Taxi Operations Research Articles

Is LTFRB Uber-Reacting?

This paper elaborates on the economics of the taxi sector in the Philippines, and the impact of emerging competition such as Uber. It argues that a better understanding of the economics of the taxi industry could help inform regulators on whether and to what extent they should regulate Uber in order to promote the general welfare of the riding public. Uber offers alternatives and could actually be tempering behavior by unfair taxi operators by enhancing competition.

Mobile internet applications: implications for taxi driver behavior and operations

ABSTRACT Recent information and communication technology innovations have brought with them a broad set of new urban transportation options (e.g. ridesourcing, scooters, etc.), as well as the opportunity to improve transportation systems. The taxi industry, historically linked to communication technology, is now in the throes of a massive upheaval due to the introduction and use of internet-based mobile technologies in personal devices. Taxi e-hailing cellphone applications (apps) pair users looking to ride with available taxis. Do apps replace the radiophone? Do drivers behave differently? Is it more profitable for them to operate with apps? This paper answers these and more questions related to the effects of these technological innovations on taxi operations and taxi drivers’ behavior, based on a before-after survey in Bogotá, Colombia. We applied descriptive statistics, hypothesis testing, and multivariate regression analysis based on surveys involving the city’s taxi drivers, conducted in 2009 and 2016.

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.

Robust Vehicle Pre‐Allocation with Uncertain Covariates

Motivated by a leading taxi operator in Singapore, we consider the idle vehicle pre‐allocation problem with uncertain demands and other uncertain covariate information such as weather. In this problem, the operator, upon observing its distribution of idle vehicles, proactively allocates the idle vehicles to serve future uncertain demands. With perfect information of demand distribution, the problem can be formulated as a stochastic transportation problem. Yet, the non‐stationarity and spatial correlation of demands pose significant challenges in estimating its distribution accurately from historical data. We employ a novel distributionally robust optimization approach that can utilize covariate information as well as the moment information of demand to construct a scenario‐wise ambiguity set. We further illustrate how the key parameters required by the new ambiguity set, such as the scenarios and their probabilities, can be estimated via multivariate regression tree. Although information about uncertain covariates provides no value when there is perfect knowledge of demand distribution, we show that it could alleviate the over‐conservativeness of the robust solution. The resulting distributionally robust optimization problem can be exactly and tractably solved using linear decision rule technique. We further validate the performance of our solution via extensive numerical simulations, and a case study using trip and vehicle status data from our partner taxi operator, paired with the rainfall data from the Meteorological Service Singapore.

Changing women's lives and livelihoods: motorcycle taxis in rural Liberia and Sierra Leone

In rural Liberia and Sierra Leone about half of motorcycle taxi passengers are female, with this proportion increasing on market days. However, all motorcycle taxi operators in rural areas are male. This study assessed if and how motorcycle taxis have contributed to the livelihoods of rural women and whether there is appetite among them to become operators themselves. Data were gathered through male and female focus group discussions, roadside traffic counts and operator and passenger surveys. The study was conducted in three districts in rural Sierra Leone and one rural county in Liberia. The Liberia field site was the location of a pioneering pilot project on upgrading footpaths to motorcycle-accessible tracks. This project, funded by a German development agency, aimed to connect remote villages to the feeder road network. Both men and women were involved in track construction and this study assessed whether the women's involvement made them more likely to take up commercial motorcycle riding. Women nearly universally praised rural motorcycle taxis, indicating that they have made access to markets and (maternal) health much easier. However, while many expressed the desire to become operators themselves, they identified a number of barriers, the most significant being lack of friends or business persons willing to rent motorcycles to female operators.

Measuring Traffic Congestion Based on the Taxi Operations of Traditional and On-Demand Taxis in Yangon

The current urbanization and motorization have caused a gradual negative impact on the existing transport infrastructure in Yangon City. Currently, the road network throughout Yangon operates at or above its desired capacity during the peak periods. At present, there are over 62,886 registered taxis operating in Yangon City. These taxis provide two different services to passengers: non-metered taxi (traditionally hailed on the street) service and metered taxi (on demand) service. Private cars and taxis constitute 70% of the modes of transport in Yangon City; this may lead to traffic congestion. However, there is lack of relevant data and taxi trip pattern information on how taxi service is related to traffic congestion. Therefore, studies on taxi surveying using Global Positioning Systems (GPS) need to be conducted, and investigations on the effect of taxi services on traffic congestion from these GPS data need to be performed. This study explores the comparison between hourly and daily trips’ frequencies as well as spatial and temporal variations of taxi trips between the two services. Field survey data collected through the GPS and Geographic Information System (GIS) were used to estimate the different taxi travel times that can be applied in predicting the occupied and vacant times in the study area. The specific objective of this research is to examine vacant taxi movement and stationary time (parking time and congestion time) of the two services to quantify the impact of taxi travel time on traffic congestion in Yangon. Moreover, by knowing how the two services vary in terms of operation, the main solution for reducing the congestion in Yangon City can be established. Further, the taxi stationary duration information is useful for knowing the taxi trip hotspot points in each township in Yangon. This may lead to support in defining proposed taxi stands in Yangon City.

