Q-Learning-Based Coordinated Variable Speed Limit and Hard Shoulder Running Control Strategy to Reduce Travel Time at Freeway Corridor

To increase traffic mobility and safety, several types of active traffic management (ATM) strategies, such as variable speed limit (VSL), ramp metering, dynamic message signs, and hard shoulder running (HSR), are adopted in many countries. While all kinds of ATM strategies show promise in releasing traffic congestion, many studies indicate that stand-alone strategies have very limited capability. To remedy the defects of stand-alone strategies, cooperative ATM strategies have caught researchers’ attention and different combinations have been studied. In this paper, a coordinated VSL and HSR control strategy based on a reinforcement learning technique—Q-learning—is proposed. The proposed control strategy bridges up a direct connection between the traffic flow data and the ATM control strategies via intensive self-learning processes, thus reducing the need for human knowledge. A typical congested interstate highway, I-270 in Maryland, United States, was selected as the study area to evaluate the proposed strategy. A dynamic traffic assignment simulation model was introduced to calibrate the network with real-world data and was used to evaluate the regional impact of the proposed algorithm. Simulation results indicated that the proposed coordinated control could reduce corridor travel time by up to 27%. The performance of various control strategies were also compared. The results suggested that the proposed strategy outperformed the stand-alone control strategies and the traditional feedback-based VSL strategy in mitigating congestion and reducing travel time on the freeway corridor.

Travel Time Prediction Model of Freeway Corridor Based on Real-Time Safety Reliability

By considering the feature of vehicle driving on the event management unit of the freeway corridor, according to the system target, a method to divide the management unit of the road network was put forward. The relative safety braking deceleration was taken as the evaluation index of single-vehicle driving risk. The reliability graph relationship and structure-function between the management unit and subunit were analyzed. Then, dynamic safety reliability and real-time safety reliability were determined on the basis of driving risk. In addition, the queuing and dissipating characteristics of the management unit under traffic incidents were analyzed based on the wave theory. The incident duration and dissipation time were also calculated. At the same time, the travel time prediction model of the incident management unit was set up when the real-time safety reliability was taken as a road resistance function. Finally, an improved travel time prediction model established in this paper is of great significance to improve traffic safety and efficiency, and the research results will provide an important theoretical foundation in the freeway corridor route decision.

Optimum Volume of Freeway Corridors

Conventional methods to assess the quality of service on freeways are based on the comparison of a specific peak hour traffic demand to the capacity of the facility, which is usually measured at a single uniform bottleneck section. However, estimating the quality of service of one bottleneck section may not be sufficient to assess the performance of an entire freeway facility. A driver traveling along a freeway corridor may actually encounter multiple flow breakdowns at independent bottleneck sections, which affect the overall quality of service. This paper introduces a comprehensive approach that considers an entire freeway corridor as a system consisting of successive independent bottlenecks with different characteristics, and can be used to estimate the optimum sustainable volume. The methodology is based on the sustained flow index, which is defined as the product of traffic volume and the probability of survival at this volume. Optimum volumes of two real-world corridors are estimated based on the new derivations. The empirical results reveal that the optimum volume and the capacity of an entire corridor is less than those of its most restrictive bottleneck.

Data analytics approach for travel time reliability pattern analysis and prediction

Travel time reliability (TTR) is an important measure which has been widely used to represent the traffic conditions on freeways. The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor. A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte, North Carolina. A number of influential factors are considered when analyzing TTR, which include, but are not limited to, time of day, day of week, year, and segment location. A time series model is developed and used to predict the TTR. Numerical results clearly indicate the uniqueness of TTR patterns under each case and under different days of week and weather conditions. The research results can provide insightful and objective information on the traffic conditions along freeway segments, and the developed data-driven models can be used to objectively predict the future TTRs, and thus to help transportation planners make informed decisions.

Validating the Adaptability of Travel Time Reliability Measurements using Probe Data

Travel time reliability (TTR) is considered a critical piece of information in highway performance evaluation. The L02 project from Strategic Highway Research Program 2 (SHRP2) has developed a holistic method using statistical probability functions of travel time as the TTR measure to build highway performance evaluation and monitoring systems. Compared with single-value reliability measures, the L02 measure is able to identify sources of unreliability and quantify their associated impacts. To validate the adaptability of L02 measure, TTR analysis on the I-15 freeway corridor in Salt Lake City, Utah using probe data has been conducted. The result is compared against output from the quadrant-based TTR measure that is currently used by the Utah Department of Transportation. Through cross-validation, it is determined that the two suites of measures demonstrate good consistency in relation to reliability assessment and unreliability source diagnoses. In addition, the study provides a method to calibrate the quadrant-based TTR measure, and new critical values were developed based on the cross-validation.

Using Google’s Aggregated and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco, California

With rising urban freeway congestion and limited funds available for highway expansion, it may be essential to manage traffic growth by using high-occupancy toll lanes and other travel demand management (TDM) measures. To prepare for and help guide freeway corridor management planning in the US-101 and I-280 corridors in San Francisco, California, information describing trip origins and destinations by time of day was desired. Observed roadway facility-specific origin–destination (O-D) flows can help researchers to understand spatial distribution of demand and impute willingness to pay, actions that are useful in evaluating various TDM strategies. This paper describes a new passively collected O-D data source—Google’s aggregated and anonymized trip (AAT) data—obtained under Google’s Better Cities program. Aggregate hourly flow matrices for 85 districts covering California’s nine-county Bay Area specific to four freeway segments in San Francisco were obtained. Because AAT data account for only a sample of travelers, Google provides relative flows rather than absolute counts. Linear regression models were estimated to relate relative flows in the AAT data set and observed traffic volumes from the California Department of Transportation’s Performance Measurement System. The models were applied to convert relative flows to trips and derive facility-specific, time-dependent O-D matrices. Comparison of these facility-specific O-D matrices to select link O-D matrices from a regional travel demand model show that there is a higher correlation in terms of productions at origin districts and attractions at destination districts than at the O-D flow level. Some opportunities and limitations of the new data source are discussed, along with recommendations for future research.

Model-Based Fault Detection Among Freeway Loop Sensors

Loop detectors are installed along many of the freeways across the state of California to provide real-time and historical traffic data. These data are used by Caltrans for traffic management operations, such as freeway ramp metering, and to evaluate the performance of freeway corridor traffic management systems. These data are also being used to calibrate traffic flow models and to perform model-based predictions of freeway corridor congestion and traffic throughput performance. However, such detection is prone to contain errors and inconsistencies, which can pose problems in further use of the data, and is also of such large quantities that identification of errors can be tedious. This paper proposes a fault detection algorithm associating loop detector data to the cell transmission model to identify significant errors among such detectors. It discusses how such an algorithm would apply to loop detection along the mainline freeway, as well as extends the algorithm to determine errors along on and off ramp detectors. It also gives a real-life example with appropriate identification of detectors in error.