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on Transport Economics |
| By: | Circella, Giovanni; Jaller, Miguel; Sun, Ran; Qian, Xiaodong; Alemi, Farzad |
| Abstract: | California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals. Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications. View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, Autonomous vehicles, Connected vehicles, Energy consumption, Forecasting, Impact, Modal split, Pollutants, Pricing, Simulation, Travel demand, Vehicle miles of travel, Zero emission vehicles |
| Date: | 2022–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt0rb439tv&r= |
| By: | Todd, Mike; Scora, George; Luo, Jill |
| Abstract: | This report details the development and application of a spreadsheet tool which enables the evaluation and use of electric and hydrogen Zero Emission Vehicles (ZEVs) within the Caltrans fleet. The spreadsheet tool assists with both the placement of ZEVs and determining placement of new fueling stations to obtain the maximum benefit. The ZEV tool created as a result of this project allows Caltrans to maximize the usage of ZEVs that will be procured within Caltrans. The ZEV tool enables a strategic adoption of ZEVs within the Caltrans fleet by analyzing: fleet parameters, vehicle technology, vehicle usage, refueling infrastructure development, and operational needs. This report summarizes how available information, trip activity, and EVSE have been integrated within a database tool to analyze ZEV integration possibilities. The ZEV tool development provides Caltrans an architecture to integrate evolving data and fleet characteristics while optimizing ZEV placement and utilization into the future. Caltrans staff will be able to utilize the ZEV tool to strategically select vehicles as ZEV capable based on vehicle specifications, refueling infrastructure, and prior vehicle activity. The ZEV tool provides evaluation techniques by vehicle classification, region or refueling/charging capabilities. Utilization of the tool will assist California in the transition to ZEV platforms that are either battery electric or hydrogen fuel cell. The report serves as a guide to utilize the ZEV tool for deployment of ZEVs in the Caltrans fleet while maintaining or improving the effectiveness of operations. The optimized deployment strategy includes the operational and performance criteria of ZEVs while considering the location of refueling/recharging stations for EVSE (electric vehicle supply equipment) and hydrogen ZEVs. View the NCST Project Webpage |
| Keywords: | Business, Engineering, Zero Emission Vehicles, ZEV analysis, ZEV compatibility, ZEV utilization, database tool, trip activity analysis, EVSE utilization, hydrogen refueling, hydrogen refueling utilization, vehicle range analysis, vehicle utilization |
| Date: | 2022–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt6rp1z76p&r= |
| By: | De Wolf, Daniel; Diop, Ngagne; Kilani, Moez |
| Abstract: | We extend a transport model to simulate an increase of the market share of electric vehicles. The main results of our model is that the increase in the share of electric cars in the network must then be accompanied by the implementation of charging stations in sufficient quantity and optimal location, to reduce, note only the waiting times for charging the electrical vehicles, but also to reduce the polluting gas emission of other vehicles. The simulation framework we use is based on the model developed. |
| Keywords: | Transport modeling and simulation ; Electric vehicles ; Deployment of charging stations ; Local pollution ; North of France |
| JEL: | H23 Q5 R4 |
| Date: | 2022–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvco:2022011&r= |
| By: | Roy, Avipsa; Law, Mankin |
| Abstract: | Electric vehicles (EV) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors which may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 74.18% of predicted EVCS placements in Orange County will lie within a low spatial equity zone – indicating populations with the lowest accessibility may require the highest investments in EVCS placements. Within the low spatial equity areas, 14.86% of the area will have a low density of predicted EVCS placements, 50.32% will have a medium density of predicted EVCS placement, and only 9% tend to have high EVCS placements. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all. |
| Date: | 2022–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:hvw2t&r= |
| By: | Hugo Salas Rodríguez (IPA); Pedro I. Hancevic (CIDE) |
| Abstract: | Approximately 70 countries worldwide implement a daylight-saving time (DST) policy: setting their clocks forward in the spring and back in the fall. The main purpose of this practice is to save on electricity. However, by artificially changing the distribution of daylight, this practice can have unforeseen effects. This document provides an analysis of the impact of DST on traffic accidents in Mexico, using two empirical strategies: regression discontinuity design (RDD) and difference-in-differences (DD). The main finding is that setting the clocks forward an hour significantly lowers the total number of traffic accidents in the country’s metropolitan areas. However, there is no clear effect on the number of fatal traffic accidents. |
| Keywords: | : traffic accidents, daylight saving time, difference-in-differences, regression discontinuity, municipalities in Mexico |
