Browsing by Author "Du, Jianhe"
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- Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transportAredah, Ahmed; Du, Jianhe; Hegazi, Mohamed; List, George; Rakha, Hesham A. (Elsevier, 2024-02-15)This study assesses the energy efficiency and environmental implications of six powertrain technologies in the U.S. freight rail network: diesel, biodiesel, diesel-hybrid, biodiesel-hybrid, hydrogen fuel cell, and electric. Utilizing a simulation model, energy consumption at the tank across different demand scenarios and geographical regions is conducted. The study revealed electric powertrains as the standout, slashing energy consumption at the tank by 56% compared to traditional diesel, with the potential for zero CO2 emissions when powered by green energy sources. Biodiesel and biodiesel-hybrid also outperformed conventional diesel, cutting CO2 tank emissions by 6% and 21%, respectively. Diesel-hybrid registered a 16% reduction in both tank energy and diesel consumption, while hydrogen fuel cells demonstrated a 15% energy consumption drop at the tank and zero emissions. Implementing these advanced technologies requires considerable infrastructure investment and adaptation, which is beyond the scope of our analysis. While centered on the U.S. rail network, our findings offer valuable insights for global freight rail systems, underpinning the push for sustainable powertrain transitions.
- Comparison of Microscopic and Mesoscopic Traffic Modeling Tools for Evacuation AnalysisAljamal, Mohammad Abdulraheem (Virginia Tech, 2017-03-06)Evacuation processes can be evaluated using different simulation models. However, recently, microscopic simulation models have become a more popular tool for this purpose. The objectives of this study are to model multiple evacuation scenarios and to compare the INTEGRATION microscopic traffic simulation model against the MATSim mesoscopic model. Given that the demand was the same for both models, the comparison was achieved based on three indicators: estimated evacuation time, average trip duration, and average trip distance. The results show that the estimated evacuation times in both models are close to each other since the Origin-Destination input file has a long tail distribution and so the majority of the evacuation time is associated when travelers evacuate and not the actual evacuation times. However, the evaluation also shows a considerable difference between the two models in the average trip duration. The average trip duration using INTEGRATION increases with increasing traffic demand levels and decreasing roadway capacity. On the other hand, the average trip duration using MATSim decreases with increasing traffic demand and decreasing the roadway capacity. Finally, the average trip distance values were significantly different in both models. The conclusion showed that the INTEGRATION model is more realistic than the MATSim model for evacuation purposes. The study concludes that despite the large execution times of a microscopic traffic simulation, the use of microsimulation is a worthwhile investment.
- Data Mining and Gap Analysis for Weather Responsive Traffic Management ProgramKrechmer, Daniel; Rakha, Hesham A.; Howard, Mark; Huang, Weimin; Zohdy, Ismail H.; Du, Jianhe (United States. Federal Highway Administration, 2010)Weather causes a variety of impacts on the transportation system. An Oak Ridge National Laboratory study estimated the delay experienced by American drivers due to snow, ice, and fog in 1999 at 46 million hours. While severe winter storms, hurricanes, or flooding can result in major stoppages or evacuations of transportation systems and cost millions of dollars, the day-to-day weather events such as rain, fog, snow, and freezing rain can have a serious impact on the mobility and safety of the transportation system users. Despite the documented impacts of adverse weather on transportation, the linkages between inclement weather conditions and traffic flow in existing analysis tools remain tenuous. This is primarily a result of limitations on the data used in research activities. The overall goal of this research was to identify gaps in the data necessary to develop weather responsive traffic management studies. Activities conducted to achieve this included 1) A comprehensive search and documentation of traffic and weather data in the United States and abroad that could be used for WRTM; 2) surveys, phone calls and site visits with organizations that have suitable traffic data on inclement weather; 3) identification of critical gaps in regards to the collection and processing of traffic data on inclement weather conditions; and 4) recommendation of strategies for gathering and processing data that will be used in WRTM studies. The study found that there are a number of useful research efforts underway both domestically and internationally that are yielding useful data for WRTM analysis. In some cases the scopes are limited and confidentiality issues were found in a number of European studies. There is increasing availability of quality traffic and weather data being generated by transportation and public/private weather information sources in the U.S. The analysis conducted for this project found that this data can be helpful in identifying adverse weather impacts on speed and lane usage. The report recommends that FHWA work closely with agencies as they expand their RWIS to assure that weather data is of adequate quality for WRTM analysis. FHWA also should continue to fund specific research and evaluation activities in conjunction with the Integrated Corridor Management Program or other WRTM initiatives.
- Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion: SHRP 2Rakha, Hesham A.; Du, Jianhe; Park, Sangjun; Guo, Feng; Doerzaph, Zachary R.; Viita, Derek; Golembiewski, Gary A.; Katz, Bryan J.; Kehoe, Nicholas; Rigdon, H. (National Research Council (U.S.). Transportation Research Board, 2011)Nonrecurring congestion is traffic congestion due to nonrecurring causes, such as crashes, disabled vehicles, work zones, adverse weather events, and planned special events. According to data from the Federal Highway Administration (FHWA), approximately half of all congestion is caused by temporary disruptions that remove part of the roadway from use, or "nonrecurring" congestion. These nonrecurring events dramatically reduce the available capacity and reliability of the entire transportation system. The objective of this project is to determine the feasibility of using in-vehicle video data to make inferences about driver behavior that would allow investigation of the relationship between observable driver behavior and nonrecurring congestion to improve travel time reliability. The data processing flow proposed in this report can be summarized as (1) collect data, (2) identify driver behavior, (3) identify correctable driver behavior, and (4) model travel time reliability, as shown in Figure ES.1.
- Impact of Ridesharing on Vehicle Miles TraveledDu, Jianhe; Rakha, Hesham A. (National Surface Transportation Safety Center for Excellence, 2020-05-05)Due to the rapid development of the ridesharing industry, there is very limited data available for researchers and practitioners to draw a comprehensive conclusion regarding resultant changes in vehicle miles traveled (VMT). Current research on ridesharing is inconclusive and conflicting. This report summarizes our findings on the impacts of ridesharing on VMT, focusing on optimization and pairing modeling, the relationship between ridesharing and public transit, induced trips, and car ownership.
- Impacts of Vehicle-to-Everything Enabled Applications: Literature Review of Existing StudiesDu, Jianhe; Ahn, Kyoungho; Farag, Mohamed; Rakha, Hesham A. (Universal Wiser Publisher, 2023-03-10)As communication technology is developing at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing vehicle energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and vehicle-to-everything (V2X) communication, or the instant and accurate communication among vehicles, devices, pedestrians, infrastructure, network, cloud, and grid. Cellular V2X (C-V2X) has gained interest as an efficient method for this data sharing. In releases 14 and 15, C-V2X uses 4G LTE technology, and in release 16, it uses the latest 5G new radio (NR) technology. Among its benefits, C-V2X can function even with no network infrastructure coverage; in addition, C-V2X surpasses older technologies in terms of communication range, latency, and data rates. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system's capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. Achieving the full benefits of CVs requires rigorous investigation into the effectiveness, strengths, and weaknesses of different CV applications. It also calls for deeper understanding of the communication protocols, results with different CV market penetration rates (MPRs), CV- and human-driven vehicle interactions, integration of multiple applications, and errors and latencies associated with data communication. This paper includes a review of existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future research. The results of this paper will help shape future research for CV applications to realize their full potential.
- Isolated Traffic Signal Optimization Considering Delay, Energy, and Environmental ImpactsCalle Laguna, Alvaro Jesus (Virginia Tech, 2017-01-10)Traffic signal cycle lengths are traditionally optimized to minimize vehicle delay at intersections using the Webster formulation. This thesis includes two studies that develop new formulations to compute the optimum cycle length of isolated intersections, considering measures of effectiveness such as vehicle delay, fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model against simulated data. The microscopic simulation software, INTEGRATION, was used to simulate two-phase and four-phase isolated intersections over a range of cycle lengths, traffic demand levels, and signal timing lost times. Intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) were derived from the simulation software. The cycle lengths that minimized the various measures of effectiveness were then used to develop the proposed formulations. The first research effort entailed recalibrating the Webster model to the simulated data to develop a new delay, fuel consumption, and emissions formulation. However, an additional intercept was incorporated to the new formulations to enhance the Webster model. The second research effort entailed updating the proposed model against four study intersections. To account for the stochastic and random nature of traffic, the simulations were then run with twenty random seeds per scenario. Both efforts noted its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths optimized for vehicle delay only. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle demands.
