Tax Distribution Research Articles

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

Analysis of High-frequency Occurrence Areas of Beijing Taxis based on the K-means Method

As the economy grows and of the pace of life accelerates, travelling by taxi has gradually become one of the most convenient and fastest choices for residents to go out. However, because of the randomness of people's travelling and the mobility of taxi driving, there exist an imbalance between the travelling demand of regional taxi users and the supply of taxis. In order to avoid the problem of uneven distribution of taxis and to achieve accurate prediction of taxi demand, this paper selects the vehicle number, latitude and longitude, and carrying status data of Beijing taxis as the basis of the study, and researches to explore the spatial distribution characteristics of the travelling demand of taxi users. The main contents of the paper are as follows. First of all, this paper initializes the GPS data of taxis, and obtains the boarding and alighting locations. The distribution of taxi regional travel demand is described by using K-means clustering method. Finally, the paper shows five high frequency locations. It is of great significance for taxi dispatching management, reducing empty rate, saving energy consumption and maximizing passenger travel demand.

The Spatial Distribution of Taxi Stations in Bangkok

Taxis play a crucial role as an on-demand transportation mode in developing countries due to perceived inefficiencies of cities’ public transportation systems. However, studies on the locational distribution of taxis in urban areas are limited, despite the need to improve passenger service quality by balancing the demand and supply of taxi services. Notably, taxi stations possess distinct characteristics compared with other public transport stations that serve passengers directly; in contrast, taxi stations solely support taxi drivers in locations where they begin and conclude their work. This study aims to determine the spatial distribution pattern and assess the agglomeration economies of taxi stations, using Bangkok as a case study, a city with a significant number of registered taxis and dispersed taxi stations. This research takes into account various spatial variables, including land price, land use mix index, population density, and gas station locations. The primary tool for analyzing the spatial distribution pattern was the spatial statistics model, employing ArcGIS 10.8 software. The analysis consisted of three steps: testing for clustered or dispersed patterns using Moran’s I, applying Anselin’s local Moran’s I (LISA) to examine the relationship between taxi station coordinates and spatial variables such as land price, land use mix index, and population density, and evaluating the relationship between taxi stations and energy service stations using the network analysis tool. The results revealed that taxi stations exhibited a spatially clustered pattern and were closely correlated with the land use mix index, land price, and population density, as indicated by Moran’s index values of 0.425, 0.328, and 0.373, respectively, especially those located within a 3000 m radius of gas stations. These findings elucidate the location selection of taxi stations, which tend to prioritize areas that can generate maximum profits for the taxi business rather than those with minimal location costs. They also tend to be situated in proximity to market areas. Additionally, the study recommends that the government consider promoting electric taxis as a sustainable mode of urban transport in the future to reduce the usage of natural gas (NGV) and liquefied petroleum gas (LPG).

Research on Optimization Configuration of Taxi Resources Based on Matching Model

“Difficulty in taxiing” has always been a concern of the society in recent years. This paper discusses the current situation of resource allocation of taxis and the impact of taxi company subsidy programs on the difficulty of taxiing, and then introduces a better subsidy program. We first collected data based on the Drip, fast intelligent travel platform, plotted the image, calculated the average of the taxi distribution and passenger demand at each time point, and carried out tracing and analysis. On this basis, the matching model is established: the idea of nearest neighbor” is introduced, and the time points that are difficult to get a taxi are concentrated at 8-10 am, 16 pm, and 19 pm. Then analyze the subsidy program data, taxi drivers and passengers after using the taxi software, taxi vacancy rate, passenger waiting time and changes in the economic benefits of taxi drivers and passengers, etc., after analysis, we found the existence of subsidy programs can fully mobilize the enthusiasm of drivers and passengers to use the software, so that the number of people using the software continues to increase, which not only shortens the waiting time for passengers to take a taxi, but also reduces the driver’s vacancy rate, so it can effectively alleviate the problem of difficult driving.

A spatiotemporal attention mechanism-based model for multi-step citywide passenger demand prediction

In taxi dispatch systems, predicting citywide passenger pickup/dropoff demand is indispensable for developing effective taxi distribution and scheduling strategies to resolve the demand-service mismatch. Compared with predicting next-step only, predicting multiple steps is preferable since it can provide a long term view, thus preventing short-sighted strategies. However, multi-step citywide passenger demand prediction (MsCPDP) is challenging due to the complicated spatiotemporal correlations in the distribution of passenger demand and the lack of ground truth from pre-steps for the prediction of subsequent steps. In this paper, a deep-learning-based prediction model with spatiotemporal attention mechanism is proposed for MsCPDP. The model, called ST-Attn, follows the general encoder-decoder framework for modelling sequential data but adopts a multiple-output strategy without recurrent neural network units. The spatiotemporal attention mechanism learns to determine the focus on those parts of the city at certain periods that are more relevant to the passenger demand in the predicted region and time period. In addition, a pre-predicted result calculated by spatiotemporal kernel density estimation is fed to ST-Attn, which provides a reference for further accurate prediction. Experiments on three real-world datasets are carried out to verify ST-Attn’s performance, and the results show that ST-Attn outperforms the baselines in terms of MsCPDP.

