|
on Discrete Choice Models |
| By: | Miller, Marshall; Wang, Qian; Fulton, Lew |
| Abstract: | This report presents the results of a project to develop a truck vehicle/fuel decision choice model for California and to use that model to make initial projections of truck sales by technology out to 2050. The report also describes the linkage of this model to a broader scenarios model of road transportation energy use in California to 2050. A separate report provides the authors detailed assumptions about truck technologies, fuels, and projections to 2050 that are inputs to this choice modeling effort. The need for low carbon trucking in California, as in other states and countries of the world, is outlined in IPCC reports and the Paris Agreement. An 80% reduction in energy-related CO2 emissions worldwide is targeted in that agreement. For trucks to contribute anywhere near this level of reduction, new, zero emissions technologies, such as electric and hydrogen fuel cell trucks, would need to be adopted at a large scale and at a rapid pace, both unprecedented for trucks anywhere in the world to date. Many truck models create new technology market penetration scenarios through minimizing cost or in an ad-hoc manner. This model utilizes a fleet decision choice process based on real world factors identified through discussions with trucking fleets. These factors include capital and operating costs, uncertainty (risk), model availability, refueling inconvenience, green PR (perceived benefit of environmentally beneficial technologies), and various incentives. The authors have developed a spreadsheet structured as a nested multinomial logit model that monetizes these factors to calculate a generalized cost. The authors have attempted to estimate the value of these factors to different types of fleets using a series of interviews, initial survey work, a truck choice workshop, and finally expert judgment and “basic logic†on how various factors might be valued now and in the future. The factors drive the choice analysis and are highly uncertain and likely highly variant across fleet types and even fleets within a type (early adopter, late adopter, in between), so the authors use a scenario approach to explore how this uncertainty could affect their results and projections. The authors created four scenarios and variants: 1) a business as usual (BAU), 2) a zero-emission vehicle (ZEV) mandate requiring the market share of ZEVs to reach 25% by 2050 (ZEV scenario 1a), 3) the same scenario but with a low penalty assumed for refueling time and (ZEV scenario 1b) 4) a ZEV mandate requiring the market share of ZEVs to reach 50% by 2050 (ZEV scenario 2). The authors also look at some policies that could help to spur sales growth among ZEV technologies in order to reach specific targets. |
| Keywords: | Engineering |
| Date: | 2017–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt1xt3k10x&r=all |
| By: | Wei , Albee |
| Abstract: | The UC Davis Campus Travel Survey is a joint effort by the Transportation Services and the Sustainable Transportation Center, part of the Institute of Transportation Studies at UC Davis. Since 2007 the survey has been administered each fall by a graduate student at the Institute of Transportation Studies. The main purpose of the survey is to collect annual data on how the UC Davis community travels to campus, including mode choice, vehicle occupancy, distances traveled, and carbon emissions. Over the past ten years, the travel survey results have been used to assess awareness and utilization of campus transportation services and estimate demand for new services designed to promote sustainable commuting at UC Davis. Data from the campus travel survey have also provided researchers with valuable insights about the effects of attitudes and perceptions of mobility options on commute mode choice. This year’s survey is the eleventh administration of the campus travel survey. The 2017-18 survey was administered online in October and November 2017, distributed by email to a stratified random sample of 19,796 students, faculty, and staff (out of an estimated total population of 47,450). Over 20 percent (4,059 individuals) of those contacted responded to this year’s survey, with 18.9 percent actually completing it. For the statistics presented throughout this report, weighs the responses by role (freshman, sophomore, junior, senior, Master’s student, PhD student, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population. |
| Keywords: | Engineering |
| Date: | 2018–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt3jf8j1k4&r=all |
| By: | Harvey, John T.; Kendall, Alissa; Saboori, Arash; Ostovar, Maryam; Butt, Ali A.; Hernandez, Jesus; Haynes, Bruce |
