BENCHMARKING MINIMUM PASSENGER WAITING TIME IN ONLINE TAXI DISPATCHING WITH EXACT OFFLINE OPTIMIZATION METHODS

On Predicting the Taxi-Passenger Demand: A Real-Time Approach

Informed driving is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-...

Online and Real-Time Dispatching Problems

Offline Optimization for Online Ad Allocation

We consider online ad allocation from the perspective of optimizing delivery of a given set of ad reservations. Ad allocation naturally fits into the class of online bipartite matching problems: ad nodes are fixed, and impression (ad slot) nodes arrive one at a time, and must be assigned a qualified ad upon arrival. However most previous work on online matching does not model an important aspec...

A Multi-Agent Taxi Dispatching System

The taxi assignment problem may be categorized as a vehicle routing problem. ‎ When placed in the field of resource allocation, it is a dynamic problem in which ‎ the situation changes as the work progresses. This paper presents a new agent-based approach to tackle the taxi assignment problem. New parameters are ‎ introduced to increase the satisfaction of the drivers. The authors propose a new...

Online Optimization through Mining the Offline Optimum

Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized). These operators have a wealth of historical data on task assignments; can these data be mined for knowledge or rules that can help the decisionmaking? MOO is a novel application of data mining to online optimization. The idea is to mine (logged...