Rakha becomes a fellow of the Canadian Academy of Engineering
This research effort provided a comprehensive analysis of using advanced signal timing information as well as information about speed and spacing of surrounding vehicles to optimize vehicle fuel consumption levels. This system was modeled, tested and evaluated in multiple simulation environments. The report also analyzed the previous and concurrent research efforts in the field of advanced eco-driving, using signal timing information. Multiple literatures were reviewed on optimizing fuel consumption at signalized intersections but most of them lacked a comprehensive analysis or even an explicit optimization function that incorporates microscopic fuel consumption models. Most researchers assumed fuel consumption to be tied directly with vehicle acceleration levels and used that as a control to optimize fuel consumption. This claim is not necessarily true and depends on a variety of other parameters. The Algorithm Development chapter described in brief how the algorithm is modeled to include multiple constraints that are based on advanced signal information, vehicular and roadway parameters.The study also analyzed vehicle-specific modeling of Eco-Speed Control (ESC) where the system was calibrated to 30 top-sold automobiles in the US, which are attributed to six different Environmental Protection Agency (EPA) classes. MATLAB-based simulation analysis was done to test the sensitivity of the model with respect to vehicle class, bounding speed-limits and green-time delay for optimization of vehicle trajectories. The Eco-Cooperative Adaptive Cruise Control (ECACC) system was found to be sensitive to these three criteria and fuel savings were measured as absolute and relative values. Class-based analysis suggested that absolute fuel savings is highest in light-duty trucks and lowest in compact cars, whereas the relative fuel savings is vice-versa. However, the absolute and relative trends matched for other variables such as approach speed and green-time delay. A higher speed-limit caused greater fuel savings and a higher green-time delay caused lesser fuel savings. The green-time delay is defined as the time differential between the actual green time and the time to intersection of the vehicle prior to optimization. Agent-based modeling of Eco-Speed Control was performed to test the performance of the system on a fully-functional signalized intersection from downtown Blacksburg, Virginia. The traffic signal is a single lane, four legged intersection. The test uses real estimates of approach volumes and signal timings. Approach volumes considered correspond to various fractions of the current evening peak demand up to 175 percent, so as to have a scenario for over-saturated conditions (v/c > 1.0). The intersection was simulated at a microscopic level including specific features such as grade and lane geometry. Reactive agents were used to simulate vehicles that run on ESC logic with respect to changing signal conditions. Two measures of effectiveness were considered – the average fuel consumption and the average travel-time for the 400 meter vicinity of the intersection. The MOEs categorized according to the approach and also the overall intersection MOE. Washington Street is the minor approach and Main Street is the major approach.
The study demonstrated the feasibility of two eco-driving applications which reduce vehicle fuel consumption and greenhouse gas emissions: an eco-drive system and eco-Lanes applications. In particular, the study developed an eco-drive system that combines eco-cruise control logic with state-of-the-art car-following models and evaluated eco-Lanes and SPD-HARM applications and investigated the potential of developing an eco-drive system that combines an ECC system with state-of-the-art car-following models. The system makes use of topographic information, the spacing between the subject and lead vehicle, and a desired (or target) vehicle speed and distance headway as input variables. The study demonstrated that the proposed system can significantly improve fuel efficiency while maintaining reasonable vehicle spacing. One of the test vehicles, a 2011 Toyota Camry, saved 27 percent in fuel consumption with an average spacing of 47 m along the I-81 study section. The study found that the car-following threshold setting significantly affects the fuel economy and the spacing between vehicles.Furthermore, the study also demonstrated that a dynamic car-following spacing threshold significantly reduces the average vehicle spacing compared to a fixed car-following spacing threshold. The study also evaluated the impacts of variable vehicle power and found that vehicle operations at lower power demands significantly enhance vehicle fuel economy (up to 49 percent). The study finally demonstrated that non-ECC-equipped vehicles can significantly reduce their own fuel consumption just by following a lead