Revealing New York taxi drivers' operation patterns focusing on the revenue aspect

Dong, Yongqi; Zuo, Zhuang; Fu, Rui; Xie, Na

doi:10.1109/wcica.2016.7578771

Cited by 7 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Hu et al [2] found that the location in which taxi drivers' cruise and search for the next passenger' pick-up location will be near the previous drop-off location by analyzing the statistical results of the distance between each taxi driver's pick-up locations' mean center and drop-off locations' mean center. Dong et al [5] classified drivers into three groups based on revenue and compared different driver groups in terms of spatiotemporal and strategy aspects. Their conclusion was that top drivers tend to maximize their usage of a limited working time to serve more trips and cover more mileage, which is the main reason that they earn more revenue.…”

Section: Spatial Pattern Of Taxicabsmentioning

confidence: 99%

“…Cabdrivers' mobility patterns and operating strategies have been widely studied since large-scale taxi GPS trajectories became obtainable due to the rapid development of information and communication technologies (ICT). For example, previous studies analyzed the distance and direction distributions of intraurban trips [1], urban cabdrivers' activity distribution [2], spatial variations in urban taxi ridership [3], cabdrivers' operation strategies with respect to revenue [4,5], and alternative taxi services strategies [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Fang

Lian³

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns.

show abstract

Section: Spatial Pattern Of Taxicabsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Fang

Lian³

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…These studies have presented diverse perspectives and methods to investigate taxi drivers' incomes. The considered factors encompass the duration (Liu et al, 2010;Rong et al, 2016;Sirisoma et al, 2010) and distance (Liu et al, 2010;Sirisoma et al, 2010) of passenger search/delivery, fare (Liu et al, 2010), likelihood of pickups (Qu et al, 2014;Rong et al, 2016), operational region (Dong et al, 2016;Gao et al, 2012;Zhang et al, 2015), ridership (Gao et al, 2012, and delivery speed (Zhang et al, 2015). Despite abundant factors affecting incomes being considered, further work is still required to be conduct with regard to summarizing these factors in a comprehensive perspective, finding significant factors, and quantifying how much the incomes are in association with these significant factors.…”

Section: Overview Of Taxi Drivers' Income Studiesmentioning

confidence: 99%

“…Spatial areas are commonly segmented into small cells in specific dimensions according to related studies (Dong et al, 2016;Liu et al, 2010;Powell et al, 2011). In this paper, we segment the study area into a significant number of 100m×100m cells, and separate the time span into six 30-min intervals, as a means to obtain summary statistics (e.g.…”

Section: Please Place Fig 2 About Herementioning

confidence: 99%

“…In addition to such regulator-side studies, an increasing number of driver-side studies emerged in the wake of available taxi GPS data (Cao et al, 2005;Huang et al, 2015;Li et al, 2009;Veloso et al, 2011;Yuan et al, 2013;Zheng et al, 2011;Zheng et al, 2009). Some of them specifically focused attentions on the low-income problem and intended to seek underlying crowd intelligence from GPS patterns, in which several factors affecting incomes were explored respectively, including duration and distance (Liu et al, 2010;Sirisoma et al, 2010) of passenger search/delivery, fare (Liu et al, 2010), likelihood of pickups (Qu et al, 2014;Rong et al, 2016), operational region (Dong et al, 2016;Gao et al, 2012;Zhang et al, 2015), ridership (Gao et al, 2012, and delivery speed (Zhang et al, 2015). Despite abundant factors, there is little knowledge of how much these factors will affect the income level, hence unknown significance of each factor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mining factors affecting taxi drivers’ incomes using GPS trajectories

Qin

et al. 2017

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Mining underlying factors affecting the income level will not only benefit the newcomers and low-income drivers but also assist in developing effective optimization algorithms for taxi operations. This paper intends to disclose the factors affecting incomes along with their quantitative influence by mining over 167 million GPS records from nearly 8000 taxis in Shanghai. We first identify a marked difference in drivers' incomes and categorize drivers into three income levels accordingly. We next investigate the overall search-delivery process, thereby defining several factors that may affect the income level. We then develop a generalized multi-level ordered logit (GMOL) model to find the significant factors that influence incomes. Finally, we compute the elasticity for those significant factors and present their contributions, as well as challenge some preconceived ideas regarding how to earn high incomes.

show abstract

Taxi High-Income Region Recommendation and Spatial Correlation Analysis

2020

View full text Add to dashboard Cite

Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Analyzing the variation trend of taxi operation efficiency indicators throughout the day, mining high-income order hot-spots and high-income regions at different periods, will effectively improve the average hourly incomes (AHI) of drivers. This paper selects the order data for each day of holidays, working days, and non-working days through the taxi order dataset of October 2019 in Xi'an. Firstly, we analyze the variation trend of taxi operation efficiency indicators in the three days. We next divide the orders into four income levels based on the Natural Breaks accordingly. Then, we use Tyson polygon and mash map matching methods to visualize the high-income order hot-spots and high-income regions. It is significantly to analyze and summarize the visualization results. Finally, we compute the Moran'I index to measure the spatial correlation between high-income order regions and high-income regions. The results show that (1) the number and the spatial distribution of high-income order hot-spots and high-income regions at different periods are different. (2) Some places are hot-spots, but neither high-income order hot-spots nor high-income regions. (3) The high-income order regions and high-income regions have a strong correlation in spatial distribution. This study provides suggestions and insights to taxi companies and taxi drivers to increase their average hourly income (AHI) and enhance the efficiency of the taxi industry.

show abstract

Revealing New York taxi drivers' operation patterns focusing on the revenue aspect

Cited by 7 publications

References 7 publications

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Mining factors affecting taxi drivers’ incomes using GPS trajectories

Taxi High-Income Region Recommendation and Spatial Correlation Analysis

Contact Info

Product

Resources

About