Taxi Supply Research Articles

Spatiotemporal Characteristics and Self-Organization of Urban Taxi Dispatch

This paper proposes a matching degree to study dynamic spatiotemporal characteristics of urban taxi and offers a novel understanding of self-organization taxi dispatch in hotspots on top of the Fermi learning model. The proposed matching degree can not only reflect the overall spatiotemporal characteristics of urban taxi supply and demand but also show that the density of distribution and the distance between the taxis supply and the city center will affect the satisfaction of demand. Besides, it is interesting to note that supply always exceeds demand and they will self-organize into an equilibrium state in hotspots. To understand the phenomenon, we develop the Fermi learning model based on the prospect theory and compared the results with the popular reinforcement learning model. The results demonstrate that both models can account for self-organization behavior under different scenarios. We believe our work is crucial to explore taxis data and our indicator can provide a significant suggestion for urban taxis development.

Understanding the Long-Term Evolution of Electric Taxi Networks

Due to the ever-growing concerns over air pollution and energy security, more and more cities have started to replace their conventional taxi fleets with electric ones. Even though environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures, and inadequate taxi supply due to the long charging time. In this article, we conduct the first longitudinal measurement study to understand the long-term evolution of mobility and charging patterns by utilizing 5-year data from one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, (1) we first perform an electric taxi contextualization about their operation and charging activities; (2) then we design a generic charging event extraction algorithm based on GPS data and charging station data, and (3) based on the contextualization and extracted charging activities, we perform a comprehensive measurement study called ePat to explore the evolution of the electric taxi network from the mobility and charging perspectives. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolution process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolution process. Our ePat as a comprehensive measurement of the electric taxi network mobility and charging evolution has the potential to advance the understanding of the evolution patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver shifts from big trace data without the information of drivers’ identities. The first step is to pick out the frequent spatiotemporal sequential patterns from all parking events based on the spatiotemporal sequence analysis. The second step is to construct a Gaussian mixture model based on prior knowledge for further identifying taxi-driver shifts from all frequent spatiotemporal sequential patterns. The spatiotemporal distribution of taxi-driver shifts is analyzed based on two indicators, namely regional taxi coverage intensity and taxi density. Taking the city of Wuhan as an example, the experimental results show that the identification precision and recall rate of taxi-driver shift events based on the proposed method can achieve about 95% and 90%, respectively, by using big taxi trace data. The occurrence time of taxi-driver shifts in Wuhan mainly has two high peak periods: 1:00 a.m. to 4:00 a.m. and 4:00 p.m. to 5:00 p.m. Although taxi-driver shift behaviors are prohibited during the evening peak hour based on the regulation issued by Wuhan traffic administration, experimental results show that there are still some drivers in violation of this regulation. By analyzing the spatial distribution of taxi-driver shifts, we find that most taxi-driver shifts distribute in central urban areas such as Wuchang and Jianghan district.

Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach

In this paper, we define and investigate a novel model-free deep reinforcement learning framework to solve the taxi dispatch problem. The framework can be used to redistribute vehicles when the travel demand and taxi supply is either spatially or temporally imbalanced in a transportation network. While previous works mostly focus on using model-based methods, the goal of this paper is to explore the policy-based deep reinforcement learning algorithm as a model-free method to optimize the rebalancing strategy. In particular, we propose an actor-critic algorithm with feed-forward neural networks as approximations of both policy and value functions, where the policy function provides the optimal dispatch strategy and the value function estimates the expected costs at each time stamp. Our numerical studies show that the algorithm converges to the theoretical upper bound with less than 4% optimality gap, whether the system dynamics are deterministic or stochastic. We also investigate the scenario where we consider user priority and fairness, and the results indicate that our learned policy is capable of producing a superior strategy that balances equity, cancellation, and level of service when user priority is considered.

Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.

Quantifying the Impact of Rainfall on Taxi Hailing and Operation

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.