ECC-equipped vehicle. This research also investigated the feasibility of Eco-Lanes applications that attempt to reduce system-wide fuel consumption and GHG emission levels through lane management strategies. The study focused its efforts on evaluating various Eco-Lanes and SPD-HARM applications using the INTEGRATION microscopic traffic simulation software. The study demonstrated that the proposed Eco-Lanes system can significantly improve fuel efficiency and air quality while reducing average vehicle travel time and total system delay. For this case study, the proposed system reduced travel time, delay, fuel consumption, HC, CO, and CO2 emissions by 8.5%, 23%, 4.5%, 3.1%, 3.4%, and 4.6%, respectively, compared with the base case scenario. The study also examined the feasibility of a predictive Eco-Lanes system. This system predicts the onset of congestion and starts the Eco-Lanes system before congestion occurs. The simulation study found that the 30-minute predictive Eco-Lanes system produced greater reductions in fuel consumption and CO2 emissions compared with the non-predictive Eco-Lanes system. The study also found that the optimum throttle levels and the optimum eco-speed limits can significantly improve the mobility, energy savings, and air quality of such systems. Furthermore, the study demonstrated that SPD-HARM as an Eco-Lanes application produced reductions in delay, fuel consumption, HC, CO, NOx, and CO2 emissions by 7.6%, 6.3%, 23.9%, 26.1%, 17.2%, and 4.4%, respectively, compared with the base case scenario. The t-test results indicated that there were significant benefits to the MOEs when SPD-HARM was operated. Future research should quantify the potential benefits of using the proposed Eco-Lanes systems on different networks with various vehicle types. Also, further studies are required to characterize the optimum eco-lanes specifications, such as the spatial and temporal eco-lanes boundaries, and to enhance the optimum eco-speed limit algorithms. Furthermore, the car-following behavior of non-eco-vehicles should be investigated. Finally, further research is needed to validate the simulation outputs using field tests.
The study developed eco-routing algorithms and investigated and quantified the system-wide impacts of implementing an eco-routing system considering two large metropolitan networks, namely downtown Cleveland and Columbus, OH. Recently navigation tools and trip planning services have introduced a vehicle routing option that is designed to minimize vehicle fuel consumption and emission levels, known as eco-routing. The eco-routing option selects the most fuel efficient route, which is not necessarily the shortest distance or the fastest travel time route. Consequently, there is a need to develop such systems and test them on large-scale networks considering different levels of system market penetration. In this study, two eco-routing connected vehicle (CV) algorithms are developed: one based on vehicle sub-populations (ECO-Subpopulation Feedback Assignment or ECO-SFA) and another agent-based system that considers individual drivers (ECO-Individual Feedback Assignment or ECO-IFA). Both approaches initially assign vehicles based on fuel consumption levels for travel at the facility free-flow speed when the network is empty and no information is available. Subsequently, fuel consumption estimates are refined based on experiences of other vehicles within the same class by communicating their experiences to the Cloud. This stochastic, multi-class, dynamic traffic assignment framework was demonstrated to work for both test scenarios (ECO-SFA and ECO-IFA). The study also quantifies the system-wide impacts of implementing a dynamic eco-routing system, considering various levels of market penetration and levels of congestion in downtown Cleveland and Columbus, Ohio, USA. The study concludes that eco-routing systems can reduce network-wide fuel consumption and emission levels in most cases; the fuel savings over the networks range between 3.3% and 9.3% when compared to typical travel time minimization routing strategies. The study demonstrates that the fuel savings achieved through eco-routing systems are sensitive to the network configuration and level of market penetration of the eco-routing system. The results also demonstrate that an eco-routing system typically reduces vehicle travel distance but not necessarily travel time. The study demonstrates that the configuration of the transportation network is a significant factor in defining the benefits of eco-routing systems. Specifically, eco-routing systems appear to produce larger fuel savings on grid networks compared to freeway corridor networks. The study also demonstrates that different vehicle types produce similar trends with regard to eco-routing strategies. Finally, the system-wide benefits of eco-routing generally increase with an increase in the level of the market penetration of the system.
