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Draft:Hesham Rakha

  • Comment: Hiya, just so you know there is a template called {{Infobox academic}} which is a better fit (and meant for this purpose) than the current thing you are doing with the tables. EatingCarBatteries (contributions, talk) 07:51, 22 November 2025 (UTC)
Hesham Rakha in 2025
Alma mater Cairo University; Queen's University
Awards MVASEM; FCAE; F.ASCE; FIEEE; IEEE ITS Outstanding Research Award
Scientific Career
Institutions Queen's University; Virginia Tech
Thesis A Simulation Approach for Modeling Real-Time Traffic Signal Controls (1993)

Hesham Ahmed Rakha is the Samuel Reynolds Pritchard Professor of Engineering in the Charles E. Via, Jr. Department of Civil and Environmental Engineering and a courtesy professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He directs the Center for Sustainable Mobility at the Virginia Tech Transportation Institute. He conducts research in the area of multi-modal large-scale transportation system optimization, modeling, and assessment. He is a fellow of the Canadian Academy of Engineering.[1] and was elected to the Virginia Academy of Science, Engineering, and Medicine[2] and the National Academy of Artificial Intelligence. He is a fellow of the American Society of Civil Engineers[3] and the Institute of Electrical and Electronics Engineers. He is the recipient of the 2021 IEEE Intelligent Transportation Systems Outstanding Research Award for contributions to large-scale transportation system optimization, modeling and assessment[4].

Education and career

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Rakha studied engineering at Cairo University and graduated with honors in 1987. He then moved to Canada in 1998 to complete his M.Sc. and PhD degrees under the supervision of the late Michel Van Aerde at Queen's University in 1990 and 1993, respectively. His M.Sc. thesis was entitled An Evaluation of the Benefits of User and System Optimized Route Guidance Strategies. He published two papers from this work[5][6]. His PhD thesis was entitled A Simulation Approach for Modeling Real-Time Traffic Signal Controls.

Rakha worked as as an engineer for Science Applications International Corporation and a postdoctoral fellow at Queen's University from 1993 to 1997. In 1997, he joined the Virginia Tech Transportation Institute (formerly known as the Center for Transportation Research) as a Research Scientist. He then became an Assistant Professor in the Charles E. Via, Jr. Department of Civil and Environmental Engineering in 1999 and moved up the ranks and is currently the Samuel Reynolds Pritchard Professor of Engineering[7] at Virginia Tech and directs the Center for Sustainable Mobility (CSM) at the Virginia Tech Transportation Institute.

Research

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Rakha conducts research in the area of multi-modal large-scale transportation system optimization, modeling, and assessment. He has conducted fundamental research (publishing approximately 600 peer-reviewed papers and book chapters) in this area culminating in the continuous development of the INTEGRATION software (conceived and developed by his advisor, the late Michel Van Aerde), a transportation modeling tool. His research includes the modeling of human travel and driver behavior; traffic flow theory; optimizing transportation system operations; optimizing the longitudinal and lateral motion of various ground transportation modes (cars, buses, trucks, and trains); the development of calibration methodologies of transportation modeling tools; the large-scale modeling multi-modal transportation systems and the inter-dependencies of communication and transportation systems; intelligent transportation systems; connected and automated vehicles; and the development of models to compute various measures of effectiveness, including: vehicle delay, stops, energy/fuel consumption, emissions, and crash risk.

Traffic flow theory and large-scale transportation system modeling

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Rakha continued work on the INTEGRATION software after the passing of the late Dr. Michel Van Aerde, who first developed the INTEGRATION software in his PhD research in the mid 1980s at the University of Waterloo. Rakha’s work on the INTEGRATION software has required developing the theory needed to model traffic microscopically. This entailed developing fundamental research in the longitudinal vehicle motion modeling considering vehicle dynamics, the driver’s characteristics, and the surrounding traffic. Rakha introduced vehicle dynamics into car-following modeling. While others use a constant acceleration constraint or use a vehicle kinematics models, none consider the forces acting on the vehicle. To achieve this novel modeling approach, Rakha and his team first characterized and developed models to capture the maximum acceleration envelope of a vehicle[8][9][10]. Rakha then extended these models to capture the driver’s input in the acceleration behavior[11]. Rakha further extended this modeling framework to the modeling of driver gear shifting[12]. Rakha then developed novel models that capture both driver input and vehicle dynamics in a single integrated longitudinal modeling framework[13][14][15].