- Naturalistic Driving Data for the Analysis of Car-following ModelsRakha, Hesham A.; Sangster, John; Du, Jianhe (United States. Department of Transportation, 2013-02-21)This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existing car following models. The research presented analyzes the simplified behavioral vehicle longitudinal motion model, currently implemented in the INTEGRATION software, known as the Rakha-Pasumarthy-Adjerid (RPA) model. This model utilizes a steady-state formulation along with two constraints, namely: acceleration and collision avoidance. An analysis of the model using the naturalistic driving data identified a deficiency in the model formulation, in that it predicts more conservative driving behavior compared to naturalistic driving. Much of the error in simulated car-following behavior occurs when a car-following event is initiated. As a vehicle enters the lane in front of a subject vehicle, the spacing between the two vehicles is often much shorter than is desired; the observed behavior is that, rather than the following vehicle decelerating aggressively, the following vehicle coasts until the desired headway/spacing is achieved. Consequently, the model is enhanced to reflect this empirically observed behavior. Finally, a quantitative and qualitative evaluation of the original and proposed model formulations demonstrates that the proposed modification significantly decreases the modeling error and produces car-following behavior that is consistent with empirically observed driver behavior.
- Naturalistic Driving Data for the Analysis of Car-Following ModelsSangster, John David (Virginia Tech, 2011-12-05)The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified. This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation.
- Network-wide Assessment of Eco-Cooperative Adaptive Cruise Control Systems on Freeway and Arterial FacilitiesTu, Ran (Virginia Tech, 2016-06-20)The environmental impact of a transportation system is critical in the assessment of the transportation system performance. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems attempt to minimize vehicle fuel consumption and emission levels by controlling vehicle speed and acceleration levels. The majority of previous research efforts developed and applied Eco-CACC systems on either freeway or signalized intersections independently on simple and small transportation networks without consideration of the interaction among these controls. This thesis extends the state-of-the-art in Eco-CACC evaluation by conducting a comprehensive evaluation on a complex network considering Eco-CACC control on both freeways and arterials individually and simultaneously. The goal of this study is to compare Eco-CACCs on arterial facilities (Eco-CACC-A), freeway facilities (Eco-CACC-F) and both facilities (Eco-CACC-F+A). The effects of Eco-CACC are evaluated considering various Measures of Effectiveness (MOEs), including: average vehicle delay, fuel consumption, and emission levels using simulated results from INTEGRATION, a microscopic traffic assignment and simulation software, considering different freeway speed limits, traffic demand levels and system market penetration rates. In total, 19 traffic scenarios for each of the four different cases (Eco-CACC-A, Eco-CACC-F and Eco-CACC-F+A plus a base no control case) were tested. In total 760 simulation runs were conducted (4 cases * 19 scenarios * 10 repetitions). T-tests and pairwise mean comparison (Tukey HSD) were conducted to identify any statistical differences between control cases and the base case from the simulation results. This thesis shows that arterial and freeway Eco-CACCs can work well together and their effects will be largely influenced by network characteristics.
- Operational Analysis of Alternative IntersectionsSangster, John (Virginia Tech, 2015-09-09)Alternative intersections and interchanges, such as the diverging diamond interchange (DDI), the restricted crossing u-turn (RCUT), and the displaced left-turn intersection (DLT), have the potential to both improve safety and reduce delay. However, partially due to lingering questions about analysis methods and service measures for these designs, their rate of implementation remains low. This research attempts to answer three key questions. Can alternative intersections and interchanges be incorporated into the existing level of service and service measure schema, or is a new service measure with an updated level of service model required? Is the behavior of drivers at alternative intersections fundamentally similar to those at conventional intersections, such that traffic microsimulation applications can accurately model the behaviors observed in the field? Finally, is the planning level tool made available through FHWA an accurate predictor of the relative performance of various alternatives, or is an updated tool necessary? Discussion and case study analysis are used to explore the existing level of service and service measure schema. The existing control delay measure is recommended to be replaced with a proposed junction delay measure that incorporates geometric delay, with the existing level of service schema based on control type recommended to be replaced by a proposed schema using demand volume. A case study validation of micro- and macroscopic analysis methods is conducted, finding the two microscopic methods investigated to match field observed vehicle delays within 3 to 7 seconds for all designs tested, and macroscopic HCM method matching within 3 seconds for the DDI, 35 seconds for the RCUT, and 130 seconds for the DLT design. Taking the critical lane analysis method to be a valid measure of operations, the demand-volume limitations of each alternative design is explored using eighteen geometric configurations and approximately three thousand volume scenarios, with the DLT design predicted to accommodate the highest demand volumes before failure is reached. Finally, six geometries are examined using both the planning-level tool and the validated microsimulation tool, finding that the curve of the capacity-to-delay relationship varies for each alternative design, invalidating the use of critical lane analysis as a comparative tool.
