2017 · 235 citations · 19 references
PlanningDecision SciencesTransportation PlanningTransportTransport PlanningTransportationDestination ChoiceIntelligent Transportation SystemsTransport ModellingTransportation ModelsTravel BehaviorTravel DemandDispatch OptimizationOptimal DispatchResponse TimeTaxi OperationsTaxi SchedulingTransport DispatchTravel Behavior ModelingTravel Demand Analysis
Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.
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Greedy function approximation: A gradient boosting machine.
Jerome H. Friedman · The Annals of Statistics · 2001 · 29K citations · Full text
Boosting Algorithms as Gradient Descent
Llew Mason, Jonathan Baxter, Peter L. Bartlett et al. · 1999 · 712 citations