Rakha was one of the first researchers to study the impact of inclement weather and roadway surface conditions on vehicle longitudinal motion and traffic stream behavior[16][17][18][19]. This was then extended to the modeling of driver gap acceptance behavior, when vehicles have to execute a maneuver through an opposing flow (e.g. merging onto a roundabout, a permissive left turn, or right turn on red at a traffic signal). This work considered among other factors the effect of inclement weather, surrounding vehicles, and the time the driver is waiting in search of an acceptable gap[20][21][22]. The research resulted in the development of a left turn assist system for connected vehicles[23] and the development of an agent-based modeling framework[24].

Rakha extended traffic flow theory and traffic modeling in the area of vehicle lateral movement with the modeling of vehicle lane changing behavior and validated these models against empirical data from different weaving sections[25]. The INTEGRATION software was then used to develop analytical models for the analysis of merge, diverge and weaving sections[26][27]. Rakha and his team used game theory to model driver lane changing behavior at merge sections using empirical data continuing on work pioneered by the late Dr. Hani Mahmassani. Rakha extended this research by considering a game between the driver of the subject vehicle and drivers in vehicles surrounding the subject vehicle[28][29][30]. The model was then extended to general lane changing behavior[31].

Rakha also conducted research in the area of modeling traffic flow dispersion[32] and addressed shortcomings in state-of-the-art modeling procedures[33]. This fundamental research was then used to develop traffic dispersion calibration procedures[34].

Rakha studied various underlying fundamental diagrams and together with the late Michel Van Aerde developed the Van Aerde fundamental diagram[35]. Using this theory, Rakha developed calibration approaches for various traffic simulation models[36][37]. Further, Rakha extended the body of knowledge in the area of moving bottlenecks (e.g. a slow moving truck within vehicular traffic flow)[38] and studied the effect of the underlying fundamental diagram on the behavior of moving bottlenecks for the computation of vehicular passing rates[39]. Rakha also looked at how the fundamental diagram varies across lanes on a freeway[40].

Rakha pioneered research in large-scale microsopic traffic modeling. For example, in the early 1990's the late Michel Van Aerde and Rakha were able to develop a microscopic model of the Greater Salt Lake City area to optimize and plan various construction strategies in preparation for the 2002 Winter Olympic Games[41]. The ability to model the entire transportation system within a city at the microscopic level represented a major breakthrough at the time. In later work, Rakha and his team developed a novel large-scale agent-based multi-modal (car, bus, train, bicycle and walking) transportation modeling system[42]. The framework simultaneously utilizes microscopic and mesoscopic modeling techniques to take advantage of the strengths of each modeling approach (microscopic provides high fidelity modeling while mesoscopic offers high computational speeds). In order to increase the model scalability, decrease the model complexity, and achieve reasonable computational loads, the proposed framework utilizes parallel simulation through two partitioning techniques: spatial partitioning by separating the network geographically and vertical partitioning by separating the network by transportation mode for modes that interact minimally. The proposed framework creates multi-modal plans for each trip and tracks the traveler’s trips each deci-second across different modes. The model was tested on the greater Los Angeles area modeling 3+ million traveler trips. This achievement represents a breakthrough in agent-based microscopic modeling of large urban areas.

Rakha conducted research in the area of integrated transportation and communication system modeling. To achieve this objective, Rakha and his colleagues, devised groundbreaking modeling procedures to develop fully integrated transportation and communication system models[43]. The approach was used to develop analytical DSRC models that were integrated with traffic modeling[44] and then used to evaluate the performance of a dynamic router[45]. The model was extended to the modeling of Direct C-V2X Release 14 (4G/LTE)[46] and then tested on a dynamic route guidance system[47]. Again, the uniqueness of the approach is the ability to model integrated transportation and communication systems at scale.

Rakha also contributed to the development of procedures to estimate various measures of effectiveness for the assessment of alternative traffic operational, intelligent transportation system, and connected automated vehicle applications. These include the estimation of delay[48], the estimation of vehicle stops[49], the estimation of the vehicle crash risk[50], and the estimation of vehicle energy consumption and emission levels[51]. These modeling tools are described later in more detail.