Recharging systems and business operations to improve the economics of electrified taxi fleets

While vehicle electrification offers great benefits to society, mass-market adoption of battery electric vehicles remains a challenge owing to the long recharging times and limited recharging infrastructure. High opportunity costs tied to long recharging times are particularly problematic for commercial fleet operators. With an aim to improve the economics of electrified taxi fleets, we present a framework for techno-economic analysis, examining the cost competitiveness of various recharging business models (i.e., combined ecosystems of recharging systems and taxi operations).When considering the achievable throughput of the recharging systems, we find that—on a per-kilometer basis—1) battery swapping emerges as a cost-effective option although it requires higher upfront investments for the battery inventory requirement; 2) increasing vehicle fleet size enhances the economic viability of double-shift taxi electrification. We expect that an electrified taxi fleet relying on the right recharging systems/operations could achieve cost parity with a gasoline-powered taxis system by 2022. Between now and then, the electrification of high-use vehicles requires government support; policies discussed include purchase subsidies and revenue-neutral gas tax imposition. By using real-world financial data taken from an operating electrified taxi fleet in Beijing, this paper provides a theoretical and practical reference for cities moving toward electric taxi ecosystems and sustainability.

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.

DHPA

Many real-world human behaviors can be modeled and characterized as sequential decision-making processes, such as a taxi driver’s choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and improves the driver’s working efficiency. Understanding the dynamics of such preferences helps accelerate the learning process of taxi drivers. Prior works on taxi operation management mostly focus on finding optimal driving strategies or routes, lacking in-depth analysis on what the drivers learned during the process and how they affect the performance of the driver. In this work, we make the first attempt to establish Dynamic Human Preference Analytics. We inversely learn the taxi drivers’ preferences from data and characterize the dynamics of such preferences over time. We extract two types of features (i.e., profile features and habit features) to model the decision space of drivers. Then through inverse reinforcement learning, we learn the preferences of drivers with respect to these features. The results illustrate that self-improving drivers tend to keep adjusting their preferences to habit features to increase their earning efficiency while keeping the preferences to profile features invariant. However, experienced drivers have stable preferences over time. The exploring drivers tend to randomly adjust the preferences over time.

Centralized and decentralized autonomous dispatching strategy for dynamic autonomous taxi operation in hybrid request mode

The combination of self-driving technology and taxis has a considerable potential in improving service quality and economic efficiency that can completely change traditional taxi operation. This study accordingly focuses on the autonomous taxi (aTaxi) dispatching problem in hybrid request mode where travelers can either request immediate rides or reserve taxi services ahead of time. In this study, a centralized dispatcher and decentralized autonomous dispatchers are designed to plan short-term and long-term routes for aTaxis in real time, respectively. The centralized dispatcher integrates vehicle-to-passenger assignment with empty vehicle rebalance to guarantee solution quality. Decentralized autonomous dispatchers distribute a portion of the calculation to aTaxis, thus reducing the centralized dispatcher’s workload. The dispatching strategy ensures that the response to traveler requests can be made immediately, and if such requests are accepted, the travelers will receive the expected services. Finally, experiments are conducted based on the actual road network and the trip data of Manhattan to investigate the dispatching strategy. The results confirm the high performance of the proposed dispatching strategies in terms of service quality and economic efficiency. It is also found that the increase in reservation rates can enhance the robustness of the method with respect to errors in predicted requests. Moreover, when the number of vehicles is adequate and reservations are made longer ahead of time, the completion rate of requests and the revenue improve.

