Where to find my next passenger

Yuan, Jing; Zheng, Yu; Zhang, Liuhang; Xie, Xing; Sun, Guoqiang

doi:10.1145/2030112.2030128

Cited by 309 publications

(136 citation statements)

References 11 publications

Supporting

Mentioning

135

Contrasting

Unclassified

Order By: Relevance

“…Moreover, Wong et al emphasized that taxi drivers' cruising decisions are significantly affected by the probability of successfully picking up passengers along this cruising route [4]. Yuan et al focused on extracting passenger waiting areas for taxi drivers and computing the probability of picking up their next passengers based on the time spent, road segment information and accessibility to waiting areas [3,22]. Qian proposed a method that transformed the taxi-routing issue into a Markov decision process of pick-up locations [52].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Yin

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Yin

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…The goal of the system is to maximize the probability of business success and reduce energy consumption. T-Finder [Yuan and Zheng et al 2011a, 2014] provides taxi drivers with some locations and the routes to these locations, toward which they are more likely to pick up passengers quickly (during the routes or in these locations) and maximize the profit of the next trip.…”

Section: Taxi Recommendation Systemsmentioning

confidence: 99%

Urban Computing: Key Challenges and Issues of Traffic Management System

Mahiya¹,

Corneille²

2018

IJCA

View full text Add to dashboard Cite

Urban Computing collects heterogeneous data from diverse sources,integrates it and analyzes it to deal with the challenges faced by the Urban cities. Due to rapid urbanization it has emerged as an important research area. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models,and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. It has various areas of application include cultural archiving, energy consumption, health, social interaction, transportation and environment. In this paper we discuss various key challenges and issues related to traffic management in urban cities.

show abstract

“…And some research is done to help passenger to get cab easily. Few of them are studied here.Jing Yuan et al [4] presented a direction system for drivers to help them to reach particular place as soon as possible. For this they used GPS system to get information about source location of trip and destination of same trip.…”

Section: Related Work:-mentioning

confidence: 99%

Recommendation System to Increase Taxi Driver’s Business.

ShaikhNazneen¹

2016

IJAR

View full text Add to dashboard Cite

Where to find my next passenger

Cited by 309 publications

References 11 publications

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Urban Computing: Key Challenges and Issues of Traffic Management System

Recommendation System to Increase Taxi Driver’s Business.

Contact Info

Product

Resources

About