Taxi Fleet Research Articles

A systematic methodology for mid-and-long term electric vehicle charging load forecasting: The case study of Shenzhen, China

More and more adoptions of electric vehicles (EVs) would bring a potential threat on the existing electric grid. In this context, a systematic methodology is presented in this paper to predict the additional loads resulting from EV charging in the mid-and-long term. It includes probabilistic models for describing the EV charging profiles and forecast models for predicting the future EV ownership. It is impractical to develop a method to simulate the charging profiles of the entire EV fleet due to the diversity of EV charging behaviors. As a consequence, the entire EV fleet is divided into four categories viz. private EV, electric taxi, electric bus and official EV so as to predict their charging loads respectively. The proposed method is conducted in the city of Shenzhen, which currently has the largest electric bus and electric taxi fleet in the world. Results indicate that the maximum value of the predicted EV charging profile in 2025 would occur at 21:30, reaching 1,760 MW under high oil price, which could elevate the existing load peak by 11.08 %.

Analysis of Methods for Determining the Optimal Number of Taxi Cars in Megacities

In modern large cities, taxi fleet is a significant component of the urban transport system. The degree of development and impact of taxi services on quality of transport services for residents and users traveling in the city depends on many factors. But one of the basic ones is the optimal ratio of the number of taxi cars and the demand for transportation by them. The objective of the article was an analytical review of scientific research and practical work in the field of methods for solving the problems of organizing and operating taxi services, in particular, determining the existing population demand for taxi transportation and the required number of taxi cars. For analysis, the research used the method of content analysis of the works of authors of several countries, including Russian ones. The conducted analysis of existing approaches to determining the demand of the population for taxi transportation and to calculating the required number of taxi cars showed that at present there is no single universally recognized method for this. The methods used are based on various methods which comprise statistical, sociological (surveys, observations), mathematical methods. The analysis of big data obtained using information systems of megacities is increasingly being used. It is concluded that there is an objective need for research in the field of developing a universal methodology for determining the required (limit) number of taxi cars in metropolitan areas.

Spatiotemporal Variation of Taxi Demand

The growth of urban areas has made taxi service become increasingly more popular due to its ubiquity and flexibility when compared with, more rigid, public transportation modes. However, in big cities taxi service is still unbalanced, resulting in inefficiencies such as long waiting times and excessive vacant trips. This paper presents an exploratory taxi fleet service analysis and compares two forecast models aimed at predicting the spatiotemporal variation of short-term taxi demand. For this paper, we used a large sample with more than 1 million trips between 2014 and 2017, representing roughly 10% of Lisbon’s fleet. We analysed the spatiotemporal variation between pick-up and drop-off locations and how they are affected by weather conditions and points of interest. More, based on historic data, we built two models to predict the demand, ARIMA and Artificial Neural Network (ANN), and evaluated and compared the performance of both models. This study not only allows the direct comparison of a linear statistical model with a machine learning one, but also leads to a better comprehension of complex interactions surrounding different urban data sources using the taxi service as a probe to better understand urban mobility-on-demand and its needs.

Solar-powered Hydrogen Refueling Stations: A techno-economic analysis

Fuel cell vehicles are a possible alternative for allowing a replacement of fossil-fuel based transportation. Thereby, this work's methodology proposes a Hydrogen Refueling Station (HRS) design powered by a photovoltaic plant for supplying the taxi fleet in a Brazilian city considering different scenarios and assuming that hydrogen-powered vehicles replace the current fleet. Results show that in order to supply 100% of the taxi fleet with hydrogen, 185.4 kgH2/day are necessary, while only 19.8 kgH2/day are enough to supply just 10% of it. Results also reveal slight variations in energy intensity. Hydrogen production costs are inversely proportional to the HRS's production capacity. Hydrogen costs about US$ 8.96/kg for larger HRSs and US$ 13.55/kg for smaller ones. Finally, the proposed system is an attractive alternative in the future nationwide, due to the fact that the cost of electricity has been escalating year after year above inflation rates.

Mandating the use of the electric taxis: The case of Florence

Abstract This paper discusses a policy enacted in 2016 by the city of Florence mandating the use of electric vehicles for the taxi service. We interviewed the taxi license owners (carriers), their co-operatives and the city administrators to evaluate the policy implementation, its acceptability and the cost-competitiveness of electric taxis (e-taxis) with respect to their hybrid and diesel alternatives. Using the data collected during the interviews, we estimated the total cost of ownership (TCO) of e-taxis and their conventional counterparts for the models comprising the Florence taxi fleet. We find that e-taxis already represent a valid alternative to the conventional ones not only from an environmental point of view but also from a private economic one. In particular, when the Florence-specific discounted purchase prices are considered and the base case scenario applies, e-taxis are the cheapest choice in terms of TCO and no financial incentives are needed. When the manufacturer suggested retail prices are considered, e-taxis are slightly less cost-competitive than hybrid taxis but more cost-competitive than the diesel ones. Sensitivity analyses are performed to evaluate the impact of the annual distance travelled, Ecobonus purchase subsidy, net revenue loss, annual percentage rate charged to finance the taxi acquisition and insurance premium.

