Temporal, seasonal and weather effects on cycle volume: an ecological study

<p>Abstract</p> <p>Background</p> <p>Cycling has the potential to provide health, environmental and economic benefits but the level of cycling is very low in New Zealand and many other countries. Adverse weather is often cited as a reason why people do not cycle. This study investigated temporal and seasonal variability in cycle volume and its association with weather in Auckland, New Zealand's largest city.</p> <p>Methods</p> <p>Two datasets were used: automated cycle count data collected on Tamaki Drive in Auckland by using ZELT Inductive Loop Eco-counters and weather data (gust speed, rain, temperature, sunshine duration) available online from the National Climate Database. Analyses were undertaken using data collected over one year (1 January to 31 December 2009). Normalised cycle volumes were used in correlation and regression analyses to accommodate differences by hour of the day and day of the week and holiday.</p> <p>Results</p> <p>In 2009, 220,043 bicycles were recorded at the site. There were significant differences in mean hourly cycle volumes by hour of the day, day type and month of the year (<it>p </it>< 0.0001). All weather variables significantly influenced hourly and daily cycle volumes (<it>p </it>< 0.0001). The cycle volume increased by 3.2% (hourly) and 2.6% (daily) for 1°C increase in temperature but decreased by 10.6% (hourly) and 1.5% (daily) for 1 mm increase in rainfall and by 1.4% (hourly) and 0.9% (daily) for 1 km/h increase in gust speed. The volume was 26.2% higher in an hour with sunshine compared with no sunshine, and increased by 2.5% for one hour increase in sunshine each day.</p> <p>Conclusions</p> <p>There are temporal and seasonal variations in cycle volume in Auckland and weather significantly influences hour-to-hour and day-to-day variations in cycle volume. Our findings will help inform future cycling promotion activities in Auckland.</p

The challenges of measuring transportation efficiency

In the US, concerns about dependence on oil (foreign oil in particular) have intensified and the transportation sector has garnered increased attention for its contribution to global warming. In response to these concerns, there is an increasing focus on the efficiency of our transportation systems. The objectives of this paper are to provide a framework for defining transportation efficiency (TE), highlight the ambiguities present in the transportation-research literature and in public policy, and describe the challenges that come with attempts to comprehensively measure TE. Of particular concern are the many studies which simply describe policy strategies associated with TE, without attempting to comprehensively define or measure it. Defining “transportation efficiency” as the maximization of services at the lowest possible cost will require that a variety of costs and service variables be assimilated into a comprehensive measurement tool. Such a tool could be used to find a TE index for temporal and operation TE comparisons. However, temporal applications are susceptible to rebound effects and operational comparisons are susceptible to shifting effects. Few assimilative measures of TE have been found which even attempt to deal with these potential sources of error. Better models are needed to adequately assimilate a wide variety of economic, environmental, human, energy, and operational variables on TE, and to assess rebound and shifting effects

Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach

A wide range of relatively short-term disruptive events such as partial flooding, visibility reductions, traction hazards due to weather, and pavement deterioration occur on transportation networks on a daily basis. Despite being relatively minor when compared to catastrophes, these events still have profound impacts on traffic flow. To date there has been very little distinction drawn between different types of network-disruption studies and how the methodological approaches used in those studies differ depending on the specific research objectives and on the disruption scenarios being modeled. In this paper, we advance a methodological approach that employs different link-based capacity-disruption values for identifying and ranking the most critical links and quantifying network robustness in a transportation network. We demonstrate how an ideal capacity-disruption range can be objectively determined for a particular network and introduce a scalable system-wide performance measure, called the Network Trip Robustness (NTR) that can be used to directly compare networks of different sizes, topologies, and connectivity levels. Our approach yields results that are independent of the degree of connectivity and can be used to evaluate robustness on networks with isolating links. We show that system-wide travel-times and the rank-ordering of the most critical links in a network can vary dramatically based on both the capacity-disruption level and on the overall connectivity of the network. We further show that the relationships between network robustness, the capacity-disruption level used for modeling, and network connectivity are non-linear and not necessarily intuitive. We discuss our findings with respect to Braess' Paradox.Network-disruption Network modeling Network Robustness Index (NRI) Isolating links Link capacity reduction

