Stop-and-go traffic is a frequently observed phenomenon in congested highway traffic, but it has ... more Stop-and-go traffic is a frequently observed phenomenon in congested highway traffic, but it has not been accurately modeled in existing traffic models. Car-following models based on kinematic flow theory cannot model stop-and-go traffic. Other approach assumed traffic states deviating from the equilibrium curve in the fundamental diagram, and the transitions between them, but no explanation was provided on the reason
Proceedings of the Eastern Asia Society for Transportation Studies The 9th International Conference of Eastern Asia Society for Transportation Studies, 2011, 2011
This paper investigates characteristics of the stop-and-go traffic wave that occurs frequently in... more This paper investigates characteristics of the stop-and-go traffic wave that occurs frequently in congested traffic, and describes its development and evolution in time and space. Using NGSIM trajectory dataset, we investigated the relationship between the development of stop-and-go waves and lane changing events which causes deceleration of vehicles and subsequent wave growth and dissipation. Asymmetric traffic theory assuming the separation between acceleration and deceleration behavior was used as a framework for interpretation and explanation of the observed results. And, reciprocal interactions between consecutive stop-and-go waves were studied. Finally, we concluded that the characteristics of stop-and-go waves are closely related to asymmetric driving behavior.
Transportation Research Board 88th Annual MeetingTransportation Research Board, 2009
ABSTRACT The paper presents a microscopic asymmetric traffic flow theory proposed based on the ob... more ABSTRACT The paper presents a microscopic asymmetric traffic flow theory proposed based on the observation of individual vehicle trajectories from the NGSIM database. The findings clearly show the asymmetry in vehicle’s acceleration and deceleration and define five traffic phases: free flow, acceleration, deceleration, coasting, and stationary. The proposed theory provides detailed description and mechanism of phase transitions. Extensions of the basic theory address common driver behavioral characteristics such as maneuvering error and anticipation. The application of the proposed theory provides reasonable and intuitive explanations verified with experimental data on common traffic phenomena that cannot to date satisfactorily be addressed by existing macroscopic or microscopic theories. These phenomena include traffic hysteresis, capacity drop, and relaxation after lane change.
A vehicle Predictive Cruise Control system has been developed to improve the fuel efficiency of v... more A vehicle Predictive Cruise Control system has been developed to improve the fuel efficiency of vehicle and traffic flow performance based on the asymmetric traffic theory. The Predictive Cruise Control system consists of four parts: (1) Deceleration based Safety Surrogate Measure, (2) Adaptive Cruise Control, (3) Multi-vehicle measurement, and (4) Predictive Cruise Control. Adaptive Cruise Control basically decides the acceleration/deceleration action based on the estimated deceleration-based safety surrogate measure of the first leader vehicle. Then, Predictive Cruise Control adjusts the acceleration/deceleration action based on the multi-vehicle measurements, which represent the future traffic condition of the subject vehicle. The developed system is tested by simulating the real vehicle trajectories from the NGSIM data and comparing the results with real following pattern. It was found that the newly proposed Predictive Cruise Control system can contribute to energy consumption and traffic flow performance, because it can effectively suppress the shockwave from the downstream and remove the unnecessary deceleration and acceleration action.
Transportation Research Part C-emerging Technologies, Dec 1, 2016
Abstract Safety warning systems generally operate based on information from sensors attached to i... more Abstract Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.
Computing in Civil and Building Engineering (2014), Jun 17, 2014
Current safety warning systems generally operate based on the information from sensors attached t... more Current safety warning systems generally operate based on the information from sensors attached to individual vehicles. This vehicle-sensor based system can only estimate the collision potential situation in close proximity of a subject vehicle, and it requires additional communication technologies such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies in order to obtain a wide range of information. The device requirements for such technologies will lead to the price increase of the collision warning system. So, in this study, we propose the collision warning system that utilizes the information from the fusion of loop detector data and smartphone data. The proposed collision warning system can be directly applied without any additional cost, because the databases of loop detector and smartphones are available to be used. The developed system is tested by simulating a real vehicle trip based on the NGSIM data and comparing its results to vehicle-sensor based system and infrastructure information based system. It was found that the newly proposed collision risk warning system can show the similar performance with the V2V-based collision warning system.
In the intelligent transportation system field, there has been a growing interest in developing c... more In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network-based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.
