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Impact assessment of future fleet compositions in vehicle emissions in urban areas: A methodological framework and a case study
Abstract
This study explores the impact of emerging vehicle technologies on direct urban traffic emissions. It investigates emission reduction potential from shifts in fleet compositions and modal choices, especially considering climate change. To achieve this, three key research areas are explored for historical, current, and future scenarios (up to 2050): mode choice, emission factors for different vehicle categories, and diverse vehicle propulsion technologies. The estimation of the modal split is pivotal, developing a methodology utilizing Stated Preference survey data, discrete choice modeling, Monte Carlo simulations, and macroscopic traffic simulations. Future scenarios derive from the reference year’s modal split, and emission factors and fleet compositions are predetermined via an extensive literature review, aiding the assessment of their respective emissions. A subsequent sensitivity analysis identifies the impact of specific parameters on emissions, guiding future research focus. Study results underscore differences in greenhouse gas emissions and primary air pollutants between base and future scenarios.
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People make numerous trips every day for a variety of purposes. Transport mode choice directly impacts travel time, congestion, and environmental conditions. It also depends on various economic, social, environmental, and personal related factors. This paper investigates the association between identity characteristics and transport mode choices in Thessaloniki, Greece. A customized questionnaire survey was carried out with 506 individuals in 2019 to collect data on nine self-declared personal statuses (affiliation with environment, place of residence, career, companionship, etc.) and trip frequencies of all available transport options in Thessaloniki. We ran latent class analyses to uncover three identity clusters. The Active individuals prefer public transport over private car, and they are mostly young, sporty, and with low incomes. Additionally, the Family-Oriented individuals are comparatively older, and they have greater access to private cars and higher incomes, while the Typical Urban population exhibits a slightly higher use of cars and public transport than the Active one. Trips on foot and by car (as passengers) are equally preferred by all latent classes’ populations. Our findings highlight the role of individuals’ identities in the development of travel behavior and may assist with the design of targeted policies and marketing strategies, which will facilitate sustainable urban mobility behaviors.
Through the lens of the Social Identify Theory (Tajfel, 1974), this research aims to understand how social identity affects the perception of cycling as a mode of transport among women from different socio-income backgrounds. Using the case study of Tel-Aviv-Jaffa (Israel), we found that cycling is associated with distinct social categories rather than seen as a 'socially neutral' practice. In particular, we found cycling to be associated with 'being a Tel-Avivian' and with a healthy and active lifestyle. Such distinct identification of cycling is likely to enhance cycling uptake among more privileged groups, who are often able to identify with these social categories. In contrast, it may create a barrier for underprivileged groups, who do not identify with these social categories. In addition, we show how e-bikes – which are not identified with privileged groups – do not provide an identifiable alternative for women from all groups, as it is identified with “tough” and “violent” men. Furthermore, we show how cycling, in general, is perceived as “tough”, “dangerous” and as requiring a “constant struggle” over space with other road users, and hence fits a typical “masculine” behavior. Finally, we show how currently cycling is perceived by the underprivileged as a threat to their way of life or even as a symbol of them being pushed out of their neighborhood – a perception that limits cycling uptake among these social groups. These findings underscore the importance of accounting for social identity in cycling research and policymaking, especially in low-cycling contexts.
The COVID-19 pandemic is a significant public health problem and the leading cause of reduction in the demand and supply of public transit in the last three years. It has caused an unprecedented reduction in the demand for public road transportation, and it has changed passengers’ perceptions from positive to negative when it comes to using public transportation amenities. There is an increasing concern that the public road transportation systems are witnessing a new horizon, especially now that developed and developing countries are trying to resume their daily activities post COVID-19 pandemic. In this research, we focused on the overview of literature reviews of research within the past three years that focused on the challenges and solutions of the impact of the COVID-19 pandemic on public road transportation systems, especially when it comes to the enforcement of physical distancing in public transportation, usage of face masks and government interventions. The inclusion and exclusion criteria were used in reviewing the articles used for this literature review, and an original 140 articles were reduced to 100 articles that were used in this literature review. This research aims to create an overview literature review that highlights the impact of the pandemic on public transportation and identifies strategic ways in which urban planners and public road transportation researchers can develop constructive and efficient public road transportation planning to ensure the sustainability of public transportation systems in the post-COVID-19 era. This literature review extends our knowledge of the COVID-19 pandemic in public road transportation. It also provides additional evidence concerning the effectiveness of physical distancing and the use of face masks to prevent the COVID-19 virus in public road transportation networks.
The COVID-19 pandemic has significantly influenced people’s lifestyles including their travel choices. The pandemic resulted in placing restrictions in travelling throughout Italy due to the obligatory need for social distancing and changes in public transport services. City residents changed their mobility patterns and started using their private vehicles more often instead of public transport, while choosing to walk or cycle for short distance trips. Governments and local authorities encouraged citizens to use sustainable travel modes, particularly walking, during the pandemic period. However, the high number of infections and deaths, especially in Italy, has strongly influenced the propensity of walking due to the emotional aspects of travelling. This paper presents a statistical analysis based on data gathered through a questionnaire in urban areas of Sicily focusing on travel by walking for either leisure or work. The evaluation of negative emotions that people who habitually walk for short distances in the study areas is the main focus of the present work. The data indicated a variation between three emotions: anxiety, stress, and fear. These emotions had a potential to influence people’s daily life and, as a result, their travel habits.
Introduction:
The aim of this study was to use photovoice methodology to explore perceptions and lived experiences of cycling for transport. It was envisaged that the contextual information gathered from this qualitative study would provide novel insights into the meanings people ascribe towards cycling for transport.
Methods:
Fifteen adult participants from Liverpool, England each generated three photographs representing what they like and what they dislike about cycling for transport, and what cycling for transport means to them, and completed a subsequent photo-elicitation telephone interview. Data were analysed through an iterative deductive and inductive process, firstly using the ecological model of active living as a thematic framework, and then inductively to enable emergent themes to be further explored. Pen profile figures were constructed to illustrate key emergent themes.
Results:
The photovoice methodology generated complimentary and interconnected visual and narrative data which confirmed and uncovered new insights into intrapersonal, sociocultural and environmental factors relevant to cycling for transport experiences. Intrapersonal and perceived environmental factors had a stronger influence on cycling for transport than sociocultural factors. Road safety concerns were reported by all participants, and the provision of cycling infrastructure was reported as an enabler and barrier to transport cycling. The motivations and meanings ascribed to cycling for transport principally related to time saving and cost saving, as well as reliability.
Conclusions:
Convenience was a recurring theme in the present study. Emphasising the relative speed and cost efficiency of cycling for transport, as well as the potential health and environmental benefits, could lead to more cycling for transport uptake.
