Extent and correlates of walking in the USA

This paper examines data about walking trips in the US Department of Transportation’s 2001 National Household Travel Survey. The paper describes and critiques the methods used in the survey to collect data on walking. Using these data, we summarize the extent of walking, the duration and distance of walk trips, and variations in walking behavior according to geographic and socio-demographic factors. The results show that most Americans do not walk at all, but those who do average close to thirty minutes of walking a day. Walk trips averaged about a half-mile, but the median trip distance was a quarter of a mile. A significant percentage of the time Americans’ walk was spent traveling to and from transit trips. Binary logit models are used for examining utility and recreational walk trips and show a positive relationship between walking and population density for both. For recreational trips, this effect shows up at the extreme low and high ends of density. For utility trips, the odds of reporting a walk trip increase with each density category, but the effect is most pronounced at the highest density categories. At the highest densities, a large portion of the effect of density occurs via the intermediary of car ownership. Educational attainment has a strong effect on propensity to take walk trips, for both for utility and recreation. Higher income was associated with fewer utility walk trips but more recreational trips. Asians, Latinos, and blacks were less likely to take utility walk trips than whites, after controlling for income, education, density, and car ownership. The ethnic differences in walking are even larger for recreational trips.     

Data Fusion-Based Traffic Density Estimation and Prediction

Traffic congestion has become a major challenge in recent years in many countries of the world. One way to alleviate congestion is to manage the traffic efficiently by applying intelligent transportation systems (ITS). One set of ITS technologies helps in diverting vehicles from congested parts of the network to alternate routes having less congestion. Congestion is often measured by traffic density, which is the number of vehicles per unit stretch of the roadway. Density, being a spatial characteristic, is difficult to measure in the field. Also, the general approach of estimating density from location-based measures may not capture the spatial variation in density. To capture the spatial variation better, density can be estimated using both location-based and spatial data sources using a data fusion approach. The present study uses a Kalman filter to fuse spatial and location-based data for the estimation of traffic density. Subsequently, the estimated data are utilized for predicting density to future time intervals using a time-series regression model. The models were estimated and validated using both field and simulated data. Both estimation and prediction models performed well, despite the challenges arising from heterogeneous traffic flow conditions prevalent in India.     

Constraints to green vehicle ownership: A focus group study

We consider constraints that prevent people with environmental concerns from buying “green” vehicles that are smaller, more fuel-efficient, and less polluting by using a series of focus group discussions. We find that the features of vehicles currently on the market, family and work responsibilities, residential choices, and routines and preferences all act as constraints. Serious misunderstandings about the environmental impacts of owning and using vehicles also were noted, making it difficult for many to accurately assess their alternatives. For some, environmental concerns are unlikely to influence future vehicle purchase decisions, even if constraints were removed altogether; other priorities have taken and will take precedence over the environmental impacts of their choices.     

Green transportation taxes and fees: A survey of public preferences in California

This paper presents the results of a survey testing whether California residents would support the concept of “green” transportation taxes and fees. Green taxes and fees would be set at variable rates, with higher rates for more polluting vehicles and lower rates for those that pollute less. The results show that the concept of green transportation taxes and fees strongly appeals to Californians. The survey data were also analyzed to identify if sub-groups within the state were particularly likely to support or oppose green transportation taxes and fees. Support for the green taxes and fees held at 50% or higher across most population sub-groups. Bivariate analysis showed that demographic factors were generally poor predictors of support, but that some attitudinal and knowledge variables did correlate with increased support for the green taxes and fees. Multivariate analysis confirmed that pro-environment and pro-government attitudes are significant and strong predictors of support for increasing transportation revenues.     

Would Americans pay more in taxes for better transportation? Answers from seven years of national survey data

Transportation - This paper explores U.S. public opinion about raising new federal transportation revenues, using the results from a national, random-digit-dial phone survey that was conducted for...     

Impacts of Pokémon GO on route and mode choice decisions: exploring the potential for integrating augmented reality,gamification, and social components in mobile apps to influence travel decisions

Transportation - This study aims to understand the impacts of Pokémon GO, a popular location-based augmented reality (AR) mobile gaming app, on route and mode choices. Pokémon GO...     

Ambient air pollution in selected small cities in India: Observed trends and future challenges

Exposure to ambient air pollution is a major threat to human health in most Indian cities. Recent studies have reported that more than three-quarters of the people in India are exposed to pollution levels higher than the limits recommended by the National Ambient Air Quality Standards in India and significantly higher than those recommended by the World Health Organization. Despite the poor air quality, the monitoring of air pollution levels is limited even in large urban areas in India and virtually absent in small towns and rural areas. The lack of data results in a minimal understanding of spatial patterns of air pollutants at local and regional levels. This paper presents particulate air pollution trends monitored over one year in three small cities in India. The findings are important for framing state and regional level policies for addressing air pollution problems in cities, and achieve the sustainable development goals (SDGs) linked to public health, reduction in the adverse environmental impact of cities, and adaptation to climate change, as indicated by SDGs 3.9, 11.6 and 11.b.