Understanding Factors Affecting Arterial Reliability Performance Metrics

In recent years, the importance of travel time reliability has become equally important as average travel time. However, the majority focus of travel time research is average travel time or travel time reliability on freeways. In addition, the identification of specific factors (i.e., peak hours, nighttime hours, etc.) and their effects on average travel time and travel time variability are often unknown. The current study addresses these two issues through a travel time-based study on urban arterials. Using travel times collected via Bluetooth data, a series of analyses are conducted to understand factors affecting reliability metrics on urban arterials. Analyses include outlier detection, a detailed descriptive analysis of select corridors, median travel time analysis, assessment of travel time reliability metrics recommended by the Federal Highway Administration (FHWA), and a bivariate Tobit model. Results show that day of the week, time of day, and holidays have varying effects on average travel time, travel time reliability, and travel time variability. Results also show that evening peak hours have the greatest effects in regards to increasing travel time, nighttime hours have the greatest effects in regards to decreasing travel time, and directionality plays a vital role in all travel time-related metrics

ARTERIAL PROBABILISTIC TRAFFIC MODELING AND REAL-TIME TRAVEL TIME PREDICTION WITH VEHICLE PROBE DATA USING MACHINE LEARNING

This study proposes a probabilistic modeling framework for the estimation and prediction of link-based arterial travel time distribution using GPS data. The spatiotemporal correlations of the network are modeled using a directional acyclic graphical model, and several external variables in the prediction model are included to yield a better prediction in a variety of situations. This study also aims to investigate the effects of each factor on the travel time and the uncertainty associated with it. In the proposed model, factors such as weather conditions, seasons, time of day, and day of the week are added as external variables in the graphical model. After determining the structure of the model, Streaming Variational Bayes (SVB) is used for training and parameter inference; this offers a valuable option when constant streaming data is utilized. SVB adaptively changes its parameters gradually with a lower computational cost, which makes the process less time-consuming and more efficient. The analysis shows that incorporating external variables can improve the model performance. The data used in this study is INRIX vehicle trajectory raw data from four months - February, June, July, and October of 2015 - which makes it possible to take into account the effects of seasons and weather conditions on travel time and its uncertainty. One of the products of this study is a framework for vehicle trajectory data cleaning process including trip identification, removing outliers, and cleaning the trips data. Once the data are cleaned and ready to use, they should be mapped to the roads. The Hidden Markov Model (HMM) map matching algorithm is used to map the GPS latitude/longitude data to the Open Street Map (OSM) base map and find the traversed links between each pair of GPS points of vehicle trajectories. Finally, a novel procedure to compare any travel time prediction model with any available commercial routing API is proposed and tested to compare the proposed model with Google API