Project Description: A recent study conducted by the National Transportation Operations Coalition (NTOC) scored the overall operation of the total of 265,000 traffic signals in the United States at a D-. A self assessment completed by 378 agencies in the United States reported unnecessary delay, increased fuel consumption, and increased pollution as a result of inefficient signal operation. The NTOC concluded that “never before has the need for good traffic signal operation been greater”. Traffic signal systems are currently operated using a very archaic traffic detection simple binary logic (vehicle presence/non-presence information). The logic was originally developed to provide input for old electro-mechanical controllers that were developed in the early 1920’s, and was sufficient for that purpose only. Many decades later, both the controller and detection technology have evolved significantly. Vehicle infrastructure integration (VII) promises to “bridge the gap” between the infrastructure and individual drivers. VII can offer significant benefits to traffic operations and control. Nevertheless, basic research in this area is still lacking, and does not provide enough guidance on how to use the existing system to its fullest potential. There is a wide range of underutilized control capabilities, including advanced traffic signal timing, the use of second-by-second vehicle location data to estimate approach delays and queue size information. Currently, only vehicle presence is provided to and used by the existing controllers [3-5]. There is therefore a need to investigate the potential of using VII data to enhance traffic signal control capabilities. Furthermore, in conjunction with traffic signal control there is a need to reduce the traffic demand on a network. One of these approaches includes the use of roadway tolling. VII again can assist in the charging of roadway usage given that the location of vehicles will be known to the second-by-second level. The objectives of the proposed research effort are: a. Research and investigate the potential to utilize VII data to characterize system operation and estimate system-wide measures of performance. b. Develop advanced signal timing procedures that can capitalize on VII data and enhance the operations of traffic signal system operations.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC) and the Virginia Department of Transportation (VDOT).
Project Description: The objectives of this project are: 1. Develop models to characterize driver deceleration, perception-reaction times, and stop/go decisions at the onset of a yellow indication 2. Evaluate the weather impact on driver behavior 3. Develop new procedures for estimating yellow timings 4. Investigate the potential for use of IntelliDrive applications to enhance signalized intersection safety and performance.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The objectives of this project are: 1. Develop a novel multi-stage model for travel time reliability evaluation and reporting. 2. Construct a simulation test bed along a section of I-66 to investigate the impact of different factors on travel time reliability. 3. Construct a database of field loop detector and incident data for the same I-66 study section. 4. Develop a multi-stage model to quantify the impact of incidents on travel time reliability using the field and simulation test bed. 5. Develop algorithms to use vehicle probe data to estimate dynamic roadway travel times.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The objectives of this project are to: 1. Develop energy and emission models for light- and heavy-duty vehicles 2. Assess the energy and emission impacts of alternative traffic calming measures 3. Assess the impact of high emitting vehicles on Green House Gases (GHGs) 4. Evaluate the energy and environmental impact of roundabouts. This characterization is critical to any successful Vehicle Infrastructure Integration (VII) or Advanced Traveler Information System (ATIS) initiatives.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC).
Project Description: Project Abstract: Within the context of transportation modeling, driver route selection behavior is typically captured using mathematical programming. These approaches assume that drivers have full knowledge of the transportation network state in attempting to minimize some objective function. Typically, drivers are assumed to either minimize their travel time (user equilibrium) or minimize the total system travel time (system optimum). Given the dynamic and stochastic nature of the transportation system, the assumption of a driver’s perfect knowledge is at best questionable. Consequently, there is a need to develop novice approaches for the modeling of driver route choice. Unlike most route choice research that is primarily focused on the conscious part of the route selection task, this research effort explores the subconscious nature of route selection. Conscious route selection assumes that drivers are constantly conscious to their route selection behavior in selecting the travel route that provides them with the maximum utility. However, it is well documented in human psychological behavior that humans tend to minimize their cognitive efforts, and follow simple heuristics to reach their decisions, especially under uncertainty and time constraints. In addition, with repetition, cognitive activities descend to the subconscious level. The project tasks include: 1. Conduct a simulation study of driver route choice behavior. 2. Conduct an in-field study of driver route choice behavior. 3. Develop a behavioral route choice model. 4. Analyze typical driver route choice behavior using naturalistic data.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC).