The Real Stranded Assets of Carbon Lock-In

Climate debates over stranded assets are focusing on the wrong assets. Fossil-fuel companies and petrostates argue that they will suffer economic losses from climate policies. Although true, this ignores assets that will be stranded in the absence of climate policy. These are the real stranded assets of carbon lock-in and could prove much more costly.

Comparison between electric vehicle technology and gasoline in regular operation. Pilot experience in public service

Urban transport is an indispensable component for the development of cities, however, the massive use of motorized modes of transport with polluting technologies has generated the non-sustainability of the sector. Colombian cities are no strangers to this problem and as an improvement strategy, the testing and implementation of electric transport technologies is considered as an opportunity for modernization, efficient use of energy, reduction of emissions and energy diversification taking advantage of the energy resources available in Colombia. In Bogotá, the taxi fleet, due to its high energy demand and responsibility in polluting emissions, is a segment where electric vehicles can offer benefits to the operator, the citizen and the city. A description of Bogota‘s taxi fleet, the presentation of electric vehicle technology and a comparison between a gasoline vehicle and an equivalent electric vehicle in regular operation are presented, as well as some results from the pilot‘s early years. The technology has demonstrated reliability and efficiency, benefits to drivers and significant savings in greenhouse gases, but requires new incentives for massification.

SharedCharging

Our society is witnessing a rapid vehicle electrification process. Even though being environmental-friendly, electric vehicles have not reached their full potentials due to prolonged charging time. Moreover, unbalanced spatiotemporal charging demand/supply along with the uneven number of charging stations between heterogeneous fleets make electric vehicle management more challenging, e.g., surplus charging stations across a city for electric buses but limited charging stations in some regions for electric taxis, which severely limit the charging performance of the whole electric vehicle network in a city. In this paper, we first analyze a large-scale real-world dataset from two heterogeneous electric vehicle fleets in the Chinese city Shenzhen. We investigate their mobility and charging patterns and then verify the practicability and necessity of shared charging. Based on the insights we found, we design a generic real-time shared charging scheduling system called sharedCharging to improve overall charging efficiency for heterogeneous electric vehicle fleets. Our sharedCharging also considers sophisticated real-world constraints, e.g., station spaces, availability of charging points, real-time timetable guarantee, etc. More importantly, we take the electric bus and electric taxi fleets as a concrete example of heterogeneous electric vehicle fleets given their different operating patterns. We implement and evaluate sharedCharging with streaming data from over 13,000 electric taxis and 16,000 electric buses, coupled with the charging station data in the Chinese city Shenzhen, which is the largest public electric vehicle network in the world. The evaluation results demonstrate that the proposed sharedCharging reduces the waiting time by 63.5% and reduces the total charging time by 15% on average for e-taxis.

Mobile phone related crashes among motorcycle taxi drivers

In many countries, motorcycle taxis remain an important mode of travel due to their fast, flexible, and inexpensive service. The recent advent of ride-hailing services has led to dramatic growth in the fleet of motorcycle taxis and additional types of motorcycle taxi drivers. Furthermore, mobile phone use while riding a motorcycle is an emerging safety issue, particularly among ride-hailing motorcycle taxi drivers. This paper investigates mobile phone use while riding, crashes and mobile phone related crashes among ride-hailing, traditional, and hybrid motorcycle taxi drivers, using data from a survey in Hanoi, Vietnam. Results show that ride-hailing motorcycle taxi drivers had the highest prevalence of mobile phone use while riding a motorcycle taxi (95.3%), followed by hybrid (88.6%) and traditional taxi drivers (64%). Approximately 32.6%, 19.3%, and 9.7% of motorcycle taxi drivers reported being involved in a crash, injury crash, and mobile phone related crash respectively. Mobile phone related crashes represent 20.5% of all reported crashes. Logistic and negative binomial regression were used to explore factors influencing mobile phone use while riding and crash frequencies. Regression results indicate that ride-hailing taxi drivers were more likely to be involved in a mobile phone related crash. Delivery trips were found to be associated with increases in crashes whereas passenger trips were found to be associated with decreases in crashes. Policy implications are also discussed.