Demand, Supply, and Performance of Street-Hail Taxi

Travel decisions are fundamental to understanding human mobility, urban economy, and sustainability, but measuring it is challenging and controversial. Previous studies of taxis are limited to taxi stands or hail markets at aggregate spatial units. Here we estimate the dynamic demand and supply of taxis in New York City (NYC) at street segment level, using in-vehicle Global Positioning System (GPS) data which preserve individual privacy. To this end, we model taxi demand and supply as non-stationary Poisson random fields on the road network, and pickups result from income-maximizing drivers searching for impatient passengers. With 868 million trip records of all 13,237 licensed taxis in NYC in 2009 - 2013, we show that while taxi demand are almost the same in 2011 and 2012, it declined about 2% in spring 2013, possibly caused by transportation network companies (TNCs) and fare raise. Contrary to common impression, street-hail taxis out-perform TNCs such as Uber in high-demand locations, suggesting a taxi/TNC regulation change to reduce congestion and pollution. We show that our demand estimates are stable at different supply levels and across years, a property not observed in existing matching functions. We also validate that taxi pickups can be modeled as Poisson processes. Our method is thus simple, feasible, and reliable in estimating street-hail taxi activities at a high spatial resolution; it helps quantify the ongoing discussion on congestion charges to taxis and TNCs.

Modeling framework for supporting taxi policy making

Taxi services account for a significant part of the daily trips in most cities around the world. These services are regulated by a central authority, which usually monitors the performance of the taxi services provision and defines the policies applied to the taxi sector. In order to support policy makers, fleet managers and individual taxi drivers, there is a need for developing models to understand the behavior of these markets. Most of the models developed for analyzing the taxi market are based on econometric measurements and do not account for the spatial distribution of both taxi demand and supply. Only few simulation models are able to better understand the operational characteristics of the taxi market. This paper presents a framework for the development of taxi models both aggregated and simulation-based. It is aimed at assessing policy makers, taxi fleet managers and individual drivers in the definition of the optimum operation mode and the number of vehicles.

The analysis of urban taxi operation efficiency based on GPS trajectory big data

With the development of social economy, a series of problems such as an imbalance of supply and demand, unreasonable resource allocation and low operation efficiency appear in the urban taxi market. Based on the GPS trajectory data of taxis, this paper calculates and analyzes the operation efficiency of taxis in terms of time and space dimensions in Chengdu, Sichuan Province by using the capacity utilization rate. The results show that, firstly, the weekly variation trend of taxi capacity utilization rate is obviously periodic and its daily variation trend is unbalanced, which is reflected in the morning peak, afternoon peak, evening peak and night peak. The daily variation trend of taxi capacity utilization rate is significantly different between workdays and weekends, and the capacity utilization rate on weekends is much lower. Secondly, there is a spatial imbalance in the distribution of taxi capacity utilization rate. The capacity utilization rate of the main urban district is relatively high, and that of the other urban districts negatively correlates with the economic level and population density. The supply and demand of taxis are balanced within the various functional zones but unbalanced between different functional zones. It is suggested that the relevant departments shall reasonably allocate taxi resources according to the imbalance of taxi capacity utilization rate in time and space to improve the overall operation efficiency, and integrate market mechanism with government regulations to promote the sustainable development of urban taxis.

Vito Tanzi: The ecology of tax systems—factors that shape the demand and supply of taxes

Vito Tanzi: The ecology of tax systems—factors that shape the demand and supply of taxes

Optimization Model of Taxi Fleet Size Based on GPS Tracking Data

A reasonable taxi fleet size has a significant impact on the satisfaction of urban traffic demand, the alleviation of urban traffic congestion, and the stability of taxi business groups. Most existing studies measure the overall scale by using macro indices, and few studies are from the micro level. To meet the transportation demand for taxis, mitigating the mismatch between taxi supply and demand, this research proposes an urban taxi fleet size calculating model based on GPS tracking data. Firstly, on the basis of road network segmentation, the probability model of a passenger taxi-taking a road section as a unit is built to evaluate the difficulty of taxi-taking on a road section. Furthermore, a user queuing model is built for the “difficult to take a taxi” road section in the peak period, and the service mileage required by potential taxi users is calculated. After that, a transportation capacity measurement model is built to estimate the number of taxis required in different time periods, Finally, the income constraint model is used to explain the impact of different vehicle fleet sizes on the income of taxi groups, so as to provide a reference for the determination of the final fleet size. The model is applied to data from Xi’an. The calculation results are based on data from May 2014, and show that the scale of taxi demand is about 654–2237, and after considering the impact of different fleet size increases on income, when the income variation index is limited to 0.10, i.e., the decrease of drivers’ income will not exceed 10%, an increase of 1286 taxis will be able to meet 66% of the unmet demand in the peak period. The conclusion indicates that the model can effectively calculate the required fleet size and formulate the constraint solutions. This method provided can be considered as a support for formulating the regulation strategy of an urban taxi fleet size.

