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.

No abstract

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 state's 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.

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.

Lack of electric vehicle charging infrastructure is a major barrier to electric vehicle (EV) adoption although the environmental benefits of EVs are well documented. Deployment of this infrastructure should be optimized to maximize use and facilitate adoption of this new technology. Forecasting the amount and location of demand for EVSE will help utilities anticipate and plan for this new electricity load. We present a methodology to forecast the demand for public electric vehicle supply equipment (EVSE) and identify priority locations. This methodology uses travel behavior data from the National Household Travel Survey (NHTS), projections of EV ownership, and spatial data of employer (non-residential) locations. We provide a case study of EVSE deployment in the state of Vermont through 2023. We estimate that each public EVSE in Vermont will serve 0.04 EVs, and that 226 charging stations will be needed by 2023. Cumulative cost estimates of required infrastructure range from $1.6 to $4.7 million over the course of 2013-2023. We identified 40 areas in the state with significant density of EV priority employment. Although forecasting demand for the state in aggregate is helpful for budgetary reasons, planning agencies should also consider the importance of spatial coverage of EVSE, as well as potential clustering of EVs, which may cause clustering of EVSE demand.Finally, we provide a discussion of a variety of business models that can be used to fund EVSE infrastructure installation and maintenance. These models include: subscription services, fee for use, pairing EVSE with solar power, generation and sale of renewable fuel credits (RINs), and public land swaps. Innovative financing methods may allow quicker penetration of EVs and reduce the financial burden of public EVSE installation.