi Availability: A Simulation Study

Cited by 22 publications

References 20 publications

Modified Differential Evolution Algorithm for a Transportation Software Application

Modified Differential Evolution Algorithm for a Transportation Software Application

A Predictive Taxi Dispatching System for Improved User Satisfaction and Taxi Utilization

Data Modeling and Optimization for Wireless Drive-Through Applications

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