| Abstract: | A multitude of goals have been stated for complete streets including non-motorized travel safety, reduced costs and environmental burdens, and creation of more livable communities, or in other words, the creation of livable, sustainable and economically vibrant communities. A number of performance measures have been proposed to address these goals. Environmental life cycle assessment (LCA) quantifies the energy, resource use, and emissions to air, water and land for a product or a system using a systems approach. One gap that has been identified in current LCA impact indicators is lack of socio-economic indicators to complement the existing environmental indicators. To address the gaps in performance metrics, this project developed a framework for LCA of complete streets projects, including the development of socio-economic impact indicators that also consider equity. The environmental impacts of complete streets were evaluated using LCA information for a range of complete street typologies. A parametric sensitivity analysis approach was performed to evaluate the impacts of different levels of mode choice and trip change. Another critical question addressed was what are different social goals (economic, health, safety, etc.) that should be considered and how to consider equity in performance metrics for social goals. This project lays the foundation for the creation of guidelines for social and environmental LCAs for complete streets. View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, Complete streets, life cycle assessment, equity, social goals, environmental impacts |
| Date: | 2018–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt0vw335dp&r=all |
| By: | Jianwei Xing; Benjamin Leard; Shanjun Li |
| Abstract: | The emissions reductions from the adoption of a new transportation technology depend on the emissions from the new technology relative to those from the displaced technology. We evaluate the emissions reductions from electric vehicles (EVs) by identifying which vehicles would have been purchased had EVs not been available. We do so by estimating a random coefficients discrete choice model of new vehicle demand and simulating counterfactual sales with EVs no longer subsidized or removed from the new vehicle market. Our results suggest that vehicles that EVs replace are relatively fuel-efficient: EVs replace gasoline vehicles with an average fuel economy of 4.2 mpg above the fleet-wide average and 12 percent of them replace hybrid vehicles. Federal income tax credits resulted in a 29 percent increase in EV sales, but 70 percent of the credits were obtained by households that would have bought an EV without the credits. By simulating alternative subsidy designs, we demonstrate the distributional and efficiency outcomes across different policy alternatives. |
| JEL: | Q4 Q48 Q55 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25771&r=all |
| By: | Circella, Giovanni; Lee, Yongsung; Alemi, Farzad |
| Abstract: | In the last decade, advances in information and communication technologies and the introduction of the shared economy engendered new forms of transportation options and, in particular, shared mobility. Shared mobility services such as carsharing (e.g., Zipcar and Car2go), dynamic ridesharing (e.g., Carma), ridehailing (e.g., Uber and Lyft), and bike/scooter sharing (e.g., CitiBike, Jump Bike, Bird, and Lime) have gained growing popularity especially among subgroups in the population including college-educated or urban-oriented young adults (e.g., millennials). These emerging transportation services have evolved at an unprecedented pace, and new business models and smartphone applications are frequently introduced to the market. However, their fast-changing nature and lack of relevant data have placed difficulties on research projects that aim to gain a better understanding of the adoption/use patterns of such emerging services, not to mention their impacts on various components of travel behavior and transportation policy and planning, and their related environmental impacts. This report builds on an on-going research effort that investigates emerging mobility patterns and the adoption of new mobility services. In this report, the authors focus on the environmental impacts of various modality styles and the frequency of ridehailing use among a sample of millennials (i.e., born from 1981 to 1997) and members of the preceding Generation X (i.e., born from 1965 to 1980). The total sample for the analysis included in this report includes 1,785 individuals who participated in a survey administered in Fall 2015 in California. In this study, the researchers focus on the vehicle miles traveled, the energy consumption and greenhouse gas (GHG) emissions for transportation purposes of various groups of travelers. They identify four latent classes in the sample based on the respondents’ reported use of various travel modes: drivers, active travelers, transit riders, and car passengers. They further divide each latent class into three groups based on their reported frequency of ridehailing use: non-users, occasional users (who use ridehailing less than once a month), and regular users (who use it at least once a month). The energy consumption and GHG emissions associated with driving a personal vehicle and using ridehailing services are computed for the individuals in each of these groups (12 subgroups), and the authors discuss sociodemographics and economic characteristics, and travel-related and residential choices, of the individuals in each subgroup. View the NCST Project Webpage |
| Keywords: | Social and Behavioral Sciences, Demographics, Energy consumption, Greenhouse gases, Mobility, Mode choice, Travel behavior, Travel surveys, Vehicle sharing |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt31v7z2vf&r=all |
| By: | Wong, Stephen; Shaheen, Susan PhD; Walker, Joan PhD |