During the beginning of the Covid-19 pandemic, cities became large residential consumers of energy. In general, energy demand has decreased, but the users who used the most energy during the pandemic were the people in their homes creating a change compared to the past. How have household habits changed affecting energy uses during the lockdown? Has energy demand changed equally in all homes? What factors help explain the change in daily household habits and the change in energy uses? Via distribution of a questionnaire completed by 3519 people living in Italy during the first lockdown #StayAtHome, the change in daily habits and consequent energy uses were investigated. It collected data on socio-demographic and household characteristics and the material context in which people live. The results were interpreted according to the social practice approach that has been used in the past to analyse energy habits and uses of households, for example, for cooking. The results can support the interpretation of energy demand studies in the pandemic period and address decisions and policymaking for sustainable energy transition.
Population aging is a conspicuous demographic trend shaping the world profoundly. Walking is a critical travel mode and physical activity for older adults. As such, there is a need to determine the factors influencing the walking behavior of older people, especially in the era of population aging. Streetscape greenery is an easily perceived built-environment attribute and can promote walking behavior, but it has received insufficient attention. More importantly, the non-linear effects of streetscape greenery on the walking behavior of older adults have not been examined. We therefore use readily available Google Street View imagery and a fully convolutional neural network to evaluate human-scale, eye-level streetscape greenery. Using data from the Hong Kong Travel Characteristic Survey, we adopt a machine learning technique, namely random forest modeling, to scrutinize the non-linear effects of streetscape greenery on the walking propensity of older adults. The results show that streetscape greenery has a positive effect on walking propensity within a certain range, but outside the range, the positive association no longer holds. The non-linear associations of other built-environment attributes are also examined.
Nowadays, the diffusion of electric-powered micro Personal Mobility Vehicles (e-PMVs) worldwide—i.e., e-bikes, e-scooters, and self-balancing vehicles—has disrupted the urban transport sector. Furthermore, this topic has captured many scholars and practitioners’ interest due to multiple issues related to their use. Over the past five years, there has been strong growth in the publication of e-PMV studies. This paper reviews the existing literature by identifying several issues on the impact that e-PMVs produce from different perspectives. More precisely, by using the PRIMA’s methodological approach and well-known scientific repositories (i.e., Scopus, Web of Science, and Google Scholar), 90 studies between 2014 and 2020 were retrieved and analyzed. An overview and classification into endogenous issues (e.g., impact on transport and urban planning) and exogenous issues (e.g., impact on safety and the environment) are provided. While several issues are deeply investigated, the findings suggest that some others need many improvements. Therefore, the status quo of these studies is being assessed to support possible future developments.
Carsharing is becoming an increasingly popular mode of transportation in many cities around the world. Previous research has revealed that users tend to be young, are highly educated, have high incomes and live in densely populated neighbourhoods. However, this does not explain why people who have similar socioeconomic characteristics do not adopt carsharing when residing in comparable urban contexts. To assess the critical differences between users and non-users of carsharing, the current research uses the theory of planned behaviour as a theoretical framework to analyse how aspects of an individual’s social and self-identity determine their intentions to participate in carsharing. In-person intercept questionnaire data were collected in the Berlin neighbourhoods Schloßstraße, Steglitz and Glasower Straße, Neukölln in the fall of 2019 (N = 216). Exploratory and confirmatory factor analysis and structural equation modelling were used to analyse the collected data. The model results suggest that having a pro-technology self-identity and negative pro-car identity are significantly associated with the intention to participate in carsharing. These associations are present in both the structural and alternative models. Moreover, both models indicate a negative relationship between individuals’ degree of environmental self-identity and the degree of their pro-car identity. The results suggest that, in order to promote carsharing, regional governments should focus on attracting new users who are currently already using mobility technology. The findings can be used by regional governments to identify potential carsharing users and to specifically target individuals who are likely to be willing to adopt and participate in carsharing.
In the German project Traffic Development and the Environment an advanced model chain was built up that includes traffic models, fleet composition developments, new driving technologies, and emission factors in order to produce spatio-temporal emission distributions for use in atmospheric chemistry transport models. This novel model chain was first used to calculate current day traffic emissions in Germany and then to develop consistent future scenarios for 2040. In all scenarios, NOx emissions from traffic decrease by approximately 80% while PM emissions show a lower reduction. The scenarios Free Play, which is based on a free market economics logic, and Regulated Shift, which considers stricter environmental regulations, represent large differences in traffic emissions. NOx emissions will be 32% lower and PM emissions 13% lower in the Regulated Shift scenario compared to the Free Play. The data can be combined with other anthropogenic emissions for investigating air quality with chemistry transport models.
Today, transportation contributes significantly to greenhouse gas emissions and air pollution. However, new technologies are emerging and existing technologies are being further improved. This article presents the results of a comprehensive study on the development of drive-trains, resulting electricity and fuel consumption as well as emissions of CO 2 and selected air pollutants in land transport in Germany. This includes a quantitative assessment of technological potentials and of resulting energy consumption and emissions. The scenario building followed an explorative approach. Only the scenario depicting a consistent transition towards electrified mobility and regulated emissions from the energy system leads to a significant reduction in transport related CO 2 emissions. Yet, the analysis shows that it is unlikely that the German emission reduction target in 2030 for the transport sector will be met. Nevertheless, the integrated scenario analysis demonstrates that only a joint de-carbonization of both transport and electricity systems lead to a significant reduction of emissions.
Private car-use is a major contributor of greenhouse gases. Car-sharing is often hypothesised as a potential solution to reduce car-ownership, which can lead to car-sharing users reducing their car-use. However, there is a risk that car-sharing may also increase car-use amongst some users. Existing studies on the impacts of car-sharing on car-use are often based on estimates of the users’ own judgement of the effects; few studies make use of quasi-experimental methods. In this paper, the impact of car-sharing on car-ownership and car-use in Flanders, Belgium is estimated using survey data from both sharers and non-sharers. The impact on car-use is estimated using zero-inflated negative binomial regression, applied to matched samples of car-sharing users and non-users. The results show that the car-sharing may reduce car-use, but only if a significant number of users reduce their car-ownership. Policy intervention may therefore be required to ensure car-sharing leads to a reduction in car-use by, for example, discouraging car-ownership. Further research using quasi-experimental methods is required to illuminate whether the promise of car-sharing is reflected in reality.
