Shared (Fully) Automated Vehicles
Both short and long distance travel will become more convenient for everyone following AV implementation, and vehicle-miles traveled (VMT) is predicted to rise, as travelers experience falling values of travel time (easier travel),” (Kockelman et al., 2017, p. 138). This is likely to increase congestion on our roadways unless some changes are made, including increasing vehicle occupancy.
Shared (Fully) Automated Vehicles and Dynamic Ride Sharing
Ride Sharing Today
Ride-sourcing services, such as Uber and Lyft, have grown their presence in the world of transportation in the recent years, and through uberPOOL and LyftLine these services have popularized the concept of ride sharing as we know it. Ride sharing offers a number of advantages as, “these shared services typically charge less than regular ride-sourcing offerings and allow for dynamic changing of routes as passengers request pickups in real time,” (Stocker and Shaheen, 2017, p. 11). According to a study conducted by Paolo Santi, adding a shareability network to millions of New York City taxi trips, “cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service,” (Santi et al., 2014). As AVs are implemented, ride sharing is expected to grow even more. SAVs “are a fleet of AVs that provide low-cost service to travelers, possibly replacing the need for personal vehicles,” (Levin et al., 2017, p. 2). SAVs are made more efficient through a system called dynamic ride sharing (DRS).
What is DRS?
Dynamic ride sharing is a form of ride sharing in which people hail a vehicle using their smartphones and they are grouped with a carpool of strangers whose destinations are located on or near a common path. A study using cellphone GPS data in Orlando, Florida found that “nearly 60% of the single-person trips could be shared with other individuals traveling solo and with less than 5 minutes added travel time (to arrive at their destinations), and this value climbs to 80% for 15 to 30 minutes of added wait or travel time,” (Gurumurthy and Kockelman, 2018, p. 1). Brownell and Kornhauser (2014) predict that automated taxis will use road space more efficiently and with a lower incident rate than automobiles, suggesting reduced trip times. DRS should be convenient and reliable enough to be used on a daily basis.
The illustration below shows the differences between a system with and without DRS (Gurumurthy and Kockelman, 2018, p. 7).
A simulation-based study found that SAV systems that used DRS outperformed those without DRS in nearly every aspect, as shown in the chart below (Fagnant and Kockelman, 2016, p. 9).
Austin Network-Based Model Results across Various Scenarios
(56,324 person-trips over 24 hours)
|Measure||With DRS||Without DRS||+30% DRS travel time||+40% DRS travel time|
|Vehicle replacement rate||10.77||>10.77||11.24||11.53|
|Avg. waiting time (min.)||1.18||1.87||1.27||1.37|
|Avg. PM peak waiting time (min.)||4.49||8.96||4.82||4.99|
|Avg. total service (min.)||14.71||14.97||15.20||15.69|
|% Waiting ≥ 10 min.||1.45%||5.65%||1.71%||1.90%|
|% Waiting ≥ 15 min.||0.22%||2.08%||0.27%||0.43%|
|# Shared trips||6,151||0||9,233||11,723|
|% Shared miles||4.83%||0.00%||8.32%||11.20%|
As shown, systems using DRS had reduced passenger wait time and total service time.
Effects on Vehicle Occupancy
According to a report by Dr. Daniel Fagnant and Dr. Kara Kockelman, SAVs offering DRS can increase average vehicle occupancy and reduce regional VMT (Fagnant and Kockelman, 2016). By maximizing the amount of time that an SAV is in use by more than one person at a time, SAVs become a crucial component in increasing vehicle capacity. The continued use of transit, especially automated buses and trains, will also become important in terms of increasing shared trips. The impact of incorporating sharing into the current transportation system can be illustrated in this infographic.
The Institute for Transportation Development and Policy predicts that the number of vehicles on the road by 2050 will decrease by 1.6 billion vehicles through maximizing shared vehicle trips.
Through policy changes, shared trips can be encouraged and single occupancy travel can be discouraged. The following are a few measures recommended by ITDP (ITDP, 2017).
- Discourage or restrict private ownership of AVs
- Added fees for vehicular travel
- Travel subsidies for high occupancy vehicles
While these measures are likely to increase AVO, special attention should be taken to decrease empty driving as much as possible in order to further reduce congestion and VMT associated with AVs.
One of the main concerns and unknowns associated with AVs is the possibility that they will worsen congestion due to empty driving, among other factors. Measures will need to be taken to increase vehicle occupancy in order to achieve maximum benefits.
Causes of Empty Driving
When driverless cars become a reality, people are likely to take advantage of empty driving capabilities.
Empty driving will likely occur in the following scenarios.
- Shared AVs driving to pickup location
- Personal AVs driving to parking after drop-off
- Personal AVs driving home after drop-off
- Shared electric AVs (SAEVs) driving to and from charging stations
- Personal AVs sent to run errands
SAVs and Empty Driving
While SAVs will inevitably lead to a certain amount of empty driving. A study led by Dr. Jun Liu involving a large-scale micro-simulation of SAVs found that, “empty vehicle miles traveled by the fleet of SAVs ranged from 7.8% to 14.2%.” The concern is that this level of empty vehicle travel, while seemingly harmless from an environmental standpoint, “could still turn already-congested cities into far more congested cities, due to the convex nature of link performance functions,” (Liu et al., 2016, p. 2).
Another study led by Dr. Donna Chen yielded similar results, “SAEV fleets are predicted to generate an additional 7.1 to 14.0% of travel miles,” due to empty charging and passenger pickup (Chen et al., 2016). While empty travel could be reduced by strategic charging, “model results also reveal the inherent tradeoffs between reduction of induced ’empty’ travel and improvement of user experience (as measured by wait times and percent of trips served).” In order to enhance the user experience while still reducing empty travel, a dynamic pricing scheme can be utilized, “which penalizes trips that incur more relocation miles” and “incentivizes trips that coincide with strategic relocation,” (Chen et al., 2016, p. 16).
Similarly, IMInnovation discusses the justification of a Road User Charge (RUC) to help manage and minimize the use of empty HAVs, “the charge is likely to be set to reflect the externalities, congestion and emissions, of empty movements.” This would encourage carpooling as well as the use of transit systems (IMInnovation, 2017).
Transportation Network Companies
Schaller Consulting (2018) released a report TNCs providing SAV services around the U.S. Lead author, Bruce Schaller, is Deputy Commissioner for Traffic and Planning at the New York City Department of Transportation and Policy Director at the NYC Taxi and Limousine Commission. He mentions the “heaven or hell” possibilities for SAVs: “In the ‘heaven’ scenario, people rely on SAVs and expanded public transit; electric vehicles replace gasoline power thus reducing greenhouse gas emissions; and acres of surface parking are replaced with parks, affordable housing and other active land uses. In the ‘hell’ scenario, AVs induce sprawl as people are less concerned about long commutes; miles driven and traffic congestion increase in both cities and suburbs; empty cars cruise city streets instead of paying for parking; and public support for bus and rail service erodes, leaving lower-income people stranded.” The resulting outcome depends on consumers, businesses, and each community’s, state’s and nation’s policymakers.
Schaller (2018) does not expect that ride sharing (between strangers, in a self-driving minivan, for example) will become a major component of U.S. travel, since TNC experience has proven the appeal of private rides, in part due to strong U.S. incomes. The report states, “What seems far more likely (for New York City) is the continued centrality of two time-honored modes: door-to-door private ride taxis and fixed-route transit. Both modes can be enhanced by technologies now in use by TNCs and microtransit to provide greater transparency and manage operations in real-time, and by autonomous technologies that promise to dramatically improve safety and reduce costs.” (Schaller, 2018)