ITS Benefits >> Free floating, one-way car sharing model reduces GHG per household by up to 18 percent

This study was designed to better understand how car2go is used; how it changes travel behavior; and its impacts on vehicle ownership, driving, and greenhouse gas (GHG) emissions. Car2go is a one-way carsharing service provider with a free-floating operational model.

Car2go is currently the largest carsharing operator in the world, with a presence in nine countries and nearly 30 cities. It operates as a one-way instant access carsharing system within a pre-defined urban zone. The University of California Berkeley’s Transportation Sustainability Research Center (TSRC) conducted a one-way carsharing impact study and found that car2go’s flexible one-way carsharing model can work in tandem with existing mass transit options, reduces the overall number of vehicles on the road (resulting in less air pollution) and ultimately improves mobility in densely-populated areas. The study gathered and analyzed car2go activity data from approximately 9,500 car2go members residing in Calgary, San Diego, Seattle, Vancouver and Washington, DC to determine the impacts on vehicle ownership, modal shift, VMT, and greenhouse gas (GHG) emissions.

Between 2014 and 2015, an online survey was distributed to car2go members in the cities of San Diego, Seattle, Vancouver, Calgary, and Washington, D.C. Across all cities, the survey was completed by 9,497 car2go members.

Overall, the results of this study suggest that car2go one-way carsharing is substantively impacting travel behavior, miles driven, GHG emissions, and the number of vehicles on urban roads within operating regions.

Across the five study cities, it is estimated that:

  • Car2go members sold between 1 to 3 vehicles per car2go vehicle (on average)
  • Car2go members suppressed the need for between 4 to 9 vehicles per car2go vehicle (on average)
  • Overall, when considering both effects together, each car2go vehicle removed between 7 to 11 vehicles from the road of the five cities studied (on average)
  • On balance car2go reduced VMT by 6 percent to 16 percent, per car2go household
  • GHG emissions were reduced by 4 percent to 18 percent per car2go household

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ITS Benefits >> Study shows that utilizing distributed parking facilities can improve the overall performance of an Automated Mobililty-on-Demand system

This paper introduces an extension for SimMobility—a high-fidelity agent-based simulation platform— for simulating and evaluating models for Autonomous mobility on demand (AMOD) systems. As a demonstration case study, preliminary simulations were designed to evaluate the effect of a new policy restricting private vehicle usage within in the high-traffic Central Business District (CBD) in Singapore, with AMOD being introduced as an alternative mode of transportation.

Methodology

This extension was used to explore the effects of different fleet sizes on customer waiting times for two models:

    1. A station-based model where cars self-drive back to stations
    2. A free-floating model where cars self-park at drop-off locations. The simulations were run for the period of 2 hours during evening peak (5:00PM to 7:00PM).

Demand Generation: For this study the SimMobility model assumes all private vehicle trips as a combined modal trip (i.e., Private vehicle + AMOD) if part of the trip is inside the CBD. The mode choice model is modified by making it sensitive to AMOD waiting times and additional cost terms. Further parking prices for private vehicles is reduced as now they have been parked outside the CBD region. For the base case, the total number of AMOD trips for the simulated period was 28,525 trips.

Facility Location and Fleet Sizes: In the station-based model, sets of 10, 20, 30 and 40 high-demand facility locations were analyzed. There was no capacity constraint on the facilities, i.e., the facility could hold as many cars as required. In the free-floating model, initial stations were assumed in the same manner as for the station-based model; however, in the free-floating model, cars were not required to return to these stations. Twelve different fleet sizes were simulated, i.e., from 2000 to 7500 AMOD vehicles in the system. At the beginning of the simulation, vehicles were uniformly distributed over the facilities.

Results

The free- floating model was compared against the station-based model with a varying number of facilities and the effect of different fleet sizes on the performance of AMOD system was assessed.

Number of Customers Served

  • In both models, increasing the vehicle fleet size resulted in a linear increase in the number of passengers served. In the free floating scheme, every additional 100 cars provisioned increased the average demand served by 3.7 percent (1055 people-trips). For the station-based model, this increase was smaller at 2.2 percent (627.55 people-trips).
  • The free-floating model was able to serve 90% of the demand, significantly more than the station-based model (68% of the requested trips). The low service rate in station-based model was likely caused by heavier traffic due to empty vehicle rides.
  • The above is consistent with the average travel time (which can be seen as a proxy metric for road congestion) of both models. The average travel time in the station-based model was higher on average, e.g., with 40 stations and 7500 vehicles the average travel time for the station-based model was 14.17 minutes, ≈ 30% higher than in the free-floating model (10.59 minutes).

