Frederick, MD, United States
Frederick, MD, United States

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Ogallo H.O.,Maryland State Highway Administration | Jha M.K.,Beulah Group | Jha M.K.,Morgan State University
Journal of Transportation Engineering | Year: 2014

This paper presents a methodology for critical gap analysis at signalized intersections with unprotected opposing left-turn movements. Highway capacity manual (HCM) methodology for estimating potential capacity uses base critical gap and base follow-up time, which are adjusted to reflect specific conditions of each intersection. That methodology assumes an unobstructed line-of-sight for drivers while executing left-turn maneuvers. However, the line-of-sight is not always unimpeded. Previous studies have shown that leading noncompact (larger and taller) vehicles and vehicles in the opposing left-turn lane may impede the line-of-sight. Specifically, the studies have demonstrated that the impedance may result in a capacity reduction. In order to quantify the capacity reduction, we present a methodology to modify the HCM critical gap and follow-up time model when the line-of-sight of unprotected left turn maneuver is obstructed. We introduce new adjustment factors to account for vehicles in the opposing left-turn lane obstructing the line-of-sight during the left-turn maneuver. Using field data from Baltimore and Annapolis, Maryland, we show that the obstruction increases the left-turn critical gap and the left-turn follow-up time, and hence decreases the potential capacity for left turns at intersections with unprotected left-turn movements. This capacity reduction may be a significant contributor to systemwide delay during rush hour, and may influence dilemma zone and red light running behavior, which are subjects of future research. © 2014 American Society of Civil Engineers.

Maji A.,Maryland State Highway Administration | Jha M.K.,Morgan State University
Advances in Transportation Studies | Year: 2011

The available Highway Alignment Optimization (HAO) algorithms use either single-objective or multiobjective approaches. These algorithms consider different highway alignment costs along with restricted land use information such as forests, wetland, etc. as the primary objectives which are minimized in the process of highway alignment optimization. In single-objective HAO approaches, different highway alignment sensitive costs are formulated and added to obtain the total highway alignment cost in monetary value. The total highway alignment cost is used as the primary objective function in the single-objective HAO. This method, upon optimal search, yields a highway alignment with minimum total highway alignment cost. As part of the total highway alignment cost, the restricted land use is expected to be minimized in the optimization process. Whereas in the multi-objective optimization approach the highway alignment cost and restricted land use information are maintained separate and optimized simultaneously. This way, the multi-objective HAO helps to yield a set of Pareto-optimal solution with trade-off between highway alignment cost and the restricted land use information. The restricted land use information used in both optimization processes is derived from a geographical information system (GIS) database. It is cumbersome to express the highway alignment and associated objectives in mathematical formulations as required in classical optimization techniques. Hence, both methods use unconventional genetic algorithm (GA) based optimization approach. This paper compares single-objective HAO with multi-objective HAO and discusses their merits and demerits in a real-world example application.

Subramanian R.,Maryland State Highway Administration | Thanikachalam T.,University of Florida | Najafi F.T.,University of Florida
ASEE Annual Conference and Exposition, Conference Proceedings | Year: 2012

Civil engineering is a discipline that amalgamates art and science to create and refine infrastructure work, provides solutions according to the needs of modern civilization, and protects the environment. The dynamics of the current global marketplace suggests that civil engineers are among the best-positioned professionals to be able to utilize the cutting edge technology. Civil engineers find numerous opportunities in industry, be it through consulting practices, research or development. However, for civil engineering to maintain its importance in a global business setting, it is imperative that institutions' curricula be regularly revised to meet the world's perpetual evolving social and environmental needs. Both the civil engineering programs leading to a bachelor's of science degree are four-year programs. The College of Engineering at the University of Florida has 11 academic departments, while the College of Engineering at the Anna University has 31. Civil engineering is one of the departments in the College of Engineering at both universities. However, the required credits required between the programs are quite varied. The curriculum leading to the Bachelor of Science in Civil Engineering degree at the University of Florida consists of 131 credits, while that of the Anna University consists of a minimum of 191 credits. The University of Florida allows for 52 general education credits while the Anna University provides for 54. In terms of core engineering credits, the University of Florida requires 79 engineering-based credits, while Anna University requires 137. This paper compares and analyzes the current civil engineering undergraduate curriculum at the Anna University with that of the University of Florida's. The results of this study indicate that both curricula meet the educational requirements of both the United States and that of the Republic of India. © 2012 American Society for Engineering Education.

