This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 26% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 20% at locations where average number of observed probes is between 30 and 47 vehicles/hr, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.
Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles. There is only a limited literature on methods using data input from probe vehicles. The spatio-temporal dependencies captured by existing data driven approaches are either too detailed or very simplistic. We strike a balance of the existing data driven approaches to account for varying degrees of influence a given road may experience from its neighbors, while controlling the number of parameters to be learnt. Specifically, we use a NoisyOR conditional probability distribution (CPD) in conjunction with a dynamic Bayesian network (DBN) to model state transitions of various roads. We propose an efficient algorithm to learn model parameters. We also propose an algorithm for predicting travel times on trips of arbitrary durations. Using synthetic and real world data traces we demonstrate the superior performance of the proposed method under different traffic conditions.
We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability densities as mixture components. Gaussian densities fail to capture the positive skew in travel time distributions and, consequently, large numbers of mixture components are needed for reasonable fitting accuracy when applied as mixture components. They also assign positive probabilities to negative travel times. To address these issues, this paper develops a mixture distribution with asymmetric components supported on the positive numbers. We use sparse estimation techniques to ensure parsimonious models. Specifically, we derive a novel generalization of Gamma mixture densities using Mittag-Leffler functions, which provides enhanced fitting flexibility and improved parsimony. In order to accommodate within-day variability and allow for online implementation of the proposed methodology (i.e., fast computations on streaming travel time data), we introduce a recursive algorithm which efficiently updates the fitted distribution whenever new data become available. Experimental results using real-world travel time data illustrate the efficacy of the proposed methods.
This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.
With the development of Connected Vehicle (CV) technology, temporal variation of roadway traffic can be captured by sharing Basic Safety Messages (BSMs) from each vehicle using the communication between vehicles as well as with transportation roadside infrastructures (e.g., traffic signal) and traffic management centers. However, the penetration of connected vehicles in the near future will be limited. BSMs from limited CVs could provide an inaccurate estimation of current speed or space headway. This inaccuracy in the estimated current average speed and average space headway data is termed as noise. This noise in the traffic data significantly reduces the prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in predicting traffic condition. To improve the real time prediction accuracy with low penetration of CVs, we developed a traffic data prediction model that combines the LSTM with a noise reduction model (the standard Kalman filter or Kalman filter based Rauch Tung Striebel (RTS)). The average speed and space headway used in this study were generated from the Enhanced Next Generation Simulation (NGSIM) dataset, which contains vehicle trajectory data for every one tenth of a second. Compared to a baseline LSTM model without any noise reduction, for 5 percent penetration of CVs, the analyses revealed that combined LSTM\RTS model reduced the mean absolute percentage error (MAPE) from 19 percent to 5 percent for speed prediction and from 27 percent to 9 percent for space headway prediction. The overall reduction of MAPE value ranged from 1 percent to 14 percent for speed and 2 percent to 18 percent for space headway prediction compared to the baseline model.