Project Description: Inclement weather is one of the key causes of congestion because drivers typically attempt to drive at lower speeds with increased caution. Additionally, inclement weather contributes to more than 1.5 million crashes every year. Several studies have quantified the effect of inclement weather on macroscopic traffic stream behavior, including its impact on the roadway capacity and speed. However, it is hard to find studies that characterize individual driver behavior under adverse weather conditions and that analyze the variability in driver behavior. Furthermore, it is difficult to find studies that specifically assess the impact of icy pavement conditions on driver behavior. The objective of this study is to quantify the impacts of icy roadway conditions on driver behavior at a microscopic level, using field-measured car-following data, in addition to the study of the typical variability in driver behavior. In addition the study quantifies the impact of rain and snow on driver gap acceptance behavior and finally develops procedures to incorporate inclement weather effects in a microscopic traffic modeling environment.
Project Sponsors: Federal Highway Administration (FHWA).
Project Description: During the past several years, the Federal Highway Administration’s (FHWA) Road Weather Management Program (RWMP) has carried out a series of studies to gain a better understanding of travelers’ behavior and responses to inclement weather conditions. The FHWA envisions that effective weather responsive traffic management (WRTM) strategies will be developed and implemented based on findings and recommendations generated from the efforts of last several years. An important lesson learned from WRTM studies carried out so far is lack of relevant and sufficient traffic and weather data available for the analysis. Efforts were devoted at onset of previous WRTM studies to identify and obtain suitable data, but yielded limited results. Hence, this project looks into the issues related to ‘traffic and inclement weather data.' Four objectives have been identified for this project as listed below: 1. Conduct a comprehensive search and documentation of traffic and weather data in the United States and abroad that could be used for WRTM; 2. Establish contacts with organizations that have suitable traffic data on inclement weather, and determine procedures/requirements to obtain these data; 3. Identify critical gaps in regards to the collection and processing of traffic data on inclement weather conditions; and 4. Recommend strategies and generate guidelines for gathering and processing data that will be used in WRTM studies.
Project Sponsors: Federal Highway Administration (FHWA).
Project Description: Dynamic traffic routing is defined as the process of dynamically selecting the sequence of roadway segments from a trip origin to a trip destination. Dynamic routing typically entails using time-dependent roadway travel times to compute this sequence of roadway segments. As with the general case of modeling human behavior, modeling driver travel behavior has always been complicated, never accurate enough, and in constant demand for further research. Among the early attempts to model human choice behavior is the economic theory of the “economic man”; who in the course of being economic is also “rational” (Simon 1955). According to Simon’s exact words, “actual human rationality-striving can at best be an extremely crude and simplified approximation to the kind of global rationality that is implied, for example, by game-theoretical models.” This project combines energy and emission models with navigation programs. The idea is to help consumers make "greener" choices about their routes. For example, an earlier study by the principal investigator found that choosing an artery-based route that takes about five minutes longer than a highway-based route reduced fuel usage by 23 percent (it was shorter and had slower speeds). Over the past year, that would have amounted to almost $300 in savings for a commuter. Adding real-time traffic information would help, too. For example, some research found that mildly congested roads actually promote fuel efficiency, since they slow drivers down and make for a more even flow. It's strange to think of willingly following a navigation program's directions to a more congested route but this could result in significant environmental savings. This task will investigate the potential of integrating energy and environmental measures within the traffic routing decision framework. The impact of this routing strategy on the network-wide efficiency (vehicle delay) will be quantified and the potential for integrating system efficiency with environmental measures will be investigated. This task is divided into several sub-tasks, as follows: 1. Incorporate energy and emissions within current routing algorithms. 2. Investigate the impact of such routing strategies using sample networks assuming perfect knowledge of system performance. 3. Quantify the minimum number of probe vehicles required for successful implementation of the algorithms. 4. Evaluate the routing strategies associated with different vehicle types.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC) and NAVTEQ.