Customer Waiting Time Analysis

  • As expected, increasing the AMOD fleet size resulted in a fall in waiting times, since more vehicles were available to service the requested trips. For example, with 20 initial stations, the median waiting time decreased from 20.74 to 1.80 minutes as the fleet size grew from 2000 to 7500 (similarly, the variance in the waiting times decreased from 31.38 to 6.09).
  • Unlike the effect on total demand served, this waiting time change is non-linear and shows diminishing returns—the rate of improvement decreases with increasing fleet size and appears minimal beyond 6000 vehicles.
  • The initial distribution of vehicles (i.e. at the beginning of the day) also influenced the performance of the system; increasing the number of initial stations decreased passenger waiting times. The biggest difference is between 10 and 20 stations, where we observed an average improvement of approximately 4 minutes across fleet sizes. However, further increases in the number of stations resulted in only minimal decreases in waiting times (< 1.5 minutes).

Read more: ITS Benefits >> Study shows that utilizing distributed parking facilities can improve the overall...

ITS Benefits >> Inductive charging on Utah State Univeristy electric bus enables lighter, cheaper batteries while achieving 90 percent power transfer efficiency

This report provides a status review of several emerging Wireless Power Transfer (WPT) technology options for dynamic or stationary charging for electric bus (EB) batteries and rail transit vehicles that promise further advances.

One such funded project includes the WPT electric bus implementation at Utah State University (USU) and later the University of Utah. A start-up at USU named WAVE (Wireless Advanced Electric Vehicles) Inc. is commercializing Inductive Power Transfer (IPT) technology for electric buses after developing WAVE transit technology within its Electrodynamics Lab.

The WAVE electric bus operates by wirelessly charging its battery from a charging pad located under the road surface. A primary transmitter of 50kW power at 20kHz is embedded in the roadway, and an identical secondary receiver is mounted underneath the bus, allowing wireless power transfer over a large air gap of 6-10 inches.

Unveiled in 2012, the initial WAVE technology bus prototype was a USU campus shuttle, the Aggie Bus, which modified a 22-foot electric eBus to recharge its nickel cadmium battery (NiCd) for 5 minutes every 15 minutes. The Aggie Bus has achieved 90 percent power transfer efficiency for 25 kW at 20 kHz across several inch air gaps during station stops over road-embedded powered coil generating the IPT magnetic field. With FTA TIGGER-3 funding, USU and the Utah Transit Authority (UTA) later implemented the WAVE bus at the University of Utah, where it has been operational since October 2014.

Benefits of WPT technology

  • Higher energy efficiency than conventional wired alternatives.
  • Reduces vehicle cost by allowing for smaller, lighter, and lower capacity batteries.
  • Reduces noise pollution.
  • Has the potential to improve system operational safety, since road-embedded infrastructure has no exposed high voltage cables or power outlets for plug-in hybrid and electric buses.

Read more: ITS Benefits >> Inductive charging on Utah State Univeristy electric bus enables lighter, cheaper...

ITS Benefits >> Study finds that by 2035, a 50 percent penetration of electric drayage trucks for near-dock and off-dock service in the LA-area would reduce GHG emissions by 30 percent.

The Ports of Los Angeles and Long Beach (POLA/LB) account for a significant percentage of diesel particulate matter and NOx emissions within the South Coast Basin. POLA/LB’s 2010 Clean Air Action Plan (CAAP) outlined several goals under technology advancement including achieving higher efficiency and zero emissions through the utilization of Zero Emissions Container Movement and Zero Emissions Drayage Trucks (ZEDTs).

A study out of the University of California, Davis looked at evaluating emissions reductions that would result from the adoption of electric drayage trucks over a period from 2015 - 2035 and assessed how performance-based regulation might increase the rate of electrification in drayage trucking.

For the analyses, two scenarios for drayage truck electrification were compared to a reference case scenario.

  • Scenario 1- conservative electrification scenario (CES) where 50 percent of near-dock vehicle-miles-traveled (VMT) are met with electric trucks by 2035.
  • Scenario 2- optimistic scenario (OPT) where 50 percent of both near and off dock drayage VMTs are electrified by 2035.

The reference case considered a "business as usual" approach, where drayage transportation fuel consumption continues to be dominated by diesel through 2035.

Drayage truck VMTs were modeled based on the volume of twenty-foot equivalent unit (TEU) containers at the POLA/LB. Truck travel distances and loads were modeled based on container trip destinations. The model assumed linear growth in both container volume and truck VMT generation. Conventional drayage truck emissions were estimated using the California Mobile Emissions Factor Database (EMFAC) which contains both historical data and forecasts for emissions rates of all types of heavy duty transport.

Findings

The CES scenario produced only moderate reductions in emissions for each of the three environmental flows considered.

  • 50 percent electrification of near dock trips reduced 2035 drayage GHG emissions by 4 percent (~9500 tons CO2e), NOX emissions by 3 percent
  • PM2.5 emissions were basically unchanged from the reference case (14 metric tons PM2.5)

The OPT scenario resulted in emissions trajectories that begin to diverge more significantly from the reference case.

