Justine Sears

Weather factor impacts on commuting to work by bicycle

OBJECTIVE Quantify the impact of weather conditions on individual decisions to commute to work by bicycle among a diverse panel of adults who commute ≥2 miles each way. METHOD Working adults (n=163) in a northern U.S. state reported transportation mode for four seven-day periods in 2009-2010 that maximized seasonal weather variations. Personal characteristics, trip to work distances, and commuting mode data were linked to location- and time-specific weather data and daylight hours. Analyses focused on effect of weather conditions on reports of commuting by bicycle. RESULTS Participants were diverse in age, gender and bicycle use, but were relatively well-educated; they traveled to work by bicycle on 34.5% of the logged commuting days. Modeling indicated that the likelihood of bicycle commuting increased in the absence of rain (odds ratio=1.91; 95% confidence interval 1.42, 2.57) and with higher temperatures (1.03; 1.02, 1.04), and decreased with snow (0.90; 0.84, 0.98) and wind (0.95; 0.92, 0.97). Independent effects also were found for bicycle commuting distance, gender, and age, but not for daylight hours. CONCLUSION Precipitation, temperature, wind and snow conditions had significant and substantial independent effects on the odds of travel to work by bicycle among a diverse panel of adult bicycle commuters.

Travel Demand and Charging Capacity for Electric Vehicles in Rural States: Vermont Case Study

As the number of electric vehicles (EVs) increases, planners must consider not only how this fuel switch may affect the electrical power infrastructure but also mobility. The suitability and charging requirements of these vehicles may differ in rural areas, where the electrical grid may be less robust and the number of miles driven higher. Although other studies have examined issues of regional power requirements of EVs, none has done so in conjunction with the spatial considerations of travel demand. For the forecast of both the future spatial distribution of EVs and the ability of these vehicles to meet current daily travel demand, this work used three data sets: the National Household Travel Survey, geocoded Vermont vehicle fleet data, and a geocoded data set of every building in the state. The authors considered spatial patterns in daily travel and home-based tours to identify optimal EV-charging locations and any area types that are unsuited for widespread electric vehicle adoption. Hybrid vehicles were found to be more likely to be adjacent to other hybrids than were conventional vehicles. This apparent clustering of current hybrid vehicles, in both urban and rural areas, suggests that the distribution of future EVs may also cluster. The analysis estimated that between 69% and 84% of the states vehicles could be replaced by EVs with a 40-mi range, but that estimate was dependent on the availability of workplace charging. Problematic areas for EV adoption may be suburban areas, where both residential density (and potential clustering of hybrids) and miles driven are high. The results suggest that EVs are viable for rural mobility demand but require special consideration for power supply and vehicle-charging infrastructure.

To Bike or Not to Bike: Seasonal Factors for Bicycle Commuting

Research with a panel of working adults in northern communities was conducted to assess the impact of weather on commuting to work by bicycle. Participants commuted at least 2 mi each way and commuted by bike more than twice annually. Transportation mode was recorded for four 7-day periods in 2009 and 2010 (one sampling period per season). Mode, personal characteristics, and commute length were linked to location- and time-specific weather conditions and to daylight hours on commuting days. Analyses focused on the effects of season, weather, and other factors to develop binary models for commuting by bicycle. The likelihood of bike commuting increased 3% with every 1°F increase in morning temperature and decreased by 5% with a 1 mph increase in wind speed. Likelihood of biking to work was more than double on days with no morning precipitation. Hours of daylight had no discernible effect, although study participants cited this as a barrier in the baseline survey. Distance to work negatively affected the likelihood of bike commuting. Men were nearly twice as likely to bike commute on a given day as were women. Separate models for men and women suggested that these groups responded similarly to adverse weather conditions, although some effects were less pronounced among women because of a smaller sample size. An appreciable portion of participants biked to work throughout the year in a variety of weather conditions, a result that suggested that a northern climate might not necessarily preclude year-round bike commuting. Multimodal commuting was prevalent in the sample: on 20% of the days that participants reported biking to work, they reported returning home by another mode. Helping cyclists learn to deal safely with cold and dark conditions and facilitation of multimodal bicycle commuting may promote wider use of bicycle commuting and extend the northern bicycle commute season.