Project Description: Numerous variables influence vehicle energy and emission rates. These variables can be classified into six broad categories, as follows: travel-related, weather-related, vehicle-related, roadway-related, traffic-related, and driver-related factors. In order to reduce fuel consumption and emissions, significant efforts are required to decrease the total trip distance and improve the vehicle technologies and road infrastructure. Several research efforts have studied that the impact of aggressive driving on fuel consumption and emission rates (Nam, Gierczak et al. 2003; Nesamani and Subramanian 2006; Tzirakis, Pitsas et al. 2006). One study from Sierra Research found that aggressive driving is responsible for 15 and 14 times higher CO and HC emissions for the same trip (NRC 1995). Figure 1 illustrates the speed and acceleration profiles of the ARTA drive cycle super-imposed on the instantaneous CO emissions as measured on a dynamometer. The figure demonstrates that CO emissions vary considerably as a function of the vehicle speed and acceleration levels. In addition, Figure 1 illustrates that 1%, 5%, and 10% of the total trip (7, 37, and 74 seconds of 735 second) contributes 14%, 34%, and 49% of the total CO emissions on average and as much as 53%, 76%, and 82% of total CO emissions. These small portions of a trip are caused by high engine-load conditions. Consequently, a reduction of high-emitting driving behavior can significantly improve air quality.
Project Sponsors: Mid-Atlantic University Transportation Center (MAUTC) and NAVTEQ.
Project Description: The objective of this project is to evaluate the potential safety benefits of the Bendix ESP system for heavy trucks using hardware-in-the-loop (HIL) simulations. The evaluation attempts to quantify safety benefits, and also provide relative safety performance between different pre-crash scenarios. The project will be limited to application of the Bendix ESP in predetermined 5-axle tractor semi-trailer configuration. The approach used in the study will include HIL simulations using the real-time version of TruckSim (TruckSim RT). The TruckSim model will provide all necessary signals to the brake system hardware. Additional interfaces and signal conditioners will be required. Bendix will be providing support in developing the software/hardware interface.
Project Sponsors: National Highway Transportation Safety Administration (NHTSA).
Project Description: The objective of this project was to determine the feasibility of using existing in-vehicle video data to make inferences about driver behavior that would allow the investigation of the relationship of observable driver behavior to non-recurring congestion in order to improve travel time reliability. The use of other data sources, such as infrastructure-based video and traffic data for example, was also evaluated for the potential to identify ways to modify driver behavior to improve travel time reliability.
Project Sponsors: National Academy of Science.
Project Description: This study investigated a possible fuel saving and greenhouse emission reduction strategy using an Eco-Cruise Control system. The eco-cruise control system employs adaptive cruise control integrated with road topography information. The potential benefits of such a system are significant since the transportation sector is responsible for nearly two-thirds of the total gasoline consumption in the United State. Specifically, since 2003, more than 20 million barrels of fuel are consumed by the surface transportation sector in the United States on a daily basis . In addition, transportation activities contribute significantly to CO2 emissions by accounting for 33 percent of CO2 emissions in the U.S. in 2007. Furthermore, almost 60 percent of the emissions are a result of gasoline consumption caused by personal vehicle use. Consequently, even small fuel consumption reductions could produce significant green house gas emission reductions and fuel cost savings. The project involves three research efforts: (a) development of a vehicle powertrain model; (b) development of simple vehicle fuel consumption models; and (c) development of an eco-cruise control system.
Project Sponsors: NAVTEQ.
Project Description: There are a number of publications that provide guidance at the national level with regard to the distance from the interchange ramps in which access should be controlled on the crossroad. These publications include suggested spacing between the interchange ramp and the first right-in/right-out access, the first unsignalized full access, and the first signalized intersection. The research investigates the operational impacts of varying access lengths based on field observations, data collection and analysis, and microscopic simulation. Crash experience at existing interchanges with varying access control lengths will be evaluated to help define the influence area and possibly be used to develop safety analysis procedures to predict crashes for varying configurations. The research will synthesize the state of the practice in other states. Older driver reaction and decision making times, vehicle acceleration and deceleration, signing and markings, and potential pedestrian conflicts will also be considered in the investigation of the optimal limited access length as measured from the ramp terminals.