| JEL: | O18 R41 D04 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:aoz:wpaper:106&r= |
| By: | Butt, Ali Azhar; Harvey, John; Fitch, Dillon; Kedarisetty, Sampat; Lea, Jeremy D.; Lea, Jon; Reger, Darren |
| Abstract: | The results presented in this report are part of Phase II of a two-phase study. Based on the results from mechanistic models of additional fuel consumption in vehicles due to the structural response of the pavement structure, Phase I of this study concluded that pavement has a small but important enough effect on vehicle fuel consumption to warrant field investigation. The goal of the Phase II study was to measure vehicle fuel consumption in the field on different pavement types in winter and summer and at different speeds, and to use the data collected to develop empirical models for this fuel-consumption effect. The field investigation presented in this report included 21 California pavement sections with different pavement types: flexible, semi-rigid, jointed plain concrete, continuously reinforced concrete, and composite structures. The vehicles selected and instrumented for the fuel economy measurements included a five-axle semi-trailer tractor, a diesel truck, a sports utility vehicle (SUV), a gasoline-fueled car, and a diesel-fueled car. Vehicles were run on cruise control and data were recorded at 45 and 55 mph on state roads and at 35 and 45 mph on local roads. The data from the field investigation were analyzed and used to develop an empirical modeling framework considering road geometry, wind, temperature, and pavement structural and surface (roughness and texture) effects on vehicle fuel consumption. Based on the final framework, a final empirical model was developed for each section. The report presents results of a factorial analysis of the effects of each variable using the final model for each vehicle type on each pavement type and in different California climate regions. The within-section variability is almost always greater than the variability between sections for a given pavement type and efficiency condition (tailwind, speed, and climate region) and the within-section variability is also usually larger than the variability between pavement types. Only the data for the heavy heavy-duty truck (HHDT) showed any meaningful difference in results between sections, but that variability is not tied to pavement type and is only present under certain conditions of speed, tailwind, and air temperature (tied to climate region). These results indicate that missing variables (or errors in the existing variables) need to be reduced in further experiments to observe measurable effects of pavements on fuel consumption in real-world driving. While air temperature interacted with cruise control speed for the HHDT, there was a lack of clear evidence that asphalt roads cause more fuel consumption for the HHDT even under the conditions where the most possible effect of pavement type was found. This suggests that pavement type is not the correct explanation for that variation. Instead, the variation in the effect of air temperature by cruise control speed for the HHDT likely has to do with differences in engine efficiency under different conditions. |
| Keywords: | Engineering, Physical Sciences and Mathematics, Pavement deflection, deflection energy, excess fuel consumption, fuel economy, field testing, mechanistic-empirical analysis, energy models, forward modeling |
| Date: | 2022–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt8zc70841&r= |
| By: | Jaiswal, Sreeja; Bensch, Gunther; Navalkar, Aniket; Jayaraman, T. |
| Abstract: | Railways are a key infrastructure that facilitates trade and regional integration with potential consequences on local development and the environment in hitherto backward regions. In this article, we study the medium- to long-term socio-economic and environmental infrastructure impacts for the case of the Konkan Railway, which is one of the biggest railway construction endeavours in independent India. We employ a quasi-experimental mixed-methods design to explore the impact of the Konkan Railway on population, workforce composition and land cover types using census and satellite data. We find that the Konkan Railway led to an increase in the female-to-male sex ratio and a negative effect on the share of male workers among the working population. In combination with qualitative evidence, this suggests that the railway access has reinforced the pre-existing pattern of high levels of male migration. We also find an increase in population and the workforce participation rate without disparate workforce effects across sectors suggesting that the railway had moderate effects across the local economies. In terms of land use, the analysis could not substantiate concerns regarding substantive loss of forest cover induced by the railways. The findings encourage policy makers - in assessing the effects of transport infrastructure - to take into consideration the impact on migration, labour mobility and labour market outcomes in sending and receiving regions. |
| Keywords: | Infrastructure,railway access,migration,impact evaluation,mixed methods,India |
| JEL: | N75 O18 O40 R11 R41 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:936&r= |
| By: | Tatsuya Abe |