Until today, road transport is largely fossil fuel driven and contributes significantly to greenhouse gas emissions and air pollutants. In order to assess the impact of development pathways of future transport, new emission factors for emerging technologies and a shift in the assessment framework that extends the emissions to include electricity generation is needed. The focus of this study is to provide emission factors for future passenger cars and fleets and offer an approach to comprehensively assess emission effects in future studies. Our scenario storyline approach imbeds different levels of changes in consideration of plausibility and consistency. We developed three pathways for Germany up to 2040 in order to capture the interdependencies of measures and developments. We hereby consistently modified the progress in transport technologies and in power generation together with changes in fleet compositions. Furthermore, we developed emission factors and energy consumption factors for plug-in hybrid and electric vehicles and expanded the conventional tank-to-wheel emission factors by including emissions derived from consistent energy scenarios. The development of emission factors depends on multiple factors, including vehicle and engine size. Furthermore, electrification shifts the emissions from tailpipe to power generation. Particularly for nitrogen oxides and particulate matter emissions, electric power generation for transport purposes could contribute significantly to ambient air emissions in the future, while tailpipe emissions can be expected to decline substantially.
We present an optimization model for the passenger car vehicle fleet transition—the timedependent fleet composition—in Germany until 2050. The goal was to minimize the cumulative greenhouse gas (GHG) emissions of the vehicle fleet taking into account lifecycle assessment (LCA) data. LCAs provide information on the global warming potential (GWP) of different powertrain concepts. Metaanalyses of batteries, of different fuel types, and of the German energy sector are conducted to support the model. Furthermore, a sensitivityanalysis is performed on four key influence parameters: the battery production emissions trend, the German energy sector trend, the hydrogen production path trend, and the mobility sector trend. Overall, we draw the conclusion that—in any scenario—future vehicles should have a plugin option, allowing their usage as fully or partly electrical vehicles. For short distance trips, battery electric vehicles (BEVs) with a small battery size are the most reasonable choice throughout the transition. Plugin hybrid electric vehicles (PHEVs) powered by compressed natural gas (CNG) emerge as promising longrange capable solution. Starting in 2040, longrange capable BEVs and fuel cell plugin hybrid electric vehicles (FCPHEVs) have similar lifecycle emissions as PHEVCNG.
This paper presents a methodology to forecast the changes of NO2 immissions due to changes in fleet composition of passenger vehicles and emission classes. Within this scope, possible changes in vehicle technologies (e.g. lower emission of diesel engines) are considered in different scenarios using the example of Munich. In line with this purpose, the traffic volumes and origin-destination data are obtained from a macroscopic traffic model, local registration statistics of vehicles are used to estimate fleet compositions. In addition, for immission modelling data on characteristic roadside structure as well as weather conditions of the investigation area are considered. For different scenarios with adapted passenger vehicle fleet compositions, the reductions of NO2 immissions levels as well as the special distribution in the road network is calculated. Additionally, the remaining length of the road network, where the legal threshold of daily average of 40 µg/m³ NO2 in Germany is exceeded, is shown for each scenario. Moreover, the contribution of the sub-fleet of commercial cars to these changes in emission is calculated.
The results show that the higher share of low emission classes (e.g. Euro 6 RDE for diesel engines) lead to continues reductions of NO2 immissions. The highest decrease of NO2 immission in the main road network of Munich is calculated for use of petrol cars with emission class Euro 6 for all passenger vehicles. To achieve the goal that the predominant part of the road network is fulfilling the legal threshold for NO2 immissions, a high share of electric vehicles is needed.
Passenger cars are an important source of air pollution, especially in urban areas. Recently, real-driving emissions (RDE) test procedures have been introduced in the EU aiming to evaluate nitrogen oxides (NOx) and particulate number (PN) emissions from passenger cars during on-road operation. Although RDE accounts for a large variety of real-world driving, it excludes certain driving situations by setting boundary conditions (e.g., in relation to altitude, temperature or dynamic driving). The present work investigates the on-road emissions of NOx, NO2, CO, particle number (PN) and CO2 from a fleet of 19 Euro 6b, 6c and 6d-TEMP vehicles, including diesel, gasoline (GDI and PFI) and compressed natural gas (CNG) vehicles. The vehicles were tested under different on-road driving conditions outside boundaries. These included 'baseline' tests, but also testing conditions beyond the RDE boundary conditions to investigate the performance of the emissions control devices in demanding situations. Consistently low average emission rates of PN and CO were measured from all diesel vehicles tested under most conditions. Moreover, the tested Euro 6d-TEMP and Euro 6c diesel vehicles met the NOx emission limits applicable to Euro 6d-TEMP diesel vehicles during RDE tests (168 mg/km). The Euro 6b GDI vehicle equipped with a gasoline particulate filter (GPF) presented PN emissions < 6 × 1011 #/km. These results, in contrast with previous on-road measurements from earlier Euro 6 vehicles, indicate more efficient emission control technologies are currently being used in diesel and gasoline vehicles. At the same time, the results suggest that particular attention should be given to CO and PN emissions of certain types of vehicles when driven under dynamic conditions, and possibly additional work is necessary. In particular, the emissions of CO (measured in this study during the regulated RDE test, but without an emission limit associated to it) or PN from PFI vehicles (presently not covered by the Euro 6 standard) showed elevated results in some occasions. Emissions of CO were up to 7.5 times higher when the more dynamic tests were conducted and the highest PN emissions were measured from a PFI gasoline vehicle during dynamic driving. Although based on a limited sample of cars, our work points to the relevance of a technology- and fuel-neutral approach to vehicle emission standards, whereby all vehicles must comply with the same emission limits for all pollutants.
City transport systems often struggle to cope with high volumes of traffic and become congested, despite the use of various traffic management strategies. The concentration of traffic around city centres results in pollution and poor urban air quality, although the increasing popularity of electric vehicles is helping ameliorate these effects. One reason for the growing momentum behind electric vehicles is the emergence of mobility operators such as car-sharing companies, who target users wishing to rent out vehicles on a short-term basis. There is currently rapid growth in one-way car-sharing, in which the vehicle can be dropped off at a different location to the pickup point. Crucially, one-way car-sharing gives the opportunity for travellers to utilise car-sharing in conjunction with other modes, such as public transport modes, for their journey provided the requisite intermodal connections are present. This paper looks at how one-way electric vehicle car-sharing systems have the potential to become important components of future city transport systems. The future role of shared autonomous vehicles is also considered.