Project Sponsors: Virginia Department of Transportation.
Project Description: In a recently completed study in the FHWA Highway Driving Simulator, it was found that about 70 percent of drivers stopped for an unanticipated warning that was intended to cause them to slow or stop to avoid collision with a hypothetical red-light violator. Ten to 20 percent more drivers responded as desired when the warning was distinctly different from a normal traffic signal phase change. Therefore, it was recommended that a field validation study be conducted using a warning similar to the most effective warning condition in the simulation. Because drivers in the simulator braked harder (decelerated faster) than is usually observed in field studies, verification was needed that drivers in actual vehicles will respond to violator warnings in a manner that will avoid collisions. The objective of the study is to partially replicate the simulator study to verify that drivers will respond to red-light violator warnings with sufficient speed and intensity to avoid a collision with the violator. The tasks of the study include: 1) conduct a field study on the Smart Road test facility using a total of 60 test subjects; 2) characterize driver behavior at the onset of a yellow phase; 3) characterize break reaction times at signalized intersection approaches; 4) characterize driver deceleration behavior at signalized intersection approaches; and 5) develop models to replicate driver stop/go behavior at the onset of a yellow phase.
Project Sponsors: Federal Highway Administration.
Project Description: Virginia’s mountainous terrain and the large number of trucks that travel on the state’s major highways have resulted in reductions in roadway capacities and levels of service. Researchers at Virginia Tech are studying this problem and its effects on traffic stream behavior. This research effort is important because current procedures for modeling trucks in the 2000 Highway Capacity Manual only consider a single truck of weight-to-horsepower ratio 200 lb/hp. The research effort develops vehicle dynamics models for modeling truck acceleration behavior, models the interaction of trucks and automobiles, enhances the HCM truck performance curves, evaluates the safety hazard of I-81, and evaluates alternative lane and truck management strategies.
Project Sponsors: Virginia Department of Transportation (VDOT) and the Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The objective of this research effort is to use GPS detection technology, together with fully-equipped vehicles, to characterize vehicle behavior in order to provide data that would allow for the enhancement of current state-of-the-art microscopic simulation tools. To achieve this objective, data are being collected along the Smart Road and along typical urban arterial and freeway sections. The data being collected include vehicle speed, acceleration, throttle level, braking indicator, fuel consumed, and emissions every second.
Project Sponsors: Virginia Department of Transportation (VDOT) and the Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The majority of transportation problems occur within urban environments. In order to manage and enhance the flow of urban traffic, transportation professionals need tools to evaluate, predict, and control the ever-growing number of vehicles on the roads. Various methods for controlling traffic are emerging, including transit signal priority, in which the timing of the traffic signal is modified to accommodate transit vehicles; adaptive signal control, in which the timing of the traffic signal adjusts according to traffic information monitored through roadway sensors; ramp metering; and toll roads. The project involves 1) developing procedures to estimate the delay upstream of bottlenecks; 2) develop analytical models to estimate the number of stops at over-saturated signalized intersection approaches; 3) develop procedures to calibrate traffic dispersion models; 4) develop procedures to model traffic dispersion microscopically; 5) develop mesoscopic models to estimate traffic stream energy consumption and emissions; and 6) develop analytical procedures to calibrate commercially available microscopic traffic simulation software.
Project Sponsors: Virginia Department of Transportation (VDOT) and the Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The goal of this project is to develop a comprehensive instrumented test bed in the town of Blacksburg to achieve the following objectives: 1) Serve as a real-life test facility for the evaluation and enhancement of traffic flow theory; 2) Develop a database of field data for conducting research on alternative means of disseminating real-time traveler information to the public; 3) Serve as a real-life test facility for enhancing and developing tools for the evaluation of network-wide energy and environmental impacts of operational-level transportation projects; 4) Serve as a real-life test facility for enhancing and developing tools for quantifying the noise impacts of operational-level transportation projects; 5) Serve as a test facility for evaluating emerging ITS technologies that can benefit transit operations; 6) Serve as a test bed for evaluating emerging surveillance and communication technologies; and 7) Serve as a unique educational tool that will allow practitioners, undergraduate students, and graduate students to access and analyze real-life traffic data.