- Real-Time Estimation of Traffic Stream Density using Connected Vehicle DataAljamal, Mohammad Abdulraheem (Virginia Tech, 2020-10-02)The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the traffic stream density using CV data only. The proposed model-driven approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized approach in downtown Blacksburg, VA. Results indicate that density estimates produced by the linear KF approach are the most accurate. A sensitivity of the estimation approaches to various factors including the level of market penetration (LMP) of CVs, the initial conditions, the number of particles in the PF approach, traffic demand levels, traffic signal control methods, and vehicle length is presented. Results show that the accuracy of the density estimate increases as the LMP increases. The KF is the least sensitive to the initial traffic density estimate, while the PF is the most sensitive to the initial traffic density estimate. The results also demonstrate that the proposed estimation approaches work better at higher demand levels given that more CVs exist for the same LMP scenario. For traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed traffic signal timings at low traffic demand levels, while the estimation accuracy is better when the adaptive phase split optimizer is activated for high traffic demand levels. The dissertation also investigates the sensitivity of the KF estimation approach to vehicle length, demonstrating that the presence of longer vehicles (e.g. trucks) in the traffic link reduces the estimation accuracy. Data-driven approaches are also developed to estimate the traffic stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The data-driven approaches also utilize solely CV data. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the dissertation compares the performance of the model-driven and the data-driven approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the linear KF approach is highly recommended in the application of traffic density estimation due to its simplicity and applicability in the field.
- To What Extent Do Ride-Hailing Services Replace Public Transit? A Novel Geospatial, Real-Time Approach Using Ride-Hailing Trips in ChicagoBreuer, Helena Kathryn (Virginia Tech, 2021-02-11)Existing literature on the relationship between ridehailing (RH) and transit services is limited to empirical studies that rely on self-reported answers and lack spatial and temporal contexts. To fill this gap, the research takes a novel approach that uses real-time geospatial analyzes. Using this approach, we estimate the extent to which ride-hailing services have contributed to the recent decline in public transit ridership. With source data on ridehailing trips in Chicago, Illinois, we computed the real-time transit-equivalent trip for the 7,949,902 ridehailing trips in June 2019; the sheer size of this sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ridehailer selecting a transit alternative to serve the specific origin-destination pair, P(Transit|CTA) . The study found that 31% of RH trips are replaceable, 61% are not replaceable, and 8% lie within the buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis, and performed a two-tailed t-test, with a 95% confidence interval. In combination with a Summation of Probabilities, the results indicate that the total travel time for a transit trip has the greatest influence on the probability of using transit, whereas the airport pass price has the least influence. Further, the walk time, number of stops in the origin and destination census tracts, and household income also have significant impacts on the probability of using transit. Lastly, we performed a Time Value Analysis to explore the cost and trip duration difference between RH trips and their transit-equivalent trips on the probability of switching to transit. The findings demonstrated that approximately 90% of RH trips taken had a transit-equivalent trip that was less expensive, but slower. The main contribution of this study is its thorough approach and fine-tuned series of real-time spatial analyzes that investigate the replaceability of RH trips for public transit. The results and discussion intend to provide perspective derived from real trips and encourage public transit agencies to look into possible opportunities to collaborate with ridehailing companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, we hope that this effort provides proof of the research benefits associated with the recording and release of ridehailing data.