Many researchers, policymakers and other stakeholders have explored and supported efforts to transition towards more sustainable forms of low-carbon mobility. Often, discussion will flow from a narrow view of consumer perceptions surrounding passenger vehicles—presuming that users act in rationalist, instrumental, and predictable patterns. In this paper, we hold that a better understanding of the social and demographic perceptions of electric vehicles (compared to other forms of mobility, including conventional cars) is needed. We provide a comparative and mixed methods assessment of the demographics of electric mobility and stated preferences for electric vehicles, drawing primarily on a survey distributed to more than 5000 respondents across Denmark, Finland, Iceland, Norway and Sweden. We examine how gender influences preferences; how experience in the form of education and occupation shape preferences; and how aging and household size impact preferences. In doing so we hope to reveal the more complex social dynamics behind how potential adopters consider and calculate various aspects of conventional mobility, electric mobility, and vehicle-to-grid (V2G) systems. In particular, our results suggest that predominantly men, those with higher levels of education in full time employment, especially with occupations in civil society or academia, and below middle age (30–45), are the most likely to buy them. However, our analysis also reveals other market segments where electric vehicles may take root, e.g. among higher income females and retirees/pensioners. Moreover, few respondents were orientated towards V2G, independent of their demographic attributes. Our empirical results can inform ongoing discussions about energy and transport policy, the drivers of environmental change, and deliberations over sustainability transitions.
Life Cycle assessments (LCAs) on electric mobility are providing a plethora of diverging results. 44 articles, published from 2008 to 2018 have been investigated in this review, in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly in regards to the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables present in the assessments, the electricity mix’s carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results.
This study examines the environmental impacts of roundtrip car sharing services by investigating transportation behavior. Car sharing should contribute to reduced greenhouse gas GHG emissions; however, such schemes include both positive and negative environmental effects, including: (1) reduced CO2e (carbon dioxide equivalent) from substituting private vehicle use for more fuel-efficient car sharing vehicles, (2) increased CO2e as car-less individuals switch from public transit to car sharing vehicles and (3) reduced CO2e due to fewer vehicles. This study examines the impacts of this modal shift on greenhouse gas (GHG) emissions using three types of models: a mixed logit model to analyze car sharing service preferences; a binary logit model to analyze whether individuals are willing to forgo vehicle ownership or planned purchases to use car sharing services; and a linear regression to determine how much private vehicle or public transportation use would be replaced by car sharing and the resulting effects on mobility. Total emissions from the current car sharing market equal 1,025,589.36 t CO2e/year. However, an increase in electric vehicle (EV) charging stations to 50% of the number of gasoline-fuel stations would increase the probability of electric car sharing vehicle use, thereby reducing emissions by 655,773 t CO2e. This study shows that forgoing vehicle purchases does not offset the increased GHG emissions caused by the shift from public transportation or private vehicle use to car sharing.
We live in rapidly changing times with new technologies emerging, but are also facing great challenges. One of these global challenges in particular is climate change driven by greenhouse gas emissions (e.g. CO2). The transport sector has a large contribution to the total CO2 emissions. In Germany, the transport sector is responsible for almost 20% of all CO2 emissions. Although there are other industries responsible for even more, e.g. the energy industry, only emissions related to the transport sector have not decreased compared to 1990 levels. Therefore, it is obvious that changes in this sector are necessary. However, choosing meaningful transport policies and measures is not straightforward. In this context, an effective method is to define different transport scenarios including different assumptions and policies. Specific transport models are required for the quantification of impacts on the transport system. The DLR Transport Program’s research project “Transport and the Environment” (Verkehrsentwicklung und Umwelt, VEU) addresses the issue of transport-related emissions by applying scenario techniques and defining different transport scenarios for the year 2040. The impact of these scenarios on travel demand and the resulting CO2 emissions is forecasted. As part of this project, the development and application of a National Transport Model for Germany was necessary. The focus of this paper is to give an overview on the methodology used to develop the model. A significant challenge in developing and applying a transport model for different scenarios is the necessity to ensure that the model is able to quantify a wide range of policy measures. These include e.g. changes in infrastructure, costs of different modes, mode availability and so on. Moreover, the project scenarios comprise urban and interregional aspects, which require more than one single model. Therefore, the German National Passenger Transport Model is divided into two travel demand models – for short and long distance travel – which have different levels of detail and consider different trip purposes and modes. Costs are skimmed from supply models that are also used for multi-class assignments together with commercial transport modes. The applicability of the model is shown by results from a project reference scenario for 2040. Beside the model concept and application, its limitations and requirements for further development are also discussed in this paper.
The Well-To-Wheels (WTW) methodology is widely used for policy making in the transportation sector. In this paper updated WTW calculations are provided, relying on 2013 statistic data, for the carbon intensity (CI) of the European electricity mix; detail is provided for electricity consumed in each EU Member State (MS). An interesting aspect presented is the calculation of the GHG content of electricity traded between Countries, affecting the carbon intensity of the electricity consumed at national level. The amount and CI of imported electricity is a key aspect: a Country importing electricity from another Country with a lower CI of electricity will lower, after the trade, its electricity CI, while importing electricity from a Country with a higher CI will raise the CI of the importing Country. In average, the CI of electricity used in EU at low voltage in 2013 was 447 gCO2eq/kWh, which is the 17% less compared to 2009. Then, some examples of calculation of GHG emissions from the use of electric vehicles (EVs) compared to internal combustion engine vehicles are provided. The use of EVs instead of gasoline vehicles can save (about 60% of) GHG in all or in most of the EU MSs, depending on the estimated consumption of EVs. Compared with diesel, EVs show average GHG savings of around 50% and not savings at all in some EU MS.
Although climate change is a global problem, specific mitigation measures are frequently applied on regional or national scales only. This is the case in particular for measures to reduce the emissions of land-based transport, which is largely characterized by regional or national systems with independent infrastructure, organization, and regulation. The climate perturbations caused by regional transport emissions are small compared to those resulting from global emissions. Consequently, they can be smaller than the detection limits in global three-dimensional chemistry-climate model simulations, hampering the evaluation of the climate benefit of mitigation strategies. Hence, we developed a new approach to solve this problem. The approach is based on a combination of a detailed three-dimensional global chemistry-climate model system, aerosol-climate response functions, and a zero-dimensional climate response model. For demonstration purposes, the approach was applied to results from a transport and emission modeling suite, which was designed to quantify the present-day and possible future transport activities in Germany and the resulting emissions. The results show that, in a baseline scenario, German transport emissions result in an increase in global mean surface temperature of the order of 0.01 K during the 21st century. This effect is dominated by the CO2 emissions, in contrast to the impact of global transport emissions, where non-CO2 species make a larger relative contribution to transport-induced climate change than in the case of German emissions. Our new approach is ready for operational use to evaluate the climate benefit of mitigation strategies to reduce the impact of transport emissions.
Nitrogen oxide (NOx) pollution is emerging as a primary environmental concern across Europe. While some large European metropolitan areas are already in breach of EU safety limits for NO2, this phenomenon does not seem to be only restricted to large industrialized areas anymore. Many smaller scale populated agglomerations including their surrounding rural areas are seeing frequent NO2 concentration violations. The question of a quantitative understanding of different NOx emission sources is therefore of immanent relevance for climate and air chemistry models as well as air pollution management and health. Here we report simultaneous eddy covariance flux measurements of NOx, CO2, CO and non methane volatile organic compound tracers in a city that might be considered representative for Central Europe and the greater Alpine region. Our data show that NOx fluxes are largely at variance with modelled emission projections, suggesting an appreciable underestimation of the traffic related atmospheric NOx input in Europe, comparable to the weekend-weekday effect, which locally changes ozone production rates by 40%.