Rakha contributed to the early development of calibration tools for use with traffic modeling software, both from the supply and demand sides. From the supply side Rakha, together with the late Michel Van Aerde, developed a unique calibration tool for the calibration of traffic stream fundamental diagrams[52][53]. Rakha’s work in this area is unique because typically the use of microscopic traffic simulation tools entails calibrating network-wide car-following models that would require the collection of microscopic vehicle-centric data, which is very expensive and time consuming to collect. Instead, Rakha and the late Michel Van Aerde approached the problem by calibrating macroscopic link-specific fundamental diagrams. This addresses two drawbacks of state-of-the-practice approaches. First, it does not require the expensive collection of microscopic car-following data, but instead can be calibrated using macroscopic stationary sensor data. Second, the car-following models are link-specific rather than constant over the entire network. This is key given that drivers drive differently on different roadway segments. Rakha also conducted research in the area of origin-destination (O-D) demand calibration. His early work on the topic addressed the calibration of static time-dependent traffic demands[54]. This approach was extended to the calibration of dynamic O-D matrices[55].

Transportation system optimization and traffic control

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Rakha's work in this area started with his PhD research where he developed an adaptive traffic signal controller that optimizes traffic signal timings to minimize network-wide delay, vehicle stops, and vehicle energy consumption[56][57]. Rakha continued his work on traffic signal control developing a de-centralized game-theoretic (Nash Bargaining (NB)) traffic signal controller (DNB controller) that was first tested on an isolated traffic signal[58]. The novelty of the proposed traffic signal controller is that it abandons the concept of a fixed sequence of phases and fixed cycle length; instead the controller calls phases as needed to alleviate congestion on their respective movements. The traffic signal controller was then tested on an arterial network[59] and on the downtown Los Angeles network, which is composed of 257 signalized intersections[60]. In a later paper, Rakha and his team compared the proposed DNB controller to various centralized traffic signal control strategies, including traffic gating based on the Network Fundamental Diagram (NFD) and demonstrated that the proposed DNB controller outperformed all state-of-the-practice and known state-of-the-art controllers[61]. Further work has extended the DNB controller to the use of the dual-ring eight-phase NEMA controller logic[62]. A key input to the DNB controller is the traffic stream density on the various approaches to the intersection. Consequently, Rakha and his team of researchers developed real-time traffic density estimation techniques based solely on connected vehicle data. The novelty of this work entails using a variable polling interval that allows the algorithm to deal with very low levels of market penetration of connected vehicles and the use of machine learning in conjunction with Kalman filtering techniques to use only connected vehicle data or use minim amounts of stationary sensor data[63][64][65][66][67]. The Kalman filtering approach was integrated with the DNB controller and demonstrated to perform well[68].

In addition, Rakha and his team have worked in the area of traffic control using the NFD concept, in which congested regions in a network are identified and then gating of traffic is performed to disperse traffic congestion. The control is either achieved through traffic signal control or through vehicle control. Examples of these applications included extending previous work on feedback control through the tuning of the controller parameters for inclement weather and accounting for different traffic stream densities[69]. New novel controllers were then developed using sliding mode control[70]. The sliding mode controller was then extended and used to develop a freeway CAV controller that dynamically identifies traffic congestion and then regulates traffic upstream of it to disperse the congestion[71]. A key input to these NFD controllers is the ability to construct the NFD. Consequently, Rakha conducted fundamental research on the construction of the NFD. For example, Rakha and his team were one of the first to publish on the use of connected vehicle data to construct NFDs[72]. This work was then extended to use synthetic origin-destination demand estimation for the construction of the NFD[73]. Rakha also worked in the area of combined traffic departure time and routing optimization for vehicle departures from large events[74].

Rakha conducted research in traffic state estimation and prediction for the use in traffic control. For example, Rakha with his advisor the late Michel Van Aerde investigated the use of connected vehicle (CV) data for the estimation of origin-destination matrices and travel times[75]. This work was done in the early 1990s before the introduction of vehicle connectivity. He continued work on this topic with the publication of a paper that developed a model for estimating travel times for very low levels of market penetration of CVs[76]. Rakha and his team continued that work with a number of novel publications[77][78][79][80][81][82][83]. Rakha also conducted research to identify and predict traffic congestion[84] and then used to identify bottlenecks along freeway sections[85].