| Abstract: | In September 2017, Hurricane Irma prompted one of the largest evacuations in U.S. history of over six million people. This mass movement of people, particularly in Florida, required considerable amounts of public resources and infrastructure to ensure the safety of all evacuees in both transportation and sheltering. Given the extent of the disaster and the evacuation, Hurricane Irma is an opportunity to add to the growing knowledge of evacuee behavior and the factors that influence a number of complex choices that individuals make before, during, and after a disaster. At the same time, emergency management agencies in Florida stand to gain considerable insight into their response strategies through a consolidation of effective practices and lessons learned. To explore these opportunities, we distributed an online survey (n = 645) across Florida with the help of local agencies through social media platforms, websites, and alert services. Areas impacted by Hurricane Irma were targeted for survey distribution. The survey also makes notable contributions by including questions related to reentry, a highly under-studied aspect of evacuations. To determine both evacuee and non-evacuee behavior, we analyze the survey data using descriptive statistics and discrete choice models. We conduct this analysis across a variety of critical evacuation choices including decisions related to evacuating or staying, departure timing, destination, evacuation shelter, transportation mode, route, and reentry timing. |
| Keywords: | Engineering, Law |
| Date: | 2018–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt9370z127&r=all |
| By: | Cornel Joseph; Vincent Leyaro |
| Abstract: | This paper investigates the gender differential effect of technical and vocational educational and training (TVET) in Tanzania using data from the 2014 Integrated Labour Force Survey (ILFS). The multinomial logit model results for employment mobility show that TVET training significantly improves male and female chances of entering into formal employment while reducing their probability of being in informal work, agriculture or unemployed. The effects are much higher for females relative to males for almost all categories of education and training. The results show that although TVET training, and general education, increase male and female earnings significantly, the returns to TVET and general education are substantially higher for females. The decomposed gender earnings gap using Oaxaca and Blinder (1973) method reveals a significant gender earning gap in Tanzania, where males tends to earn significantly higher income (by 58 per cent on average) than females. As TVET and general education increase the probability of females to be in the formal employment more than for males, investing in girls skills training and education helps address the challenge of rising youth unemployment and increasing formal employment. Furthermore, as returns to TVET and general education are higher females, investing in girls’ skills training and education will help address gender earnings inequality |
| Keywords: | gender, employment, returns to education, TVET, Tanzania |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:not:notcre:19/04&r=all |
| By: | Circella, Giovanni; Matson, Grant; Alemi, Farzad; Handy, Susan |
| Abstract: | Individual travel options are quickly shifting due to changes in sociodemographics, individual lifestyles, the increased availability of modern communication devices (smartphones, in particular) and the adoption of emerging transportation technologies and shared-mobility services. These changes are transforming travel-related decision-making in the population at large, and especially among specific groups such as young adults (e.g., “millennials†) and the residents of urban areas. This panel study improves the understanding of the impacts of emerging technologies and transportation trends through the application of a unique longitudinal approach. The authors build on the research efforts that led to the collection of the 2015 California Millennials Dataset and complement them with a second wave of data collection carried out during 2018, generating a longitudinal study of emerging transportation trends with a rotating panel structure. The use of longitudinal data allows researchers to better assess the impacts of lifecycle, periods and generational effects on travel-related choices, and analyze components of travel behavior such as the use of shared mobility services among various segments of the population and its impact on vehicle ownership over time. Further, it helps researchers evaluate causal relationships between variables, thus supporting the development of better-informed policies to promote transportation sustainability. View the NCST Project Webpage |
| Keywords: | Social and Behavioral Sciences, Intelligent vehicles, Mobility, Mode choice, Surveys, Technological innovations, Travel behavior, Travel surveys, Vehicle sharing |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt35x894mg&r=all |
| By: | Heckathorn, Drew |