Analysis on Taxi Operation Using GPS Data and Possibility of Its Efficiency Improvement

Analysis on Taxi Operation Using GPS Data and Possibility of Its Efficiency Improvement

Research on the Evaluation Index System of the Taxi Operation and Service in Airport Hubs

Passengers changing to taxis in airport hubs are facing problems such as complicated transfer lines, long transfer distance and time, stranded at taxi stands, and imperfect emergency safeguard measures. Combining with the trend of smart passenger services under the development of new generation of information technology, this paper constructs a set of taxi operation and service evaluation index system reflecting unique attributes of the airport hub collection and distribution system. The index system considers the rationality of taxi infrastructure configuration, the matching of passenger service facilities, collection and distribution efficiency, transfer efficiency, etc. It also discusses index quantification and evaluation methods. Aiming at the real-time prediction of inbound flight passenger flow, taxi sharing rate and taxi capacity, the realization process of prediction models is proposed based on the big data method. Meanwhile, combining with the combing of comprehensive transportation operation information systems of hubs, it proposes to extract data from information systems to calculate some index values. The establishment of this index system can provide a strong reference for the evaluation of other transportation modes and the overall hub, and provide decision support for the optimization and improvement of taxi dispatch and overall operation and service of airport hubs.

Taxi High-Income Region Recommendation and Spatial Correlation Analysis

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 orders 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 orders 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 orders regions and high-income regions. The results show that (1) the number and the spatial distribution of high-income orders hot-spots and high-income regions at different periods are different. (2) Some places are hot-spots, but neither high-income orders hot-spots nor high-income regions. (3) The high-income orders 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.

Hybrid Particle Swarm Optimization with Parameter Fixing: Application to Automatic Taxi Management

The conflicting needs of meeting the growth of air traffic, mitigating the resulting additional air pollution, and reducing costs derived from congestion at airports will push toward the modernization of ground operations and airport management. In this framework, a new solution is proposed to perform just-in-time taxi operations using autonomous electric towbarless tractors. The purpose of this solution was to eliminate queues and to reduce the environmental and economic impacts of ground operations so as to meet the requirements for future air traffic management. A hybrid particle swarm optimization algorithm is developed to provide conflict-free schedules for tractor autopilots. To improve the rate of convergence of the algorithm, a parameter-fixing algorithm has been developed, which constrains the particle elements based on the particle history.

The Influence of Parking Location and Competitive Forces on the Economic Performance of Car Taxi in Kisumu City

Car taxi business has been a source of livelihood for many people in Kisumu City. However, studies indicate that their economic performance has been on the downward trend. Whereas the decline could be attributed to several factors; it is not clear whether location of parking space and competitive forces, occasioned by city planning has influenced the decline. Therefore, the purpose of this study is to investigate the influence of parking locations and competitive forces on the economic performance of car taxi in Kisumu City. This study uses ex post facto research design, while adopting a mixed method approach which utilizes both quantitative and qualitative data. These were collected by means of interview schedules and questionnaires. Quantitative data was analyzed using Chi-square and regression analyses while qualitative data was analyzed through content analysis. The study concludes that location of car taxi parks and bargaining power of taxi operators have significant influence on the economic performance of car taxi business in Kisumu City. Consequently, it recommends that Kisumu city council should develop relevant policies to level the field for taxi operators and improve taxi service quality.

Dealing with existing theory: national fatality rates, vehicle standards and personal safety

In this paper we discuss some of the weaknesses in exiting theories and understandings behind road safety interventions and policy making. The paper deals with four main issues: road traffic fatality rates and per capita income of countries, vehicle crashworthiness standards, role of pedestrian and powered two-wheeler share in traffic on fatalities, and safety standards for vehicles other than cars. Recent data indicate that there may not be a strong relationship between income and road safety performance and it is possible for low and middle-income countries (LMIC) to decrease death rates at present income levels. Safer cars have had a major role in reducing fatality rates, but, gains in traffic safety in high income countries may be partly due to reducing exposure of vulnerable road users. Small lightweight vehicles (like tuk-tuks, three-wheeled scooter taxis) operating in many LMIC appear to have low fatality rates though they do not follow any crashworthiness standards. Very different crashworthiness standards need to be developed for low mass vehicles incapable of operating speeds greater than 50 km/h. LMIC may not be able to reduce fatality rates below about 7 per 100,000 population unless there are innovative developments in road design and all vehicle safety standards.

