Babak Mehran

Implementing Travel Time Reliability for Evaluation of Congestion Relief Schemes on Expressways

Preevaluation of the impacts of congestion relief schemes on travel time reliability is significant for road authorities. However, most existing approaches to estimating travel time reliability rely mainly on empirical data and are therefore not of help for evaluating improvement schemes before implementation. A methodology is presented to estimate travel time reliability on the basis of modeling travel time variations as a function of demand, capacity, weather conditions, and road accidents. For a subject expressway segment, patterns of demand and capacity were generated for each 5-min interval during a year by using the Monte Carlo simulation technique, and accidents were generated randomly according to traffic conditions. A whole year analysis was performed by comparing demand and available capacity for each scenario; shock wave analysis was used to estimate the queue length at each time interval. Travel times were estimated from refined speed–flow relationships. The buffer time index was estimated as a measure of travel time reliability and compared with observed values from empirical data. After validation, the methodology was applied to assess the impact on travel time reliability of opening the hard shoulder to traffic. Opening the hard shoulder to traffic during the peak periods was found to ameliorate travel time reliability significantly and mitigate congestion levels, which could result in up to a 26% cut in the number of accidents occurring.

Fusion of probe and fixed sensor data for short-term traffic prediction in urban signalized arterials

A data fusion framework is proposed to predict vehicle trajectories in urban signalized arterials. Recent advancements in data collection techniques and availability of traffic data from fixed and probe sensors suggest data fusion-based models as an alternative approach for traffic prediction. Yet, majority of the existing data fusion approaches are based on statistical models without considerations for traffic engineering principles. In addition, existing approaches do not use probe trajectory data efficiently. The proposed framework in this research is based on the kinematic wave theory and is capable of fully utilizing the probe trajectory data to reconstruct the trajectories of the other vehicles within a ‘prediction window’. The data fusion framework combines real-time and historical traffic data to predict future traffic patterns at upstream and downstream boundaries. The modelling approach is based on the kinematic wave theory and applies the variational theory as the solution method. Predicted traffic patterns are used to set the boundary conditions in the solution domain and probe trajectories are used to set additional boundary conditions. Given the boundary conditions, a dynamic programming approach is applied to reconstruct vehicle trajectories within the prediction window. The performance of the proposed framework is evaluated by using real-world traffic data, and possible directions for improving the accuracy of the model are discussed.

Considering Travel Time Reliability and Safety for Evaluation of Congestion Relief Schemes on Expressway Segments

In this paper, the authors present a methodology for estimating travel time reliability based on modeling travel time variations as a function of demand, capacity and weather conditions. Accidents are modeled as a function of traffic conditions. The methodology is able to evaluate impacts of congestion relief schemes on travel time reliability and safety. Reliability and congestion measures are first measured using data from a segment of an intercity expressway. The proposed methodology is then applied to develop a simulation model for estimating travel time reliability and the number of accidents on the test bed. The model is then applied in evaluating the impacts of opening the hard shoulder of the roadway to traffic and reducing the peak period travel time reliability and safety.

Real-Time Estimation of Saturation Flow Rates for Dynamic Traffic Signal Control Using Connected-Vehicle Data

Existing adaptive traffic signal control (ATSC) systems rely on dedicated fixed-point sensors, such as inductive loop detectors or video cameras, for measuring traffic demands and discharge saturation flow rates. The cost associated with installation, operation, and maintenance of these sensors is one of the factors that limit the deployment of ATSCs. The emergence of connected vehicles (CVs), which continuously broadcast their speed, position, heading, and other information to other vehicles and to roadside infrastructure, provides an opportunity to reduce the reliance of ATSC on data from fixed sensors and potentially to reduce ATSC deployment costs. However, so that existing ATSC systems can operate by using CV data, a methodology is needed for estimating demands and saturation flow rate based on CV data instead of fixed sensor data. This paper focuses on estimating the time-varying saturation flow rate for individual lane groups at signalized intersections solely on the basis of CV data. The accuracy of a proposed methodology is quantified through microsimulation for a range of traffic conditions, lane group configurations, and levels of market penetration (LMP) of CVs. The analysis shows that the proposed methodology can capture temporal variations in the saturation flow rate caused by road incidents, queues spilling back from downstream bottlenecks, and lane closures. The evaluation results show that the mean absolute relative error of the lane group saturation flow rate ranged from approximately 2% to 9% when LMP = 20% and only 1% to 2% when LMP = 100%.