Influences of infrastructure and attitudes to health on value of travel time savings in bicycle journeys

In this paper we investigate how attitudes to health and exercise in connection with cycling influence the estimation of values of travel time savings in different kinds of bicycle environments (mixed traffic, bicycle lane in the road way, bicycle path next to the road, and bicycle path not in connection with the road). The results, based on two Swedish stated choice studies, suggest that the values of travel time savings are lower when cycling in better conditions. Surprisingly, the respondents do not consider cycling on a path next to the road worse than cycling on a path not in connection to the road, indicating that they do not take traffic noise and air pollution into account in their decision to cycle. No difference can be found between cycling on a road way (mixed traffic) and cycling in a bicycle lane in the road way. The results also indicate that respondents that include health aspects in their choice to cycle have lower value of travel time savings for cycling than respondents that state that health aspects are of less importance, at least when cycling on a bicycle path. The appraisals of travel time savings regarding cycling also differ a lot depending on the respondents’ alternative travel mode. The individuals who stated that they will take the car if they do not cycle have a much higher valuation of travel time savings than the persons stating public transport as the main alternative to cycling.

Prediction intervals to account for uncertainties in travel time prediction

The accurate prediction of travel times is desirable but frequently prone to error. This is mainly attributable to both the underlying traffic processes and the data that are used to infer travel time. A more meaningful and pragmatic approach is to view travel time prediction as a probabilistic inference and to construct prediction intervals (PIs), which cover the range of probable travel times travelers may encounter. This paper introduces the delta and Bayesian techniques for the construction of PIs. Quantitative measures are developed and applied for a comprehensive assessment of the constructed PIs. These measures simultaneously address two important aspects of PIs: 1) coverage probability and 2) length. The Bayesian and delta methods are used to construct PIs for the neural network (NN) point forecasts of bus and freeway travel time data sets. The obtained results indicate that the delta technique outperforms the Bayesian technique in terms of narrowness of PIs with satisfactory coverage probability. In contrast, PIs constructed using the Bayesian technique are more robust against the NN structure and exhibit excellent coverage probability.

Travel time and speed of gray whales and amphipod abundance along Chukotka Peninsula in 2006

The purpose of this study was to evaluate summer and fall residency and habitat selection by gray whales, Eschrichtius robustus, together with the biomass of benthic amphipod prey on the coastal feeding grounds along the Chukotka Peninsula. Thirteen gray whales were instrumented with satellite transmitters in September 2006 near the Chukotka Peninsula, Russia. Nine transmitters provided positions from whales for up to 81 days. The whales travelled within 5 km of the Chukotka coast for most of the period they were tracked with only occasional movements offshore. The average daily travel speeds were 23 km/day (range 9-53 km/day). Four of the whales had daily average travel speeds <1 km/day suggesting strong fidelity to the study area. The area containing 95% of the locations for individual whales during biweekly periods was on average 13,027 km**2 (range 7,097-15,896 km**2). More than 65% of all locations were in water <30 m, and between 45 and 70% of biweekly kernel home ranges were located in depths between 31 and 50 m. Benthic density of amphipods within the Bering Strait at depths <50 m was on average ~54 g wet wt/m**2 in 2006. It is likely that the abundant benthic biomass is more than sufficient forage to support the current gray whale population. The use of satellite telemetry in this study quantifies space use and movement patterns of gray whales along the Chukotka coast and identifies key feeding areas.

Real-time reliability test for a CPV module based on a power degradation model

Models based on degradation are powerful and useful tools to evaluate the reliability of those devices in which failure happens because of degradation in the performance parameters. This paper presents a procedure for assessing the reliability of concentrator photovoltaic (CPV) modules operating outdoors in real-time conditions. With this model, the main reliability functions are predicted. This model has been applied to a real case with a module composed of GaAs single-junction solar cells and total internal reflection (TIR) optics

The value of travel time: new estimates developed using a speed-choice model. Final report.

Mode of access: Internet.

Effect of freeway corridor attributes upon motorist diversion responses to real-time travel time information.

Texas Department of Transportation, Austin

Travel time models for forest roads : a verification of the Forest Service Logging road handbook /

"9677 1202--SDTDC"--Cover.

The valuation of travel time savings in least developed countries:theoretical and empirical challenges and results from a field study

In developed countries travel time savings can account for as much as 80% of the overall benefits arising from transport infrastructure and service improvements. In developing countries they are generally ignored in transport project appraisals, notwithstanding their importance. One of the reasons for ignoring these benefits in the developing countries is that there is insufficient empirical evidence to support the conventional models for valuing travel time where work patterns, particularly of the poor, are diverse and it is difficult to distinguish between work and non-work activities. The exclusion of time saving benefits may lead to a bias against investment decisions that benefit the poor and understate the poverty reduction potential of transport investments in Least Developed Countries (LDCs). This is because the poor undertake most travel and transport by walking and headloading on local roads, tracks and paths and improvements of local infrastructure and services bring large time saving benefits for them through modal shifts. The paper reports on an empirical study to develop a methodology for valuing rural travel time savings in the LDCs. Apart from identifying the theoretical and empirical issues in valuing travel time savings in the LDCs, the paper presents and discusses the results of an analysis of data from Bangladesh. Some of the study findings challenge the conventional wisdom concerning the time saving values. The Bangladesh study suggests that the western concept of dividing travel time savings into working and non-working time savings is broadly valid in the developing country context. The study validates the use of preference methods in valuing non-working time saving values. However, stated preference (SP) method is more appropriate than revealed preference (RP) method.

How to manual:the valuation of rural travel time savings in least developed countries (LDCs)

The manual is designed to bring out issues that are relevant in the valuation of rural travel time savings in Least Developed Countries (LDCs). It should also be relevant for other developing countries which do not have LDC status but have rural economy features typical of low income developing countries. The manual elaborates step-by-step procedures on how to design and execute studies to estimate the value of time (VoT) savings of rural travellers.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^