Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Liu, Xinmin; Sun, Luna; Sun, Qiuxia; Gao, Ge

doi:10.1155/2020/7621576

Cited by 20 publications

(20 citation statements)

References 45 publications

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“…Previous studies on taxi demand prediction are generally based on historical taxi trajectory data. Previous studies have shown the feasibility of obtaining predictions from historical taxi trajectory data [1,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Methods of traffic demand prediction can be classified into three types: linear system theory (such as the autoregressive moving average model [24], Kalman filtering model, and time series model), nonlinear system theory (such as the neural network model, gray prediction model, and random forest model (RFM)), and combination forecasting model (CFM).…”

Section: Introductionmentioning

confidence: 99%

Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

Liu

Chen

et al. 2020

Journal of Advanced Transportation

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Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on historical taxi trajectory data. In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). Thereafter, we considered environmental and meteorological factors to predict the taxi demand in hotspots. Finally, the importance of indicators was analyzed, and the essential elements were the time, temperature, and weather factors. The results indicate that the prediction effect of CFM is better than those of RFM and RRM. The experiment obtains the relationship between taxi demand and environment and is helpful for taxi dispatching by considering additional factors, such as temperature and weather.

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Section: Introductionmentioning

confidence: 99%

Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

Liu

Chen

et al. 2020

Journal of Advanced Transportation

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“…Zhang et al studied the relationship between travel intensity and POIs based on car-hailing data, and the results show that some types of POIs such as traffic facilities have great impacts on pick-up and drop-off [39]. Liu et al argued that higher proportion of commercial area and public service area produced greater taxi demand, while the proportion of residential area and land use mix have negative impacts on taxi demand [40]. Based on the strong relationship between taxi traffic demand and POI, taxi trajectory data could be used to identify the urban functional regions [41].…”

Section: Introductionmentioning

confidence: 99%

Revealing Urban Traffic Demand by Constructing Dynamic Networks With Taxi Trajectory Data

Zhang

Zhang²,

Che

et al. 2020

IEEE Access

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As a crucial travel mode, taxi plays a significant role in residents' daily travel. Uncovering taxi traffic demand has become a hotspot in transport studies. Previous researchers pay more attention to the statistical characteristics of taxi trips, while few studies focus on the dynamic features in different periods of a day. In this paper, we study the taxi travel demand by constructing dynamic networks based on taxi trajectory data. In addition, relationship between travel intensity and point of interest (POI) in Xiamen, China is discussed. Firstly, the study area is divided by 1km×1km uniform cells. The pickup and drop-off activities of passengers are recorded for each cell. Secondly, the networks are constructed by regarding each cell as a node and regarding taxi trips from a cell to another cell as an edge. On this basis, we divide a day into 12 periods by two hours and construct the networks for different periods. Finally, correlation between travel intensity and POI intensity is detected with regression analysis. Results show that the taxi trip networks have large clustering coefficient and small shortest path length, which indicates they are 'small world' networks. Moreover, the taxi trip networks are disassortative networks that hotspot areas tend to connect with the common areas. Furthermore, the taxi trip length in a day follows a lognormal distribution and the peak hour of taxi trip appears around midnight. Finally, a cubic polynomial curve could fit the relationship between travel intensity and POI intensity. Our findings provide a new insight for understanding the traffic demand of taxi. INDEX TERMS Urban traffic demand, dynamic spatial-interaction network, spatiotemporal characteristics of taxi trips, taxi GPS trajectory data, point of interest This work is licensed under a Creative Commons Attribution 4.

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“…One is that it is more accurate in spatial structure study. Secondly, when a city is taken as the research area to study the taxi travel demand problem, it is usually divided into 500 m×500 m square grids [ 13 , 29 , 40 ]. The total number of analysis units was 3486 ( Fig 5B ), which is similar to the scale of the street units used in planning research.…”

Section: Study Area and Datamentioning

confidence: 99%

“…In order to overcome this defect, some scholars have utilized the GWR model. Since the GWR model explains the spatial distribution differences of the research objects well [24][25][26], it has been widely used in the analysis of spatial heterogeneity [27][28][29][30].…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of the mixed degree of urban functions on the taxi travel demand

et al. 2021

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As an important service industry in cities, taxis provide people with an all-weather travel mode. And its demand is greatly affected by the internal functions of the city. It is very important to understand the relationship between the mixed degree of urban internal functions and the residents’ taxi travel demand to alleviate traffic congestion and formulate corresponding urban traffic strategies. This paper combined two heterogeneous data in the main urban area of Xi’an, urban points of interest (POIs) and taxi GPS. Firstly, a spatial information entropy model was constructed to quantitatively evaluate the mixed degree of functions in different spaces within the city. Secondly, the kernel density estimation method was used to analyze the spatial distribution evolution characteristics of residents’ taxi travel demand. A geographically weighted regression (GWR) model was further used to study the spatial and temporal influences of the mixed degree of urban internal functions on taxi travel demand. Results indicate that there is an obvious spatiotemporal pattern in the impact of the mixed degree of urban functions on taxi travel demand. And the GWR model is used to study the impact is superior to the ordinary least squares (OLS). In more developed areas, improving the mixed degree of urban functions will be more attractive than backward areas. It is also found that although the single function of the city has an impact on the taxi travel demand, the result of the single function is not ideal. This study can provide a reference for the optimal combination of basic units of urban space in urban planning, promote the balance of supply and demand of urban taxis, rationalize urban taxis’ operation and allocation, and solve the problems of urban transportation systems.

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Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Cited by 20 publications

References 45 publications

Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

Revealing Urban Traffic Demand by Constructing Dynamic Networks With Taxi Trajectory Data

Impact of the mixed degree of urban functions on the taxi travel demand

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