Browsing by Author "Chen, Hao"
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- Air pollution perception in ten countries during the COVID-19 pandemicLou, Baowen; Barbieri, Diego Maria; Passavanti, Marco; Hui, Cang; Gupta, Akshay; Hoff, Inge; Lessa, Daniela Antunes; Sikka, Gaurav; Chang, Kevin; Fang, Kevin; Lam, Louisa; Maharaj, Brij; Ghasemi, Navid; Qiao, Yaning; Adomako, Solomon; Mirhosseini, Ali Foroutan; Naik, Bhaven; Banerjee, Arunabha; Wang, Fusong; Tucker, Andrew; Liu, Zhuangzhuang; Wijayaratna, Kasun; Naseri, Sahra; Yu, Lei; Chen, Hao; Shu, Benan; Goswami, Shubham; Peprah, Prince; Hessami, Amir; Abbas, Montasir M.; Agarwal, Nithin (2021-06-21)As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.
- Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized IntersectionsChen, Hao; Rakha, Hesham A. (MDPI, 2020-05-12)This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.
- Cooperative Automated Vehicle Movement Optimization at Uncontrolled Intersections using Distributed Multi-Agent System ModelingMahmoud, Abdallah Abdelrahman Hassan (Virginia Tech, 2017-02-28)Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network.
- Developing and Field Testing a Green Light Optimal Speed Advisory System for BusesChen, Hao; Rakha, Hesham A. (MDPI, 2022-02-17)In this study, a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed. The proposed B-GLOSA system was implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy-to-calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the relationship between vehicle speed and distance to the intersection are used to construct an optimization problem. A moving-horizon dynamic programming problem solved using the A-star algorithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios, including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recommended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized intersections, thus producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1%, on average, respectively.
- Dynamic Travel Time Prediction using Pattern RecognitionChen, Hao; Rakha, Hesham A.; McGhee, Catherine C. (TU Delft, 2013)Travel-time information is an essential part of Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of travel times. From the perspective of travelers such information may assist in making better route choice and departure time decisions. For transportation agencies these data provide criteria with which to better manage and control traffic to reduce congestion. This study proposes a dynamic travel time prediction algorithm that matches current traffic patterns to historical data. Unlike previous approaches that use travel time as the control variable, the approach uses the temporal-spatial traffic state evolution to match traffic states and predict travel times. The approach first identifies candidate historical time intervals by matching real-time traffic state data against historical data for use in prediction purposes. Subsequently, the selected candidates are used to predict the temporal-spatial evolution of traffic. Lastly, dynamic travel times are constructed using the identified candidate historical data. The proposed algorithm is tested on a 37-mile freeway segment from Newport News to Virginia Beach along the I-64 and I-264 freeways using historical INRIX data. The prediction results indicate that the proposed method produces predictions that are more accurate than the state-of-the-art K-Nearest Neighbor methods reducing the prediction error by 15 percent to less than 3 minutes on a 50-minute trip.
- Enhancing Freeway Merge Section Operations via Vehicle ConnectivityKang, Kyungwon (Virginia Tech, 2019-11-12)Driving behavior considerably affects the transportation system, especially lane-changing behavior occasionally cause conflicts between drivers and induce shock waves that propagate backward. A freeway merge section is one of locations observed a freeway bottleneck, generating freeway traffic congestion. The emerging technologies, such as autonomous vehicles (AVs) and vehicle connectivity, are expected to bring about improvement in mobility, safety, and environment. Hence the objective of this study is to enhance freeway merge section operations based on the advanced technologies. To achieve the objective, this study modeled the non-cooperative merging behavior, and then proposed the cooperative applications in consideration of a connected and automated vehicles (CAVs) environment. As a tactical process, decision-making for lane-changing behaviors is complicated as the closest following vehicle in the target lane also behaves concerning to the lane change (reaction to the lane-changing intention), i.e., there is apparent interaction between drivers. To model this decision-making properly, this study used the game theoretical approach which is the study of the ways in which interacting choices of players. The game models were developed to enhance the microscopic simulation model representing human driver's realistic lane-changing maneuvers. The stage game structure was designed and payoff functions corresponding to the action strategy sets were formulated using driver's critical decision variables. Furthermore, the repeated game concept which takes previous game results into account was introduced with the assumption that drivers want to maintain initial decision in competition if there is no significant change of situations. The validation results using empirical data provided that the developed stage game has a prediction accuracy of approximately 86%, and the superior performance of the repeated game was verified by an agent-based simulation model, especially in a competitive scenario. Specifically, it helps a simulation model to not fluctuate in decision-making. Based on the validated non-cooperative game model, in addition, this study proposed the cooperative maneuver planning avoiding the non-cooperative maneuvers with prediction of the other vehicle's desired action. If a competitive action is anticipated, in other words, a CAV changes its action to be cooperative without selfish driving. Simulation results showed that the proposed cooperative maneuver planning can improve traffic flow at a freeway merge section. Lastly, the optimal lane selection (OLS) algorithm was also proposed to assist lane selection in consideration of real-time downstream traffic data transferred via a long-range wireless communication. Simulation case study on I-66 highway proved that the proposed OLS can improve the system-wide freeway traffic flow and lane allocation. Overall, the present work addressed developing the game model for merging maneuvers in a traditional transportation system and suggesting use of efficient algorithms in a CAV environment. These findings will contribute to enhance performance of the microscopic simulator and prepare the new era of future transportation system.