Dynamic traffic assignment and vehicle routing

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Rakha has conducted research in the area of traffic assignment and vehicle routing. His work in this area started with his M.Sc. thesis in which he, together with the late Michel Van Aerde, demonstrated that routing vehicles using the link marginal travel time would produce the system optimum traffic assignment (i.e. produce a traffic loading that minimizes the total system travel time). This work was one of the first research efforts that demonstrated this novel routing approach[86]. Rakha continued his work on traffic routing with the evaluation of the first field operational test of a dynamic route guidance system (TravTek)[87]. Rakha wrote a chapter in the Encyclopedia of Complexity and Systems Science on this topic[88].

Some of Dr. Rakha’s novel contributions to this area of research include developing a Genetic algorithm for the combined computation of trip distribution and traffic assignment[89][90]. Rakha is most known for his work in the area of energy-efficient routing of vehicles. These include the INTEGRATION framework for modeling eco-routing strategies[91], quantifying the benefits of network-wide eco-routing strategies[92], eco-routing of battery electric vehicles[93], considering the impact of vehicular communication in dynamic eco-routing systems[94], a unique vehicle-specific dynamic eco-router that applies to any vehicle powertrain[95], and developing a dynamic multi-modal energy-efficient routing application[96]. His work on energy efficient routing was enhanced with the introduction of multi-objective routing of battery electric vehicles[97].

Rakha also led efforts on the modeling and assessment of travel time reliability. Specifically, Rakha proposed modeling travel times using multistate distributions[98]. Procedures were then developed to calibrate these multistate models[99]. These models were then enhanced to consider skewed distributions[100].

A key component of DTAs is the ability to predict the transportation system evolution. To address this need Rakha and his team developed used AI and statistical techniques to predict the spatiotemporal evolution of the transportation system. These approaches included prediction of travel times using a particle filter with a non-explicit state transition model[101], using genetic programming[102], using agent-based modeling[103], through trajectory construction[104], and using pattern recognition techniques[105]. The latter paper received the best scientific paper award at the 20th ITS World Congress in Tokyo, Japan. Further research involved developing deep learning techniques to predict the spatiotemporal evolution of traffic states[106].

Connected automated vehicles

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Rakha and his team have conducted research on Green Light Optimized Speed Advisory (GLOSA) vehicle control, also known as eco-cooperative adaptive cruise control (Eco-CACC) in the vicinity of traffic signals. In one of the early publications on this topic, Rakha and his team leveraged Signal Phasing and Timing (SPaT) data to compute vehicle optimum trajectories[107], which is a highly-cited paper on the topic. The GLOSA controller was then enhanced to solve the problem using dynamic programming[108]. Further improvements to the controller were made to speed up the computations so that they can run in real-time[109]. The algorithm was then tested in a simulation virtual environment[110] and network-wide impacts were quantified[111]. Later enhancements were made to the algorithm to predict the time the queue would be dissipated[112] and to consider multiple intersections within the control algorithm[113]. The system was then implemented and tested in a controlled field environment[114][115]. These were the first controlled full field deployment and testing of a GLOSA system considering the latency and loss of packets in an actual deployment. Further extensions of the system were made to optimize trajectories of buses[116], battery electric vehicles[117], and hybrid electric vehicles[118]. The GLOSA system was then integrated with an energy-optimum router and tested[119]. Other extensions include optimizing vehicle trajectories in the vicinity of actuated traffic signals[120] and considering the effect of queues[121]. A key input to the system entails the prediction of traffic signal switch times. Consequently, Rakha and his team developed machine learning approaches to predict traffic signal switch times at actuated traffic signals[122][123][124].

Rakha has also conducted fundamental research in the area of connected automated vehicle control and platooning on freeways. This has entailed the development of several platooning algorithms[125], identifying the optimum platoon speeds to reduce vehicle energy consumption[126], and developing an ad-hoc platoon controller[127]. In addition, Rakha and his team developed general models that capture the impact of the distance of a vehicle to its lead and follower in a platoon, its position in a platoon, and its speed on the vehicle’s drag coefficient and its effect on vehicle fuel consumption[128].

Rakha and his team also worked on the optimization and control of CAV movements through uncontrolled intersections[129][130][131][132]. This work was extended to the traversing of roundabouts[133][134]. The algorithm was extended to consider a fully-distributed system to minimize communication loads[135]. Other work in this area entailed use of game-theory to optimize the intersection performance[136]. Rakha and his team extended the algorithm to optimize vehicle trajectories when approaching uncontrolled intersections to minimize the total energy consumption and delay for all vehicles approaching the intersection[137]. This algorithm was then extended for real-time applications[138].

