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.     

Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel

This study intends to predict the influence of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with waste cooking palm oil based biodiesel using the artificial neural network (ANN) model. To acquire data for training and testing in the proposed ANN, experiments were carried out in a single cylinder, four stroke direct injection diesel engine at a constant speed of 1500 rpm and at full load (100%) condition. From the experimental results, it was observed that waste cooking palm oil methyl ester provided better engine performance and improved emission and combustion characteristics at injection pressure of 280 bar and timing of 25.5° bTDC. An ANN model was developed using the data acquired from the experiments. Training of ANN was performed based on back propagation learning algorithm. Multilayer perceptron (MLP) network was used for non-linear mapping of the input and output parameters. Among the various networks tested the network with two hidden layers and 11 neurons gave better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and was found to be very well correlated.     

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...     

A Gendered Perspective of the Shelter–Transport–Livelihood Link: The Case of Poor Women in Delhi

Abstract

Women form an important part of the workforce originating from the slums in the city of Delhi, India. The paper illustrates that women spend more time travelling on slower modes of transport to access work; the faster modes are more expensive. Their time–poverty demands they look for work at shorter distances from home. The basic argument presented is that their ability to contribute to the alleviation of their standard of living and their status in society is severely curtailed by their limited mobility and the constrained accessibility to the transport system of the city. This transport deprivation becomes further exacerbated by the process of forced eviction and relocation of low‐income households to the periphery of the city, causing the women to lose livelihood opportunities.