Conditions for electric vehicle taxi: A case study in the Greater Stockholm region

This study investigates the usability of electric vehicles (EVs) in a taxi company in Greater Stockholm, Sweden. By investigating cost and revenue data of both electric and conventional taxi vehicles, as well as by interviewing taxi drivers and carriers, an assessment has been made of the financial and operational implications of using EVs in a company’s taxi fleet. Both the drivers’ and the carriers’ perspectives have been examined. The main findings are that the investigated e-taxis have a similar or lower Total Cost of Ownership and slightly higher profitability than the investigated conventional taxis. For taxi drivers, using e-taxis implies more advanced planning and revenue service time being sacrificed for charging. However, certain customers’ preferences for EVs, as well as benefits such as corporate clients favoring e-taxis and a zero emission priority queuing system at Stockholm’s main international airport (partly) compensate for time devoted to charging. In order to facilitate increased use of e-taxis, more fast charging facilities should become available at strategic locations. Besides that, there are signs that carriers’ lack of information about the opportunities and consequences of shifting towards e-taxis hamper a wider deployment of e-taxis.     

Assessing greenhouse gas emissions from electric vehicle operation in Australia using temporal vehicle charging and electricity emission characteristics

ABSTRACT

Significant interest exists in the potential for electric vehicles (EVs) to be a source of greenhouse gas (GHG) abatement. In order to establish the extent to which EVs will deliver abatement, however, a realistic understanding of the electricity and transport sector GHG emissions impacts arising from different approaches to integrating EVs into the power system is required. A key issue in this regard is the extent to which GHG emissions are a function of where and when EV charging will be enabled (or disabled) by the provision of recharging infrastructure and implementation of charging management strategies by the electricity industry. This article presents an investigation of the GHG emissions arising from electricity and gasoline consumption by plug-in hybrid EVs under a range of standard EV-power system integration scenarios. An assessment framework is presented, and GHG emissions from EV use are assessed for the New South Wales (NSW) and South Australian (SA) pools of the Australian National Electricity Market (NEM) using retrospective electricity system generation data for 2011. Results highlight that there is a range of possible outcomes depending on the integration scenario and emissions accounting approach used. This range illustrates value of a temporally explicit assessment approach in capturing the temporal alignment of electricity sector emission intensity and EV charging. Results also show the importance of a clean electricity generation mix in order for EVs to provide a GHG abatement benefit beyond what would be achieved by a hybrid (but non-plug-in) vehicle. The extent to which overnight charging in NSW is observed to produce higher emissions relative to unmanaged charging also illustrates a possible trade-off between GHG emissions and benefits for electricity industry from EV charging at times of low demand.     

Modeling electric taxis' charging behavior using real-world data

Charging behavior is critical to the development and deployment of electric vehicle (EV) systems, given its impacts in EV adoption, the energy and environmental performance of EVs, potential load change to the electric grid, etc. However, the general characteristics of practical charging behavior have not been well studied. Existing studies are mostly based on travel data from conventional internal combustion engine vehicles, modeled with assumed and simplified charging scenarios. The use of public charging infrastructure is often neglected. Few studies evaluate real-world charging behaviors of EVs currently in operation using public charging stations. To address this gap, this study analyzes the data of 39,372 charging events from 129 unique electric taxis in Shenzhen, China to study the distributions of daily charging frequency, charging start time, and charging duration. The insights we learned from this study are: 1) the daily frequency for a vehicle to visit charging stations is unlikely to exceed five times; 2) the distribution of charging start time have multiple peaks and can be fitted with Gaussian Mixture Models; 3) charging duration is influenced by charging start time; and 4) charging dynamics can be modeled using the distributions of daily charging frequency, charging start time, and charging duration. Results from this study can inform charging behavior modeling for EVs and charging infrastructure development.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

Supply chain leading models of building charging stations: Leaders,subsidy policies,and cost sharing

Consumer willingness to pay for electric vehicles (EVs) is severely limited by their driving range. The expansion of a charging network could alleviate this dilemma. This paper focuses on determining whether the manufacturer or the dealer is more suitable to extend charging network. In scenario 1 (wholesale price is exogenous), M-Investing (the manufacturer invests on charging stations) better facilitates EV adoption at the early stage of EV market. By contrast, D-Investing (the dealer invests on charging stations) better facilitates the EV adoption when the market becomes mature. However, neither of the two investors have an incentive to offer building investment. In scenario 2 (wholesale price is a decision made by the manufacturer), M-Investing is consistently better than D-Investing in terms of facilitating EV adoption. The manufacturer is voluntary even with high building costs. In addition, we address two subsidy policies: producer subsidy and consumer subsidy, to determine which is more effective in facilitating EV adoption in M-Investing and D-Investing, respectively. Moreover, we extend our model by allowing cost sharing between the manufacturer and the dealer. We observe some cases in which the profit and the EV adoption level are improved.     

