A Bolbol

Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification

Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Nowadays, attempts have been made to automatically infer transportation modes from positional data, such as the data collected by using GPS devices so that the cost in time and budget of conventional travel diary survey could be significantly reduced. Some limitations, however, exist in the literature, in aspects of data collection (sample size selected, duration of study, granularity of data), selection of variables (or combination of variables), and method of inference (the number of transportation modes to be used in the learning). This paper therefore, attempts to fully understand these aspects in the process of inference. We aim to solve a classification problem of GPS data into different transportation modes (car, walk, cycle, underground, train and bus). We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVMs) classification. The framework was tested using coarse-grained GPS data, which has been avoided in previous studies, achieving a promising accuracy of 88% with a Kappa statistic reflecting almost perfect agreement.

Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings

The assignment of short-term counts to groupings of seasonal adjustment factors is the most critical step in the annual average daily traffic estimation process; this step is also extremely sensitive to error resulting from engineering judgment. In this study, discriminant analysis is examined, and several variable selection criteria are investigated to develop 12 assignment models. Continuous traffic volume data, obtained in the state of Ohio during 2005 and 2006, are used in the analysis. Seasonal adjustment factors are calculated with individual volumes of the two directions of travel as well as the total volume of a roadway segment. The results reveal that the best-performing directional volume–based model, which employs the Raos V algorithm, produces a mean absolute error (MAE) of 4.2%, which can be compared with errors reported in previous studies. An average decline in the MAE by 58% and in the standard deviation of the absolute error by 70% is estimated over the traditional roadway functional classification. In addition, time-of-day factors are slightly more effective in identifying similar patterns of short-term counts than when they are combined with the average daily traffic. When directional-specific factors are used instead of total volume–based seasonal adjustment factors, the improvement in the average MAE is approximately 41%. This conclusion is consistent with previous research findings and may result from the division of the data set by direction essentially doubling the sample size, which in turn increases the number of assignment options for a short-term count.

Effects of Tube Strikes on Journey Times in Transport Network of London

The goal of this study was to investigate the impact of five underground strikes on journey times in Londons transport network during 2009 and 2010. The main data source for this study was automatic number plate recognition cameras, which were installed on the entrances and exits of 670 travel links that covered the vast majority of the network and were equivalent to a total length of 1,740 km. The determination of spatio-temporal differences of strike effects between the first and the remaining strike days, the identification of changes in departure and arrival times, and the estimation of travel time delays within central, inner, and outer London, as well as between inbound and outbound traffic, were the main objectives of the study. The total travel time within the examined areas, the excess delay, and the corresponding percentage difference in journey times were the main performance measurements used. The most significant results showed that the second day of strikes resulted in significant delays as opposed to the first strike days. The peaks elongated by approximately 45 to 60 min, while the unique full-day strike had the highest percentage increase in travel times, especially during the evening period (74%). Central London was generally affected the most, especially during the morning peak, which experienced an average increase in travel times of 35%, while Central London also had the highest percentage of negatively affected links (80%). The inbound traffic experienced, on average, high delays during the morning peak; the outbound traffic yielded greater delays during the evening period.