Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand ...Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand provided models for predicting operating speeds.However, less attention has been paid to multi-lane highwaysespecially in Egypt. In this research, field operatingspeed data of both cars and trucks on 78 curve sections offour multi-lane highways is collected. With the data, correlationbetween operating speed （V85） and alignment isanalyzed. The paper includes two separate relevant analyses.The first analysis uses the regression models toinvestigate the relationships between V85 as dependentvariable, and horizontal alignment and roadway factors asindependent variables. This analysis proposes two predictingmodels for cars and trucks. The second analysisuses the artificial neural networks （ANNs） to explore theprevious relationships. It is found that the ANN modelinggives the best prediction model. The most influential variableon V85 for cars is the radius of curve. Also, for V85 fortrucks, the most influential variable is the median width.Finally, the derived models have statistics within theacceptable regions and they are conceptually reasonable.展开更多
The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles’merging and lane-changing behaviors,while ensuring safety and optimizing traffic flow.However,the...The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles’merging and lane-changing behaviors,while ensuring safety and optimizing traffic flow.However,there are few studies that have addressed the merging problem of ramp vehicles and the cooperative lane-change problem of mainline vehicles within a unified framework and proposed corresponding optimization strategies.To tackle this issue,this study adopts a cyber-physical integration perspective and proposes a graph-based solution approach.First,the information of vehicle groups in the physical plane is mapped to the cyber plane,and a dynamic conflict graph is introduced in the cyber space to describe the conflict relationships among vehicle groups.Subsequently,graph decomposition and search strategies are employed to obtain the optimal solution,including the set of mainline vehicles changing lanes,passing sequences for each route,and corresponding trajectories.Finally,the proposed dynamic conflict graph-based algorithm is validated through simulations in continuous traffic with various densities,and its performance is compared with the default algorithm in SUMO.The results demonstrate the effectiveness of the proposed approach in improving vehicle safety and traffic efficiency,particularly in high traffic density scenarios,providing valuable insights for future research in multi-lane merging strategies.展开更多
Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we...Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.展开更多
Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic re...Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed.First,the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk.Second,a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning.Finally,the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles.The proposed framework is validated in both low-density and high-density traffic scenarios.The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.展开更多
Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for int...Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for intersections with potential growth i~ future traffic, there has been a lack of existing studies with field data that provide reference values in terms of capacity and delay measurements. With the intent of providing such reference values, a case study was conducted by using the East DowlinC Road Roundabouts in Anchorage, Alaska, which are currently operating with extensive queues during the evening peak hours. This research used multiple video camcorders t（ capture vehicle turning movements at the roundabouts as well as the progressior~ of vehicle queues at the roundabout entrance approaches. With these video records, the number of vehicles in the queues can be accurately counted in any single minute during the peak hours. This study shows that unbalanced entrance flow patterns （i.e., ~ne entrance has significant higher flow than others） can intensify the queue and delay fo., the overall roundabouts. Then various software packages including RODEL, SIDRA and VISSIM were used to estimate several performance measurements, such as capacity. queue length, and delay, compared with the collected field data. With the comparison, it is found that all the three software packages overestimate multi-lane roundabout ca pacity before calibration. With default parameters, SIDRA and VISSIM tend to underes timate delays and queue lengths for the multi-lane roundabouts under congestion, while RODEL results in higher delay and queue length estimations at most of the entrance approaches.展开更多
Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over m...Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over multiple lanes.However,these disparities are not considered in current specifications.To address this drawback,a multi-coefficient MLF model was developed based on an improved probabilistic statistical approach that considers the presence of multiple trucks.The proposed MLF model and approach were calibrated and demonstrated through an example site.The model sensitivity analysis demonstrated the significant influence of lane disparity of truck traffic volume and truck weight distribution on the MLF.Using the proposed approach,the experimental site study yielded MLFs comparable with those directly calculated using traffic load effects.The exclusion of overloaded trucks caused the proposed approach,existing design specifications,and conventional approach of ignoring lane load disparity to generate comparable MLFs,while the MLFs based on the proposed approach were the most comprehensive.The inclusion of overloaded trucks caused the conventional approach and design specifications to overestimate the MLFs significantly.Finally,the benefits of the research results to bridge practitioners were discussed.展开更多
文摘Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand provided models for predicting operating speeds.However, less attention has been paid to multi-lane highwaysespecially in Egypt. In this research, field operatingspeed data of both cars and trucks on 78 curve sections offour multi-lane highways is collected. With the data, correlationbetween operating speed （V85） and alignment isanalyzed. The paper includes two separate relevant analyses.The first analysis uses the regression models toinvestigate the relationships between V85 as dependentvariable, and horizontal alignment and roadway factors asindependent variables. This analysis proposes two predictingmodels for cars and trucks. The second analysisuses the artificial neural networks （ANNs） to explore theprevious relationships. It is found that the ANN modelinggives the best prediction model. The most influential variableon V85 for cars is the radius of curve. Also, for V85 fortrucks, the most influential variable is the median width.Finally, the derived models have statistics within theacceptable regions and they are conceptually reasonable.