This paper empirically investigates the causes for transit use cessation, focusing on the influence of users’ personal experiences, resulting levels of satisfaction, and subsequent behavioral intentions. It builds on a novel data set in which observed, objective measures of travel times are mapped to smartphone-based surveys where participants assess their travel experience. An integrated choice and latent variable model is developed to explain the influence of satisfaction with operations (travel times) and satisfaction with the travel environment (e.g., crowding) on behavioral intentions. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated desire and intention to continue using public transportation. The results show how delays, in particular in-vehicle delays but also transfer times and being left behind at stops, contribute to passengers’ intentions to cease transit use. Furthermore, a number of critical incidents, i.e., particularly memorable negative experiences, are found to have negative and significant effects on overall satisfaction and on willingness to continue using public transportation. The usefulness of the framework is demonstrated in a set of simulations in which the effect of three types of delays on passengers’ willingness to remain transit riders is modeled. This work highlights the value and potential of using new data collection methods to gain insights on complex behavioral processes, and it is intended to form the basis for new modeling tools to understand the causes of transit use cessation and the impact of various strategies and service quality improvements to reduce ridership turnover.
Previous well-to-wheels (WTW) analyses on electric vehicles (EVs) have reported tremendous results of potential energy and environmental effects. However, there remains a challenge to lower the uncertainties that were introduced when obtaining life-cycle parameters from a macro perspective (e.g., nationwide or regional scales). This study takes Beijing as a case, because it is an important regional hub for EV promotion and represents megacities with severe urban air pollution issues and congested traffic conditions. We collected up-to-date data concerning the electricity generation mix, fuel transport, end-of-pipe controls, real-world fuel economy and emissions, and estimated the WTW energy consumption and CO2 and air pollutant emissions for various light-duty passenger vehicle technologies currently (2015) and in the mid-term future (2030). Unlike previous results, battery electric vehicles (BEVs) are shown to significantly reduce WTW CO2 emissions by 32% for the present model year (MY) 2015 compared with their conventional gasoline counterparts, primarily due to the shift from coal to gas in local power plants in Beijing and the significantly higher real-world fuel consumption of conventional vehicles compared with the type-approval value. By 2030, WTW CO2 emissions by BEVs should approach 100 g km-1 due to the increased importation of non-fossil electricity, even lower than that of hybrid electric vehicles. Furthermore, significant improvements in end-of-pipe controls for coal-fired power plants have effectively lowered WTW emissions of air pollutants. In terms of VOCs and NOX that are of most concerns among all pollutants emitted from passenger vehicles, the WTW emissions of VOCs for MY 2015 BEV are already significantly lower than their conventional counterparts by 95%. Although WTW NOX emissions for BEVs are currently higher by 66% than conventional gasoline vehicles, we expect that BEVs can achieve WTW emission reduction benefit of NOX (41%) by 2030. This study indicates the significance of fine-grained and real-world features when assessing the WTW energy and environmental effects of EVs.
Current logistics and transportation (L&T) systems include heterogeneous fleets consisting of common internal combustion engine vehicles as well as other types of vehicles using "green" technologies, e.g., plug-in hybrid electric vehicles and electric vehicles (EVs). However, the incorporation of EVs in L&T activities also raise some additional challenges from the strategic, planning, and operational perspectives. For instance, smart cities are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the number, location, and capacity of these stations. Similarly, the limited driving-range capabilities of EVs, which are restricted by the amount of electricity stored in their batteries, impose non-trivial additional constraints when designing efficient distribution routes. Accordingly, this paper identifies and reviews several open research challenges related to the introduction of EVs in L&T activities, including: (a) environmental-related issues; and (b) strategic, planning and operational issues associated with "standard" EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems.
Shared micro-mobility services are rapidly expanding yet little is known about travel behaviour. Understanding mode choice, in particular, is quintessential for incorporating micro-mobility into transport simulations in order to enable effective transport planning. We contribute by collecting a large dataset with matching GPS tracks, booking data and survey data for more than 500 travellers, and by estimating a first choice model between eight transport modes, including shared e-scooters, shared e-bikes, personal e-scooters and personal e-bikes. We find that trip distance, precipitation and access distance are fundamental to micro-mobility mode choice. Substitution patterns reveal that personal e-scooters and e-bikes emit less CO2 than the transport modes they replace, while shared e-scooters and e-bikes emit more CO2 than the transport modes they replace. Our results enable researchers and planners to test the effectiveness of policy interventions through transport simulations. Service providers can use our findings on access distances to optimize vehicle repositioning.
Background
The UK Government restrictions on non-essential work in response to the coronavirus disease 2019 (COVID-19) pandemic forced millions of working aged-adults into an unplanned lifestyle change. We present data on changes in commuting behaviour in response to COVID-19 and describe the facilitators and barriers to switching commuting behaviours, with a specific focus on cycling and walking.
Methods
An online survey queried individuals’ transport mode to/from work before and when becoming aware of COVID-19, when restrictions were in place and the transport mode they may use once restrictions are lifted. Free-form text responses were collected on why they may switch to a sustainable commute mode in the future and what would help/allow them to achieve this. Quantitative and qualitative data on those who commuted by car (single occupant) and public transport (bus/rail/park & ride) were analysed and presented separately.
Results
Overall, 725 car and public transport commuters responded; 72.4% were car commuters and 27.6% were public transport commuters before COVID-19. Of the car commuters, 81.9% may continue travelling by car once restrictions are lifted while 3.6% and 6.5% might change to walking and cycling, respectively. Of the public transport commuters, 49.0% might switch modes. From the free-form text responses three themes were identified: (a) perceived behavioural control towards cycling and walking (infrastructure and safety of roads, distance, weather) (b) key motivators to encourage a switch to cycling and walking (provision to support cycling, personal and environmental benefits); (c) the demands of current lifestyle (job requirements, family and lifestyle commitments).
Conclusion
These UK data show how the COVID-19 pandemic has been an “external shock” causing some individuals to reassess their commuting mode. This provides an opportunity for theory-based behaviour change interventions tackling motivations, barriers and beliefs towards changing commute mode.
