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on Transport Economics |
| By: | Ramji, Aditya; Das, Anannya; Ladha, Rijhul; Palia, Ridhi |
| Keywords: | Social and Behavioral Sciences, transportation policy, electric vehicles, electric trucks, India |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt22s2v6k5 |
| By: | Circella, Giovanni PhD; Iogansen, Xiatian; Matson, Grant; Makino, Keita; Malik, Jai K. PhD; Lee, Youngsung PhD |
| Abstract: | This report summarizes the findings from ten sets of analyses that investigated ways the COVID-19 pandemic transformed people's activity-travel patterns. Data were collected through three waves of surveys in Spring 2020, Fall 2020, and Summer 2021 in California and the rest of the US. We found that there was a substantial shift among California workers from physical commuting to exclusive remote work in 2020, followed by a transition to hybrid working schedules by Summer 2021. The adoption of remote work and hybrid work varied significantly among population subgroups, with higher income, more educated individuals, and urban residents showing the greatest shift to these arrangements. In terms of mode use and vehicle ownership, increased concerns about the use of shared modes of travel correlated with an increasing desire to own a car. We observed a major decrease in walking for commuting purposes and a significant increase in walking and biking for non-work trips. The study also found a reduction in the demand for, and/or an elevated aversion to, ridehailing because of the shared nature of the service. Regarding shopping patterns, the study found a nearly five-fold increase in the number of respondents who shopped online at least once per week between Fall 2019 and Spring 2020. However, part of this increase vanished by Fall 2020. Overall, the pandemic brought both temporary changes and longer-term impacts. The study proposes strategies to promote sustainable transportation and social equity among different population groups as communities strive to recover from the pandemic. |
| Keywords: | Engineering, COVID-19, mobility, travel behavior, activity choices, online shopping, telecommuting, data collection |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt2102b2zq |
| By: | Teevrat Garg; Ryan Hanna; Jeffrey Myers; Sebastian Tebbe; David G. Victor |
| Abstract: | To minimize the environmental costs of electric vehicles (EVs) and support decarbonizing electric grids, drivers must charge their EVs when renewable energy generation is abundant. To induce a shift in charging behavior toward daytime hours with ample solar energy, we conducted a field experiment (n = 629) at a large workplace to measure the influence of environmental nudges and financial incentives on the usage and timing of workplace charging. Environmental nudges led drivers to shift from early to later morning charging, whereas discounts to charge at work increased total workplace charging and prompted a shift from daytime to early morning and overnight charging. We identify three clusters of mechanisms explaining these temporal shifts: the utilization and reliability of the charging network, concerns about charger scarcity, and driver characteristics. Finally, we compute the societal effects of CO2 emissions and marginal electricity costs of these shifts in workplace charging sessions. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11445 |
| By: | Lu, Hongyu; Rodgers, Michael O.; Guensler, Randall |
| Abstract: | In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis. View the NCST Project Webpage |
| Keywords: | Engineering, Synthetic Household, Synthetic Fleet, Emission Modeling, Travel Demand Model |
| Date: | 2024–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt20h266r6 |
| By: | Stephen J. Redding |
| Abstract: | This paper reviews recent quantitative urban models. These models are sufficiently rich to capture observed features of the data, such as many asymmetric locations and a rich geography of the transport network. Yet these models remain sufficiently tractable as to permit an analytical characterization of their theoretical properties. With only a small number of structural parameters (elasticities) to be estimated, they lend themselves to transparent identification. As they rationalize the observed spatial distribution of economic activity within cities, they can be used to undertake counterfactuals for the impact of empirically-realistic public-policy interventions on this observed distribution. Empirical applications include estimating the strength of agglomeration economies and evaluating the impact of transport infrastructure improvements (e.g., railroads, roads, Rapid Bus Transit Systems), zoning and land use regulations, place-based policies, and new technologies such as remote working. |
| Keywords: | cities, commuting, transportation, urban economics |
| Date: | 2024–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2053 |
| By: | Panle Jia Barwick; Hyuk-Soo Kwon; Shanjun Li; Yucheng Wang; Nahim B. Zahur |
| Abstract: | This paper examines the impact of industrial policies (IPs) on innovation in the global automobile industry. We compile the first comprehensive dataset linking global IPs with patent data related to the auto industry from 2008 to 2023. We document a major shift in policy focus: by 2022, nearly half of all IPs targeted electric vehicles (EV)-related sectors, up from almost none in 2008. In the meantime, there has been a clear technological transition from internal combustion engine (GV) technologies to EV innovations. Our analysis finds a positive relationship between policy support and innovation activity. At the country level, a one-standard-deviation increase in five-year cumulative EV-targeted IPs is associated with a four-percent rise in new EV patent applications. Firm-level analyses (using OLS, IV, and PPML) indicate that a ten-percent increase in EV financial incentives received by automakers and EV battery producers leads to a similar four-percent increase in EV innovations. We confirm the importance of path dependence in the direction of technology change in the automobile industry but find no evidence that EV-targeted IPs stimulate innovation in GV technologies. |
| JEL: | H20 L5 L60 L62 O3 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33138 |
| By: | Reichard, Will; Guensler, Randall |
| Abstract: | This study applies bootstrap analysis to historic transportation project item cost data to develop improved estimates of item costconfidence bounds for use in transportation project cost uncertainty analysis (a component of lifecycle analysis). Bootstrap regression results of confidence bounds will then be integrated into AASHTOWare Project Cost Estimator so that Monte Carlo procedures can estimate project-level confidence intervals for use in lifecycle project cost analysis and transportation capital planning. Data and functions contained within AASHTOWare (a cost estimation software licensed to the Departments of Transportation for over 40 states and the District of Columbia) are employed in the analyses. Coordinating with the Georgia Department of Transportation to obtain a research license for AASHTOWare took longer than expected, resulting in projectdelays. This report summarizes the work completed to date and describes the remaining steps required to finish the study and for the primary author to publish a final dissertation. View the NCST Project Webpage |
| Keywords: | Engineering, AASHTOWare, construction cost estimation confidence bounds, infrastructure design and alternatives assessment. |
| Date: | 2024–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt5xj9b8jm |
| By: | Leslie Sheng Shen; Hillary Stein |
| Abstract: | Global shipping costs have soared to record highs in recent years. Costs spiked during the COVID-19 pandemic, driven by supply chain disruptions, labor shortages, and port congestion. Costs had eased by mid-2023, but they began rising again later that year and into 2024 due to Houthi violence off the coast of Yemen that restricted access to the Suez Canal and a drought at the Panama Canal that limited vessel traffic and forced rerouting around the Cape of Good Hope. As global shipping costs have soared, US import prices also have increased. |
| Keywords: | shipping costs; import prices; cost-price pass-through; inflation |
| JEL: | E31 F14 |
| Date: | 2024–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbcq:99072 |