基金supported by the National Key R&D Program of China(2022YFB2503200)the National Natural Science Foundation of China,Science Fund for Creative Research Groups(52221005).
文摘The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles’merging and lane-changing behaviors,while ensuring safety and optimizing traffic flow.However,there are few studies that have addressed the merging problem of ramp vehicles and the cooperative lane-change problem of mainline vehicles within a unified framework and proposed corresponding optimization strategies.To tackle this issue,this study adopts a cyber-physical integration perspective and proposes a graph-based solution approach.First,the information of vehicle groups in the physical plane is mapped to the cyber plane,and a dynamic conflict graph is introduced in the cyber space to describe the conflict relationships among vehicle groups.Subsequently,graph decomposition and search strategies are employed to obtain the optimal solution,including the set of mainline vehicles changing lanes,passing sequences for each route,and corresponding trajectories.Finally,the proposed dynamic conflict graph-based algorithm is validated through simulations in continuous traffic with various densities,and its performance is compared with the default algorithm in SUMO.The results demonstrate the effectiveness of the proposed approach in improving vehicle safety and traffic efficiency,particularly in high traffic density scenarios,providing valuable insights for future research in multi-lane merging strategies.
基金supported by the National Nature Science Foundation of China under Grant No.61502226the Jiangsu Provincial Transportation Science and Technology Project No.2017X04the Fundamental Research Funds for the Central Universities
文摘Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.
基金support of the National Engineering Laboratory of High Mobility antiriot vehicle technology under Grant B20210017the National Natural Science Foundation of China under Grant 11672127+2 种基金the Fundamental Research Funds for the Central Universities under Grant NP2022408the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX21_0188the Chinese Scholar Council under Grant 202106830118.
文摘Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed.First,the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk.Second,a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning.Finally,the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles.The proposed framework is validated in both low-density and high-density traffic scenarios.The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.
基金sponsored by Alaska University Transportation Center(AUTC,No.RR08.08)Alaska Department of Transportation(AK DOT)
文摘Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for intersections with potential growth i~ future traffic, there has been a lack of existing studies with field data that provide reference values in terms of capacity and delay measurements. With the intent of providing such reference values, a case study was conducted by using the East DowlinC Road Roundabouts in Anchorage, Alaska, which are currently operating with extensive queues during the evening peak hours. This research used multiple video camcorders t（ capture vehicle turning movements at the roundabouts as well as the progressior~ of vehicle queues at the roundabout entrance approaches. With these video records, the number of vehicles in the queues can be accurately counted in any single minute during the peak hours. This study shows that unbalanced entrance flow patterns （i.e., ~ne entrance has significant higher flow than others） can intensify the queue and delay fo., the overall roundabouts. Then various software packages including RODEL, SIDRA and VISSIM were used to estimate several performance measurements, such as capacity. queue length, and delay, compared with the collected field data. With the comparison, it is found that all the three software packages overestimate multi-lane roundabout ca pacity before calibration. With default parameters, SIDRA and VISSIM tend to underes timate delays and queue lengths for the multi-lane roundabouts under congestion, while RODEL results in higher delay and queue length estimations at most of the entrance approaches.
基金This work was supported by the National Natural Science Foundation of China(Grant No.51808148)Natural Science Foundation of Guangdong Province,China(No.2019A1515010701)Guangzhou Municipal Science and Technology Project(No.201904010188).
文摘Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over multiple lanes.However,these disparities are not considered in current specifications.To address this drawback,a multi-coefficient MLF model was developed based on an improved probabilistic statistical approach that considers the presence of multiple trucks.The proposed MLF model and approach were calibrated and demonstrated through an example site.The model sensitivity analysis demonstrated the significant influence of lane disparity of truck traffic volume and truck weight distribution on the MLF.Using the proposed approach,the experimental site study yielded MLFs comparable with those directly calculated using traffic load effects.The exclusion of overloaded trucks caused the proposed approach,existing design specifications,and conventional approach of ignoring lane load disparity to generate comparable MLFs,while the MLFs based on the proposed approach were the most comprehensive.The inclusion of overloaded trucks caused the conventional approach and design specifications to overestimate the MLFs significantly.Finally,the benefits of the research results to bridge practitioners were discussed.