Transportation safety modeling and assessment

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Rakha’s distinctive technical contributions in the area of transportation safety include: 1) developing microscopic crash prediction models for the safety assessment of Intelligent Transportation System (ITS) and CAV applications; 2) integrating these safety models with microscopic traffic simulation software; 3) designing traffic signal yellow times to reduce vehicle crashes at signalized intersections; 4) assessing the safety impacts of access management strategies and designing guidelines for access spacing in the vicinity of highway ramps; 5) detecting driver distraction using on-board vehicle data to reduce vehicle crashes; 6) assessing the safety impacts of adaptive cruise control (ACC) systems; 7) assessing the safety impacts of forward collision warning (FCW) systems; 8) assessing the safety impacts of electronic stability program (ESP) systems on trucks; and 9) developing safe and ecological optimal vehicle control systems.

Rakha led a project that attempted to determine the feasibility of using existing in-vehicle video data to make inferences about driver behavior that would allow for the investigation of the relationship between observable driver behavior and non-recurring congestion (congestion caused by vehicle crashes) 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[139]. Further work included developing Artificial Intelligence techniques to detect driver distraction using vehicle kinematics data[140]. This work won the User Information Systems (AND20) Best Paper Award at the Transportation Research Board Annual Meeting.

Rakha together with the late Michel Van Aerde evaluated ACC systems in the late 1990s. There work investigated the potential safety impacts and driver acceptance of these systems. Rakha’s work concluded that time gap settings of 2.0 and 1.5s were longer than that used by human drivers and thus produced undesirable safety outcomes. In their work, Rakha and Van Aerde concluded that a setting of 1.0s was more consistent with manual driving. Current car manufacturers set the time gap at a 1.0s gap as recommended in this work[141][142]. Rakha, together with researchers in Australia, developed vehicle control algorithms that ensure safe and energy-efficient ACC systems[143].

Rakha and his team evaluated the safety of Forward Collision Warning (FCW) systems. This effort evaluated the US nation-wide safety impacts of a FCW system on trucks using naturalistic data[144]. This study, together with other studies, helped NHTSA approve FCW systems on trucks.

Rakha also led an effort to develop a hardware-in-the-loop system that can be used to test in-truck safety technology.  This effort entailed developing various hardware and software components and validation tests of the system[145].  The hardware-in-the-loop was then used to evaluate the safety impacts of an Electronic Stability Program (ESP) system[146]. This study assisted NHTSA in approving ESP systems on trucks given that it demonstrated the effectiveness of the system in reducing truck rollover crashes. The paper[145] was awarded the 2011 Best Paper Award in the Transportation Research Board Truck and Bus Safety Committee and was one of six papers nominated for the Patricia Waller Award for the best safety-related paper of the year at the 2011 Transportation Research Board Annual Meeting, the largest transportation conference.

Rakha led several projects aimed at characterizing driver perception/reaction times, deceleration behavior, and stop/go decisions at the onset of yellow indications to better design traffic signal yellow timings to reduce vehicle crashes at signalized intersections[147][148][149]. The outcome of this effort entailed developing a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone)[150]. The work included characterizing light-duty vehicle driver behavior in clear weather conditions. Further studies analyzed the impact of surrounding vehicles on light-duty driver behavior[151]. Further field tests developed procedures for designing yellow times in inclement weather and for slippery roadway conditions[152][153]. This work was extended to predicting driver stop-run decisions using historical behavior for the development of in-vehicle driver violation warning systems[154]. Further research was conducted to design traffic signal yellow times while accounting for trucks and buses[155][156].

Rakha also led research efforts to develop crash prediction models for use in microscopic traffic simulation software. This research entailed developing a safety model using a US national crash database (GES database)[157]. The model was then incorporated in the INTEGRATION microscopic traffic simulation software. The model computes the crash risk for 14 different crash types as a function of the facility speed limit and a time-dependent measure of exposure. The novelty of the approach is the use of a time-dependent measure of exposure that allows the model to capture differences in crash risk that result from differences in the transportation system efficiency. The model also computes the vehicle damage and level of injury to the passengers involved in the crash based on the vehicle's instantaneous speed. The use of the instantaneous speed means that the crash damage and injury level is responsive to the level of congestion. Consequently, the model can capture the safety impacts of operational-level applications including Intelligent Transportation Systems[158].