| Abstract: | This paper examines the efficiency and distributional effects of the fuel tax and feebate policies. I employ a model with households' two-stage decisions on car ownership and utilization and estimate model parameters by combining micro-level data from a household survey and macro-level aggregate data for the Japanese new car markets from 2006 through 2013, with a car price endogeneity being dealt with. Counterfactual analyses show that the Japanese feebate results in a significant increase in social welfare while augmenting environmental externalities. In particular, the rebound effect induced by the feebate cancels out about 7% of the reduction in CO2 emissions that would originally have been attained by the fuel economy improvement. In addition, I find that the fuel tax at the current tax rate in Japan is 1.7 times less costly than the product tax, an alternative feebate scheme considered in the counterfactuals, in all income classes to reduce environmental externalities by the same amount, with no difference between the regressivity of the two policies. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:tcr:wpaper:e172&r= |
| By: | Matsumoto, Deanna; Mace, Caitlin; Reeb, Tyler; O'Brien, Thomas |
| Abstract: | Critical to freight movement in Southern California are environmental plans at the Port of Los Angeles (POLA) and Port of Long Beach (POLB). The combined port complex is the single largest fixed source of air pollution in the South Coast Air Basin. This white paper presents three case studies from the San Pedro Bay Ports Clean Air Action Plan (CAAP), including brief analyses of their effects on freight movement in the region. This research also includes a case study of a private-sector, yet-to-be-built infrastructure project designed to support the faster movement of freight out of the San Pedro Bay Ports called the Southern California International Gateway (SCIG). The case studies are provided to elucidate how self-regulating agreements and operator-led programs contribute to regional environmental goals for freight operations. The findings indicate in part that stakeholder power relationships influence the ability to both develop environmental strategies and determine their outcomes. They also indicate that port-focused plans are more effective when their impact on the entire supply chain is considered. The research also helps to illustrate examples of unintended consequences of freight-related environmental measures which will prove useful to policymakers and operators alike. View the NCST Project Webpage |
| Keywords: | Business, Law, Ports, Clean Air Action Plan, Emissions, Drayage, Ocean Going Vessels, Vessel Speed Reduction, Air Quality Action Plans, global supply chain, intermodal rail, freight, cargo handling equipment, ZE trucks |
| Date: | 2022–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt5jb232mt&r= |
| By: | Koike, Atushi; Sakaguchi, Takuhiro; Seya, Hajime |
| Abstract: | This study investigates the relationship between road infrastructure stock and total factor productivity (TFP) using R-JIP2017, a database of productivity by industry for each prefecture in Japan, which allows us to estimate TFP with considering the quality of inputs. Specifically, using the growth accounting method, we estimated TFP for each industry in each prefecture from 1972 to 2012, after the period of high economic growth. Afterwards, we conducted a panel data analysis to explain the estimated TFP by road stock. The results of a panel unit root test indicated the existence of unit roots in the road infrastructure stock. Therefore, unlike many previous studies, a panel autoregressive distributed lag (ARDL) model was used as the empirical model, considering the nonstationarity of the variables. The results of the analysis indicated that road stock had a positive and significant relationship with TFP at the 5% level in the majority of industries, even after the period of rapid economic growth. Further, we found that the two-way fixed effects model, which does not consider the non-stationarity of road infrastructure stock, could produce misleading results. |
| Keywords: | Total factor productivity (TFP); R-JIP; Road infrastructure; ARDL model |
| JEL: | R11 R40 R42 |
| Date: | 2022–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112375&r= |
| By: | Anderson, Anders (Mistra Center for Sustainable Markets (Misum)); Hong, Harrison (Columbia University) |
| Abstract: | Electric bikes are a potentially important tool to address global warming since they can be a viable alternative to cars in urban areas. Governments are using subsidies to promote household adoption. Welfare analyses are challenging, requiring pass-through estimates from transactions, incidence of non-additionality (i.e. those who would have bought even without the subsidy), and resulting substitution from driving. We combine administrative, insurance and survey data from a large-scale Swedish subsidy program in 2018, which is similar to other programs around world, to evaluate these implications. We find (1) complete pass through of the average $500 subsidy to consumers, (2) a near doubling of E-bikes sold but one-third of adopters are non-additional; and (3) a savings of 1.3 tons of carbon emissions during the life of the E-bike. Combining these estimates, an E-bike subsidy program can only be justified with a social cost of carbon that is several hundred dollars higher than what is typically used. |