| Abstract: | The UC Davis Campus Travel Survey is a joint effort by the Transportation & Parking Services (TAPS) and the National Center for Sustainable Transportation, part of the Institute of Transportation Studies at UC Davis. Since 2007 the survey has been administered each fall by a graduate student at the Institute of Transportation Studies. The main purpose of the survey is to collect annual data on how the UC Davis community travels to campus, including mode choice, vehicle occupancy, distances traveled, and carbon emissions. Over the past ten years, the travel survey results have been used to assess awareness and utilization of campus transportation services and estimate demand for new services designed to promote sustainable commuting at UC Davis. Data from the campus travel survey have also provided researchers with valuable insights about the effects of attitudes and perceptions of mobility options on commute mode choice. This year’s survey is the tenth administration of the campus travel survey. The 2016-17 survey was administered online in October and November 2016, distributed by email to a stratified random sample of 24,029 students, faculty, and staff (out of an estimated total population of 45,380). About 19 percent (4,448 individuals) of those contacted responded to this year’s survey, with 16.1 percent actually completing it. For the statistics presented throughout this report, we weight the responses by role (freshman, sophomore, junior, senior, Master’s student, PhD student, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population. The weighting methodology depends on an accurate estimate of the campus population by role and gender. For the 2016-17 survey, campus administrators used a new protocol to estimate faculty and staff population for the campus. The new protocol produced a higher estimate of the number of staff and a lower estimate of the number of faculty in 2016-17 than in 2015-16, meaning that the responses of staff are given more weight and those of faculty less weight in this year’s results (see Appendix H: Weighting by role and gender†for more information). This change in protocol affects the comparison of 2016-17 results to 2015-16 results, and the comparisons presented below may not accurately reflect the true changes in travel to campus. The 2017-18 survey will use the new protocol and will thus provide a more accurate estimate of changes from 2016-2017 to 2017-18. |
| Keywords: | Engineering |
| Date: | 2017–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7g77p8hp&r=all |
| By: | Joana Passinhas; Isabel Proença |
| Abstract: | We research gender differences in unemployment incidence and persistence during the debt crisis in Portugal. A dynamic random effects probit model is estimated to control for unobserved individual heterogeneity and for the ‘initial conditions’ problem. The estimation uses data from four waves of the Survey on Income and Living Conditions (ICOR) between 2010 and 2013. We find strong evidence of persistence in unemployment, and an indication that men are more prone to endure the negative implications of previous unemployment. Simultaneously, we found evidence of higher probabilities of unemployment for women through a fixed effect that aimed to capture gender discrimination in an unstable labour market. Results suggest that policies to boost employment should accommodate a gender dimension and also have a special focus on the long-term unemployed. |
| Keywords: | unemployment, persistence, unobserved heterogeneity, dynamic random effects models, gender discrimination |
| JEL: | C23 C25 J21 J24 J71 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp0792019&r=all |
| By: | Rodier, Caroline; Michaels, Julia |
| Abstract: | Ride-hailing services, which allow consumers to order and pay for rides through smart phone applications, have grown to a substantial proportion of the transportation market. Today, an estimated 15% of adults across the U.S. and 21% living in major U.S. cities have used ride-hailing services. The growth of ride-hailing services has raised questions about their overall effects on the transportation system. While they clearly offer a new form of mobility, there is concern they may increase congestion and air pollutant emissions. A limited number of studies have attempted to quanitfy changes associated with the increased use of ride hailing services. UC Davis researchers examined how ride-hailing affects the total amount of driving (measured in vehicle miles traveled, VMT) as well as greenhouse gas (GHG) emissions. The researchers developed a framework of categories for analyzing the multiple aspects of transportation that may be affected by ride-hailing. These categories are: automobile ownership; number of vehicle trips generated; choice of mode of travel; empty (passenger-less) travel between drop-off and pick-up points, known as “network travel†; and destination choice and land use. Thirteen (13) studies were analyzed using this new framework: 8 used surveys of riders or recorded data on rider and driver activity; and 5 used simulated (“modeled†) travel in and around cities by automated taxis. By compiling multiple studies in the framework, stronger and more certain conclusions could be reached. View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, Automobile ownership, Greenhouse gases, Mode choice, Travel behavior, Trip generation, Vehicle miles of travel |
| Date: | 2019–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt4vz52416&r=all |
| By: | Yang, Bill Huajian |