Rakha led efforts to quantify the crash risk associated with access management. The adequate spacing and design of access to crossroads in the vicinity of freeway ramps is critical to the safety and traffic operations of both the freeway and the crossroad. This research effort developed a novel methodology to evaluate the safety impact of different access road spacing standards[159]. The results highlighted the shortcomings of the American Association of Highway Transportation Officials (AASHTO) standards and the benefits of enhancing these standards. The models developed as part of this research effort were utilized to compute the crash rate associated with alternative section spacing. The study demonstrated that the models satisfied the statistical requirements and provide reasonable crash estimates. The results demonstrate an eight-fold decrease in the crash rate when the access road spacing increases from 0 to 300 m. An increase in the minimum spacing from 90 m (300 ft) to 180 m (600 ft) results in a 50% reduction in the crash rate. The models were used to develop lookup tables that quantify the impact of access road spacing on the expected number of crashes per unit distance[160]. The models were used by the Virginia Department of Transportation (VDOT) in the design of access roads in the vicinity of freeway exits.

Rakha conducted research to identify the factors that affect crash severity among elderly drivers[161]. This approach was then extended to consider AI techniques for crash prediction and compared it to traditional statistical techniques[162]. Finally, the work was extended to study the temporal effect of the COVID pandemic on the factors affecting crash severity among elderly drivers[163].

Rakha also conducted research on vehicle headlights on the procedures used for roadway design of sag curves[164]. This effort then proposed enhancements to the current procedures to address changes in the vehicle headlight designs.

Transportation energy and environmental assessment

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Rakha’s contributions in the area of transportation energy and the environment include: 1) developing various vehicle energy/fuel consumption and emission models that are used world-wide to assess the energy and environmental impacts of Intelligent Transportation System (ITS) applications and emerging Connected Automated Vehicle (CAV) systems (e.g. VT-Micro, VT-Meso, the VT-CPFM, the VT-CPEM, and the VT-CPHEM models); 2) integrating these models with large-scale microscopic traffic simulation software; 3) developing various vehicle and infrastructure based optimization approaches that minimize the vehicle and traveler’s carbon footprint; 4) developing energy-efficient dynamic routing algorithms; 5) designing traffic signal control algorithms that minimize vehicle GHG emissions; 6) developing safe and ecological optimal vehicle control systems.

Rakha and his team developed vehicle fuel/energy consumption and emission models that are used worldwide to quantify the energy and environmental impacts of various transportation system modifications and various in-vehicle systems (e.g. vehicle automation). He is recognized as a pioneer in this area. His first paper on the topic[165] has been cited over 1100 times (Google Scholar). In this regard, Rakha first developed the VT-Micro model[166], which is available on his website. The VT-Micro model computes hot stabilized gasoline vehicle fuel consumption and emissions of hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO2), and oxides of nitrogen (NOx) emissions based on instantaneous vehicle speed and acceleration levels, which was a novelty at the time given that it does not require vehicle engine data to compute vehicle emissions. The model was developed using data collected on a chassis dynamometer by the Oakridge National Laboratory (ORNL) and additional data collected by the US Environmental Protection Agency (EPA). The model was then extended to model cold starts[167], high emitting vehicles[168], and vehicles with catalytic converter malfunctions[169]. The VT-Micro model was then used to develop the VT-Meso model, which computes vehicle fuel consumption and emissions using more aggregated data (vehicle average travel time, stops, and delay)[170]. Rakha recognized that one of the major drawbacks of all state-of-the-art vehicle fuel consumption and emission models was the fact that they required the collection of fuel consumption and emission data either in the field or in a laboratory, which is very expensive to conduct and has to be done on a continuous basis. In an attempt to generate a general model, Rakha and his team developed the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM)[171]. This model is a major breakthrough given that it provides a model that can be calibrated using publicly available vehicle data (e.g. EPA fuel ratings and various vehicle parameters found on automotive manufacturer websites) and thus overcomes the need to purchase expensive vehicle emission measurement devices and the need to collect data in the field. Rakha provides a free model calibration tool on his personal website for anyone to use (https://sites.google.com/a/vt.edu/hrakha/). Rakha and his team enhanced the VT-CPFM modeling framework to the modeling of diesel-powered buses[172], diesel-powered trucks[173], diesel-powered trains[174], and compressed natural gas (CNG) buses[175]. The model was then extended to the modeling of hybrid electric buses[176] and Battery Electric Vehicles (BEVs) to develop the VT-CPEM model[177]. The model was further developed to model Plugin Hybrid Electric Vehicles (PHEVs)[178] and Hybrid Electric Vehicles (HEVs)[179]. Recently the modeling framework was extended to the modeling of electric trains[180]. These models are critical to the development of various traffic and vehicle control algorithms and the quantification of the GHG emissions associated with these applications.

