Work zones create one of the most challenging environments for drivers. Implementing work zones on urban freeways creates many issues, especially with respect to mobility. Decisions made regarding the work zone should be informed by quantitative data, collected in work zones, to ensure that the mobility impacts of the work zone treatments implemented are mitigated. A new decision-making process, which addresses the shortcomings in the current decision-making processes, was developed through the course of this research. The new process incorporates a Performance Measure/Treatment matrix, which recommends multiple performance measures, each of which is chosen to measure the mobility impacts particular to a specific work zone implementation. Most importantly, the revised decision-making process incorporates a feedback loop. Quantitative data collected in work zones is analyzed after the work zone is complete, to determine the impacts specific decisions had on mobility in the work zone. The lessons learned in previous work zones are then incorporated into the decision-making process, lessening the mobility impacts of future work zones. This thesis develops the new decision-making process, and examines the issues with the application of the process.

Thesis (M.S.C.E.)--University of Massachusetts Amherst, 2008. / Includes bibliographical references (p. 71-72).

Construction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their safety and daily commute time. These problems represent major challenges to construction planners as they are required to plan vehicle routes around construction zones in such a way that maximizes the safety of construction workers and reduces the impact on daily commuters. This research aims at developing a framework for optimizing the planning of construction detours. The main objectives of the research are to first identify all the decision variables that affect the planning of construction detours and secondly, implement a model based on shortest path formulation to identify the optimal alternatives for construction detours. The ultimate goal of this research is to offer construction planners with the essential guidelines to improve construction safety and reduce construction zone hazards as well as a robust tool for selecting and optimizing construction zone detours.

As the United States continues to build and repair the ageing highway infrastructure, the bearing of freeway work zones will continue to impact the capacity. To predict the capacity of a freeway work zone, there are several tools available for engineers to evaluate these work zones but only microsimulation has the ability to simulate the driver behavior. One of the limitations of current car-following models is that they only account for one overall behavioral condition. This dissertation hypothesizes that drivers change their driving behavior as they drive through a freeway work zone compared to normal freeway conditions which has the potential to impact traffic operations and capacity of work zones. Psycho-physical car-following models are widely used in practice for simulating car-following. However, current simulation models may not fully capture car-following driver behavior specific to freeway work zones. This dissertation presents a new multidimensional psycho-physical framework for modeling car-following based on statistical evaluation of work zone and non-work zone driver behavior. This new framework is close in character to the Wiedemann model used in popular traffic simulation software such as VISSIM. This dissertation used two methodologies for collecting data: (1) a questionnaire to collect demographics and work zone behavior data and (2) a real-time vehicle data from a field experiment involving human participants. It is hypothesized that the parameters needed to calibrate the multidimensional framework for work zone driver behavior can be derived statistically by using data collected from runs of an Instrumented Research Vehicle (IRV) in a Living Laboratory (LL) along a roadway. The design of this LL included the development of an Instrumented Research Vehicle (IRV) to capture the natural car-following response of a driver when entering and passing through a freeway work zone. The development of a Connected Mobile Traffic Sensing (CMTS) system, which included state-of-the-art ITS technologies, supports the LL environment by providing the connectivity, interoperability and data processing of the natural, real-life setting. The IRV and CMTS system are tools designed to support the concept of a LL which facilitates the experimental environment to capture and calibrate natural driver behavior. The objective is to have these participants drive the instrumented vehicle and collect the relative distance and the relative velocity between the instrumented vehicle and the vehicle in the front of the instrumented vehicle. A Phase I pilot test was conducted with 10 participants to evaluate the experiment and make any adjustments prior to the full Phase II driver test. The Phase II driver test recruited a group of 64 participants to drive the IRV through an LL set up along a work zone on I-95 near Washington D.C. in order to validate this hypothesis In this dissertation, a new framework was applied and it demonstrated that there are four different categories of car-following behavior models each with different parameter distributions. The four categories are divided by traffic condition (congested vs. uncongested) and by roadway condition (work zone vs. non-work zone). The calibrated threshold values are presented for each of these four categories. By applying this new multidimensional framework, modeling of car-following behavior can enhance vehicle behavior in microsimulation modeling. This dissertation also explored driver behavior through combining vehicle data and survey techniques to augment the model calibrations to improve the understanding of car-following behavior in freeway work zones. The results identify a set of survey questions that can potentially guide the selection of parameters for car-fallowing models. The findings presented in this dissertation can be used to improve the performance of driver behavior models specific to work zones. This in return will more acutely forecast the impact a work zone design has on capacity during congestion.

