Promoting transportation cycling for women : the role of bicycle infrastructure

Objective.
Females are substantially less likely than males to cycle for transport in countries with low bicycle transport mode share. We investigated whether female commuter cyclists were more likely to use bicycle routes that provide separation from motor vehicle traffic.
Methods.
Census of cyclists observed at 15 locations (including off-road bicycle paths, on-road lanes and roads with no bicycle facilities) within a 7.4 km radius of the central business district (CBD) of Melbourne, Australia, during peak commuting times in February 2004.
Results.
6589 cyclists were observed, comprising 5229 males (79.4%) and 1360 females (20.6%). After adjustment for distance of the bicycle facility from the CBD, females showed a preference for using off-road paths rather than roads with no bicycle facilities (odds ratio [OR] = 1.43, 95% confidence interval [CI]: 1.12, 1.83), or roads with on-road bicycle lanes (OR = 1.34, 95% CI: 1.03, 1.75).
Conclusions.
Consistent with gender differences in risk aversion, female commuter cyclists preferred to use routes with maximum separation from motorized traffic. Improved cycling infrastructure in the form of bicycle paths and lanes that provide a high degree of separation from motor traffic is likely to be important for increasing transportation cycling amongst under-represented population groups such as women.

An optimal transportation routing approach using GIS-based dynamic traffic flows

This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Occupational light vehicle use: characterising the at-risk population

Previous occupational light vehicle research has concentrated on employees using cars. The aim of this study was to identify and characterise the total occupational light vehicle-user population and compare it with the privately-used light vehicle population. Occupational light vehicle and private light vehicle populations were identified through use-related 2003 registration categories from New South Wales Roads and Traffic Authority data. Key groups of occupational light vehicle registration variables were comparatively assessed as potential determinants of occupational light vehicleuser risks. These comparisons were expressed as odds ratios with 95% Confidence Intervals. The occupational light vehicle population vehicles (n=646,201) comprised 18% of all light vehicle registrations. A number of statistical differences emerge between the two populations. For instance, 86% of occupational light vehicle registrants were male versus 65% of private registrants, and 56% of the occupational users registered load shape vehicles versus 20% of the private registrants. Occupational light vehicles registered for farming or taxi use were more than six times more likely to belong to sole-traders than organisations. Sole-traders were nearly twice as likely to register light-trucks, and twice as likely to register older vehicles, than organisations. This study demonstrates that the occupational light vehicle user population is larger and more diverse than previously shown with characteristics likely to increase the relative risks of motor vehicle crashes. More occupational light vehicles were load shapes and therefore likely to have poorer crashworthiness ratings than cars. Occupational light vehicles are frequently used by sole-traders for activities with increased OHS risks including farming and taxi use. Further exploration of occupational light vehicle-user crash risks should include all vehicle types, work arrangements and small ‘fleets’.