Rakha was one of the first researchers to integrate traffic and vehicle energy and emission modeling. Specifically, he was one of the first researchers to recognize the need to develop a fully integrated traffic and energy/emission modeling tool, namely INTEGRATION[181]. Previous work entailed running the two modeling frameworks in a decoupled fashion, namely running the traffic simulation software first and generating vehicle trajectories then plugging these trajectories to vehicle energy and emission models to compute vehicle energy consumption and emission levels. Rakha also recognized that in order to compute accurate vehicle energy consumption and emission levels, not only was there a need to develop vehicle energy and emission models that were sensitive to vehicle instantaneous speed and acceleration levels, but more importantly existing car-following models needed to be modified to conform with vehicle dynamics characteristics in order to provide accurate instantaneous vehicle speed and acceleration levels as input to these energy/emission models. Consequently, Rakha conducted research in the area of vehicle longitudinal modeling developing models that are restricted by the vehicle dynamics. This resulted in a number of novel publications in this area. First models were developed to compute the maximum possible acceleration levels for trucks[182][183], cars[184], passenger trains[185], freight trains[186], and ships[187] in order to ensure that any acceleration output from the INTEGRATION software and other software are valid. Subsequently work focused on characterizing the typical acceleration levels of drivers to better match empirical data[188]. Dr. Rakha pioneered the introduction of these vehicle dynamics models as constraints within car-following modeling[189][190][191]; others only introduced a constant maximum acceleration level as a constraint, which in most cases violated vehicle dynamics constraints. The integrated car-following and vehicle energy/emission modeling framework was validated against naturalistic empirical data[192].

Rakha was part of a collaborative research effort with the Palo Alto Research Center (PARC) entailed developing a Collaborative Optimization and Planning for Transportation Energy Reduction (COPTER) system that identified the energy-efficient multi-modal routes most likely to be adopted by a traveler. The system model used currently available data from navigation tools, public transit, and intelligent transportation systems to simulate the Los Angeles transportation network consisting of roadways and trains as well as its energy use. This model was built on top of the INTEGRATION microsimulation framework. The team modeled the Greater Los Angeles network capturing the movement of 3+ million travelers at real-time computational speed. A final report summarizing the project approach and results was developed[193]. As part of this effort a multi-modal agent-based modeling framework was developed[194] that was built on the INTEGRATION modeling framework and was applied to the Greater Los Angeles network[195]. Models were developed to model train transit trips[196]. In addition, artificial intelligence techniques were devised to tailor optimum multi-modal trips to the specific person[197].

Rakha and his team were one of the first groups to conduct research in the area of energy-efficient vehicle routing. His first paper on the topic[198] was cited over 600 times. This was a first attempt at quantifying the impact of route choice decisions on Internal Combustion Engine Vehicle (ICEV) fuel consumption and emission levels using empirical data collected from a Global Positioning System (GPS). The study demonstrated that the fastest route was not necessarily the most fuel-efficient route and also demonstrated that the use of the MOBILE6 (EPA emission model based solely on the vehicle’s average speed) could produce wrong conclusions. Consequently, this study demonstrated the need to estimate vehicle trip fuel consumption and emission levels by integrating instantaneous fuel consumption and emission levels computed using instantaneous speed and acceleration levels. Rakha and his team then developed a fuel-efficient dynamic route guidance system and implemented it in the INTEGRATION software[199]. This paper received the “Most Cited Paper Award” in the International Journal of Transportation Science and Technology in 2018. Continuing on this work, Rakha and his team extended the work to BEVs[200]. Rakha and his team then developed a general framework that does all computations on the edge using data shared with the cloud to compute powertrain-specific optimum energy routing strategies[201]. The uniqueness of this dynamic router is that it shares eight common variables from all connected vehicles, but then uses those variables to compute on the edge powertrain-specific optimum routes, something that has never been done before. Rakha and his team then demonstrated that eco-routing strategies for BEVs can result in a 100% increase in BEV travel times. Consequently, Rakha and his team developed multi-objective routing systems that consider both travel time and energy optimization[202].