Cheevarunothai P.,University of Washington | Zhang G.,University of New Mexico | Zheng J.,Maryland State Highway Administration | Wang Y.,University of Washington | An S.,Harbin Institute of Technology
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations | Year: 2012

Traffic congestion is getting worse and has resulted in increased travel delays and costs. In order to develop effective intelligent transportation systems (ITS) strategies to mitigate traffic congestion on freeways, a good understanding of its causes and impacts is vital but has not been achieved at a satisfactory level. Over the past several decades, deterministic queuing theory (DQT) has been widely used to evaluate freeway travel delays resulted from traffic congestion. However, several studies evaluated the accuracy of its delay estimates and claimed that the DQT method consistently underestimates vehicle delays. The reason for the underestimation, however, had not been clearly identified. This study aims at exploring the main cause of such underestimation problems and proposing a solution to fix it. Based on theoretical analysis and empirical justification, it was found the underestimation resulted primarily from the inappropriate estimates of the time offsets, that is, the travel times between the queue starting point and the immediate upstream and downstream traffic sensor locations. To address this issue, a microscopic approach was developed and implemented in a computer application to enhance the time offset estimation. This proposed approach was tested using the real vehicle delay data manually extracted from traffic surveillance video cameras. The test results indicated that the improved DQT-based vehicle delay estimates with appropriate time offset settings were very close to the ground-truth data. The underestimation problem associated with the traditional DQT method can be effectively addressed and fairly accurate estimates of vehicle delay can be achieved by the proposed method. © 2012 Copyright Taylor and Francis Group, LLC.

Zheng J.,Maryland State Highway Administration | Ma X.,University of Washington | Wu Y.-J.,University of Arizona | Wang Y.,Harbin Institute of Technology
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations | Year: 2013

Safety and quality of travel on arterial networks tie closely to the performance of signalized intersections. Measures commonly used for intersection performance evaluations are control delay, queue length, and cycle failure. However, these variables are not directly available from typical configurations of traffic sensors designed for intersection signal control. Collecting vehicle control delay data manually for intersection performance measurement has been a task too time-consuming and labor-intensive to be practical. Video image processors (VIPs) have been increasingly deployed for intersection signal control in recent years. This study aims to use the extra detection capabilities of VIPs for performance monitoring at signalized intersections. Most VIPs can support up to 24 virtual loops, but normally less than half of the virtual loops are used. By properly configuring the spare virtual loops and analyzing the loop measurements, intersection performance can be monitored in real time. In this research, we propose an approach for measuring queue length and vehicle control delay at signalized intersections based on traffic count data collected with traffic sensors. This algorithm has been implemented in a computerized system called In-PerforM. The In-PerforM system was evaluated by both field tests and simulation experiments. Although the VIP's counting errors do affect the accuracy of field test results, we still received encouraging results on queue lengths and control delay measurements in both the field tests and simulation experiments. This demonstrates that the In-PerforM system, and therefore the proposed algorithm, has the potential to be a cost-effective approach for performance measurement at signalized intersections. © 2013 Copyright Taylor and Francis Group, LLC.