Allocation of Intrahousehold Motorized Vehicles: Exploration with the 2009 National Household Travel Survey

This study examines allocation of intrahousehold vehicles to drivers and trips by using data from the 2009 National Household Travel Survey and is motivated by the knowledge that reallocating household vehicles is a reasonable short-term action to reduce fuel and associated emissions. Models are developed for households in the national sample and for segmentations by population and census regions. Binomial logistic regression is used to model whether a household fleet is optimal and is a high-potential saver (HPS). Of households with two or more vehicles, 31% are classified as HPS. Linear regression is used to model the number of gallons of fuel a household can potentially save per year with vehicle reallocation. About 59% of households can reduce fuel consumption by an estimated 5.2%, or approximately 5 billion gallons of fuel nationally, if they reallocate their fleet. Household size and life-cycle, travel behavior, and fleet composition are related to allocation of intrahousehold vehicles. Similar variables are significant predictors of potential gallons of savings and whether a household is an HPS. Models are consistent across regions with minor exceptions. Rural areas had differences from more urban areas. This study has demonstrated that appreciable savings in fuel consumption and associated emissions are plausible through vehicle reallocation, and the ability to pursue this countermeasure in the short-term motivates further research to provide fuller understanding of the causal mechanisms and target households for intervention.

Measure for Measure: Energy Utility Model for Standardized Evaluation of Transportation Efficiency Measures

As the environmental and financial costs of conventional gasoline become more apparent, interest in the concept of transportation efficiency is growing. Broadly, this concept involves using less energy to meet current travel demand and often uses a systems-level approach. The transportation sector has much to learn from the electric and thermal energy sectors, whose demand management strategies have used established screening tools to assess the environmental and financial benefits of efficiency measures for years. The adoption of such screening tools may be ideal for the transportation sector as electric vehicles (EVs) bring these two sectors together. An example discusses how the Vermont state screening tool can be used to evaluate a transportation measure: switching from a conventional vehicle to an EV. Screening tool results demonstrate that the estimated cost benefits of an EV vary from –

Assessment of Level 1 and Level 2 Electric Vehicle Charging Efficiency

15,911 to

A comparison of electric vehicle Level 1 and Level 2 charging efficiency

24,645, depending on the EV model, miles driven annually, and externalities considered, among other factors. The cost-effectiveness of EVs was improved by including avoided health costs because of reduced tailpipe emissions. More broadly, results showed that cost-effectiveness screening tools used in the electric and thermal energy sectors provided a meaningful way to assess potential gains in transportation efficiency and could be used to evaluate other transportation efficiency measures (e.g., bicycle and walking infrastructure, transit). The use of such screening tools will increase communication and collaboration between the energy and transportation sectors while facilitating a systems-based approach to transportation planning and demand management.

Forecasting demand of public electric vehicle charging infrastructure

North American plug-in electric vehicle (EV) sales are projected to grow steadily in the next decade, and EVs are expected to become a significant portion of the vehicle feet. Widespread electrification of personal transport will require coordination between the electricity and transportation sectors. The coordination could include application of electric efficiency incentives that are commonly used in the energy sector for more efficient products, such as energy star appliances. The use of incentives for more efficient vehicles and charging equipment will facilitate a faster transition to this transformative technology. If EVs and electric vehicle supply equipment (EVSE) are found to be eligible for incentives, electric utilities could create programs that would accelerate EV deployment. The efficiency of 120-V Level 1 and 240-V Level 2 EVSE was compared with FleetCarma logger data collected from 1,008 Chevrolet Volt charging events. On average, Level 2 charging was 3% more efficient than Level 1 charging, but this percentage increased with shorter charge times. When less than 2 kW-h was drawn from the grid, Level 2 charging was 13% more efficient than Level 1. Although Level 2 charging was more efficient at all temperatures, the differences in efficiency were greater at high (above 708F) and low (less than 538F) temperatures. The greatest efficiency gains for Level 2 charging are expected at public charging locations, where charge times tend to be shorter and weather conditions more variable, as opposed to residential sites. Providing incentives for efficient EV charging infrastructure through utility and government programs will ensure optimal long-term investment in this new technology, reduce energy use, and facilitate more rapid uptake of EVs. EV charging infrastructure is eligible for federal funding under the Moving Ahead for Progress in the 21st Century Act. Thus, transportation agencies could include efficiency benchmarks as a requirement for project financing.