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

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

Peer reviewedPostprin

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

Exploring Car-Ownership and Declining Carlessness in the United States during the COVID-19 Pandemic

This paper examines changes in car-ownership levels before and after the COVID-19 pandemic in the US. In contrast to the two years before the pandemic, the propensity of households to be carless decreased for all households considered, as well as for low- and middle-income, and minority households. There is also evidence of an increase in the average number of vehicles for low-income households. The results highlight the additional financial burden faced by households during the pandemic as a result of higher levels of car-ownership, and that the recovery of public transportation ridership may be negatively impacted with the rise in car-ownership among transit-using groups

Will Psychological Effects of Real-Time Transit Information Systems Lead to Ridership Gain?

This paper examines whether the psychological effects of real-time transit information on commuters will lead to a gain in transit ridership. A conceptual model, which posits a simultaneous structure among psychological and behavioral constructs, was developed on the basis of cognitive models of behavior. Path analysis was used to analyze such a process. A detailed stated preference survey for Chicago commuters composed the data-gathering approach. The analysis results showed that real-time transit information systems might achieve the goal of increasing transit ridership through their psychological effects on commuters. The results indicated that the provision of real-time transit information might serve as an intervention to break current transit nonusers\u27 travel habits and in consequence increase the mode share of transit use. Moreover, the results of this study suggest that real-time transit information may be more successful in increasing transit ridership if combined with facilitating programs that enhance commuters\u27 opportunities to be exposed to such systems before using them