| Abstract: | Monotonic estimation for the survival probability of a loan in a risk-rated portfolio is based on the observation arising, for example, from loan pricing that a loan with a lower credit risk rating is more likely to survive than a loan with a higher credit risk rating, given the same additional risk covariates. Two probit-type discrete-time hazard rate models that generate monotonic survival probabilities are proposed in this paper. The first model calculates the discrete-time hazard rate conditional on systematic risk factors. As for the Cox proportion hazard rate model, the model formulates the discrete-time hazard rate by including a baseline component. This baseline component can be estimated outside the model in the absence of model covariates using the long-run average discrete-time hazard rate. This results in a significant reduction in the number of parameters to be otherwise estimated inside the model. The second model is a general form model where loan level factors can be included. Parameter estimation algorithms are also proposed. The models and algorithms proposed in this paper can be used for loan pricing, stress testing, expected credit loss estimation, and modeling of the probability of default term structure. |
| Keywords: | loan pricing, survival probability, Cox proportion hazard rate model, baseline hazard rate, forward probability of default, probability of default term structure |
| JEL: | C02 C13 C18 C40 C44 C51 C52 C53 C58 C61 C63 |
| Date: | 2019–03–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93398&r=all |
| By: | Bhaskar, Venkataraman; Roketskiy, Nikita |
| Abstract: | We examine the implications of consumer privacy when preferences today depend upon past consumption choices, and consumers shop from different sellers in each period. Although consumers are ex ante identical, their initial consumption choices cannot be deterministic. Thus ex post heterogeneity in preferences arises endogenously. Consumer privacy improves social welfare, consumer surplus and the profits of the second-period seller, while reducing the profits of the first period seller, relative to the situation where consumption choices are observed by the later seller. |
| Keywords: | consumer privacy; dynamic demand; endogenous screening; Nonlinear Pricing |
| JEL: | D11 D43 L13 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13686&r=all |
| By: | Niemeier, Debbie; Qian, Xiaodong |
| Abstract: | Bikeshare programs are increasingly popular in the United States, and they are an important part of sustainable transportation systems. They offer an important alternative mode choice for many types of last mile trips. Most of the current research on bikeshare focuses on bikeshare benefits (e.g., how to replace auto trips with bike trips and reduce greenhouse-gas emissions) and bikeshare system management (e.g., bike repositioning between stations). Far less attention has been paid to the programmatic potential for providing greater access to jobs and essential services for underserved communities. To date, there is virtually no quantitative research aimed at designing bikeshare systems for underserved communities. The authors develop a new spatial index that identifies bikeshare station locations exhibiting a high potential for providing service for underserved communities. The index can: 1) facilitate the identification of priority areas for bikeshare investment based on current infrastructure and the potential for increased job or essential service access; 2) inform the siting of bikeshare stations and investment in bike infrastructure to better assist underserved populations, and finally 3) provide an estimate of the potential for improved job and social services access via bike†to†transit. |
| Keywords: | Engineering |
| Date: | 2018–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7g49j69q&r=all |
| By: | Zhou, Kun; Wang, Yanqiao; Li, Jingquan; Wachs, Marty; Walker, Joan; Meng, Huadong; Friedman, Jason; Zhang, Wei-Bin |
| Keywords: | Engineering |
| Date: | 2018–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt3mk6q1k0&r=all |
| By: | Rodier, Caroline |
| Abstract: | Towards the close of the first decade of the 21st Century, ride-hailing services began to enter the transportation market through smart phone applications that allowed consumers to hail and pay for a ride from drivers using their own vehicle. The information and communication technologies used by these platforms allow for more reliable service, to more locations, with shorter wait times, and at a lower cost than traditional taxi services and, perhaps, public transit. Today, an estimated 15% of adults across the U.S. and 21% in major cities have personally used these services. The successful entrance of ride-hailing services into the transportation market has raised questions about their effect on the overall transportation system, including congestion, total vehicle miles traveled (VMT), and greenhouse gas emissions (GHGs). Reliable answers are limited, in large part, because of their rapid expansion and the lack of publicly available data from these private ride-sharing companies. However, there is now a small body of research, most conducted in 2016 and 2017, that provides some initial evidence on the impacts of these services. This research includes population representative survey data, targeted ride-hailing user survey data, and measured ride-hailing driver and passenger activity data. In addition, the recent interest in automated vehicles has produced modeling studies that also provide insight into the potential effects of ride-hailing services. The following framework was developed to identify the range of possible travel effects, both positive and negative, on users of ride-hailing services. This includes the effects of ride-hailing on auto ownership, trip generation, destination choice, mode choice, network vehicle travel, and land use. |
| Keywords: | Engineering |
| Date: | 2018–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt2rv570tt&r=all |