A novel two-stage location model of charging station considering dynamic distribution of electric taxis

Electric taxi (ET), with zero exhaust emission, plays a vital role in low-carbon sustainable development of urban public traffic to reduce fossil fuel consumption and air pollution. As the key part of ET development, charging station (CS) directly impacts ET’s widespread application by figuring out the charging difficulty of ET. Therefore, to tackle the location issues of electric taxi charging station (ETCS), a novel two-stage location model is built in consideration of ET dynamic distribution and charging requirements in this paper. Firstly, taking the complex features of ET into account, such as dynamic location and dispersed distribution, the dynamic distribution clustering model of ET is formed based on k-means method, by which lots of scattered ET locations are grouped into certain clustering blocks. Secondly, according to aforementioned blocks, the location model of ETCS is established with barycentric method to get the location of ETCS, in view of both the latitude and longitude location and charging demands of each clustering block. Finally, the two-stage location model is employed in the case study of ETCS location in Chengdu, China. The result shows that the chosen location meets the charging requirements and the characteristic of taxi distribution in Chengdu, proving the validity of the model. The two-stage location model is an improved approach of traditional static barycentric method by processing ET’s dynamic distribution and decentralized charging requirements with k-means clustering algorithm, and is of theoretical and practical significance to provide quantitative ETCS location solutions to help deal with actual CS location problems in urban management.

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

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.

Evaluation of taxi carpooling feasibility in different urban areas through the K-means matter–element analysis method

A K-means matter–element model for the evaluation of taxi carpooling feasibility was proposed. The model was established on the basis of the influential factors and characteristics of urban taxi carpooling and data of taxi traffic. The first step taken to improve the shortcomings of the existing element model is to determine the index interval of the matter–element characteristic quantity using the K-means clustering algorithm. The entropy method is used to determine index weight. Taxi carpooling feasibility can be evaluated when passenger point density, taxi passenger rate, taxi distribution density, average carpool kilometres, average carpool return and average successful carpooling index information are described. Results showed that the algorithm can be used to effectively evaluate the effects feasibility of taxi carpooling.

Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China

Air pollutions from transportation sector have become a serious urban environmental problem, especially in developing countries with expending urbanization. Cleaner technologies advancement and optimal regulation on the transporting behaviors and related design in infrastructures is critical to address above issue. To understand the spatial and temporal emissions pattern within transportation lays the foundation for design on better infrastructures and guidance on low-carbon transportation behaviors. The feasibility of Global Positioning System (GPS) and emerging big data analysis technique enable the in-depth analysis on this topic, while to date, applications had been rather few. With this circumstance, this paper analyzed the taxi's energy consumption and emissions and their spatial-temporal distribution in Shanghai, one of the most famous mega cities in China, applying big data analysis on GPS data of taxies. Spatial and temporal features of energy consumptions and pollutants emissions were further mapped with geographical information system (GIS). Results highlighted that, spatially, the energy consumption and emission presented a distribution of dual-core cyclic structure, in which, two hubs were identified. One was the city center, the other was Hongqiao transport hub, the activities and emission was more concentrated in the west par of Huangpu River. Temporally, the highest activity and emission moment was 9–10AM, the second peak occurred in 7–8PM, which were both the traffic rush period. The lowest activity/emission moment was 3–4AM. Causal mechanism for such distribution was further investigated, so as to improve the driving behaviors. Through the exploration of spatial and temporal emissions distribution of taxis via big dada technique, this paper provided enlightening insights to policy makers for better understanding on the travel patterns and related environmental implications in Shanghai metropolis, so as to support better planning on infrastructures system, demand side management and the promotion on low-carbon life styles.

Taxi Bidirectional Search System Based on Smart Phone

For the issues of unreasonable taxi distribution, high load rate and low dispatching efficiency etc. in the current city calling taxi services, the paper presents some key technologies of implementation of taxi driver-passenger bidirectional search system based on smart phones, including map matching, hotspot analysis, taxi route planning and bidirectional search UI. The results help achieve efficiency of urban taxi service.