Annual Energy Outlook

We develop a conceptual framework that allows us to define the sharing economy and its close cousins and we understand its sudden rise from an economic-historic perspective. We then assess the sharing economy platforms in terms of the economic, social and environmental impacts. We end with reflections on current regulations and future alternatives, and suggest a number of future research questions.

https://cyberleninka.org/article/n/1477434.pdf

Putting the sharing economy into perspective

Historically, most demographic data for states and substate areas were collected from the long version of the decennial census questionnaire. A “snapshot” of the characteristics of the population on the April 1 census date was available once every 10 years. The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005. Instead of a snapshot in which all of the data are gathered at one time, the ACS aggregates data collected over time, making the results more difficult to interpret. However, the ACS data are updated annually.

A practical guide to the American Community Survey (3-year estimates)

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Environment and Planning B: Planning and Design

There are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous electric vehicles (SAEVs) in a regional, discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas. Results based on 2009 NHTS trip distance and time-of-day distributions indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. SAEVs can serve 96–98% of trip requests with average wait times between 7 and 10 minutes per trip. However, due to the need to travel while “empty” for charging and passenger pick-up, SAEV fleets are predicted to generate an additional 7.1–14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low-mileage households, and thus be competitive with current manually-driven carsharing services and significantly cheaper than on-demand driver-operated transportation services. When Austin-specific trip patterns (with more concentrated trip origins and destinations) are introduced in a final case study, the simulation predicts a decrease in fleet “empty” vehicle-miles (down to 3–4% of all SAEV travel) and average wait times (ranging from 2 to 4 minutes per trip), with each SAEV replacing 5–9 privately owned vehicles.

/pdf/operations-of-a-shared-autonomous-electric-vehicle-fleet-1pjl2av2rr.pdf

Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions

Access to electric vehicle (EV) charging stations will impact EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. This work uses parking information from over 30,000 personal-trip records in the Puget Sound Regional Council’s 2006 household travel survey to determine public (non-residential) parking locations and durations. Regression equations predict parking demand variables (total vehicle-hours per zone/neighborhood and parked-time per vehicle-trip) as a function of site accessibility, local jobs and population densities, trip attributes, and other variables available in most regions and travel surveys. Several of these variables are key inputs for a mixed integer programming problem, developed here for optimal EV-charging-station location assignments. The algorithm minimizes EV users’ station access costs while penalizing unmet demand. This useful specification was used to determine top locations for installing a constrained number of charging stations within 10 miles of Seattle’s downtown, showing how charging location schemes’ access costs respond to parking demand and station locations. The models developed here are generalizable to data sets available for most any region, and can be used to make more informed decisions on station locations around the world.

/pdf/the-electric-vehicle-charging-station-location-problem-a-54rni0o14g.pdf

The electric vehicle charging station location problem: a parking-based assignment method for seattle

The market potential of a fleet of shared autonomous electric vehicles (SAEVs) is explored by using a multinomial logit mode choice model in an agent-based framework and different fare settings. The mode share of SAEVs in the simulated midsize city (modeled roughly after Austin, Texas) is predicted to lie between 14% and 39% when the SAEVs compete with privately owned, manually driven vehicles and city bus service. The underlying assumptions are that SAEVs are priced between $0.75/mi and $1.00/mi, which delivers significant net revenues to the fleet owner–operator under all modeled scenarios; that they have an 80-mi range and that Level 2 charging infrastructure is available; and that automation costs are up to $25,000 per vehicle. Various dynamic pricing schemes for SAEV fares indicate that specific fleet metrics can be improved with targeted strategies. For example, pricing strategies that attempt to balance available SAEV supply with anticipated trip demand can decrease average wait times by 19% to 23%...

Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes:

This paper examines the life-cycle inventory impacts on energy use and greenhouse gas (GHG) emissions as a result of candidate travelers adopting carsharing in US settings. Here, households residing in relatively dense urban neighborhoods with good access to transit and traveling relatively few miles in private vehicles (roughly 10% of the U.S. population) are considered candidates for carsharing. This analysis recognizes cradle-to-grave impacts of carsharing on vehicle ownership levels, travel distances, fleet fuel economy (partly due to faster turnover), parking demand (and associated infrastructure), and alternative modes. Results suggest that current carsharing members reduce their average individual transportation energy use and GHG emissions by approximately 51% upon joining a carsharing organization. Collectively, these individual-level effects translate to roughly 5% savings in all household transport-related energy use and GHG emissions in the U.S. These energy and emissions savings can be primarily attributed to mode shifts and avoided travel, followed by savings in parking infrastructure demands and fuel consumption. When indirect rebound effects are accounted for (assuming travel-cost savings is then spent on other goods and services), net savings are expected to be 3% across all U.S. households.

Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions

19 This paper examines the life-cycle inventory impacts on energy use and greenhouse gas (GHG) 20 emissions as a result of travel ers adopting the carsharing m ode in US settings where shared-car 21 use is reasonable. Here, households already residing in relatively dense urban neighborhoods 22 with good access to transit and traveling relative ly few m iles in private vehicles (roughly 10 23 percent of the U.S. population) are considered good candidates for carsharing. This analysis 24 recognizes the cradle-to-grave im pacts of cars haring on vehicle owne rship levels, travel 25 distances, fleet fuel econom y (partly a result of faster fleet turnover) , parking demand (and the 26 associated infrastructure), and alternative mode use. Analytical results suggest that current 27 carsharing candidates reduce their average individual transportation energy use and GHG 28 emissions by approximately 51% upon joining a carsharing organization. Collectively, these 29 individual-level effects transl ate to roughly a 5% savings in all household transport-related 30 energy use and GHG e missions in the U.S. Thes e energy and e missions savings can be m ostly 31 attributed to m ode shifts and avoided travel, followed by savi ngs in parking infrastructure 32 demands and fuel consum ption. If indirect rebound effects are taken into account (where 33 individuals’ travel-cost savings is then spent on other goods and services), these savings may fall 34 to as little as 3% across all U.S. households. 35

https://www.caee.utexas.edu/prof/kockelman/public_html/TRB15carsharingLCA.pdf

T. Donna Chen

Papers

Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions

The electric vehicle charging station location problem: a parking-based assignment method for seattle

Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes:

Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions

Carsharing's life-cycle impacts on energy use and greenhouse