Modeling Taxi Demand and Supply in New York City Using Large-Scale Taxi GPS Data

Yang, Ci; Gonzales, Eric J.

doi:10.1007/978-3-319-40902-3_22

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…Dispatchers often call on non-Hala taxis when Hala taxis are unavailable. Unlike publicly available dynamic mobility datasets such as those from New York City [12], the Dubai Taxi dataset contains the unique license plate number of each vehicle. The RTA anonymized the driver information by replacing their names with unique identifiers.…”

Section: Methods and Datamentioning

confidence: 99%

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Bridgelall

Tolliver

et al. 2018

Journal of Advanced Transportation

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On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

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Section: Methods and Datamentioning

confidence: 99%

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Bridgelall

Tolliver

et al. 2018

Journal of Advanced Transportation

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“…GPS based system is also utilized on taxis of New York City to track them and to analyze taxi ridership with such data source. Yang and Gonzales (2017) processed the GPS taxi data of New York City and used negative binomial method to capture the variation of taxi pick-up demand. Six explanatory variables were used in their study including population, It is well-proven that spatial information increases the accuracy of prediction specifically in congestion traffic and for longer horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models

Safikhani

Kamga

Mudigonda

et al. 2020

International Journal of Forecasting

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A highly dynamic urban space in a metropolis such as New York City, the spatio-temporal variation in demand for transportation, particularly taxis, is impacted by various factors such as commuting, weather, road work and closures, disruption in transit services, etc. To understand the user demand for taxis through space and time, a generalized spatio-temporal autoregressive (STAR) model is proposed in this study. In order to deal with the high dimensionality of the model, LASSO-type penalized methods are proposed to tackle the parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and it is found that the proposed models outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and practical to be applied by taxi operators. Efficiency of the proposed model also helps in model estimation in real-time applications.

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“…Morgul and Ozbay [14] present an empirical assessment of taxicab drivers labor supply. Yang and Gonzales [15] identify locations and times of day where there is a mismatch between the availability of taxicabs and taxi service demand. Zhao et al [16] use entropy and the temporal correlation of human mobility to measure the demand uncertainty at the building block level.…”

Section: A Prediction Applications Using Taxi Datamentioning

confidence: 99%

Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Information

Markou

Rodrigues

Pereira

2020

IEEE Trans. Intell. Transport. Syst.

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In transportation, nature, economy, environment, and many other settings, there are multiple simultaneous phenomena happening that are of interest to model and predict. Over the last few years, the traffic data that we have at our disposal have significantly increased, and we have truly entered the era of big data for transportation. Most existing travel demand prediction methods mainly focus on capturing recurrent mobility trends that relate to habitual/routine behavior, and on exploiting short-term correlations with recent observation patterns. However, valuable information that is often available in the form of unstructured data is neglected when attempting to improve forecasting results. Particularly, under non-recurrent conditions, such as large events, or incidents, we need much better models. In this paper, we explore time-series data and semantic information combinations using machine learning and deep learning techniques in the context of creating a prediction model that is able to capture in real-time future stressful situations of the studied transportation system. We apply the proposed approaches in event areas in New York using publicly available taxi data. We empirically show that the proposed models are able to significantly reduce the error in the forecasts. The importance of semantic information is highlighted in all presented methods and the final mean absolute error of our prediction is decreased by 23.8% for a three months testing period.

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Modeling Taxi Demand and Supply in New York City Using Large-Scale Taxi GPS Data

Cited by 24 publications

References 17 publications

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models

Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Information

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