Percepciones del riesgo asociado a la práctica del mototaxismo en Cartagena, Colombia

El objetivo fundamental de este artículo consiste en analizar los factores de riesgo asociados al oficio de mototaxista en Cartagena. Otro objetivo es el de estudiar la influencia que estos factores tienen en la percepción de los mototaxistas respecto a sufrir lesiones causadas por accidentes de tránsito. Los datos relacionados con los factores de riesgo y las percepciones sobre lesiones causadas por el tránsito, se obtuvieron de una encuesta estructurada construida por el equipo de investigadores, a partir de referencias de la Organización Internacional del Trabajo, la Organización Mundial de la Salud y de la comunidad académica, y aplicada a un total de 403 personas que ejercen el oficio de mototaxista en Cartagena, asumiendo una población infinita y tomando como referencia un nivel de confianza del 95% y un error muestral del 4,9%. Los resultados arrojan que el oficio del mototaxismo lleva a quienes lo ejercen a estar constantemente expuestos a altos niveles de riesgos ambientales, físicos y psicológicos. Otro resultado fue que un gran porcentaje de los mototaxistas reconocen que el oficio representa un gran riesgo que los expone a accidentes de tránsito; sin embargo, se ven en la obligación de ejercer esta actividad al carecer de suficientes oportunidades de conseguir otro tipo de empleos.

Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles

The variation in BEV energy consumption and driving range under different weather and driving conditions can affect the usefulness and consumer acceptance of these vehicles. Thus, there is a need to better understand and quantify seasonal factors that affect consumption and range under real-world driving conditions. In this paper, a dataset representing the real-world driving activity of 197 BEVs of the same model recorded over 12 months at a polling frequency of 0.1 Hz is analyzed to estimate BEV performance across different driving applications (personal driving, taxi operation, and ridesharing) and seasons (spring/autumn, summer, and winter). The results show that the electricity consumption, travel patterns, and charging patterns of BEVs vary significantly by both vehicle application and season. For example, BEV models with a range of 160 km, recharged every 1.6 days on average, can meet most trip demands of personal vehicles. However, the same BEV model when used for ridesharing or taxi purposes, is driven much more and recharged more frequently. The results also show that actual BEV electricity consumption (EC) differs significantly from the consumption predicted by the New European Driving Cycle (NEDC) test, with real-world EC being 7%–10% higher than predicted by the NEDC test cycle. Furthermore, the real-world range of personal-use BEVs in winter is only 64% of the NEDC-estimated range. The study found that, when the ambient temperature is lower than 10 °C, electricity consumption increases 2.4 kWh/100 km for every 5 °C decrease in temperature. When it is higher than 28 °C, EC increases 2.3 kWh/100 km for every 5 °C increase in temperature. These findings imply that manufacturers should design BEVs with application-appropriate driving ranges and make R&D investments for improving battery performance in cold environments.

Robust Vehicle Pre-Allocation with Uncertain Covariates

Motivated by a leading taxi operator in Singapore, we consider the idle vehicle pre-allocation problem with uncertain demands and other uncertain covariate information such as weather. In this problem, the operator, upon observing its distribution of idle vehicles, proactively allocates the idle vehicles to serve future uncertain demands. With perfect information of demand distribution, the problem can be formulated as a stochastic transportation problem. Yet, the non-stationarity and spatial correlation of demands pose significant challenges in estimating its distribution accurately from historical data. We employ a novel distributionally robust optimization approach that can utilize covariate information as well as the moment information of demand to construct a scenario-wise ambiguity set. We further illustrate how the key parameters required by the new ambiguity set, such as the scenarios and their probabilities, can be estimated via multivariate regression tree. Although information about uncertain covariates provides no value when there is perfect knowledge of demand distribution, we show that it could alleviate the over-conservativeness of the robust solution. The resulting distributionally robust optimization problem can be exactly and tractably solved using linear decision rule technique. We further validate the performance of our solution via extensive numerical simulations, and a case study using trip and vehicle status data from our partner taxi operator, paired with the rainfall data from the Meteorological Service Singapore.

Multi-period Planning of Electric Taxi Charging Station Based on Genetic Tabu Hybrid Algorithm

As an auxiliary facility for electric taxi operation, the charging station has important significance for the promotion of electric taxis in terms of location, scale and construction time. Most of the existing research on charging station location focuses on static problems, ignoring the impact of station time on the location model. Therefore, based on the site selection and queuing theory, this paper proposes a multi-period and multi-level electric taxi charging station model, with the goal of the total construction cost, the cost of the station and the minimum waiting cost. Combined with the multi-starting point of genetic algorithm and the strong local search ability of tabu algorithm, the genetic tabu hybrid algorithm for solving this model is designed. The simulation example is used to analyze and verify the validity of the model and algorithm, which can provide important reference for practical multi-period charging station planning.