The Ecology of Tax Systems: Factors that Shape the Demand and Supply of Taxes, by Vito Tanzi (Edward Elgar, Cheltenham, 2018), pp. 175.

The Ecology of Tax Systems: Factors that Shape the Demand and Supply of Taxes, by Vito Tanzi (Edward Elgar, Cheltenham, 2018), pp. 175.

Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas

A popular phenomenon in the street-hailing taxi system is the imbalanced mobility services between city central and outside downtown areas, which leads to unmet demand outside downtown areas and competitions in city central areas. Understanding taxi drivers’ customer-searching behaviors is crucial to addressing the phenomenon and redistributing the taxi supply. However, the current literature ignores or simply models the taxi drivers’ behaviors, in particular, lacks the in-depth discussions on individuals’ heterogeneity. This study introduces the latent class model to identify the internal and external factors influencing the taxi drivers’ destination choice after the last drop-offs. Beyond the influencing factors, the modeling structure captures the heterogeneity in vacant taxicab drivers through introducing latent classes. The proposed model outperforms other discrete choice models, for instance, multinomial logit, nested logit, and mixed logit, based on the two study cases developed from the New York City yellow taxicab system. The empirical results first statistically indicate the existence of latent classes, which further empirically prove the heterogeneity in the choices by vacant taxicab drivers while searching customers. Moreover, we obtain a set of internal and external factors influencing the customer searching behaviors. For example, the taxicab drivers are sensitive to the demand at the search destination areas and the distance from the last drop-off location to the search destination areas and behave identically in particular under the conditions of high demand and short search distance. On the other hand, the external variables have different impacts on customer searching behaviors across the different groups of drivers in the both study cases, including peak hours, weekday, holiday, earned fare from last occupied trip, raining hours, and flight arrivals at airports. In final, the proposed modeling structure and findings are useful as a sub-model of taxi system modeling while developing strategies, as well as as a regional planning tool for taxi supply estimations.

Modeling Framework for Comparing Taxi Operational Modes: Case Study in Barcelona

Abstract This paper presents an aggregated mathematical model for the estimation of key performance indicators of the taxi market based on the system’s generalized cost function, which is calculated using the expected statistical values of customers’ trip distance, waiting/access time and the cost of the involved actors, including externalities, who are the taxi drivers, the taxi customers and the city represented by the rest of the drivers and the citizens. Optimum values for the taxi supply are obtained from mathematical formulations depending on the demand level and the size of the city. The model is developed for stand, hailing and dispatching taxi markets and the results are compared, presenting conclusions for the best type of market for each demand level and city size. The model is applied in the city of Barcelona, presenting useful conclusions on the performance indicators of the taxi services and the impact of the applied policies as well as the optimum number of taxis for each operational mode, ranging between 30 and 40 vehicles per hour and km2.

Research on taxi software policy based on big data

Through big data analysis, statistical analysis of a large number of factors affect the establishment of the rally car index set, By establishing a mathematical model to analyze the different space-time taxi resource “to match supply and demand” degree, combined with intelligent deployment to solve the “taxi difficult” this hot social issues. This article takes Shanghai as an example, the central park, Lu Xun park, century park three areas as the object of study. From the “sky drops fast travel intelligence platform” big data, Extracted passenger demand and the number of taxi Kongshi data. Then demand and supply of taxis to establish indicators matrix, get the degree of matching supply needs of the region. Then through the big data relevant policies of each taxi company. Using the method of cluster analysis, to find the decisive role of the three aspects of the factors, using principal component analysis, compare the advantages and disadvantages of the existing company’s programs. Finally, according to the above research to develop a reasonable taxi software related policies.

