Stephen Greaves

Collecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology: Issues and Potential Applications

The assessment of strategies designed to manage the continued growth in road-based freight and associated externalities has been hampered by a paucity of disaggregate data on commercial vehicle movements. When disaggregated data are available, the analysis of commercial vehicle route and trip chain structure can provide insightful information about urban commercial vehicle tours, travel patterns, and congestion levels. Over the past 15 years, the ability to collect detailed travel information has been expanded by developments in global positioning system (GPS) technology. In mid-2006, a GPS survey of commercial vehicles was piloted in Melbourne, Australia, to support a major update of freight data and modeling capabilities in the metropolitan region. The survey used passive GPS methods in which the truck drivers involvement in the data collection effort was minimal. The contributions of this research to the field of urban freight data collection were fourfold: (a) describing implementation issues with the data collection, (b) detailing the algorithms used to process the raw GPS data into meaningful travel and trip information, (c) presenting a discussion of pilot survey data tour results, and (d) discussing potential uses and limitations of GPS technology in urban freight modeling and planning. Despite processing challenges, GPS provides an appealing method to enrich commercial vehicle data collection and enhance our understanding of on-road behavior. Because increasing numbers of commercial vehicles become equipped with GPS receivers, only privacy concerns remain as a major barrier to gathering and using such data on a widespread basis in the future.

Reducing Burden and Sample Sizes in Multiday Household Travel Surveys

Traditional household travel surveys ask respondents to report their travel behavior for a 24-h period, even though it is well known that travel patterns vary from day to day. Although this information provides an indication of average household behavior or travel on an average weekday, evidence suggests this method may not be the most cost-effective way to collect the data because day-to-day travel variability is substantial, requiring larger sample sizes. In addition, collecting multiday data provides a richness of information that simply cannot be captured with a 1-day survey, offering insights into, for example, differences in weekday versus weekend travel, the longer-term travel impacts of flexible work hours, and trip substitution and cycling patterns that emerge over the course of a week or more. Despite the intuitive appeal of multiday surveys, few examples and little information on sampling issues and sample size requirements are available. Given this situation, the reasons given for not doing multiday surveys (which center around respondent fatigue), why these issues are fast becoming irrelevant (through the use of new passive data-recording technologies), the sample size implications of extending a survey, and the potential for estimator efficiency and cost savings (even accounting for the cost of new technologies) when conducting multiday surveys are explored. The use of Global Positioning System–based travel distance data from Adelaide, South Australia, indicates that the sample size reductions are large; collecting multiday data is feasible, offering a richness not available in 1-day data; and the method results in cost-effective gains in estimator efficiency.

Development of a Global Positioning System Web-Based Prompted Recall Solution for Longitudinal Travel Surveys

This paper details the development of a Global Positioning System collection solution for a longitudinal (10-week) survey of driving behavior in Sydney, Australia: the primary purpose is to investigate behavioral responses to variable rate charging regimes. The study calls for data to be transmitted regularly (wirelessly) to check the quality of data as they are being collected and provide the basis for a web-based prompted recall (PR) survey in which participants can view their trips, confirm details, and provide information on who was driving, number of passengers, and trip purpose. Following details of the technological setup, details are provided of the data processing issues involved and the development of the PR survey. Pilot testing of the approach on 30 motorists demonstrates that contrary to popular belief, highly accurate data of this nature can be collected for several weeks with little respondent burden.

Driver behaviour profiles for road safety analysis

Driver behaviour is a contributing factor in over 90 percent of road crashes. As a consequence, there is significant benefit in identifying drivers who engage in unsafe driving practices. Driver behaviour profiles (DBPs) are introduced here as an approach for evaluating driver behaviour as a function of the risk of a casualty crash. They employ data collected using global positioning system (GPS) devices, supplemented with spatiotemporal information. These profiles are comprised of common risk scores that can be used to compare drivers between each other and across time and space. The paper details the development of these DBPs and demonstrates their use as an input into modelling the factors that influence driver behaviour. The results show that even having controlled for the influence of the road environment, these factors remain the strongest predictors of driver behaviour suggesting different spatiotemporal environments elicit a variety of psychological responses in drivers. The approach and outcomes will be of interest to insurance companies in enhancing the risk-profiling of drivers with on-road driving and government through assessing the impacts of behaviour-change interventions.

