Real time location prediction with taxi-GPS data streams

Laha, Arnab Kumar; Putatunda, Susil K.

doi:10.1016/j.trc.2018.05.005

Cited by 27 publications

(21 citation statements)

References 86 publications

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“…Secondly, similar to the problems addressed by the works of Laha and Putatunda (2018) or Ocalir et al (2010), we plan to apply methods to estimate probabilities of new taxi customers appearing in different areas of the network. In a previous paper related to the dynamic positioning of ambulances, we used Voronoi Tessellation to dynamically determine the default positions for service vehicles (Billhardt et al, 2014) based on historical data.…”

Section: Discussionmentioning

confidence: 99%

Taxi dispatching strategies with compensations

Billhardt

Fernández

Ossowskí

et al. 2019

Expert Systems with Applications

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Urban mobility efficiency is of utmost importance in big cities. Taxi vehicles are key elements in daily traffic activity. The advance of ICT and geo-positioning systems has given rise to new opportunities for improving the efficiency of taxi fleets in terms of waiting times of passengers, cost and time for drivers, traffic density, CO2 emissions, etc., by using more informed, intelligent dispatching. Still, the explicit spatial and temporal components, as well as the scale and, in particular, the dynamicity of the problem of pairing passengers and taxis in big towns, render traditional approaches for solving standard assignment problem useless for this purpose, and call for intelligent approximation strategies based on domain-specific heuristics. Furthermore, taxi drivers are often autonomous actors and may not agree to participate in assignments that, though globally efficient, may not be sufficently beneficial for them individually.This paper presents a new heuristic algorithm for taxi assignment to customers that considers taxi reassignments if this may lead to globally better solutions. In addition, as such new assignments may reduce the expected revenues of individual drivers, we propose an economic compensation scheme to make individually rational drivers agree to proposed modifications in their assigned clients. We carried out a set of experiments, where several commonly used assignment strategies are compared to three different instantiations of our heuristic algorithm. The results indicate that our proposal has the potential to reduce customer waiting times in fleets of autonomous taxis, while being also beneficial from an economic point of view.

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Section: Discussionmentioning

confidence: 99%

Taxi dispatching strategies with compensations

Billhardt

Fernández

Ossowskí

et al. 2019

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…A statistical experiment of two-way clusters proved that there was a positive relationship between the level of sunlight and tipping percentage in taxi rides with an increase of tipping percentage from 0.5 to 0.7 from dark sky day to a full sunshine day (Devaraj & Patel, 2017). The GPS data streams of the taxi were used for prediction of pick-up locations and spherical regression model was the best compared to the multivariate regression model in terms of accuracy-time trade-off criterion (Laha & Putatunda, 2018). This paper utilizes the concepts of the above mentioned methodologies to detect the key aspects that generate more revenue for the industry.window for it.…”

Section: Shylaja S Kannika Nirai Vaani Mmentioning

confidence: 99%

A Machine Learning Framework for Profitability Profiling and Dynamic Price Prediction for the New York City Taxi Trips

S*,

Vaani M

2020

IJITEE

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 Abstract: The New York City Taxi & Limousine Commission's (NYC TLC) Yellow cabs are facing increased competition from app-based car services such as Ola, Uber, Didi, Lyft and Grab which is rapidly eating away its revenue and market share. Research work: In response to this, the study proposes to do profitability profiling of the taxi trips to focus on various key aspects that generate more revenue in future, visualization to assess the departure and arrival counts of the trips in various locations based on time of the day to maintain demand and supply equilibrium and also build a dynamic price prediction model to balance both margins as well as conversion rates. Methodology/Techniques used: The NYC TLC yellow taxi trip data is analysed through a cross-industry standard process for data mining (CRISP-DM) methodology. Firstly, the taxi trips are grouped into two profitability segments according to the fare amount, trip duration and trip distance by applying K means clustering. Secondly, spatiotemporal data analysis is carried to assess the demand for taxi trips at various locations at various times of the day. Thirdly, multiple linear regression, decision tree, and random forest models are adopted for dynamic price prediction. The findings of the study are as follows, high profitable segments are characterized by airport pickup and drop trips, Count of trip arrivals to airports are more compared to departures from airports at any time of the day, and further analysis revealed that drivers making only a few numbers of airport trips can earn more revenue compared to making more number of trips in local destinations. Compared to multiple linear regression and decision tree, the random forest regression model is considered to be most reliable for dynamic pricing prediction with an accuracy of 91%. Application of research work: The practical implication of the study is the deployment of a dynamic pricing model that can increase the revenue of the NYC TLC cabs along with balancing margin and conversion rates.

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“…Al-Mayouf et al (2018) proposed an intersection-based segment aware algorithm for geographic routing in VANETs [33]. Laha and Putatunda (2018) proposed three incremental learning methods for next pickup location prediction problems [34]. Xu and Cai (2018) proposed a variable neighborhood search algorithm for the consistent Vehicle Routing Problem [35].…”

Section: Collaborative Vehicle Routing Problem Vehicle Routingmentioning

confidence: 99%

Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

Gui

2018

Journal of Advanced Transportation

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Urban traffic congestion has become a global problem and has garnered special importance in recent years in the transportation sector, especially in taxi markets. To unlock the potential of Internet of Vehicles (IoV) in Intelligent Transportation Systems (ITS), it was vital to make efficiency measurements. In this study, Distance Formula was built to calculate distances by GPS data based on mathematical equations, and Motorcade-Sharing (MS) Model was proposed to improve the efficiency of collaborative vehicles. The experimental data of 2191 GPS-equipped taxis in Sanya of China was adopted to make comparisons between original results and modelled results. Measurement results showed that MS Model had 10.54% more leisure taxis, reduced 5 overdriving taxis, and saved 33.73% running distance in total compared to the original. This indicated that the application of MS Model could not only alleviate urban traffic congestion but also optimize urban taxi markets, and it has a bright future in the field of taxi and other collaborative vehicles. Future directions could be improving MS Model and expanding data.

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Real time location prediction with taxi-GPS data streams

Cited by 27 publications

References 86 publications

Taxi dispatching strategies with compensations

Taxi dispatching strategies with compensations

A Machine Learning Framework for Profitability Profiling and Dynamic Price Prediction for the New York City Taxi Trips

Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

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