Taxis, Traffic, and Thoroughfares: The Politics of Transportation Infrastructure in China's Rapid Urbanization in the Reform Era

AbstractCities have expanded both territorially and demographically at an unprecedented speed during the recent intense process of urbanization in China. This article investigates the material, discursive, and social dimensions of an infrastructural process that has been interwoven with the discourse and practice of urban development in the city of Guangzhou since the 1990s. It takes a 2007 public hearing concerning the short supply of taxis on workdays as an entry point into the politics of urban infrastructure, threading together long‐term changes in urban forms, the ritual dimensions of the public hearing, and taxi drivers' downward mobility. The issue at the heart of this public hearing is a pattern of traffic flow caused by intense urbanization in the late Reform period that has led to important structural tensions in everyday life. Yet, under the official ideology of urban development, these structural tensions are de‐humanized and rendered a mere technical issue of supply and shortage. In present‐day China, infrastructural projects are regarded as technological solutions to a wide range of social and political issues in the process of urbanization; yet their material results often simply make these issues invisible instead. [Infrastructure; Urbanization; Taxi; Class; Mobility; China]

Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case

Taxi service is an important component of airport ground access, which affects the economic competitiveness of an airport and its potential positive impact on the surrounding region. Airports across the globe experience both taxi shortages and excesses due to various factors such as the airport’s proximity to the city center, timing and frequency of flights, and the fare structure. Since taxi drivers are independent entities whose decisions affect the taxi supply at airports, it is important to understand taxi drivers’ decision mechanisms in order to suggest policies and to maintain taxi demand and supply equilibrium at the airports. In this paper, New York City (NYC) taxi drivers’ decisions about airport pick-ups or cruising for customers at the end of each trip is modeled using logistic regression based on a large taxi GPS dataset. The presented approach helps to quantify the potential impacts of parameters and to rank their influence for policy recommendations. The results reveal that spatial variables (mainly related to proximity) have the highest impact on taxi drivers’ airport pickup decisions, followed by temporal, environmental and driver-shift related variables. Along with supplementary information from unstructured taxi driver interviews, the model results are used to suggest policies for the improvement of John F. Kennedy (JFK) airport’s ground access and passenger satisfaction, i.e. the implementation of taxi driver frequent airport server punch cards and a time-specific ride share program.

A Study on Optimal Resource Allocation of Taxis

As taxis play an increasingly important role in urban traffic system, the research on the supply and demand of taxis and the design of an optimal model of taxis supply has an importantly practical significance. In this paper, we used the traffic bureau data of Guangzhou, and determined the rate of empty driving as the index to establish the model. Nonlinear programming method was uesd to establish the optimal model of taxis quantity to meet the maximum income and maximum passenger satisfaction. Finally we got the optimal number of taxis was 37537.59

Analyzing and Modeling a City’s Spatiotemporal Taxi Supply and Demand: A Case Study for Munich

This paper presents a method for studying supply and demand in a taxi network in time and space by using the example of Munich. First, we introduce the necessary data collection that is linked to a fleet management system (FMS) operated by a local taxi agency and create a statistically sound database, which represents the mobility behavior on a trip level. Second, we derive key figures describing the city’s taxi characteristics. Here both the temporal taxi supply and demand of 420 taxis over a period of 19 weeks is considered. As the taxi demand differs according to the city district, the investigated area has to be divided into various zones. We analyze their specific characteristics and describe key factors influencing taxi requests, such as weekdays, public holidays and number of point of interests within an area. Next, a model to predict a time-variant demand for individual districts is introduced. We classify the problem and choose an appropriate algorithm to forecast spatiotemporal taxi requests. As booking actions differ over time, a nonhomogeneous Poisson distribution is suitable for counting those events. In a final step, we validate the proposed model on a local and global scale. Our model helps to provide a better understanding of taxi fleet operations on a city scale. We use the suggested demand prediction as one input parameter for an agent-based fleet simulation that represents individual taxi movements. 

Does the Age Profile of the Taxi Sector Labour Force in the Uk Affect the Provision of Transport for the Night Time Economy?

Since the early 1990s it has been public policy in the UK to support the growth of night time economy (NTE), particularly through patronage of restaurants, clubs and pubs as a way of developing the use of city centres at night. This policy has had some success however, venue managers have stated that there is still room for further growth and key to this is improving the transport links to and from venues as the easier it is to get to and from a venue the more likely people are to attend events. Following concern from venue managers that shortage of taxi supply at night was hampering the development of the NTE the London Chamber of Commerce investigated taxi services in London and found that there was a significant unmet demand for taxis at night. They suggested that this was caused in part by the age profile of taxi driver as most drivers were aged over 50 and this is the age group least likely to work at night. This study uses data from the Labour Force Survey to find out if the age profile of London taxi drivers is similar to that of the taxi sector in the UK as a whole and assesses the impact this has on choice of working patterns. This work is coupled with a survey of taxi drivers operating in Scotland to find out directly why drivers choose to operate at certain times.