The impact of weather conditions on bikeshare trips in Washington,DC

Bicycle usage can be affected by colder weather, precipitation, and excessive heat. The research presented here analyzes the effect of weather on the use of the Washington, DC, bikeshare system, exploiting a dataset of all trips made on the system. Hourly weather data, including temperature, rainfall, snow, wind, fog, and humidity levels are linked to hourly usage data. Statistical models linking both number of users and duration of use are estimated. Further, we evaluate trips from bikeshare stations within one quarter mile of Metro (subway) stations at times when Metro is operating. This allows us to determine whether Metro serves as a back-up option when weather conditions are unfavorable for bicycling. Results show that cold temperatures, rain, and high humidity levels reduce both the likelihood of using bikeshare and the duration of trips. Trips taken from bikeshare stations proximate to Metro stations are affected more by rain than trips not proximate to Metro stations and less likely when it is dark. This information is useful for understanding bicycling behavior and also for those planning bikeshare systems in other cities.     

Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach

Cycling is often promoted as a means of reducing urban congestion and improving health, social and environmental outcomes. However, the quantification of these potential benefits is not well established. This is due in part to practical difficulties in estimating cycling demand and a lack of sound methodologies to appraise cycling initiatives. In this paper we attempt to address this need by developing predictive models of cycle demand, relative to other transport modes, that capture not only the impacts of observed characteristics such as age and travel time but also the role of attitudes and perceptions. Using data from a stated preference survey, we estimate a hybrid choice model for cycle use that incorporates the role of attitudes towards cycling, perceptions of the image associated with cycling, and the stress arising from safety concerns. Model results indicate that the latent attitudes and perceptions explain an important part of the non-observable utility in a simple multinomial logit choice model. We also demonstrate policy analysis using the hybrid choice model, which allows comparisons of ‘hard’ policies such as the provision of parking facilities against ‘soft’ measures such as cycle promotion schemes.     

In-use measurement of the activity, fuel use, and emissions of eight cement mixer trucks operated on each of petroleum diesel and soy-based B20 biodiesel

In-use micro-scale fuel use and emission rates were measured for eight cement mixer trucks using a portable emission measurement system. Each vehicle was tested on petroleum diesel and B20 biodiesel. Average fuel use and emission rates increase monotonically versus engine manifold absolute pressure. A typical duty cycle includes loading at a cement plant, transit while loaded from the cement plant to work site, creeping in a queue of vehicles at the worksite, unloading, and transit without load from the site to the plant. For B20 versus petroleum diesel, there is no significant change in the rate of fuel use, CO₂ emissions, and NO emissions, and significant decreases in emissions for CO, hydrocarbons, and particulate matter. For loaded versus unloaded onroad travel, fuel use and CO₂ emissions rates are approximately 60% higher and the rates for other pollutants are approximately 30–50% higher. A substantial portion of cycle emissions occurred at the work site. Inter-vehicle and intra-cycle variability are also quantified using the micro-scale methodology.     

The construction and use of continuous autoregressive models for traffic indices

This article discusses the use of continuous autoregressive models to describe the behavior of traffic indices. From discretely sampled data, second-order differential equation models are constructed to represent dynamic traffic fluctuations as the response of a linear system to a stochastic forcing function. The results are compared to the more common M/G/∞ queueing model approach, and the analysis is demonstrated on time series of aircraft concentration in thirty-one enroute air traffic control sectors.     

The strategic transportation research study — Its origins and purposes

The highway industry in the United States spends about $35 to $40 billion annually. Management of the industry is almost wholly decentralized. This decentralization plus diminishing fuel tax revenues used to finance road improvements have caused road research efforts to decline to a very low level. Comparisons between funds for highway research and those spent by private firms in similar industries show that private firms spend from 5 to 12 times the rate of highway agencies. The problem of how much to spend on research is difficult both for private-sector and for public-sector enterprises. The level of research spending is shown to correlate well with both profitability and growth in U.S. firms. Four methods used for making research decisions in the private sector are discussed. The goals of the Strategic Transportation Research Study (STRS), which is being conducted by the Transportation Research Board to examine highway and transportation needs, are described.     

An assignment principle for urban networks based on travel budgets

A new assignment principle for traveler behavior in an urban network is described which is based on empirical findings in the theory of travel budgets. It characterizes the distribution of travelers, demand, and modal split. It treats all travel decisions (whether to travel, where to go, how to get there) and the important costs (time and money) in a single, unified way. A numerical technique is proposed and it is applied to several examples to illustrate qualitative features.     

Simplical decomposition of the asymmetric traffic assignment problem

This paper presents a convergent simplicial decomposition algorithm for the variational inequality formulation of the asymmetric traffic assignment problem. It alternates between generating minimum path trees based on the cost function evaluated at the current iterate and the approximate solving of a master variational inequality subject to simple convexity constraints. Thus it generalizes the popular Frank-Wolfe method (where the master problem is a line search) to the asymmetric problem. Rules are given for dropping flow patterns which are not needed to express the current iterate as a convex combination of previous patterns. The results of some computational testing are reported.