An analysis of household transportation spending during the 2007-2009 US economic recession

The recent economic recession in the United States led to widespread destruction of jobs, home foreclosures, credit freeze and to creditor repossessions of key assets such as personal cars. Our objective is to empirically assess transportation conditions of US households with a focus on transportation spending. The latter is examined in the context of changes in multiple metrics such as total number of household cars, zero-vehicle status, expenditures on local public transportation and gasoline, down payment and net purchase price of cars, decline in household vehicle stock, and interest rates on auto loans. Using an econometric model of repeated cross-sections of data on households from the Consumer Expenditure Survey for the period 2005 through 2009, we examine factors which affect recession-period spending. In an effort to demonstrate the effects of the recession on specific groups, as well as to examine equity implications for vulnerable populations, our overall results are disaggregated by variations in transportation spending of minority, single mother and young households. Transportation spending declined significantly between 2005 and the recession years. A large part of this was due to lower car-ownership levels and an overall increase in zero-car households. Those households that did acquire a car needed to make higher levels of down payment. They also paid higher interest rates compared to the pre-recession period. Minorities spent significantly less than non-minorities before the recession but the difference from non-minorities was not significant during the recession. Single mothers did not spend significantly less than other households overall; however, their spending level became significantly less during the recession and they were much more likely to become zero-car households during the recession. The cost of car-ownership increased drastically for young adult households and the share of carless young households greatly increased during the recession

Examining the relationship between different urbanization settings, smartphone use to access the Internet and trip frequencies

No abstract available

Privacy in context : an evaluation of policy-based approaches to location privacy protection

Peer reviewedPostprin

Multi-sensor movement analysis for transport safety and health applications

Recent increases in the use of and applications for wearable technology has opened up many new avenues of research. In this paper, we consider the use of lifelogging and GPS data to extend fine-grained movement analysis for improving applications in health and safety. We first design a framework to solve the problem of indoor and outdoor movement detection from sensor readings associated with images captured by a lifelogging wearable device. Second we propose a set of measures related with hazard on the road network derived from the combination of GPS movement data, road network data and the sensor readings from a wearable device. Third, we identify the relationship between different socio-demographic groups and the patterns of indoor physical activity and sedentary behaviour routines as well as disturbance levels on different road settings

Location Privacy Preferences : A Survey-Based Analysis of Consumer Awareness, Trade-off and Decision-Making

Peer reviewedPostprin

Probabilistic Linkage Approach to Commercial Motor Vehicle and Carrier Datasets

In this paper, a probabilistic linkage method is explored in the context of linking databases in the Commercial Motor Vehicle and Carrier (CMVC) sector as a potential solution to overcome data quality problems. An application of this method is demonstrated by linking commercial motor vehicle inspection files kept by the Illinois State Police (ISP) and the inspection files available from the Illinois portion of the Motor Carrier Management Information System (MCMIS). Since one of the files to be matched is a subset of the other, the application allows us to validate the methodology. The results show 6,228 correct identifications of true matched record pairs out of 6,335 actual true matches (more than 99%) between the two files. The number of erroneously identified record pairs is 690 (about 11% of the actual true matched pairs.) Sensitivity analysis is conducted of error rates with respect to variations in the optimal thresholds for merging the databases. A simple analysis also shows how much of a clerical examination for unclear record pairs would have to be tolerated for a reduction in dollar expenditure

Incorporating weather information into real-time speed estimates: comparison of alternative models

Weather information is frequently requested by travelers. Prior literature indicates that inclement weather is one of the most important factors contributing to traffic congestion and crashes. In this paper, we propose a methodology to use real-time weather information to predict future speeds. The reason for doing so is to ultimately have the capability to disseminate weather-responsive travel time estimates to those requesting information. Using a stratified sampling technique, we select cases with different weather conditions (precipitation levels) and use a linear regression model (called the base model) and a statistical learning model (using Support Vector Machines for Regression) to predict 30-minute ahead speeds. One of the major inputs into a weather-responsive short-term speed prediction method is weather forecasts; however, weather forecasts may themselves be inaccurate. We assess the effects of such inaccuracies by means of simulations. The predictive accuracy of the SVR models show that statistical learning methods may be useful in bringing together streaming forecasted weather data and real-time information on downstream traffic conditions to enable travelers to make informed choices

The role of numeracy and financial literacy skills in the relationship between information and communication technology use and travel behaviour

The present research examines the role of maths-related literacies, or competencies, in influencing the relationship between ICTs and travel behaviour. We adopted a Bayesian approach to jointly model the frequency of different types of internet use, and total travel distance per traveller, with respect to measures of lifewide literacies (other than reading), specifically in the form of numeracy and financial literacy questions. Our findings revealed that participants with higher levels of these literacies used the internet more frequently, and travelled further than those with fewer skills. These literacies were directly associated with total travel distance, as well as indirectly associated through internet use. Our results therefore imply that a strong policy aim to improve maths-related literacies could have implications for mitigating the effects of social exclusion in the digital age

Short-term prediction of demand for ride-hailing services: a deep learning approach

As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UBERNET, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UBERNET employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UBERNET, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UBERNET with several other approaches, we show that the prediction quality of the model is highly competitive. Further, UBERNET’s prediction performance is better when using economic, social and built environment features. This suggests that UBERNET is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services