Documentation of Context-Sensitive Design Case Studies

A major challenge for many current transportation projects is to plan, design and build to create a solution which will not only address the planning and engineering requirements, but also satisfy the human and natural environmental issues related to a specific project. Occasional projects of this type have been designed and built for many years, typically when there have been few alternatives otherwise. A key component to these types of projects has been a greater level of community interest and public involvement. Initial efforts to introduce the concept of increased sensitivity to community interests and the natural environment was labeled Thinking Beyond the Pavement. This concept was the outgrowth of a conference held in Maryland in 1998, through the joint efforts of the Maryland DOT, FHWA, and AASHTO. As part of the conference, the concept was defined, the principles of Context-Sensitive Design (CSD) were outlined, and five pilot states were identified to begin developing training courses. Those states were Connecticut, Kentucky, Maryland, Minnesota, and Utah. Various forms of information sharing and training programs have also begun in each of the pilot states. Realizing more input was needed in order to expand the concept beyond the project development stage, several state highway agencies began to seek input from construction, operations and maintenance experts, as well as resource agencies and the public. This general concept of seeking innovative solutions to achieve flexibility in highway design has begun to be implemented and aggressively expanded

A Comparison of Bicyclist Attitudes in Two Urban Areas in USA and Italy

Over the past 40 years, the number of people using bicycles as their primary means of transportation has increased significantly. Transportation agencies around the world now promote bicycling as a way to reduce pollution and traffic congestion. However, the lack of bicycling infrastructure in many cities could significantly impede the future growth of bicycle usage. This paper used a web survey to evaluate the attitudes and preferences of bicyclists in two cities: Lexington, Kentucky, USA and Catania, Sicily, Italy. The goal of the survey was to document impediments to bicycling in both cities, determine how infrastructure could be improved, and identify features of smartphone app to aid bicyclists in route selection. Descriptive statistics and test of hypothesis were applied to the survey data to analyze participant responses and their level of agreement. Confirming previous research, respondents in both cities overwhelmingly cited lack of infrastructure as a major obstacle to bicycling more often. Respondents indicated that improving bicycle infrastructure and pavement conditions would result in an increased number of bicycle trips. Participants expressed a strong desire for a smartphone app that contains information on route safety level, a feature not currently available in any apps, which mainly provide route geometry information and distance between origin-destination pairs. While the survey findings lend support to the idea that bicyclists around the world harbor similar attitudes about what improvements are needed to increase cycling and enhance their experiences, local conditions and practices also influence perceptions about the relevance of specific issue

Analysis of Traffic Growth Rates

The primary objectives of this study were to determine patterns of traffic flow and develop traffic growth rates by traffic composition and highway type for Kentucky’s system of highways. Additional subtasks included the following: 1) a literature search to determine if there were new procedures being used to more accurately represent traffic growth rates, 2) development of a random sampling procedure for collecting traffic count data on local roads and streets, 3) prediction of vehicle miles traveled based on socioeconomic data, 4) development of a procedure for explaining the relationship and magnitude of traffic volumes on routes functionally classified as collectors and locals, and 5) development of county-level growth rates based on procedures to estimate or model trends in vehicle miles traveled and average daily traffic. Results produced a random sampling procedure for traffic counting on local roads which were used as part of the effort to model traffic growth at the county level in Kentucky. Promising results were produced to minimize the level of effort required to estimate traffic volumes on local roads by development of a relationship between functionally classified collector roads and local roads. Both regression and logarithmic equations were also developed to explain the relationship between local and collector roads. County-level growth rates in traffic volumes were analyzed and linear regression was used to represent changes in ADT to produce county-level growth rates by functional class. Linear regression and Neural Networks models were developed in an effort to estimate interstate and non-interstate vehicle miles traveled