Project Sponsors: National Science Foundation, the Virginia Department of Transportation (VDOT) and the Mid-Atlantic University Transportation Center (MAUTC).
Project Description: It is common knowledge that inclement weather affects traffic stream parameters and behavior. However, it is not clear how these various forms of inclement weather impact traffic stream parameters. Consequently, this research effort combines weather and traffic data to quantify the impact of inclement weather on traffic stream parameters considering three geographic locations in the USA, namely, Baltimore, Minneapolis, and Seattle. The study will develop analytical models to account for inclement weather impacts on roadway capacity and free-flow speed. These factors will be similar to the Highway Capacity Manual (HCM) procedure heavy vehicle, lane width, and lane-changing intensity adjustment factors.
Project Sponsors: Federal Highway Administration (FHWA), the Virginia Department of Transportation (VDOT), and the Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The freeway weaving analysis procedures in the 2000 Highway Capacity Manual (HCM) are based on research conducted in the early 1970s through the early 1980s. Subsequent researches have shown that the methods’ ability to predict the operation of a weaving section is limited. Consequently, this research effort utilizes the INTEGRATION software to estimate the capacity of weaving sections. Subsequently, analytical procedures are developed using the simulated data to estimate the capacity of high intensity lane-changing sections including weaving, merge, and diverge sections.
Project Sponsors: Virginia Department of Transportation (VDOT) and the Mid-Atlantic University Transportation Center (MAUTC).
Project Description: The project develops algorithms that estimate dynamic roadway travel times. The tasks of the project include: 1) develop algorithms for matching license plate readings; 2) develop procedures to estimate trip travel time variability from segment travel time measurements; 3) characterize daily traffic demand variability; and 4) develop procedures to estimate space-mean-speed from loop detector time-mean-speed measurements.
Project Sponsors: ITS Implementation Center.
Project Description: The project investigates the merits of integrating transit signal priority (TSP) within an adaptive traffic signal control system along the Columbia Pike corridor. The tasks of the project include 1) install GPS units on five buses and sample automobiles traveling along the corridor to record second-by-second speed data; 2) develop emission models for transit vehicles; 3) estimate different MOEs from the data; and 4) conduct a statistical analysis of the aggregate data to quantify the impacts on TSP on the various MOEs.
Project Sponsors: Arlington County and the ITS Implementation Center.
Project Description: This project aims at refining the practice of screening high emitting vehicles, by supporting the Virginia Department of Environmental Quality on three tasks: 1) converting emission concentration measurements to emission rates; 2) devising techniques to account for the lag between speed/acceleration measurements and tailpipe emission measurements, and 3) devising techniques for screening high emitting vehicles.
Project Sponsors: Virginia Department for Environmental Quality (VDEQ) and the ITS Implementation Center.
Project Description: This project was a collaborative effort between Virginia Tech and the University of Virginia. The objective of the study was to develop alg0rithms to locate surveillance technology and estimate dynamic roadway travel times. The project tasks included: 1) estimate dynamic roadway travel times using Automotive Vehicle Identification (AVI) data; 2) develop algorithms to optimally locate AVI surveillance instrumentation using the Reformulation Linearization Procedure (RLT); and 3) develop a Genetic Algorithm to optimally locate surveillance instrumentation.
Project Sponsors: Virginia Department Department of Transportation and the ITS Implementation Center.
Project Description: This study involved the modeling of alternative I-77/I-81 overlap designs using microscopic traffic simulation. The tasks of the project included: 1) compare CORSIM and INTEGRATION for the modeling of bottlenecks and 2) develop calibration procedures for the CORSIM software.
Project Sponsors: Virginia Department Department of Transportation and the ITS Implementation Center.