Chang G.-L.,University of Maryland University College | Park S.Y.,University of Maryland University College | Paracha J.,Maryland State Highway Administration
Transportation Research Record | Year: 2011

Contending with recurrent congestion on commuting corridors has long been a challenging and pressing issue for responsible highway agencies. However, effective strategies to mitigate the congestion level and the accompanying safety issues on those highway segments remain to be developed. In response to such needs, this study presents an innovative system that integrates variable speed control and travel time information for alleviating day-to-day recurrent congestion on a highway corridor. The system presented in this study includes a set of algorithms for setting variable speeds for different highway segments based on traffic conditions detected from roadway sensors and a well-calibrated license-plate-recognition system for displaying the estimated travel time. Field experiments of the proposed system on MD-100 for 8 weeks showed that with proper speed control in real time, the congested highway segment can achieve a higher throughput, a stable traffic condition, and a shorter travel time.

Li Z.,University of Maryland University College | Tao R.,Maryland State Highway Administration
Transportation Research Record | Year: 2011

Congested freeway interchanges normally suffer from the spillback of off-ramp and on-ramp queues. The traffic signals around a congested interchange can also experience link-blockage and lane-blockage problems. For an overall system performance around an interchange to be improved, both freeway and arterial performance must be considered to optimize signal timings. This study extends the cell transmission concept and proposes a set of new formulations to capture the traffic dynamic with link blockage, lane blockage, and ramp spillback. On the basis of these formulations, an integrated control model for optimizing interchanges and a solution method based on a genetic algorithm are proposed. The model's performance was demonstrated in a case study with different traffic scenarios for the interchange connecting Georgia Avenue (MD-97) and the Capital Beltway (I-495) in Silver Spring, Maryland. The resulting signal timings were compared with those from TRANSYT-7F (Release 10). For fair comparison, this study used CORSIM, a third-party simulation package, as the performance evaluator. Results indicated that the proposed model was promising and could improve freeway and arterial performance.

Kim M.,Maryland State Highway Administration | Miller-Hooks E.,University of Maryland University College | Nair R.,University of Maryland University College
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations | Year: 2011

The authors present a framework for a geographic information systems (GIS)-based decision support system (DSS) designed to provide online routing instructions in response to updated information on traffic and weather conditions for vehicles transporting hazardous materials. Embedded within the DSS is a heuristic that exploits a spatially and temporally rolling horizon. The heuristic is implemented using standard, widely available tools to facilitate adoption by decision makers. The DSS's ability to dynamically update instructions in response to real-time information is demonstrated along the corridor between the metropolitan areas of Washington, D.C., and Baltimore, Maryland. Copyright © Taylor and Francis Group, LLC.

Krebs A.,Stantec Inc. | Shamayleh R.,Stantec Inc. | Habic E.,Maryland State Highway Administration
Military Engineer | Year: 2015

As transportation departments across the country deal with the pressing challenges of growth, development and aging infrastructure, one agency in Maryland is expanding that focus to include resiliency. © 2015, Society of American Military Engineers. All rights reserved.

Shirazi H.,Applied Research Associates Inc. | Ayres M.,Applied Research Associates Inc. | Speir R.,Applied Research Associates Inc. | Song W.,Maryland State Highway Administration | Hall G.,Maryland State Highway Administration
Transportation Research Record | Year: 2010

The international roughness index (IRI) is the primary index of the Maryland pavement management system and is the data source for performance trend analysis, budget allocation, and project selection. The Maryland State Highway Administration (MDSHA) makes a significant investment to collect, process, analyze, and store IRI data every year over the entire network of the roads. Such reliance on IRI data and investment instigated the investigation of the confidence level embedded in the data and of devising possible sampling scenarios. To evaluate the confidence level, repeatability errors of the measurements were assessed. IRI data were repeatedly collected over a designated test loop under normal operating conditions to mimic network-level data collection. Sampling scenarios were devised using the Monte Carlo method for network IRI data collected in 2008. The network was stratified on the basis of functional classifications. Sampling errors in estimating the percentage of the roads in each of five IRI categories established by MDSHA were evaluated. The results indicated that the repeatability error of the measurements was less than 7%. The sampling results indicated that the state may survey a third of the network each year and expect estimation errors of less than 0.5% for all IRI categories. As such, MDSHA would not incur a considerable sacrifice in the confidence of network condition evaluation.

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