Light-duty electric vehicles in the gulf? Techno-economic assessment and policy implications

We investigate the economic and technological potential for adoption of Electric Vehicles (EVs) in Abu Dhabi through a parametric assessment, a public attitude survey, and a diffusion model. Abu Dhabi, similar to fossil-fuel-rich countries in its region, features a car-dependent society and energy subsidies while being situated in hot, desert environment challenging to EVs. We compare conventional vehicles and EVs in the region accounting for the higher use of air-conditioning (AC) in the actual climatic and driving pattern conditions of Abu Dhabi developing an EV AC consumption penalty model. Average annual AC penalty in fuel consumption over normal testing conditions is 16% for conventional vehicles and 25% for EVs based on hourly weather and vehicle utilization patterns. For 250 days per year, the expected EV range is higher than 75% of the nominal value. During the five hottest days of the recorded year, it can drop to 60% but still covering the average daily driving of the majority of users with a single charge with a 25kWh battery. With partial subsidies offered for both fuel and electricity, EV adoption impacts the state in terms of opportunity costs for these fuels in addition to environmental externalities. We calculate the state and user benefits in a parametric analysis dependent on driving distance and battery costs. If the battery cost is $325/kWh, direct and internalized benefits and costs are balanced when the EV is driven 60 km/day. A diffusion scenario of 5% by 2030 results in cumulative net savings of 6.4 million barrels of oil, a reduction of 517.000 tonnes of CO₂, and a cumulative net benefit of $127 million.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

An alternative explanation of the persistent low EV-uptake: The need for interventions in current norms of mobility demand

Worldwide, electric vehicles (EVs) are regarded as a key technology in decarbonising the transport sector by integrating renewable energy sources into the grid. Considering the great potentials to disseminate this smart-grid technology, the EV uptake remains low. This tension between the recent years high anticipation of the peak-shaving and storage potential of EVs and the associated persistent minor adoption rate is discussed through an in-depth case study of a Danish mobility operator's attempt to test EVs across a variety of Danish households. Considering the operator's ambitious and strategic promotion of EVs lower cost of operation, sustainable aspects and ability to meet driving needs, almost none of the participants wanted to adopt an EV after the trial ended. Corresponding with dominant approaches, the operator reproduced conventional problem framings' focus on technology, economic rationality, and information. However, through an alternative practice-based analysis, this paper critically recommends urgent sustainable mobility interventions to identify the crucial intervention points in the complexes of interlinked social practices to help explain the persistent low EV-uptake. The paper essentially acknowledges the need for policy makers and designers to scale down the focus on technology fix and innovation, and strategical intervene in the current concepts of practice configurations. In particularly, governance of mobility is recommended to involve multiple change agents to design practice-based interventions that target to reframe and reconceptualise the norms enmeshed in current mobility demand.     

Deployment and utilization of plug-in electric vehicles in round-trip carsharing systems

Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) can reduce gasoline consumption, but increase vehicle acquisition costs and introduce operational constraints. We develop a comprehensive approach to EV/PHEV deployment and utilization in round-trip carsharing systems. First, we formulate and solve the tactical problem of utilizing a mix of gasoline vehicles and EVs/PHEVs to serve trip demand, using Mixed Integer Programming optimization to estimate the minimal gasoline consumption in a computationally efficient manner, and simulation to assess the effect of reservation order on realized gasoline consumption. Second, we use these results to inform the strategic deployment of EVs/PHEVs in the carsharing fleet, using meta-optimization. We implement our approach using data from a large carsharing provider. From the perspective of a carsharing operator, our results suggest that replacing some portion of existing gasoline fleets by EVs/PHEVs would result in gasoline savings likely to outweigh upfront investments and the constraints on vehicle utilization that it creates. Moreover, we find that easily implementable heuristics can capture some of these benefits, and that the integration of vehicle utilization patterns into the design of EV/PHEV deployment strategies can result in added benefits.     