Project Description: The Fuel Doctor technology relates to a device and process provided for treatment of a hydrocarbon or fossil fuel which is to be combusted in a combustion chamber to improve combustion of the fuel in the combustion chamber by turbulently treating the fuel with a plurality of fields of magnetic flux and subjecting the fuel to a field of differing standard electrochemical reduction potentials. The device is adapted to be connected in-line in a fuel supply line of the combustion chamber. The objective of the study was to evaluate the impact of the Fuel Doctor technology on vehicle fuel consumption and emissions.
Project Sponsors: Performance Fuel Systems.
Project Description: This project was intended to provide a comprehensive evaluation of the impact of the Metropolitan Model Deployment Initiatives (MMDI) in Seattle, Phoenix, and San Antonio . As part of his involvement in the MMDI evaluation, Dr. Rakha headed a team that quantified the impact of traffic signal coordination and variable message signs on the system throughput, energy and emissions, and safety. In doing so the team modeled a portion of Phoenix and some portions of San Antonio . Dr. Rakha was also involved in enhancing current energy and emission models together with current safety models. Furthermore, Dr. Rakha was actively involved in research in the area of driver behavior in terms of time of departure, mode of travel, and route of travel.
Project Sponsors: Federal Highway Administration.
Project Description: The objective of the project was to develop a sketch planning tool that assists Metropolitan Planning Organizations (MPO's) and Departments of Transportation (DOT's) in assessing the benefits and costs of Intelligent Transportation System (ITS) and non-ITS options. Partners involved in the project included Oak Ridge National Lab, Cambridge Systematics, and the Center for Transportation Research at Virginia Tech. As part of his work with IDAS, Dr. Rakha was part of a team that developed macroscopic emission and safety models.
Project Sponsors: Federal Highway Administration.
Project Description: This project involved evaluating the safety and throughput benefits of Adaptive Cruise Control (ACC) systems using field data that were gathered by the University of Michigan. As part of his involvement, Dr. Rakha compared the workload involved in the usage of conventional cruise control to ACC. Dr. Rakha was part of a team that developed a framework for the evaluation of the safety impacts of ACC.
Project Sponsors: Federal Highway Administration.
Project Description: The project entailed an evaluation of the SWIFT (Seattle Wide-Area Information For Travel ers) system architecture. The SWIFT system disseminated real-time traffic information via an FM sub-carrier. Three reception devices received the information, namely: Delco navigation units, PC laptops, and Seiko MessageWatch™ wrist watches. SAIC was responsible for evaluating the SWIFT system and Dr. Rakha was the Co-PI in the system architecture evaluation. This evaluation study involved a number of field tests, questionnaires, and a review of the system architecture.
Project Sponsors: Federal Highway Administration.
Project Description: Dr. Rakha conducted the Genesis modeling study in the twin cities Minneapolis/St. Paul. The Genesis study involved evaluating the traffic and environmental impacts of Personal Communication Devices (PCD's) including pager systems on the overall performance of traffic. The Genesis system was evaluated using a combination of operational field and modeling tests. Field experiments and surveys collected data and information on the Genesis test drivers. A total of 403 test vehicles were allowed to traverse the Minneapolis/St. Paul network for an entire year. The desire to examine other unobservable factors resulted in the inclusion of a modeling activity using the INTEGRATION model. The INTEGRATION model was initially calibrated to the existing conditions, and then utilized to extrapolate results for conditions that were not encountered during the field test.
Project Sponsors: Federal Highway Administration.
Project Description: Dr. Rakha was involved in evaluating the traffic and environmental impacts of the TravTek route guidance system in Orlando , Florida using the INTEGRATION simulation model. The TravTek evaluation study is the largest field evaluation of a Dynamic Route Guidance System (DRGS) in North America . As part of the TravTek evaluation study, Dr. Rakha analyzed the I-4 ( Orlando , Florida ) Freeway Traffic Management System (FTMS) data, assisted in evaluating the safety of the TravTek route guidance system, and conducted the modeling study of the TravTek system using the INTEGRATION simulation model.
Project Sponsors: Federal Highway Administration.