Spatial variation of the urban taxi ridership using GPS data

Qian, Xinwu; Ukkusuri, Satish V.

doi:10.1016/j.apgeog.2015.02.011

Cited by 223 publications

(150 citation statements)

References 16 publications

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“…For example, the crime rate would become unstable where the population is low. Transforming the dependent variable, such as log-transformation, is another commonly used method to meet the assumption [33]. Unfortunately, transformation makes it difficult to interpret the model results [34].…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Chen

Liu

Zhou

et al. 2017

IJGI

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Abstract:The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

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Section: Introductionmentioning

confidence: 99%

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Chen

Liu

Zhou

et al. 2017

IJGI

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“…An example of this method is given in Figure 3. Assuming that there are three recommended pick-up locations ( 1 , 2 , and 3 ) and 4 recommended cruising routes 1 1 , 2 1 , 3 1 and 3 2 with the weights 60%, 50%, 40% and 30%, respectively, then 10 taxis ( , ∈ [1,10]) are considered to be allocated. Based on our methods, 1 1 will be assigned to 1 , 2 , 4 and 7 ; 2 1 will be assigned to 3 , 5 and 8 ; 3 1 will be assigned to 6 and 9 ; and 3 2 will be assigned to 10 .…”

Section: Algorithm 1: the Weighted Round-robin Scheduling Algorithmmentioning

confidence: 99%

“…Taxis, with respect to their flexibility and convenience, have become one of the most popular modes of urban transportation [1]. Taxis are an indispensable component of the urban transportation system and meet the travel demands of a great number of people.…”

Section: Introductionmentioning

confidence: 99%

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

Yin

et al. 2017

IJGI

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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.

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“…Recognizing the change of spatial inequality in taxi services in the context of a subsidy war could support sustainable planning for equitable taxi services. Taxicabs play an important role in daily travel because they provide an uninterrupted door-to-door service [8]. E-hailing services (e.g., Didi in China and Uber in the U.S.) offer passengers convenient booking services that prevent them from waiting on the street for a long time, especially during a day with bad weather.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps

Fang

Hong

et al. 2018

IJGI

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Spatial inequalities in urban public transportation are a major concern in many countries but little of this research has focused specifically on taxi services. The taxi situation has grown more complex, as traditional ride-for-hire services face growing competition from e-hailing apps like Uber in the U.S., or Didi and Kuaidi in China. In 2014, Didi and Kuaidi triggered a nationwide subsidy war, with possible effects on the spatial inequality of taxi services. Taxi trajectory data from Shenzhen collected during the subsidy war shows that this competition reduced spatial inequality in the inner city but aggravated it in the outer city. In this study, a measure of service rate to depict the quantity of taxi services is proposed to calculate a Gini coefficient for evaluating change in the spatial inequality of taxi services. The Theil index and its decomposition were used to distinguish the contribution of Traffic Analysis Zones (TAZs) in the inner and the outer city and compare them to the overall spatial inequality of taxi services in Shenzhen, TAZs in the outer city had greater inequality in taxi services than the inner city. Furthermore, the primary contributor to overall inequality in taxi services was inequality within, rather than between, the inner and outer city. Moreover, the mean values for the changed service rates in the inner city were always larger than the outer city, and the inner city had a more equitable changed service rate than the outer city. These results could serve as a foundation for improving taxi services citywide.

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Spatial variation of the urban taxi ridership using GPS data

Cited by 223 publications

References 16 publications

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

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

Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps

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