KEBUTUHAN ARMADA DAN KESESUAIAN TARIF TAKSI KONVENSIONAL DI KOTA MATARAM BERDASARKAN BOK, ATP DAN WTP

With the development of technology, the thinking to use applications in the business world also develops. One of them is a business in the field of transportation. At present an application-based transport service has emerged. The emergence of these services, the Conventional Taxis have new competitors. The existence of such competition has led to disputes between conventional taxis and application-based taxis. The existence of Application-Based Taxi, causing some people in the city of Mataram have the opportunity to switch interests using Application-Based Taxi. Under these conditions, the number of fleets and the suitability of conventional taxi rates in the city of Mataram need to be reviewed. In this study, examined taxi rates according to the calculation of vehicle operating costs. Then compared with the ability to pay and the will of the people of the City of Mataram. Then the number of taxi fleet requirements for the City of Mataram will be calculated. From the results of this study it can be concluded that: 1). there is an excess number of taxi fleets, which is 550 fleet optically, but in reality the current taxi fleet operating based on real conditions in the field is as many as 636 fleets from all operators, 2). 48.75% of respondents have the ability to pay a tariff of Rp.3,900 / kilometer and the remaining 52.25% of respondents are forced to use conventional taxis, 3). Current rates are in accordance with government regulations. This can be seen from the calculation of vehicle operating costs (BOK) for the tariff per kilometer is Rp. 4,000 per kilometer while the current tariff is Rp. 3,900, - only a difference of Rp. 100

Case Article—The Rise and Fall of Taxi Club Management in New York

This case provides an interesting example of a traditional business being disrupted by a new business based on the sharing economy. Taxi Club Management (TCM) was the largest taxi fleet in New York City and was doing well until Uber came along. The case traces the entry of Uber and the disruption to TCM’s business, resulting in the eventual bankruptcy of TCM.

Development of Reduction Scenarios Based on Urban Emission Estimation and Dispersion of Exhaust Pollutants from Light Duty Public Transport: Case of Tabriz, Iran

The transportation sector is one of the major air pollution sources in cities and accounts for about 72% of total air pollution in Tabriz, Iran. The economic burden of air pollution on Iran’s economy was estimated to be 8 and 10 billion dollars in 2005 and 2010, respectively. Using air pollution models, including emission and dispersion models, has been proposed as a logical solution to overcome problems such as expensive measurement methods and in some cases, difficulty and impossibility of direct measurements. In this paper, IVE emission model was used for estimating emission amounts and emission factors of taxi fleet of Imam Khomeini Street, one of main and busiest streets in Tabriz, as a representative street and for studying effects of different scenarios. AERMOD dispersion model was used for investigating the dispersion pattern of emitted pollutants from this fleet and to estimate the effect of using local base emission factors and replacement of present taxi fleet by environmental friendly vehicles in this city. CO, CH4, and NOx pollutants with quantities of 108, 20.7, and 5.4 g/km have the highest emission factor among all pollutants. Amount of hourly emissions per each pollutant, daily emission amount by technology types, and daily emission amount of each technology by pollutant types were also examined. CO emission has the highest amount at 7–8 in the morning, and at noon hours, emission of NOx and VOC increases. Technologies with old fuel injection and emission control systems and higher mileage have higher level of pollution emission. Investigating dispersion of pollutants from this fleet in the atmosphere shows pollutants movement in the northwest direction. Performance statistics of AERMOD model, such as FB, NMSE, MG, VG, FAC2, and R by use of pollution level of two adjacent air pollution monitoring stations were determined to continue. Three air pollution scenarios are used to assay effects of using site-specific base emission factors; replacement of worn-out portion of fleet with two distinct technology types (gasoline fuel and CNG fuel) were investigated. As a result under the first and second scenarios, 6.4% and 3.9% decrease of GWP would be estimated respectively and under the third scenario, 7.8% increase of GWP relative to the present study was observed.

Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data

Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data, and geographical information for reconstructing the spatial and temporal patterns of urban traffic emissions. Vehicle emission factor models are employed to estimate emissions based on taxi trajectories. The estimated emissions are then mapped to spatial grids of urban areas to account for spatial heterogeneity. To extrapolate emissions from the taxi fleet to the whole vehicle population, we use Gaussian process regression (GPR) models supported by geographical features to estimate the spatially heterogeneous traffic volume and fleet composition. Unlike previous studies, this paper utilizes the taxi GPS data and LPR data to disaggregate vehicle and emission characteristics through space and time in a large-scale urban network. The results of a case study in Hangzhou, China, reveal high-resolution spatio-temporal patterns of traffic flows and emissions, and identify emission hotspots. This study provides an accessible means of inferring the environmental impact of urban traffic with multi-source urban data that are now widely available in urban areas.

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.

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.