An exploratory empirical analysis of willingness to hire and pay for flying taxis and shared flying car services

A new transportation mode that can simultaneously operate on land and in the air, namely the flying cars, is anticipated to penetrate the automobile fleet between 2020 and 2025. Due to their flexible mobility patterns and automated operational characteristics, flying taxi and shared flying car services are expected to expand the existing shared mobility services (such as Uber, Lyft, and similar services) of the urban transportation network. Despite their forthcoming introduction in the shared mobility market, public perceptions and expectations about these services have not been investigated in travel demand literature. This study aims to provide an exploratory analysis of public willingness to hire and pay for flying taxis and shared flying car services, and to identify the determinants of the willingness to hire and pay for such services. Using data collected from an online survey, individuals' willingness to hire and to pay for flying taxi and shared flying car services are statistically modeled within a correlated grouped random parameters bivariate probit framework. The analysis shows that various socio-demographic characteristics and individuals’ opinions towards the perceived benefits and challenges of flying cars affect public willingness to hire and pay for flying taxi and shared flying car services. Even though the awareness about the operation of flying taxis and shared flying car services is possibly limited in the public sphere, the findings of this study can provide insights into the challenges that policymakers, manufacturing companies, and shared mobility providers will face with the introduction of such flying car services in the transportation networks.     

Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs.     

A review of spatial localization methodologies for the electric vehicle charging infrastructure

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem’s spatial dimension, and derives perspectives for further research.     

Why is electric vehicle uptake low in Atlantic Canada? A comparison to leading adoption provinces

This paper investigates potential electric vehicle (EV) adoption among households in Atlantic Canada, a region lagging in terms of EV uptake. The data come from a 2015 Survey for Preferences and Attitudes of Canadians towards Electric Vehicles or SPACE, with an intent to investigate electric mobility prospects in Canada through a series of socioeconomic, attitudinal, and stated preference (SP) questions. A latent class (LC) random utility model is used to segment Atlantic respondents based on their sociodemographic and environmental attitudes, and to estimate their willingness-to-pay for different vehicular features. A separate model is estimated for leading adoption provinces (Ontario and British Columbia), and compared to the Atlantic model. Results indicate that cash incentives and the quality of battery warranty are important features shaping the choice of vehicle powertrain in the Atlantic sample. Contrary to the model obtained from the leading provinces, electric range, maintenance cost, free parking, and access to high occupancy vehicle lanes are not significant attributes in the Atlantic model. With respect to segmentation, the adoption of EVs in the Atlantic model increases with youth, education, and progressive attitudes towards the environment, while income is not a determining factor. Our results imply some support for EVs among Atlantic consumers, though at less advanced levels than the leading adoption provinces in Canada. However, the high purchase price of EVs, lack of financial incentives, and limited public charging infrastructure are seen as key reasons for low EV deployment in Atlantic Canada. The demographic profiles of the plug-in oriented group, found in this study, suggest targeted decisions regarding policy and marketing.     

A traffic engineering model for air taxi services

We propose a single-provider model for on-demand air taxi services. Data from a discrete-event model support a “flow model,” which describes the aggregate flow of passengers and aircraft for an air taxi service provider without specifying event-level operation. Using a set of hypothetical scenarios for air taxi network operation, we evaluate the accuracy of the flow model against the outputs of the discrete-event model. Finally, we illustrate how the flow model can be applied towards the optimal pricing of passenger fares for a single air taxi service provider.     

机车视频智能监控系统功能设计探讨

机车乘务员的操作行为对铁路行车安全至关重要,机车视频智能监控系统是通过监测机车运行中乘务员的操作行为,判断其是否符合相关标准规范的智能系统。基于既有系统中视频数据结构存在异状、场景较为单一、分析方法受限、现场生产效率低下等问题,围绕机车视频智能监控系统设计原则,以及地面视频智能分析、智能识别机车运行中乘务员的操作行为等设计目标,针对系统中地面设备、车载智能设备,优化其总体架构,并提出机车视频离线分析自动化、乘务员行为实时分析、视频存储轻量化、行车安全分析数据化4个关键功能,从而监督乘务员岗位操作是否符合标准规范要求,提高视频监控的效率和视频监控范围全覆盖,更好消除铁路运输中存在的重大行车安全隐患。     

The evaluation of demand responsive transport services in Europe

Over the last decade Demand Responsive Transport (DRT) services have grown in popularity for several reasons including: the shortcomings of conventional regular bus and taxi services; shortcomings of special transport services; and new developments in community transport. Traditional dial-a-ride services have often been criticised because of their relatively high cost of provision, their lack of flexibility in route planning and their inability to manage high demand. The potential for overcoming these limitations may be realised through the introduction of telematics-based DRT and this has been widely demonstrated, for example, in recent research funded by the European Commission (e.g. the DGXIII-funded SAMPO and SAMPLUS projects). This paper provides an introduction to the concept of telematics-based DRT services and presents the results of the evaluation of a set of DRT technologies and operations at urban and rural sites across Europe. The paper offers discussion in terms of the key issues that influence the introduction of DRT services.     