  • 2035 GHG emissions decreased by 30 percent, from 250 to 170 thousand metric tonnes CO2e
  • NOX emissions also decreased substantially with increased electrification of off-dock rail
  • 2035 emissions were reduced by 28 percent
  • Total emissions by 12 percent, or approximately 1,200 tonnes NOX

ZEDT deployment also shifts emissions away from the port facility. Emissions from ZEDTs take place mostly at the site of electricity generation. In the case of criteria pollutants, this could provide an important benefit in the form of relocating emissions away from sensitive receptors.

  • PM2.5 emissions were actually expected to increase with increased electrification, with a 7 percent total increase observed under the OPT scenario due to emissions from electricity production.
  • While total PM2.5 in the OPT scenario increased, emissions at the port facility actually decreased by 26 percent.
  • Operating and implementation costs for the ZEDT deployment under the conservative scenario increased total costs over the study period by 27 percent, or $124 million dollars.
  • While costs were generally higher under the conservative scenario, fuel costs did decrease by 15 percent, or $15 million, from the reference case fuel cost of $105 million.

Read more: ITS Benefits >> Study finds that by 2035, a 50 percent penetration of electric drayage trucks for...

ITS Benefits >> "Vehicle Entering When Flashing" Signs at stop-controlled intersections in North Carolina yield a 7 percent reduction in crashes.

North Carolina Department of Transportation (NCDOT) has utilized "Vehicle Entering When Flashing" (VEWF) systems to provide an active, real-time warning that delivers motorists with more information about intersection conditions. Depending on the assembly placement, they may be used to warn drivers approaching an intersection if a vehicle is entering the intersection from the minor road, or they may be used to provide guidance on gap selection for stopped drivers. The assemblies include vehicle-actuated warning signs for stopped vehicles, vehicle-actuated warning signs for through vehicles, or a combination of both.

The purpose of this project was to determine if the installation of VEWF intersection warning systems reduce the number and severity of crashes at various types of two-way stop controlled intersections. Because a variety of sign configurations are used, this study determines if a particular sign placement and usage provides more safety benefit. This study will compare the crash data of stop controlled intersections before and after the assembly installation.

Methodology

74 intersections covering four categories of VEWF intersection warning systems were evaluated.

  1. Category 1 – Overhead Signs and Flashers at the Intersection on Major, Loop on Minor (24 sites)
  2. Category 2 – Overhead Signs and Flashers at the Intersection on Minor, Loop on Major (19 sites)
  3. Category 3 – Post Mounted Signs and Flashers in Advance of Intersection on Major, Loop on Minor (23 sites)
  4. Category 4 – Locations with Combination of Category 1 through Category 3 (8 intersections)

Treatment sites were located in urban and rural areas with mainline approach speed limits ranging from 35 mph to 55 mph, although the majority of sites were rural, isolated, high speed facilities. The intersection annual average daily traffic (AADT) ranged from approximately 3,000 to 30,000 vehicles entering per day. The type of mainline facilities varied with the intersection geometry including 56 two-lane at two-lane intersections, 11 four-lane divided at two-lane intersections, and 7 multi-lane undivided at two-lane intersections.

A before and after crash analysis was performed at each intersection utilizing the Traffic Engineering Accident Analysis (TEAAS) software that accessed the North Carolina Traffic Records Database. The time periods analyzed for each location varied depending on when the treatment was installed. In most cases, the ending dates for the analyses were determined by the available crash data at the time the crash analysis was completed, which was through October 31, 2011. Some of the analysis periods were over ten years in duration, and the average before period was approximately five years.

Empirical Bayes before and after techniques were utilized to account for selection bias and to overcome the threat of regression to the mean, along with other potential deficiencies in a naïve before and after analysis. The Empirical Bayes approach requires the use of reference sites as well as before period data from the treatment site to estimate the expected safety of the treatment site had no improvements been made. Approximately five reference sites per treatment site were chosen.

Findings

  • For all 67 treatment locations, the results of the Empirical Bayes analysis with the traffic volume adjustment yielded
    • 3 percent (+/- 5 percent) reduction in target crashes
    • 7 percent (+/- 4 percent) reduction in total crashes
    • 6 percent (+/- 6 percent) reduction in injury crashes
    • 16 percent (+/- 16 percent) reduction in severe injury crashes
  • There was a noticeably greater reduction in total crashes for Categories 3 and 4 (19.3 percent, 25.1 percent) than Categories 1 and 2 (-9.1 percent, 3.5 percent). Category 1 actually showed an increase in total crashes.
  • Crash analysis results for four-lane divided at two-lane intersections (11 of the intersections studied) showed no apparent reduction in crashes. Overall, the locations with this intersection geometry did not experience a reduction in total, target, injury or severe injury crashes. The use of VEWF systems at four-lane divided at two-lane intersections may be more a “band-aid? treatment that does not address the root cause of crashes in this situation and a geometric change (i.e. directional crossover, offsetting minor road legs, or crossover closure) may be a more appropriate solution to address crash patterns likely related to gap-acceptance.

Read more: ITS Benefits >> "Vehicle Entering When Flashing" Signs at stop-controlled intersections in North...

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