Data play an indispensable role in transport modelling. The availability of data from non-conventional sources, such as mobile phones, social media, and public transport smart cards, changes the way we conduct mobility analyses and travel forecasting. Existing studies have demonstrated the multitude and varied applications of these emerging data in transport modelling. The transferability of current research and further endeavours depend mostly on the availability of these data. Therefore, the openness or public availability of the prominent data for transport modelling needs to be adequately investigated. Such a discussion should also encompass these data’s application aspects to provide a holistic overview. This paper defines a typology for the data classification based on a set of availability or openness attributes from the existing literature. Subsequently, we use the developed typology to classify the prominent transport data into four categories: (i) Commercial data, (ii) Inaccessible data, (iii) Gratis and accessible data with restricted use, and (iv) Open data. Using this typology, we conclude that the public data, which refer to the data that are accessible and free of cost, are a superset of open data. Further, we discuss the applications and limitations of the selected data in transport modelling and highlight in which task(s) certain data excel. Lastly, we synthesise our review using a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis to bring out the aspects relevant to data owners and data consumers. Public availability of data can help in various modelling steps such as trip generation, accessibility, destination choice, route choice, network modelling. Complementary datasets such as General Transit Feed Specification (GTFS) and Volunteered Geographic Information (VGI) increase the usability of other data. Thus, modellers can gain from the positive cascade effect by prioritising these data. There is also a potential for data owners to release proprietary data, such as mobile phone data, with restricted-use licenses after addressing privacy risks. Our study contributes by dealing with two problems at the same time. On the one hand, the paper analyses existing data based on their potential for mobility studies. On the other hand, we classify them based on how open they are. Hence, we identify the most promising public data for developing the next generation of transport models.
Electric vehicles (EVs) attract increasing attention among consumers worldwide due to their innovative and environmentally-friendly properties. Buying an EV is not only an environmentally-friendly behavior but also an innovation-acceptance behavior. Accordingly, consumer propensities associated with environmental-innovative products—environmental propensity and innovative propensity—are expected to influence consumers’ attitudes and intention for EV purchase. Relying on an extended theory of planned behavior, this study reveals that both environmental propensity and innovative propensity of consumers have significant indirect impacts on their intention to purchase EVs through the mediation of attitude, subjective norm, and perceived behavior control. Based on these findings, important marketing implications for promoting the EV market are suggested.
The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications this may have on commuting activity. Working from home (WFH) has been the ‘new normal’ during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the amount of weekly one-way commuting trips by car and public transport. Using Wave 1 of an ongoing data collection effort done at the height of the restrictions in March and April 2020 in Australia, we develop a number of days WFH ordered logit model and link it to a zero-inflated Poisson (ZIP) regression model for the number of weekly one-way commuting trips by car and public transport. Scenario analysis is undertaken to highlight the way in which WFH might change the amount of commuting activity when restrictions are relaxed to enable changing patterns of WFH and commuting. The findings will provide one reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed.
The mobility scenarios have been modified by the COVID-19 pandemic. Catastrophic and health events cause strong repercussions on daily activities and related mobility habits. In Italy, as in other contexts, we have witnessed a period of lockdown that has led to an almost zero travel. This work shows an overview of the changes in mobility choices before and after the COVID-19, with particular attention to sustainable forms of mobility such as shared mobility. It can reduce the use of private vehicles if properly incentivized and can be enhanced within the different forms of urban mobility, adopting ad hoc strategies and encouraging the active participation of the population in the assessment of critical issues and solutions towards sustainability. First results, analyzed at national level, provide a basis for future research steps on the assessment of the perceived safety by users in the use of different forms of mobility before and after the pandemic.
A range of technology and policy actions can be put in place to reduce carbon emissions from passenger cars, this paper aims to prioritise between them, based on their likely impact and uncertainty. Formal sensitivity analysis techniques are used for the first time to determine the relative importance of factors affecting future emissions from passenger vehicles in Great Britain.
The two most important actions to limit future life-cycle CO2 emissions involve shifting to electric vehicles and limiting trends towards larger and more powerful vehicles. According to our analysis over 80% of the uncertainty in future cumulative CO2 emissions can be attributed to uncertainty in electric vehicle uptake and vehicle size and power. These variables are a priority for transport policy makers. The analysis also highlights variables of comparatively low importance; these include the share of hybrid electric vehicles, the Rebound Effect and the utilisation factor of PHEVs.
Electric Vehicles (EVs) appear as an environmental solution for transport sector since they emit zero emissions while driving. Nonetheless, the carbon intensity (CI) of the energy sources involved in the electricity generation system could seriously compromise this solution. Hence, this study proposes a methodology to verify the sustainability of the sector by the introduction of EVs. By means of the “Well-to-Wheel” tool, it compares emissions generated by two fleets: one based on internal combustion engine vehicles (ICEVs) and another one that also contemplates different EVs penetration levels. This methodology develops an iterative process on the contribution of renewable sources to the electricity generation system until a certain level of emissions reduction is achieved. The needed evolution of the CI for the electricity system is therefore deduced. The methodology has been applied to Spain by the mid-term future, given these country policies for both a high penetration of EVs and a progressive introduction of renewable sources in its electricity system. Results indicate that the current Spanish electricity mix allows for a reduction in CO2 emissions by the introduction of EVs, but a 100% renewable system will be needed for reductions up to 74 million tons per year. This research is a first-ever study to relate the forecasted Spanish environmental policies, in terms of urban transport and configuration of the power system, with a sustainable introduction of EVs in the urban fleet. Hence, this paper would be very helpful for policy makers on evaluation of the requirements for a transport fleet electrification.
Transportation is one of the most significant sources of emissions across various industries. Projections indicate that electric vehicles (EVs) will attain a considerable portion of the vehicle market in the next decade. In this paper, Well-to-Wheels (WTW) greenhouse gas (GHG) emissions are analyzed both for conventional and EVs by using generated driving cycle. An up to date driving cycle for Istanbul is developed (so called Istanbul Driving Cycle) by using collected traffic data across various sections of the city. An internal combustion engine vehicle (ICEV) and an electric vehicle (EV) are tested on a chassis dynamometer under the same conditions to determine specific energy consumption and specific emissions. Turkey’s electricity production infrastructure is analyzed and the emission factor of electricity production of Turkey is estimated. This paper concludes that, utilization of EVs instead of internal combustion engine vehicles (ICEVs) can save about 109.5 g CO2 equivalent (CO2eq) per kilometer according to WTW methodology under the used Istanbul Driving Cycle. The WTW emissions of ICEV and EV are calculated as 183.4 g CO2eq/km and 73.9 g CO2eq/km respectively.
In this paper we present the findings from the “Transport and Environment” (VEU) project related to the development of transport demand in Germany in three scenarios up to the year 2040. The results were obtained using the German national transport model DEMO, a complex model landscape covering all areas of transport (both passenger and freight), trip purposes and modes. The main findings are expressed in terms of number of trips, mode shares and distance travelled by mode.