- Field Testing of Eco-Speed Control Using V2I CommunicationRakha, Hesham A.; Chen, Hao; Almannaa, Mohammed Hamad; Kamalanathsharma, Raj Kishore; El-Shawarby, Ihab; Loulizi, Amara (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-04-15)This research focused on the development of an Eco-Cooperative Adaptive Cruise Control (Eco-CACC) System and addressed the implementation issues associated with applying it in the field. The Eco-CACC system computes and recommends a fuel-efficient speed based on Signal Phasing and Timing (SPaT) data received from the traffic signal controller via vehicle-to-infrastructure (V2I) communication. The computed speed profile can either be broadcast as an audio alert to the driver to manually control the vehicle, or, implemented in an automated vehicle (AV) to automatically control the vehicle. The proposed system addresses all possible scenarios, algorithmically, that a driver may encounter when approaching a signalized intersection. Additionally, from an implementation standpoint, the research addresses the challenges associated with communication latency, data errors, real-time computation, and ride smoothness. The system was tested on the Virginia Smart Road Connected Vehicle Test Bed in Blacksburg, VA. Four scenarios were tested for each participant: a base driving scenario, where no speed profile data was communicated; a scenario in which the driver was provided with a “time to red light” countdown; a manual Eco-CACC scenario where the driver was instructed to follow a recommended speed profile given via audio alert; and finally, an automated Eco-CACC scenario where the AV system controlled the vehicle’s longitudinal motion. The field test included 32 participants, and each participant completed 64 trips to pass through a signalized intersection for different combinations of signal timing and road grades. The analyzed results demonstrate the benefits of the Eco-CACC system in assisting vehicles to drive smoothly in the vicinity of intersections, thereby reducing fuel consumption levels and travel times. Compared to an uninformed baseline drive, the longitudinally automated Eco-CACC system controlled vehicle drive resulted in savings in fuel consumption levels and travel times of approximately 37.8% and 9.3%, respectively.
- Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modesBarbieri, Diego Maria; Lou, Baowen; Passavanti, Marco; Hui, Cang; Hoff, Inge; Lessa, Daniela Antunes; Sikka, Gaurav; Chang, Kevin; Gupta, Akshay; Fang, Kevin; Banerjee, Arunabha; Maharaj, Brij; Lam, Louisa; Ghasemi, Navid; Naik, Bhaven; Wang, Fusong; Mirhosseini, Ali Foroutan; Naseri, Sahra; Liu, Zhuangzhuang; Qiao, Yaning; Tucker, Andrew; Wijayaratna, Kasun; Peprah, Prince; Adomako, Solomon; Yu, Lei; Goswami, Shubham; Chen, Hao; Shu, Benan; Hessami, Amir; Abbas, Montasir M.; Agarwal, Nithin; Rashidi, Taha Hossein (2021-02-01)The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents' perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.
- iTRAQ protein profile analysis of developmental dynamics in soybean [Glycine max (L.) Merr.] leavesQin, Jun; Zhang, Jianan; Wang, Fengmin; Wang, Jinghua; Zheng, Zhi; Yin, Changcheng; Chen, Hao; Shi, Ainong; Zhang, Bo; Chen, Pengyin; Zhang, Mengchen (PLOS, 2017-09-27)
- Policy and industry implications of the potential market penetration of electric vehicles with eco-cooperative adaptive cruise controlBas, Javier; Zofio, Jose L.; Cirillo, Cinzia; Chen, Hao; Rakha, Hesham A. (Pergamon-Elsevier, 2022-10)The Eco-Cooperative Adaptive Cruise Control (Eco-CACC) makes use of an algorithm to compute energy-optimized speed profiles within the vicinity of signalized intersections. We conduct a stated choice experiment to unveil the inclination of drivers towards the Eco-CACC and to calculate its potential market share. To do so, we consider the performance of the system in field and simulated tests, as well as different types of drivers. Models of discrete choice are used to identify key elements in the adoption of this technology and its market penetration. The study has been performed for gasoline and electric vehicles, as well as for different categories of roads (arterial, highways and both), separately, exploring the effect of the advantages that the Eco-CACC features bring to both. Our results demonstrate, for the gasoline-powered, that potential purchasers perceive a clear trade-off between the cost of the system and the fuel savings that it provides. This is not the case for potential electric vehicles purchasers, for whom the cost-benefit analysis is adverse, mainly due to the low cost of electricity compared to gasoline. Nevertheless, the market shares resulting from the estimated models give a significant quota to the alternatives that include the Eco-CACC, resulting from favorable attitudes towards environmentally friendly technological innovations.
- Real-time Traffic State Prediction: Modeling and ApplicationsChen, Hao (Virginia Tech, 2014-06-12)Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future.