| Keywords: | Sustainability; household behavior; subsidies; carbon emissions; welfare analysis |
| JEL: | G50 H20 |
| Date: | 2022–03–09 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:hamisu:2022_008&r= |
| By: | Luz, Gregorio; Barboza, Matheus Henrique Cunha; da Silva Portugal, Licinio; Giannotti, Mariana; van Wee, Bert |
| Abstract: | Most of the transport equity and TRSE studies assume that increasing accessibility levels lead to increased activity participation and, therefore, a reduction in social exclusion. Although this assumption makes sense from a theoretical point of view, this causal relationship has not yet been validated in practice. Previous studies investigating the accessibility-participation relationship were inconclusive, indicating that increasing accessibility has a limited impact on activity participation levels, if any. Moreover, the existing empirical evidence in the literature in the Global South context is scarce, is merely correlational and fails to infer causality between both variables. The contributions of the paper are threefold. First, (a) to provide a conceptual model of the causal relationship between accessibility, activity participation and risk of transport-related social exclusion (TRSE); second, (b) to summarise the available empirical evidence about the accessibility-activity participation relationship through a systematic literature review; and third, (c) to provide evidence of the causal relationship between accessibility and activity participation levels in a Global South context. Three Poisson regression models associated with an instrumental variable identification strategy were used to assess the causal effect between accessibility and participation in total, mandatory and discretionary activities in the city of São Paulo, Brazil. The three models showed a highly significant, strong correlation between an individuals’ accessibility level and their actual participation in total, mandatory and discretionary activities. Based on our results, we argue that low accessibility levels may severely restrict individuals’ life chances and add evidence that accessibility has to be an important instrument to support transport policies' decision-making. |
| Date: | 2022–02–19 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:2p896&r= |
| By: | Ariela MICHA; Cecilia POGGI; Francisca PEREYRA |
| Abstract: | This article inspects how the expansion of the platform economy affects gender inequalities, in some new forms as well in reinforcing pre-existing ones. It focuses on two platform occupations in the Buenos Aires Metropolitan Area: ride-hailing and delivery services. First, it explores the ways in which the platform economy constitutes a welcoming environment for female workers. Second, female versus male performance is assessed in terms of hours worked and earnings. Using a combination of qualitative and quantitative approaches it performs a gender gap analysis via linear regression. The article finds that platforms are facilitating an increase in female participation due to three main factors: the impossibility of finding another job, the impersonal recruiting mechanisms and time flexibility offered by platforms. This trend still implies significant gender gaps. The analysis suggests that the differentiated economic performance of male and female riders and drivers is mainly associated to on-the-job characteristics that are reinforced by algorithmic bias in the platform. Women experience more restrictions in terms of when and where they can work, as location and time choices are both constrained by care responsibilities and are also due to the subjective perception of exposure to insecurity and harassment during the work shift. |
| JEL: | Q |
| Date: | 2022–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:avg:wpaper:en13727&r= |
| By: | Sunbin YOO; KUMAGAI Junya; KAWABATA Yuta; MANAGI Shunsuke |
| Abstract: | We provide quantitative evidence of whether a representative sample of the newly introduced fully automatic vehicles (FAVs) are inclusive. We answer this question by examining FAV demand with a focus on natural disaster victims—people who have become physically or mentally challenged due to severe disaster damage, including those with post-traumatic stress disorder. We investigate whether the fear of natural disasters, social support, environmental concerns, the fear of potential accidents, and merits regarding FAVs are motivators of, or hindrances to, purchasing intentions of FAVs. To do so, we acquire a unique dataset covering disaster victims with traumatic disaster damages (12,286 observations in total) and people without such experiences (57,105 observations in total). Then, we construct a multigroup structural estimation model to estimate FAV demand. We conduct estimations of latent and socioeconomic variables which demonstrate people's attitudes. Our findings show that the social support of family, friends, and local authorities is a crucial factor in motivating disaster victims to appreciate and purchase FAVs. The positive impact of social support on appreciating/purchasing FAVs can offset the negative impacts of a fear of natural disasters and accidents, thus enabling more people to enjoy FAVs. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:22017&r= |