Modeling the Effect of a Road Construction Project on Transportation System Performance

Road construction projects create physical changes on roads that result in capacity reduction and travel time escalation during the construction project period. The reduction in the posted speed limit, the number of lanes, lane width and shoulder width at the construction zone makes it difficult for the road to accommodate high traffic volume. Therefore, the goal of this research is to model the effect of a road construction project on travel time at road link-level and help improve the mobility of people and goods through dissemination or implementation of proactive solutions. Data for a resurfacing construction project on I-485 in the city of Charlotte, North Carolina (NC) was used evaluation, analysis, and modeling. A statistical t-test was conducted to examine the relationship between the change in travel time before and during the construction project period. Further, travel time models were developed for the freeway links and the connecting arterial street links, both before and during the construction project period. The road network characteristics of each link, such as the volume/ capacity (V/C), the number of lanes, the speed limit, the shoulder width, the lane width, whether the link is divided or undivided, characteristics of neighboring links, the time-of-the-day, the day-of-the-week, and the distance of the link from the road construction project were considered as predictor variables for modeling. The results obtained indicate that a decrease in travel time was observed during the construction project period on the freeway links when compared to the before construction project period. Contrarily, an increase in travel time was observed during the construction project period on the connecting arterial street links when compared to the before construction project period. Also, the average travel time, the planning time, and the travel time index can better explain the effect of a road construction project on transportation system performance when compared to the planning time index and the buffer time index. The influence of predictor variables seem to vary before and during the construction project period on the freeway links and connecting arterial street links. Practitioners should take the research findings into consideration, in addition to the construction zone characteristics, when planning a road construction project and developing temporary traffic control and detour plans

Predicting vehicular travel times by modeling heterogeneous influences between arterial roads

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 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.Comment: 13 pages, conferenc

Arriving on time: estimating travel time distributions on large-scale road networks

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area

Probabilistic travel time progression and its application to automatic vehicle identification data

Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilise adequately the advanced data collection methods that provide real-time different types of information, this paper is aimed at a novel approach to the estimation of long roadway travel times, using Automatic Vehicle Identification (AVI) technology. Since the long roads contain a large number of scanners, the AVI sample size tends to reduce and, as such, computing the distribution for the total road travel time becomes difficult. In this work, we introduce a probabilistic framework that extends the deterministic travel time progression method to dependent random variables and enables the off-line estimation of road travel time distributions. In the proposed method, the accuracy of the estimation does not depend on the size of the sample over the entire corridor, but only on the amount of historical data that is available for each link. In practice, the system is also robust to small link samples and can be used to detect outliers within the AVI data

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

Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles

International audienceEstimating and analyzing traffi c conditions on large arterial networks is an inherently diffi cult task. The fi rst goal of this article is to demonstrate how arterial tra c conditions can be estimated using sparsely sampled GPS probe vehicle data provided by a small percentage of vehicles. Traffi c signals, stop signs, and other flow inhibitors make estimating arterial traffi c conditions significantly more diffi cult than estimating highway traffi c conditions. To address these challenges, we propose a statistical modeling framework that leverages a large historical database and relies on the fact that tra ffic conditions tend to follow distinct patterns over the course of a week. This model is operational in North California, as part of the Mobile Millennium tra ffic estimation platform. The second goal of the article is to provide a global network-level analysis of tra ffic patterns using matrix factorization and clustering methods. These techniques allow us to characterize spatial tra ffic patterns in the network and to analyze traffi c dynamics at a network scale. We identify tra ffic patterns that indicate intrinsic spatio-temporal characteristics over the entire network and give insight into the traffi c dynamics of an entire city. By integrating our estimation technique with our analysis method, we achieve a general framework for extracting, processing and interpreting traffi c information using GPS probe vehicle data