Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories (original) (raw)
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2010
Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per 1x1 km2 area.
iTaxi: Context-Aware Taxi Demand Hotspots Prediction
It has been estimated that over 60 thousand licensed taxis in the Great Taipei area are not occupied over 70 percent of driving time on average. However, the taxi company, TaiwanTaxi, indicates that even in rush hour, there are customers whose requests are not satisfied. The demand and supply are not paired, causing not only customers wait too long for a cab, but also taxi drivers waste time and fuel to wander around the streets. In this paper, it uses spatial statistics analysis, data mining and clustering algorithm on historical data of taxi requests to discover the demand distribution, which varies from different environment contextual information such as the location, time, and weather. Finally, the predicting system then predicts potential hotspots of taxi requests and provides hotspots information for drivers to reduce vacant time of the taxi.
On Predicting the Taxi-Passenger Demand: A Real-Time Approach
Lecture Notes in Computer Science, 2013
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-Passenger demand in a short time horizon. We did so by using learning concepts originally proposed to a well-known online algorithm: the perceptron [1]. The results were promising: we accomplished a satisfactory performance to output the next prediction using a short amount of resources.
Online Predictive Model for Taxi Services
Lecture Notes in Computer Science, 2012
In recent years, both companies and researchers have been exploring intelligent data analysis to increase the profitability of the taxi industry. Intelligent systems for online taxi dispatching and time saving route finding have been built to do so. In this paper, we propose a novel methodology to produce online predictions regarding the spatial distribution of passenger demand throughout taxi stand networks. We have done so by assembling two well-known time series short-term forecast models: the time-varying Poisson models and ARIMA models. Our tests were performed using data gathered over a period of 6 months and collected from 63 taxi stands within the city of Porto, Portugal. Our results demonstrate that this model is a true major contribution to the driver mobility intelligence: 78% of the 253745 demanded taxi services were correctly forecasted in a 30 minutes horizon.
A predictive model for the passenger demand on a taxi network
2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012
Abstract² In the last decade, the real-time vehicle location systems attracted everyone attention for the new kind of rich spatio-temporal information. The fast processing of this large amount of information is a growing and explosive challenge. Taxi companies are already exploring such information in efficient taxi dispatching and time-saving route finding. In this paper, we propose a novel methodology to produce online short term predictions on the passenger demand spatial distribution over 63 taxi stands in the city of Porto, Portugal. We did so using time series forecasting techniques to the processed events constantly communicated for 441 taxi vehicles. Our tests -using 4 months of real data -demonstrated that this model is a true major contribution to the driver mobility intelligence: 76% of the 86411 demanded taxi services were accurately forecasted in a 30 minutes time horizon.
2015
Taxi waiting queues or passenger waiting queues usually reflect the imbalance between taxi supply and demand, which consequently decrease a city’s trac system productivity and commuters’ satisfaction. In this paper, we present a queue detection and analysis system to conduct analytics on both taxi and passenger queues. The system utilizes the event-driven taxi traces and the taxi state transition knowledge to detect queue locations at a coordinate level and subsequently identify 4 di↵erent types of queue context (e.g., only passengers queuing or only taxis queuing). More specifically, it adopts the novel and easy-to-implement algorithms to selectively extract taxi pickup events and their critical features. The extracted taxi pickup locations are then used to detect queue locations, and the extracted critical features are used to infer queue context. The extensive empirical evaluations, which run on daily 12.4 million taxi trace records from nearly 15000 taxis in Singapore, demonstra...
(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories
Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems. We present an approach based on trip matching and ensemble learning, in which we leverage the patterns observed in a dataset of roughly 1.7 million taxi journeys to predict the corresponding final destination and travel time for ongoing taxi trips, as a solution for the ECML/PKDD Discovery Challenge 2015 competition. The results of our empirical evaluation show that our approach is effective and very robust, which led our team-BlueTaxi-to the 3rd and 7th position of the final rankings for the trip time and destination prediction tasks, respectively. Given the fact that the final rankings were computed using a very small test set (with only 320 trips) we believe that our approach is one of the most robust solutions for the challenge based on the consistency of our good results across the test sets.
iTaxi: Context-aware taxi demand hotspots prediction using ontology and data mining approaches
2008
It has been estimated that over 60 thousand licensed taxis in the Great Taipei area are not occupied over 70 percent of driving time on average. However, the taxi company, TaiwanTaxi, indicates that even in rush hour, there are customers whose requests are not satisfied. The demand and supply are not paired, causing not only customers wait too long for a cab, but also taxi drivers waste time and fuel to wander around the streets. In this paper, it uses spatial statistics analysis, data mining and clustering algorithm on historical data of taxi requests to discover the demand distribution, which varies from different environment contextual information such as the location, time, and weather. Finally, the predicting system then predicts potential hotspots of taxi requests and provides hotspots information for drivers to reduce vacant time of the taxi.
2011
In modern cities, more and more vehicles, such as taxis, have been equipped with GPS devices for localization and navigation. Gathering and analyzing these large-scale realworld digital traces have provided us an unprecedented opportunity to understand the city dynamics and reveal the hidden social and economic "realities". One innovative pervasive application is to provide correct driving strategies to taxi drivers according to time and location. In this paper, we aim to discover both efficient and inefficient passenger-finding strategies from a large-scale taxi GPS dataset, which was collected from 5350 taxis for one year in a large city of China. By representing the passenger-finding strategies in a Time-Location-Strategy feature triplet and constructing a train/test dataset containing both top-and ordinary-performance taxi features, we adopt a powerful feature selection tool, L1-Norm SVM, to select the most salient feature patterns determining the taxi performance. We find that the selected patterns can well interpret the empirical study results derived from raw data analysis and even reveal interesting hidden "facts". Moreover, the taxi performance predictor built on the selected features can achieve a prediction accuracy of 85.3% on a new test dataset, and it also outperforms the one based on all the features, which implies that the selected features are indeed the right indicators of the passenger-finding strategies.
Taxi-Passenger-Demand Modeling Based on Big Data from a Roving Sensor Network
IEEE Transactions on Big Data, 2017
Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on offline data collected by manual investigations, which are often dated and inaccurate for real-time analysis. To address this issue, we propose Dmodel, employing roving taxicabs as real-time mobile sensors to (i) infer passenger arriving moments by interactions of vacant taxicabs, and then (ii) infer passenger demand by customized online training with both historical and real-time data. Dmodel utilizes a novel parameter called pickup pattern based on an entropy of pickup events (accounts for various real-world logical information, e.g., bad weather) to reduce the size of big historical taxicab data to be processed. We evaluate Dmodel with a real-world 450 GB dataset of 14,000 taxicabs for a half year, and results show that compared to the ground truth, Dmodel achieves 83 percent accuracy and outperforms a statistical model by 42 percent. We further present an application where Dmodel is used to dispatch vacant taxicabs to achieve an equilibrium between passenger demand and taxicab supply across urban regions.