What Is an Effective Way to Measure Arterial Demand When It Exceeds Capacity?

This project focused on developing and evaluating methods for estimating demand volume for oversaturated corridors. Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of the study was to identify and develop methods that could be implemented in practice based on readily available data. To this end, two methods were proposed: an innovative method based on shockwave theory; and the volume delay function adapted from the Highway Capacity Manual. Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. The proposed methods were tested with simulation data and validated based on volume data from the field. The results show both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.Comment: 8 page

Performance Predication Model for Advance Traffic Control System (ATCS) using field data

Reductions in capital expenditure revenues have created greater demands from users for quality service from existing facilities at lower costs forcing agencies to evaluate the performance of projects in more comprehensive and greener ways. The use of Adaptive Traffic Controls Systems (ATCS) is a step in the right direction by enabling practitioners and engineers to develop and implement traffic optimization strategies to achieve greater capacity out of the existing systems by optimizing traffic signal based on real time traffic demands and flow pattern. However, the industry is lagging in developing modeling tools for the ATCS which can predict the changes in MOEs due to the changes in traffic flow (i.e. volume and/or travel direction) making it difficult for the practitioners to measure the magnitude of the impacts and to develop an appropriate mitigation strategy. The impetus of this research was to explore the potential of utilizing available data from the ATCS for developing prediction models for the critical MOEs and for the entire intersection. Firstly, extensive data collections efforts were initiated to collect data from the intersections in Marion County, Florida. The data collected included volume, geometry, signal operations, and performance for an extended period. Secondly, the field data was scrubbed using macros to develop a clean data set for model development. Thirdly, the prediction models for the MOEs (wait time and queue) for the critical movements were developed using General Linear Regression Modeling techniques and were based on Poisson distribution with log linear function. Finally, the models were validated using the data collected from the intersections within Orange County, Florida. Also, as a part of this research, an Intersection Performance Index (IPI) model, a LOS prediction model for the entire intersection, was developed. This model was based on the MOEs (wait time and queue) for the critical movements. In addition, IPI Thresholds and corresponding intersection capacity designations were developed to establish level of service at the intersection. The IPI values and thresholds were developed on the same principles as Intersection Capacity Utilization (ICU) procedures, tested, and validated against corresponding ICU values and corresponding ICU LOS

Modeling Clemson Football Traffic: New Techniques for Small Communities

Many communities host planned special events that generate several times the communties\u27 AADT around the event period (e.g. pro and college football games). Larger metropolises benefit from ITS to collect data from, model, plan for, and analyze potential solutions to event-caused congestion. The smaller communities, which do not have the resources for traffic management centers, could benefit from more cost-appropriate methodologies. This thesis presents a cost-effective methodology for traffic data collection before and after these events. Modelers can then use this data in a microsimulation package, such as VISSIM, to model how the transportation network performs during this period, to model treatments, and to obtain MOEs useful for making planning decisions. Furthermore, because these events cause networks to be severely over-saturated, collected data can underestimate the level of demand, as it is restricted by capacity. This thesis also presents a methodology to account for this as well. Researchers collected traffic data with these methods from games in 2014-16, developed models for base and treatment scenarios, and proposed changes to the traffic plan starting in 2015. In addition to the methodology, travel-time results from these models are provided as measures of effectiveness. The author\u27s uses his experience with this project to demonstrate that these methods can be used to microsimulate a severely-oversaturated network and predict treatment effectiveness

Accounting for midblock pedestrian activity in the HCM 2010 urban street segment analysis

The Urban Street segment analysis Chapter of the 2010 Highway Capacity Manual (HCM 2010) provides a methodology for analyzing automobile performance on signalized roadway segments within an urban roadway network. The methodology involves applying a platoon dispersion model to: a) predict the vehicle arrival flow profiles at a downstream signalized intersection; b) use the predicted arrivals to compute the proportion of vehicle arrivals on green; and c) subsequently estimate the delay, travel speed and Level of Service (LOS) under which the segment operates. Vehicles arriving during the red interval at a signalized intersection generally accumulate and form a platoon. When the signal turns green, the platoon of vehicles is discharged from the upstream intersection to the downstream intersection. As vehicle speeds fluctuate, the platoon will disperse before it arrives at the downstream intersection. This is called Platoon dispersion. Notwithstanding its importance and application in evaluating the performance of urban roadway segments, the predictive ability of the HCM 2010 platoon dispersion model under friction and non-friction traffic conditions has not been evaluated. Friction traffic conditions include midblock pedestrian activity, on-street parking activity, and medium to high truck volume. Furthermore, one key limitation of the methodology for evaluating automobile performance on urban street segment is that it does not account for the delay incurred by platoon vehicles due to pedestrian activity at midblock (or mid-segment) crosswalks Therefore, the first objective of this research is to evaluate the predictive performance of the HCM 2010 platoon dispersion model under friction and non-friction traffic conditions using field data collected at four urban street segments. The second and primary objective is to develop an integrated deterministic-probabilistic (stochastic) model that estimates the delay incurred by platoon vehicles due to midblock pedestrian activity on urban street segments. Results of the statistical model evaluation show statistically significant difference between the observed and predicted proportion of arrivals on green under traffic. The results, however, show no statistically significant difference between the observed and predicted proportion of vehicle arrivals on under no traffic friction condition. In addition, the developed delay model was validated using field measured data. Results of the statistical validation show the developed midblock delay model performs well when compared to delays measured in the field. Sensitivity analysis is also performed to study the relationship between midblock delay and certain model parameters and variables. The model parameters are increased and decreased by 50% of their baseline values

Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2020, 6,516 pedestrians were killed in traffic crashes, representing the most deaths since 1990 (NHTSA, 2020). Approximately 15% of these occurred at signalized intersections where a variety of modes converge, leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of push buttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected, especially with older devices, and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing mobility and safety and provide innovative techniques to connect pedestrians, vehicles, and infrastructure. Most research on smart and connected technologies is focused on vehicles; however, there is a critical need to harness the power of these technologies to study pedestrian behavior, as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. In this research, the investigators first review the previous studies on pedestrian behavior data and sensing technologies. Then the research team developed a pedestrian behavioral data collecting system based on the emerging LiDAR sensors. The system was deployed at two signalized intersections. Two studies were conducted: (a) pedestrian behaviors study at signalized intersections, analyzing the pedestrian waiting time before crossing, generalized perception-reaction time to WALK sign and crossing speed; and (b) a novel dynamic flashing yellow arrow (D-FYA) solution to separate permissive left-turn vehicles from concurrent crossing pedestrians. The results reveal that the pedestrian behaviors may have evolved compared with the recommended behaviors in the pedestrian facility design guideline (e.g., AASHTO’s “Green Book”). The D-FYA solution was also evaluated on the cabinet-in-theloop simulation platform and the improvements were promising. The findings in this study will advance the body of knowledge on equitable traffic safety, especially for pedestrian safety in the future

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Development and operational analysis of highway alternating merge transition zones

The design and control of work zone traffic control areas is governed by standards published by the United States Department of Transportation (US-DOT) and documented in the Manual for Uniform Control Devices (MUTCD). While these configurations have evolved over time to reflect safer and more efficient management practices and have become familiar to drivers, they are also recognized as areas of vehicle conflict that can cause congestion and safety problems. As part of this research, a new design has been developed that has the potential to lessen the detrimental effects of lane closures in work zones. This new concept, known as the “joint merge,” is configured to simultaneously merge two lanes into one. The key feature of the joint merge design is its use of a two-sided taper. In it, both lanes approaching a lane reduction are simultaneously tapered into a single lane, with neither lane having a priority, thereby influencing drivers to merge in a smooth alternating pattern. The joint merge configuration was examined at a work zone site in Louisiana and compared to the MUTCD conventional merge configuration that was tested at the same site. The performance measures collected in the field included lane-specific volume and vehicle speeds. The two designs were quantitatively compared using Analysis of Variance (ANOVA) and T-test statistical procedures. These two testing agents were used to analyze the effects each design had on volume, speed and vehicle lane distributions at several locations in advance of the work zone entrance. Using speed and volume data, the joint merge traffic control plan was found to increase the efficiency of the closed lane and better encourage the use of both lanes leading up to the work zone entrance. It was further concluded that the number of lane changes during low and high volume periods decreased when the joint merge configuration was used. While no conclusive findings could be made relative to its specific effect on capacity, the video recordings and lane usage data suggested that the joint merge strategy was understood and well received by most drivers

Vision-based Detection, Tracking and Classification of Vehicles using Stable Features with Automatic Camera Calibration

A method is presented for segmenting and tracking vehicles on highways using a camera that is relatively low to the ground. At such low angles, 3D perspective effects cause significant appearance changes over time, as well as severe occlusions by vehicles in neighboring lanes. Traditional approaches to occlusion reasoning assume that the vehicles initially appear well-separated in the image, but in our sequences it is not uncommon for vehicles to enter the scene partially occluded and remain so throughout. By utilizing a 3D perspective mapping from the scene to the image, along with a plumb line projection, a subset of features is identified whose 3D coordinates can be accurately estimated. These features are then grouped to yield the number and locations of the vehicles, and standard feature tracking is used to maintain the locations of the vehicles over time. Additional features are then assigned to these groups and used to classify vehicles as cars or trucks. The technique uses a single grayscale camera beside the road, processes image frames incrementally, works in real time, and produces vehicle counts with over 90% accuracy on challenging sequences. Adverse weather conditions are handled by augmenting feature tracking with a boosted cascade vehicle detector (BCVD). To overcome the need of manual camera calibration, an algorithm is presented which uses BCVD to calibrate the camera automatically without relying on any scene-specific image features such as road lane markings