Deep decarbonization from electrified autonomous taxi fleets: Life cycle assessment and case study in Austin, TX

Although recent studies of autonomous taxis (ATs) have begun to explore potential environmental implications of fleet deployment, little is known about their impacts over the long term. We present a life-cycle assessment framework that incorporates both direct and indirect effects of ATs at the subsystem, vehicle, and mobility-system levels. Eco-driving and intersection connectivity are the direct effects analyzed along with indirect effects that include empty kilometers, parking, charging infrastructure, powertrain rightsizing, electric vehicle adoption, ride-sharing, and fleet-turnover rates. A case study of an AT fleet in Austin, Texas from 2020 to 2050 with constant travel demand indicates the strategic deployment of an electrified AT fleet can reduce cumulative energy and greenhouse gas (GHG) emissions by 60% in the base case, with a majority of this benefit resulting from electrified powertrains. Further reductions up to 87% can be achieved with accelerated electrical grid decarbonization, dynamic ride-share, longer vehicle lifetime, more energy efficient computer systems, and faster fuel efficiency improvements for new vehicles. We highlight the major opportunities for maximizing the environmental performance of AT fleets over the long term.

An Intelligent Dispatch System Operating in a Partially Closed Environment

Taxicabs are very important in our daily lives and are reputed to be one of the mostly used forms of transportation. The cab dispatch system was first created to help passengers get through to taxi drivers and make it easier to book reservations. The evolution of cab dispatch system has moved from the ordinary callboxes to computer-aided dispatch system. These solutions were created to help organizations that own fleet of taxis manage and control their operations. Campuses and other partially closed environments also require these solutions but due to their high cost of implementation, they find it quite difficult to deploy and execute. In this paper, a smart dispatch system (SDS) is proposed. The system comprises of software and hardware units. The database and the android application make up the software unit while the microcontroller, the GSM module, and an android device constitute the hardware unit. The microcontroller intelligently reads and makes decisions based on the information received from the android device. The microcontroller also retrieves drivers’ details from a database where all the information about the vehicles and drivers are stored. The GSM module acts as the intermediary between the android device and the microcontroller, and enhances the communication between the microcontroller and other devices. The system makes use of a microcontroller that selects a driver and dispatches it based on the capacity of the vehicle corresponding to the number of passengers in need. Consequently, an android application is built to be used by the clients making the request process much easier. The proposed system reduces human operator intervention, gives the passengers the estimated time for the dispatched cab to arrive at their bus stops thereby satisfying the clients in terms of cost efficiency and improved quality of service.

Quantifying the sensing power of vehicle fleets

Sensors can measure air quality, traffic congestion, and other aspects of urban environments. The fine-grained diagnostic information they provide could help urban managers to monitor a city's health. Recently, a "drive-by" paradigm has been proposed in which sensors are deployed on third-party vehicles, enabling wide coverage at low cost. Research on drive-by sensing has mostly focused on sensor engineering, but a key question remains unexplored: How many vehicles would be required to adequately scan a city? Here, we address this question by analyzing the sensing power of a taxi fleet. Taxis, being numerous in cities, are natural hosts for the sensors. Using a ball-in-bin model in tandem with a simple model of taxi movements, we analytically determine the fraction of a city's street network sensed by a fleet of taxis during a day. Our results agree with taxi data obtained from nine major cities and reveal that a remarkably small number of taxis can scan a large number of streets. This finding appears to be universal, indicating its applicability to cities beyond those analyzed here. Moreover, because taxis' motion combines randomness and regularity (passengers' destinations being random, but the routes to them being deterministic), the spreading properties of taxi fleets are unusual; in stark contrast to random walks, the stationary densities of our taxi model obey Zipf's law, consistent with empirical taxi data. Our results have direct utility for town councilors, smart-city designers, and other urban decision makers.

Distributed Approach for Temporal–Spatial Charging Coordination of Plug-in Electric Taxi Fleet

This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance the utilization ratio for CSs, and also flatten the unevenness of charging request for power grid.

Heuristics for electric taxi fleet management at Teo Taxi

The advent of new technologies in the area of transportation has led to the creation of new business models that benefit from their capabilities. Among them, Teo Taxi, a Taxelco project, uses a unique business model: taxi drivers are employees instead of being self-employed. In addition, the company uses only electric vehicles. This shift entails new challenges regarding fleet management and process optimization. This article tackles these issues and proposes solutions to three operational problems: the dispatch, relocation and charging of electric taxis. To evaluate the performance of our algorithms, we designed a discrete-event simulator. The application models a taxi fleet and computes relative performance according to average waiting time and hourly income per taxi. We show that dispatching taxis based on the solution to an assignment problem can provide a 20% increase in income relative to the simplistic approach where requests are assigned to the nearest taxi. Furthermore, we adapt to the case of taxi fleet management a relocation strategy designed for ambulances. Finally, in order to efficiently manage battery charges, we use a strategy that involves terminal calls. This strategy stabilizes the average state of charge and eases operations overall.