Work Zone Simulator Analysis: Driver Performance and Acceptance of Alternate Merge Sign Configurations

Improving work zone road safety is an issue of great interest due to the high number of crashes observed in work zones. Departments of Transportation (DOTs) use a variety of methods to inform drivers of upcoming work zones. One method used by DOTs is work zone signage configuration. It is necessary to evaluate the efficiency of different configurations, by law, before implementation of new signage designs that deviate from national standards. This research presents a driving simulator based study, funded by the Missouri Department of Transportation (MoDOT) that evaluates a driver’s response to work zone sign configurations. This study has compared the Conventional Lane Merge (CLM) configurations against MoDOT’s alternate configurations. Study participants within target populations, chosen to represent a range of Missouri drivers, have attempted four work zone configurations, as part of a driving simulator experience. The test scenarios simulated both right and left work zone lane closures for both the CLM and MoDOT alternatives. Travel time was measured against demographic characteristics of test driver populations. Statistical data analysis was used to investigate the effectiveness of different configurations employed in the study. The results of this study were compared to results from a previous MoDOT to compare result of field and simulation study about MoDOT’s alternate configurations

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Multi-Criteria Evaluation in Support of the Decision-Making Process in Highway Construction Projects

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared

Evaluating Mobility Impacts of Construction Work Zones on Utah Transportation System Using Machine Learning Techniques

Construction work zones are inevitable parts of daily operations at roadway systems. They have a significant impact on traffic conditions and the mobility of roadway systems. The traffic impacts of work zones could significantly vary due to several interacting factors such as work zone factors (work zone location and layout, length of the closure, work zone speed, intensity, and daily active hours); traffic factors (percentage of heavy vehicles, highway speed limit, capacity, mobility, flow, density, congestion, and occupancy); road factors (number of total lanes, number of open lanes, and pavement grade and condition); temporal factors (e.g., year, season, month, weekday, daytime, and darkness); weather conditions (rainy, sunny, and snowy); and spatial factors (road lane width, proximity, and number of ramps). Utah Department of Transportation (UDOT) is continuously collecting and storing project-related data. Due to the significant impact of work zones on traffic conditions, they are interested in evaluating the impacts of work zone attributes on mobility and traffic conditions of roadway systems within the state of Utah. Such an analysis will help the UDOT personnel better understand and plan for more efficient work zone operations, select the most effective traffic management systems for work zones, and assess the hidden costs of construction operations at work zones. To help UDOT address this problem, we propose a robust, deep neural network (DNN) model capable of evaluating the impacts of the factors mentioned earlier on the mobility conditions of Utah roadway systems. DNNs can capture all the relationships between input variables and output compared to traditional machine learning algorithms. The results of this project show that work zone features have an important effect on the traffic condition. In the end, the performance of the model is evaluated using three different measures, including R2 score, RMSE, and MAE. Comparing the measurement to previously conducted research, it is the first study that has attempted to investigate the effect of work zone features on hourly traffic volume

Bus Rapid Transit: A Handbook for Partners, MTI Report 06-02

In April 2005, the Caltrans Division of Research and Innovation (DRI) asked MTI to assist with the research for and publication of a guidebook for use by Caltrans employees who work with local transit agencies and jurisdictions in planning, designing, and operating Bus Rapid Transit (BRT) systems that involve state facilities. The guidebook was also to assist to transit operators, local governments, community residents, and other stakeholders dealing with the development of BRT systems. Several areas in the state have experienced such projects ( San Diego , Los Angeles , San Francisco , and Alameda County ) and DRI wished to use that experience to guide future efforts and identify needed changes in statutes, policies, and other state concerns. Caltrans convened a Task Team from the Divisions of Research and Innovation, Mass Transportation, and Operations, together with stakeholders representing many of those involved with the BRT activities around the state. Prior to MTI’s involvement, this group produced a white paper on the topic, a series of questions, and an outline of the guidebook that MTI was to write. The MTI team conducted case studies of the major efforts in California, along with less developed studies of some of the other BRT programs under development or in early implementation phases around the state. The purpose was to clarify those issues that need to be addressed in the guidebook, as well as to compile information that would identify items needing legislative or regulatory action and items that Caltrans will need to address through district directives or other internal measures. A literature scan was used to develop a bibliography for future reference. The MTI team also developed a draft Caltrans director’s policy document, which provides the basis for Caltrans’ actions. This ultimately developed to be a project within a project. MTI submitted a draft document to Caltrans as a final product from the Institute. Task team members and Caltrans staff and leadership provided extensive review of the draft Bus Rapid Transit: A Handbook for Partners. Caltrans adopted a new Director’s Policy and published the document, BRT Caltrans. The MTI “wraparound” report presented below discusses in more detail the process that was followed to produce the draft report. The process was in many ways as much a project as the report itself