The results show that it is possible to push the transport sector toward a more sustainable state if comprehensive policy measures are established to reduce road transport and make other modes of transport more attractive. In the Regulated Shift scenario, the distance travelled on the road is 20% lower for passenger transport and 4% lower for freight transport compared to the Reference scenario. However, our results also indicate that road transport will still be the backbone of the future transport system in all scenarios, meaning that the mitigation of the environmental effects is only possible in combination with technological developments of road-based transport systems.
Road transport is a major source of anthropogenic emissions especially in the Middle East where the regulations enforcement is generally poor. This study aims to quantify the Emission Factors (EF) of traffic-related gaseous and particulate pollutants inside the Salim Slam urban tunnel in Beirut, Lebanon. The fuel-based emission factors of measured pollutants were from the carbon mass balance model. The EF determined showed general higher values than those reported in recent studies from European and American countries, even for speciated NMVOC. The average CO and NOx emission factors for the mixed fleet (HDV + LDV) were determined to be 10.52 ± 3.00 g km⁻¹ and 2.20 ± 0.57 g km⁻¹ respectively, while the EF for PM2.5 55 ± 27 mg km⁻¹. Moreover, IVOC species from gaseous phase were reported for the first time in the region. A reduction trend was observed in comparison with the previous tunnel study from Lebanon, however there is still a need to have tougher regulations to control the local practices such as removal of catalytic converter, adjustment of engine parameters for inspection, etc. The comparison of the EF to those calculated through EMEP or IPCC methodologies shows the need to take local practices while establishing national emission inventories.
In this paper, we aim to assess the potential influence of increased adoption of electric vehicles (EVs) on a well-to-wheel (WTW) basis in the four countries with highest passenger car sales (Germany, the United States, China, and Japan), and Norway which represents a highly renewable energy market on greenhouse gas emissions. To characterize these emissions, we define critical parameters regarding fleet composition, activity, efficiency and fuel production in each country. Overall, with today’s technology at a national average level, on a per km driven basis, battery electric vehicles emit fewer greenhouse gases than conventional vehicles in all countries. Though vehicle energy consumption is similar in all countries, electricity production energy efficiency and greenhouse gas emissions per kWh electricity vary considerably, with Norway and China representing the low and high emitting endpoints, respectively. As electricity generation decarbonizes, EVs have the potential to be lower greenhouse-gas emitting than gasoline vehicles in all countries considered. The complexity of EV analysis across international boundaries, time periods, and environmental media complicates communication of EV benefits to stakeholders. Analysts must continue to address and clearly communicate the influence of EV and electricity production technology advancement into the future on EV impacts on all environmental media (air, water, land).
Der mögliche Wandel der Mobilität in Deutschland steht derzeit im Fokus. Sowohl im Hinblick auf das Verkehrsaufkommen und lokale Luftqualität als auch im Hinblick auf den Klimaschutz sind weitreichende Änderungen notwendig, damit der Verkehr seinen Beitrag zur Qualitätsverbesserung leistet. Andererseits besteht die Notwendigkeit und auch das individuelle Bedürfnis eine hohe Mobilität von Personen und Gütern in Zukunft zu gewährleisten. Das Projekt „Verkehrsentwicklung und Umwelt“ hat sich der Frage gewidmet, mit welchen Wirkungen bei der Umsetzung verschiedener Maßnahmenbündel zu rechnen ist.
Comparisons of the energy and emission intensity of transportation modes are standard features of sustainable transportation research, policy, and advocacy. These comparisons are typically based on average energy and emission factors per passenger trip or per passenger-kilometer traveled. However, as acknowledged in the energy production sector, comparing average emission factors can misinform policy and other decisions because it fails to represent the marginal impact of changing demand. The objective of this paper is to quantify the difference between average and marginal energy and emission factors for passenger transportation modes. Transportation system operations data are used to estimate energy and emission factors per passenger-kilometer traveled for U.S. urban and intercity travel. Marginal emission factors range from 30% (intercity rail) to 90% (private vehicles) of average factors. For urban travel, private vehicles and public transit have similar average emission factors, but marginal factors are 50% lower for transit. The average emission factor for intercity rail is 10% lower than air travel and 30% lower than private vehicles, but the marginal factor is 60% and 80% lower, respectively. Using average energy and emission factors to represent the impacts of travel by different modes is biased against public transit and discounts the benefits of shifting travel away from private passenger vehicles.
Until today road transport remains largely fossil fuel driven and therefore significantly contributes to emissions of greenhouse gases and air pollutants, particularly nitrogen oxides and particulate matter (EEA 2015). On the other hand, new vehicle technologies are emerging and existing technologies are being improved regarding fuel efficiency and transport emissions. The assessment of technologies and their implementation with the aid of scenario analysis is an appropriate tool to evaluate effects of different development pathways. The assessment of future emissions to air needs to consider not only tailpipe emissions from engines, but also emissions associated with the generation of electricity, since transport will increasingly be electrified. In this context and with regard to the requirement to provide robust environmental assessment of transport activities this contribution addresses some of the major gaps in prospective emission estimation from future cars and vehicle fleets. The aim of this work is to provide emission factors for future cars and fleets that allow the calculation of comprehensive emission effects in scenario analysis. This work is motivated by the project Transport and the Environment (Henning et al., 2015), in which twelve institutes of the German Aerospace Center (DLR) developed three explorative scenarios of the German transport system up to 2040, in order to analyse their environmental impact. The scenarios were named Reference, Free Play and Regulated Shift, and Table 1 characterizes the main storylines of these long-term pathways. The scenario definitions provide consistent context settings, and societal levers were identified that affect both, the transport and the energy system. Emissions and environmental effects were calculated using a network of models, established at the DLR. Both, the transport and the energy system are included in the scenarios. The derivation of the scenarios including the setting of model parameter can be found in Seum et al. (forthcoming). The calculation of new car and fleet-wide emission factors, which explicitly take into account factors stemming from the electricity supply, is necessary for transport scenario analysis. It goes thereby beyond scenario approaches, where improvements in technologies are only considered as relative changes. Conventional tank-to-wheel emission factors are expanded by including well-to-tank emissions derived from defined energy scenarios. This allows a non-bias comparison of different technologies. This integrated approach to develop new emission factors is presented in this article, using the example of passenger cars. The focus is on four main pollutants of road transportation, namely carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and fine particulate matter (PM10). In a first step, we modified existing tank-to-wheel emission factors to reflect the vehicle composition of future scenario fleets. Since new propulsion technologies, particularly electric drives, are swiftly appearing, we developed new tailpipe emission factors for hybrid technologies, based on own measurements on the DLR test-bed and literature reviews. Furthermore, for plug-in-hybrid, battery-electric, and fuel cell technologies energy consumption factors are calculated using an advanced model-based approach. The generation of electricity and the resulting emissions were considered based on long-term energy scenarios. We enhanced for this purpose a bottom-up normative energy scenario by a calibrated top-down emission forecast. The final results are well-to-wheel emission factors that include raw material processing to provide the energy for transportation. The extraction and transport of raw materials is omitted, but could be added through life cycle assessment data.