Rakha and his team developed models to compute vehicle tire and brake emissions for ICEVs and BEVs. These models were then incorporated in the INTEGRATION software and used to quantify network-wide non-exhaust particulate matter emissions[203]. Additional work included projecting airborne tire wear particle emissions in the United States in the era of electric vehicles[204].

Rakha and his team were one of the first teams to work in the area of energy-efficient adaptive cruise control systems[205][206]. This work entailed developing these systems and comparing them to traditional cruise control systems and manual driving[207].

Rakha has also conducted research on various energy-efficient modes of transportation including buses, trains, BEVs, and bicycles. His work on transit systems has focused on providing priority to buses at traffic signals to reduce their delay and energy consumption[208][209][210][211][212][213][214]. Additional work included estimating transit vehicle dwell times at bus stops for the better prediction of their arrival times at signalized intersections for use in transit priority systems[215] [216].

In addition, Rakha and his team have worked on developing fuel/energy consumption models for diesel, hybrid electric, and Compressed Natural Gas (CNG) powered buses to develop a real-time energy minimization transit system while maintaining a desired level of service[217][218][219][220][221]. Additional work included using these fuel/energy consumption models to develop connected vehicle trajectory optimization systems in the vicinity of signalized intersections[222]. These systems receive real-time traffic Signal Phasing and Timing (SPaT) information via wireless communication with the traffic signal controller. The bus then optimizes its trajectory to minimize its fuel/energy consumption. This novel system was the first to be applied to buses. Similar systems were also developed for Internal Combustion Engine Vehicles (ICEVs)[223][224], Battery Electric Vehicles (BEVs)[225], and Hybrid Electric Vehicles (HEVs)[226].

Rakha and his team worked on bikeshare systems. The work includes predicting the demand at bikeshare systems for the optimization of bike availability to pickup a bike and space availability to drop off a bike in a bikeshare system[227][228][229]. Additional work on bikeshare systems include the assessment of the performance of bikeshare systems and the optimal design of bikeshare systems[230], identifying the optimum number of bikes needed at a station in a bikeshare system at the start of the day[231], developing a framework to optimize in real-time the allocation of bikes in a bikeshare system[232], predicting bike travel times within a bikeshare system[233], the overall modeling and assessment of bikeshare systems[234], modeling of bicyclist longitudinal motion[235], and the modeling of bicyclist longitudinal and lateral motion[236]. Additional work looked at e-scooter and e-bike systems and compared them to one another[237].

Finally, Rakha and his team conducted research on ridesharing aplications, also known as ride hailing (e.g. Uber, Lyft). Specifically, they developed a novel crowdsourcing model for micro-mobility in rideshare systems[238], studied the impact of ridesharing on vehicle miles of travel[239], and studied the impact of COVID-19 on ridesharing trips[240]. They also published a paper quantifying the impact of vehicle automation on vehicle miles traveled and potential rebound effects[241].

Multi-modal freight optimization and modeling

[edit]

Rakha and his team have conducted research in the area of freight optimization and modeling. This research included the development of the NeTrainSim open source software for quantifying the fuel, energy, and emissions impacts for various freight train powertrains[242]. The outputs of the software include instantaneous train speed, position, fuel use, and CO2 emissions at user-specified time intervals. The NeTrainSim software was used to optimize the energy consumed in freight train operations[243]. In addition, NeTrainSim was used to compare and quantify the carbon footprint associated with alternative train powertrains[244]. In parallel, the open-source ShipNetSim tool was developed by Rakha and his team to model freight ship and barge operations[245]. Using NeTrainSim, ShipNetSim, and the INTEGRATION software, the CargoNetSim comprehensive multi-modal freight optimization and modeling tool was developed by integrating these modeling tools into a single platform and optimizing and tracking the movement of a container from its origin to it's final destination[246]

Awards and honors

[edit]

References

[edit]
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  2. ^ a b Haugh, Lindsey. "Five faculty members elected to the Virginia Academy of Science, Engineering, and Medicine". news.vt.edu. Retrieved 2025-11-12.
  3. ^ a b Levin, Jacob. "Hesham Rakha named fellow of the American Society of Civil Engineers". news.vt.edu. Retrieved 2025-11-12.
  4. ^ Levin, Jacob. "Hesham Rakha awarded the 2021 IEEE Intelligent Transportation System Outstanding Research Award". news.vt.edu. Retrieved 2025-11-12.
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