A Combined GPS/Stated Choice Experiment to Estimate Values of Crash-Risk Reduction

Abstract This paper details the development and application of a Stated Choice (SC) experiment designed to explore motorists sensitivities to a kilometre-based charging regime focused around crash-risk reduction. Responses are gathered through a SC experiment that pivots off actual driving behaviour collected over a five week period using an in-vehicle Global Positioning System (GPS) device. This provision of greater reality using revealed preference (RP) information ensures that the alternatives in the SC experiment are embedded in reality, providing motorists with a more realistic context for their choices. The study demonstrates with the improved affordability, power and consumer familiarity with GPS devices, the integration of GPS recorded travel information with SC experiments is a now a feasible solution which can help enrich the quality of the reference alternatives in SC experiments in the future.

Evaluating changes in driver behaviour: A risk profiling approach

New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums.

Development of a Kilometer-Based Rewards System to Encourage Safer Driving Practices

Interest is growing in the use of kilometer-based financial mechanisms to encourage safer driving practices and reduce accident claims. The rationale behind such an approach is that, in addition to driver characteristics such as age and gender, crash risk is intrinsically a function of both the kilometers driven and the circumstances under which those kilometers are driven (e.g., time of day, day of week, road type, speed). Using recent accident data and travel survey data collected in the Sydney, Australia, greater metropolitan area, options were explored to design a kilometer-based reward scheme that incentivized drivers to reduce their kilometers traveled, nighttime driving, and speeding. Results show that young drivers (17 to 30 years old) would be hardest hit by the proposed scheme, and middle-aged drivers (31 to 65) would fare best. The impacts of the rewards system were assessed hypothetically by using evidence from 125 motorists who completed 5 weeks of driving in which their kilometers traveled, nighttime driving, and speeds were monitored with the latest Global Positioning System technology. Various charging scenarios and hypothesized behavioral changes were implemented to assess both their incentive for change and the overall financial impact of the project. These results were used in conjunction with the theoretical and empirical justification outlined in this paper to set the final charging regime rates on the basis of the overall study budget.

Associations between the frequency of cycling and domains of quality of life

ISSUE ADDRESSED This study examined the association between domains of quality of life (QoL) and the frequency of cycling by men and women. METHOD A cross-sectional survey of 846 healthy adults in Sydney, Australia measured cycling behaviour and self-reported QoL. Participants were aged 18-55 years and were living within 5km of the centre. Cycling frequency for all purposes was recorded as weekly, less than weekly or never cycling. QoL was measured using the four QoL domains of the WHOQOL-BREF: physical psychological, social and environment. Linear regression was used to assess the association between cycling and QoL. RESULTS Among men, at least weekly cycling was associated with physical QoL (P=0.002) and any cycling was positively associated with psychological wellbeing (at least weekly P=0.01, less than weekly P=0.01) after adjusting for age, education and income. No significant associations were observed for women. CONCLUSION Frequent cycling was associated with higher physical and psychological QoL in men, but not among women in this sample. No relationship was observed between cycling and the environment and social QoL domains. SO WHAT?: These findings suggest that cycling offers physical and psychological QoL benefits for men.

Variability of Personal Exposure to Fine Particulates for Urban Commuters Inside Automobiles

Over the past decade, a growing body of evidence has emerged to suggest a causal link between short-duration exposure to elevated levels of fine airborne particulate matter and adverse health consequences. It is believed that much of this peak exposure occurs in transport microenvironments both because of the higher levels of fine particulates associated with road traffic, primarily from diesel exhaust emissions, and because of the significant amount of time people spend traveling (for instance, 80 min/day for residents of Sydney, Australia). Although previous studies have suggested substantial differences in exposure rates because of factors such as choice of mode, route, in-vehicle conditions, and meteorological factors, current measurement techniques have restricted insights to fairly coarse sampling intervals (e.g., every hour and every trip). As a consequence, little tangible evidence is available on how pollution varies over a trip and, most critically, about the location, duration, and magnitude of peak excursions within trips. With these issues in mind, this current paper reports on a study in which capabilities of Global Positioning Systems and real-time particle monitors are combined to address the problem for an urban commute in Sydney. This ability to reference pollution data precisely and spatially, and to identify hot spots in particular, holds considerable promise for both understanding and reporting of such data in the future.

Simulating Household Travel Survey Data

Better (more comprehensive, higher quality, and more detailed) data on travel demand related to the sociodemographic and spatial characteristics of individuals and household are critical for modern transport planning and policy development. Provision of such data relies largely on household travel surveys (HTSs), in which a small sample of the population (usually about 2,000 to 5,000 households) records their travel patterns over some given time period. HTSs are notoriously expensive planning activities with current cost typically ranging from