Mining factors affecting taxi drivers’ incomes using GPS trajectories

Qin, Guoyang; Li, Tienan; Yu, Bin; Wang, Yunpeng; Huang, Zhenhua; Sun, Jian

doi:10.1016/j.trc.2017.03.013

Cited by 46 publications

(28 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Kong et al[ 23 ] proposed a time-location-relationship (TLR) model to predict passenger distribution for different social functional regions. Qin et al [ 24 ] applied a multilevel ordered logit model to investigate the significant factors that influence taxi driver’s incomes using data from 8,000 taxis in Shanghai. Yamamoto et al [ 25 , 26 ]developed an adaptive routing method to help taxi drivers acquire passengers more effectively.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of rainfall on the temporal and spatial distribution of taxi passengers

et al. 2017

View full text Add to dashboard Cite

This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The impact of rainfall on the temporal and spatial distribution of taxi passengers

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Other studies worked further to improve understanding of these taxi operation patterns, including taxi demand distribution predicting in order to recommend the best queuing locations for taxi drivers [24,25]. Aided by GPS data, Gao et al [25] and Qin et al [26] investigated the differences in drivers' operation strategies and revealed the top drivers' strategies to help ordinary drivers increase their incomes.…”

Section: Assessment Of Taxi Service and Operationsmentioning

confidence: 99%

“…In Shanghai, taxi trips are highly concentrated within the downtown area bounded by the Outer Ring Road, where about 82.25% of taxi pickups are generated, which makes it an appealing choice for our study area [26]. To facilitate the investigation of spatial variations of taxi operations, we segment the study area into small square cells with dimensions of 1 km × 1 km.…”

Section: Study Area and Data Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the Impact of Rainfall on Taxi Hailing and Operation

Sun

Qin

et al. 2020

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

Adverse weathers are well-known to impact the operation of transportation systems, including taxis. This paper utilizes taxi GPS waypoint data to investigate the quantitative impact of rainfall on taxi hailing and taxi operations to help improve service quality on rainy days. Through statistical analysis, the study proves that it is more difficult to hail taxis on rainy days, especially during morning peak hours. By modelling the difference value of factors for rainfall and nonrainfall conditions in a multivariate regression model and attaining the significance and elasticity of each factor, passenger demand, taxi supply, search time and velocity are proved to be the significant factors that lower the taxis’ level of service on rainy days. Among them, the number of passengers and taxis are two factors that have the greatest impact. It is also shown that there is no significant difference in the total taxi supply and passenger demand between rainfall and nonrainfall conditions, but a dramatic change in the spatial distribution is discovered. The results suggest that instead of simply providing more taxis on rainy days, optimally dispatching taxicabs to high demand regions can be a more effective solution.

show abstract

“…With the rapid expansion of online ride-hailing services, many conflicts of interests between online ride-hailing vehicles and traditional taxis have appeared and are becoming increasingly acute [5]. These conflicts result in shrinking of the traditional taxi market [6] and decline in the income of taxi drivers [7]. Meanwhile, many violent incidents have broken out between traditional taxi drivers and online ride-hailing drivers [8].…”

Section: Introductionmentioning

confidence: 99%

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

Xia

Shen

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

The distribution of high-quality customers (hereafter HQC) for taxis (including ride-hailing cars) has a significant impact on drivers’ revenue and taxis’ operation efficiency. Based on the taxi global positioning system data, we construct an evaluation model of passengers to discuss the imbalance of consumption in taxi market. The profit margin for each taxi order is calculated, and then a grid-based method is used to distinguish the HQC and the regions with potential higher benefits. We analyze the HQC’s distribution of taxis (including ride-hailing cars) in different areas and in different time periods. We find that the HQC are distributed mainly on the periphery of the main urban area, which indicates that traffic condition is even worse in the urban center because of factors such as congestion. The HQC are more concentrated on workdays and more scattered on nonworkdays, which implies that the public have different travel habits and demands on workdays and nonworkdays. The proportion of HQC in each administrative district or functional zone is not always positively correlated to either the proportion of total orders or the total HQC. This indicates that the distribution of HQC in each administrative district or functional zone is imbalanced. The proportion of orders and that of HQC are roughly the same in the temporal dimension, being higher in the morning and evening rush hours. Compared with the distribution of the HQC of ride-hailing, that of taxis is more imbalanced in the temporal dimension. Relevant departments should further coordinate taxi pricing, strengthen market control, and promote sustainable development in the taxi and ride-hailing markets.

show abstract

Mining factors affecting taxi drivers’ incomes using GPS trajectories

Cited by 46 publications

References 24 publications

The impact of rainfall on the temporal and spatial distribution of taxi passengers

The impact of rainfall on the temporal and spatial distribution of taxi passengers

Quantifying the Impact of Rainfall on Taxi Hailing and Operation

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

Contact Info

Product

Resources

About