Where are the electric vehicles? A spatial model for vehicle-choice count data

Electric vehicles (EVs) are predicted to increase in market share as auto manufacturers introduce more fuel efficient vehicles to meet stricter fuel economy mandates and fossil fuel costs remain unpredictable. Reflecting spatial autocorrelation while controlling for a variety of demographic and locational (e.g., built environment) attributes, the zone-level spatial count model in this paper offers valuable information for power providers and charging station location decisions. By anticipating over 745,000 personal-vehicle registrations across a sample of 1000 census block groups in the Philadelphia region, a trivariate Poisson-lognormal conditional autoregressive (CAR) model anticipates Prius hybrid EV, other EV, and conventional vehicle ownership levels. Initial results signal higher EV ownership rates in more central zones with higher household incomes, along with significant residual spatial autocorrelation, suggesting that spatially-correlated latent variables and/or peer (neighbor) effects on purchase decisions are present. Such data sets will become more comprehensive and informative as EV market shares rise. This work’s multivariate Poisson-lognormal CAR modeling approach offers a rigorous, behaviorally-defensible framework for spatial patterns in choice behavior.     

Consumer structure in the emerging market for electric vehicles: Identifying market segments using cluster analysis

This paper presents results from a segmentation analysis of the emerging market for Electric Vehicles (EVs). Data has been sourced through the application of a self-completion household questionnaire distributed over two cities in the United Kingdom (UK). A two stage cluster analysis methodology has been followed to identify market segments in a dataset of UK drivers. Five unique segments have been identified in the analysis and are characterised by their preferences for EVs, socio-economic characteristics, current car details, and psychographic profiles. These segments hold a range of different EV preference levels, from those who appear unwilling to adopt an EV to those which are clearly attracted to EVs. Moreover, the features of these segments suggest that segments might be attracted to or repelled from EVs for different reasons. These results demonstrate that a significant degree of consumer stratification is present in the emerging market for EVs, with the possible implication being that policy interventions at the segment as opposed to market, level may prove more effective due to their ability to cater for the nuances of important segments.     

Interplay between ethanol and electric vehicles as low carbon mobility options for passengers in the municipality of São Paulo

The Brazilian cities as well as many of the large urban centers in the world continue to expand, increasing the demand for mobility and transport, while, at the same time, the same cities are investing in greenhouse gas (GHG) mitigation to avoid climate change. Brazil's urbanization rate increased from 26% in 1940 to almost 70% in 1980. During this period, the Brazilian population tripled and the urban population multiplied by seven. In 2010, the transport sector in São Paulo accounted for 71% of the total emissions released by the energy sector. Ethanol has been considered a fuel with less greenhouse gas emissions, when compared with fossil fuels. However, ethanol production would have to double to meet the expected demand. Electric vehicles (EVs) market is expanding around the world, and is also an option to reduce the transport emissions, if powered by clean electricity. To assess whether the adoption of EVs might bring more benefits than the current ethanol, we develop prospective scenarios supported by the Long-range Energy Alternatives Planning (LEAP) simulation tool, taking a bottom-up tank-to-wheel approach to consider the CO₂ emissions of car in São Paulo. The scenario considering a substitution of 25% of gasoline-powered cars by EV in 2030 showed a reduction in energy consumption and CO₂ emissions, around 15% and 26% respectively in that year in comparison with 2015. We discuss the interplay between ethanol and EV, also considering emission coefficients from life cycle analysis conducted in Brazil, and concluded EV will have higher positive impact on climate change mitigation than ethanol.     

Optimizing the service area and trip selection of an electric automated taxi system used for the last mile of train trips

We propose two integer programming models for optimizing an automated taxi (AT) system for last mile of train trips. Model S1: trip reservations are accepted or rejected by the operator according to the profit maximization; model S2: any reservation on a selected zone by the model must be satisfied. Models were applied to a case-study. Results indicate that fleet size influences the profitability of the taxi system: a fleet of 40 ATs is optimal in S1 and 60 ATs in S2. Having electric ATs constrains the system for small fleets because ATs will not have time for charging.