No description available.

No description available.

The presented thesis deals with lane merging at lane drops. The theory of traffic flow is briefly introduced and put into the perspective of lane merging. Forming of queues at lane drops, the capacity drop and traffic flow behaviour at lane merging is explained. A review of existing measures at lane drops at work zones and elsewhere is provided. Measurements of traffic flow from three different locations are presented. The locations are introduced, the methods used for analysis of the data are explained and the results are provided and discussed. The capacity drop is confirmed and the first proof of concept of the later introduced metering system is presented. The behaviour of the merging drivers was found to have a significant influence on the merging capacity during congestion. Further, an overview of existing applications of traffic light in traffic flow control is provided and ramp metering and mainstream metering is explained. The new metering system for lane drops is presented including several possible modifications and extensions. Finally, the proposed metering system is tested in microsimulation software Aimsun. The simulations further confirm the viability of such systems. It brings significant capacity improvements and consequently even greater improvements of delays and travel times due to shorter queues.

<p>  </p> <p>Advancements in technology have propelled the availability of enriched and more frequent information about traffic conditions as well as the external factors that impact traffic such as weather, emergency response etc. Most newer vehicles are equipped with sensors that transmit their data back to the original equipment manufacturer (OEM) at near real-time fidelity. A growing number of such connected vehicles (CV) and the advent of third-party data collectors from various OEMs have made big data for traffic commercially available for use. Agencies maintaining and managing surface transportation are presented with opportunities to leverage such big data for efficiency gains. The focus of this dissertation is enhancing the use of CV data and applications derived from fusing it with other datasets to extract meaningful information that will aid agencies in data driven efficient decision making to improve network wide mobility and safety performance.   </p> <p>One of the primary concerns of CV data for agencies is data sampling, particularly during low-volume overnight hours. An evaluation of over 3 billion CV records in May 2022 in Indiana has shown an overall CV penetration rate of 6.3% on interstates and 5.3% on non-interstate roadways. Fusion of CV traffic speeds with precipitation intensity from NOAA’s High-Resolution Rapid-Refresh (HRRR) data over 42 unique rainy days has shown reduction in the average traffic speed by approximately 8.4% during conditions classified as very heavy rain compared to no rain. </p> <p>Both aggregate analysis and disaggregate analysis performed during this study enables agencies and automobile manufacturers to effectively answer the often-asked question of what rain intensity it takes to begin impacting traffic speeds. Proactive measures such as providing advance warnings that improve the situational awareness of motorists and enhance roadway safety should be considered during very heavy rain periods, wind events, and low daylight conditions.</p> <p>Scalable methodologies that can be used to systematically analyze hard braking and speed data were also developed. This study demonstrated both quantitatively and qualitatively how CV data provides an opportunity for near real-time assessment of work zone operations using metrics such as congestion, location-based speed profiles and hard braking. The availability of data across different states and ease of scalability makes the methodology implementable on a state or national basis for tracking any highway work zone with little to no infrastructure investment. These techniques can provide a nationwide opportunity in assessing the current guidelines and giving feedback in updating the design procedures to improve the consistency and safety of construction work zones on a national level.  </p> <p>CV data was also used to evaluate the impact of queue warning trucks sending digital alerts. Hard-braking events were found to decrease by approximately 80% when queue warning trucks were used to alert motorists of impending queues analyzed from 370 hours of queueing with queue trucks present and 58 hours of queueing without the queue trucks present, thus improving work zone safety. </p> <p>Emerging opportunities to identify and measure traffic shock waves and their forming or recovery speed anywhere across a roadway network are provided due to the ubiquity of the CV data providers. A methodology for identifying different shock waves was presented, and among the various case studies found typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were between 5.78 to 16.54 mph. The significance of this is illustrated with a case study of  a secondary crash that suggested  accelerating the clearance by 9 minutes could have prevented the secondary crash incident occurring at the back of the queue. Such capability of identifying and measuring shock wave speeds can be utilized by various stakeholders for traffic management decision-making that provide a holistic perspective on the importance of both on scene risk as well as the risk at the back of the queue. Near real-time estimation of shock waves using CV data can recommend travel time prediction models and serve as input variables to navigation systems to identify alternate route choice opportunities ahead of a driver’s time of arrival.   </p> <p>The overall contribution of this thesis is developing scalable methodologies and evaluation techniques to extract valuable information from CV data that aids agencies in operational decision making.</p>