Transport electrification through battery electric vehicle (BEV) is currently regarded as a solution for clean mobility, perfect for metropolitan decarbonization, with high impact and short-term feasibility. However, it is necessary to consider that this technological deployment will constitute only a partial solution for the problem regarding the availability of automotive fuel and its impact on air quality. Suitability of electric powertrain as a substitute for the Internal Combustion Engine Vehicles (ICEVs) is not under discussion, nor its main environmental potential benefits (no contribution to pollution, no emissions of particles or nitrogen oxides) at a local level, but the generation of electricity to be stored in the batteries pollutes the environment during its production at power plant level. Global Zero-Emissions (ZE) transport solution goes through supplying electricity using renewable energy sources (such as wind, solar and geothermal), together with a change in social habits. In this paper, the use of electric vehicles and its influence in greenhouse gas emissions (GHG) inventories is analyzed. Calculation of the GHG emissions associated to the use of BEVs is put under question, and a new approach for assessing realistic GHG emissions at power plant level puts in evidence the inaccuracy of the existing methods.
This paper models and controls a multi-region and multi-modal transportation system, given that the travelers can adjust their mode choices from day to day, and the within-day traffic dynamics in the network also evolve over days. In particular, it considers that the city network can be partitioned into two regions (center and periphery). There are park-and-ride facilities located at the boundary between the city center region and the periphery. Travelers can either drive to the city center, or take public transit, or drive to the park-and-ride facilities and then transfer to the public transit. Travelers can “learn” from their travel experience, as well as real-time information about traffic conditions, thus will adjust their choices accordingly. It follows that the dynamic traffic pattern (within-day) in the city network will evolve over (calendar) time (day-to-day). To improve traffic efficiency in the network, an adaptive mechanism, which does not need detailed travelers’ behavioral characteristics, is developed to update parking pricing (or congestion pricing) from period to period (e.g., one period can be one month). The developed doubly dynamics methodological framework coupled with a feedback pricing mechanism unfolds and influences equilibrium system characteristics that traditional static day-to-day models cannot observe. The proposed adaptive pricing approach is practical for implementation in large-scale networks as the variables involved can be observed in real life with monitoring techniques. Also, it can contribute to reduce total social cost effectively, as shown in the numerical experiments.
Developing the electric vehicle (EV) industry is generally considered to be an effective way of easing the imbalance between the supply and demand of oil, and, in addition, the pressure to reduce environmental pollution. Developed countries and most developing countries including Brazil, Russia, India, and China (so-called ‘BRIC’ countries) are actively promoting the development of EVs. By studying different types of widely-used gasoline internal combustion engine vehicles (ICEVs) and EVs, we compare the effect on the environment of utilizing EVs in both developed and developing countries. This is achieved by using a ‘well-to-wheel’ method. The results show that compared to gasoline ICEVs, EVs have a significant effect on CO2 emission reduction. However, the corresponding air pollution due to SO2, PM10, NOx, etc. for a given EV varies substantially in different countries because of the influence of several factors (electrical power structure, line loss rate, and so on). As developing countries use larger proportions of thermal power or present high line loss rates, pollutant emission produced by a certain EV is much higher than that in developed countries. Taking China as a typical developing country as an example, this research dynamically predicts the environmental effects expected in 2020 and 2025 due to a developing EV industry. Predictions are based on a method of Monte Carlo simulation and consider the government’s development plan for energy. Finally, according to the results obtained, policies and suggestions for the development of the EV industry in developing countries are proposed.
As Chinese cities continue to grow rapidly and their newly developed suburbs continue to accommodate most of the enormous population increase, rail transit is seen as the key to counter automobile dependence. This paper examines the effects of rail transit-supported urban expansion using travel survey data collected from residents in four Shanghai suburban neighborhoods, including three located near metro stations. Estimated binary logit model of car ownership and nested logit model of commuting mode choice reveal that: (1) proximity to metro stations has a significant positive association with the choice of rail transit as primary commuting mode, but its association with car ownership is insignificant; (2) income, job status, and transportation subsidy are all positively associated with the probabilities of owning car and driving it to work; (3) higher population density in work location relates positively to the likelihood of commuting by the metro, but does not show a significant relationship with car ownership; (4) longer commuting distance is strongly associated with higher probabilities of riding the metro, rather than driving, to work; (5) considerations of money, time, comfort, and safety appear to exert measurable influences on car ownership and mode choice in the expected directions, and the intention to ride the metro for commuting is reflected in its actual use as primary mode for journey to work. These results strongly suggest that rail transit-supported urban expansion can produce important positive outcomes, and that this strategic approach can be effectively facilitated by transportation policies and land use plans, as well as complemented by timely provision of high quality rail transit service to suburban residents.
Carsharing combines the positive elements of both private and public transportation. This feature renders it attractive to a significant percentage of the population, especially younger people who do not own a car. To maximize the effectiveness of such systems, the characteristics of potential users have been investigated in this research.
The propensity of people to join a carsharing scheme is being modeled using mixed data collected from Internet and paper surveys. An ordered logit model is developed to model the willingness of young Greeks to join carsharing. Several types of explanatory variables are used, including demographic characteristics and travel attributes, but also the satisfaction of the commuters about their current travel patterns, which is included as latent attitude in the modeling framework. The combination of the two datasets aims to measure the difference of the variance of the error term, generated from the paper and Internet respondents; the latter are prone to be more positive in their responses, possibly in order to satisfy the interviewer. This is verified in the current research by the positive sign of the scale parameter applied at the utility function of the Internet-based data. The use of latent classes enhances the model estimation, by measuring the parameters that determine the respondents' unobserved, underlying behaviors.
The results demonstrate that people who use taxi for social activities, those with medium to low income, and the environmentally conscious, are more willing to join a hypothetical carsharing scheme. The results are compared with a 2013 study, in order to identify the advantages of using this advanced modeling framework.
According to national statistics 87% of all trips in the U.S. are by automobile and 90% of commuters typically get to work by car. Statistics for individual trips or the main mode of commuting do not capture variability in individual travel behavior over time. This paper uses the 2001 and 2009 National Household Travel Surveys to analyze recent trends in the share of multimodal motorists who use a car and also walk, bicycle, or ride public transport during a day or week. This paper identifies trends of multimodal behavior among car users in the U.S. and provides profiles of these multimodal motorists.