IEEE Transactions on Intelligent Transportation Systems, Jun 1, 2016
Missing data imputation is a critical step in data processing for intelligent transportation syst... more Missing data imputation is a critical step in data processing for intelligent transportation systems. This paper proposes a data-driven imputation method for sections of road based on their spatial and temporal correlation using a modified k- nearest neighbor method. This computing-distributable imputation method is different from the conventional algorithms in the fact that it attempts to impute missing data of a section with multiple sensors that have correlation to each other, at once. This increases computational efficiency greatly compared with other methods, whose imputation subject is individual sensors. In addition, the geometrical property of each section is conserved; in other words, the continuation of traffic properties that each sensor captures is conserved, therefore increasing accuracy of imputation. This paper shows results and analysis of comparison of the proposed method to others such as nearest historical data and expectation maximization by varying missing data type, missing ratio, traffic state, and day type. The results show that the proposed algorithm achieves better performance in almost all of the missing types, missing ratios, day types, and traffic states. When the missing data type cannot be identified or various missing types are mixed, the proposed algorithm shows accurate and stable imputation performance.
One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-v... more One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian's crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian's crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian-vehicle collisions by capturing the pedestrian's crossing intention.
For improvement of road safety, many collision-warning systems are developed. In this study, we p... more For improvement of road safety, many collision-warning systems are developed. In this study, we propose Sampling-based Collision Warning System (SCWS) that overcomes the limitations of existing collision warning systems such as high installation cost, requirement of high market penetration rate, and the lack of consideration of traffic dynamics. SCWS gathers vehicle operation data though smartphones of drivers on the road and shares the information of surrounding vehicles' movement through a cloud server. From the pool of information on the cloud, SCWS uses sampled data, which indirectly represents the traffic state and traffic changes in the perspective of the leader vehicle. Therefore, SCWS can effectively replace the leader vehicle's information with the average behavior of sampled surrounding vehicles. The performance of SCWS is evaluated with comparison to Vehicle-to-Vehicle communication based Collision Warning System (VCWS) and Infrastructure based Collision Warning System (ICWS), where VCWS is considered the most similar measure to the actual collision risk in theory, but in practice very difficult to achieve due many limitations, such as high installation cost and market penetration. The result shows that in both aggregation and disaggregation level analysis the proposed SCWS exhibits a similar collision risk trend to the VCWS. Furthermore, the SCWS shows a high potential for practical application because it has the acceptable performance even with a low sampling ratio (40%), requiring a low market penetration rate and low installation cost by using the wide spread smartphone.
The modeling of walking behavior and design of walk-friendly urban pathways have been of interest... more The modeling of walking behavior and design of walk-friendly urban pathways have been of interest to many researchers over the past decades. One of the major issues in pedestrian modeling is path planning decision-making in a dynamic walking environment with different pedestrian flows. While previous studies have agreed that pedestrian flow influences path planning, only a few studies have dealt with the empirical data to show the relationship between pedestrian flow and path planning behavior. This study introduces a new methodology for analyzing pedestrian trajectory data to find the dynamic walking conditions that influence the path planning decision. The comparison of the pedestrians' path shows that the higher proportion of opposite flows are, the greater they influence the path selection decision. In this study, we investigate the relationship between the opposite flow changes and path planning behavior and find the spatial and temporal ranges of the opposite flow that affects the path planning behavior. Lastly, we find the ratio of pedestrians that update their paths with respect to the opposite flow rate.
Procedia - Social and Behavioral Sciences, May 1, 2016
Since the beginning of the new millennium, various ideas and methods of measuring road network vu... more Since the beginning of the new millennium, various ideas and methods of measuring road network vulnerability have been proposed in the field of transportation and infrastructure engineering. However, most of the existing measures have been focusing on large road networks like highways between cities, even though urban networks are equally important. Also, the measures require full microscopic simulations for deriving the value of travel time increase, which is used for predicting network performance in case a disaster situation occurs. In order to overcome such issue, this study provides a new evaluation method for vulnerability of urban road network based on the concept of macroscopic fundamental diagram (MFD). We call such measure as MFD-based Vulnerability Index (MVI), which is derived by comparing the MFD behaviours in normal condition and event condition, in which one or more links of a road network are failed by a disaster. For testing the measure, a simulation study based on the road network of Gangnam district, Seoul, South Korea is provided. The results show that the measure can help us investigate the different patterns of network performance loss by different traffic states. This work provides a different perspective of investigating road network vulnerability.
One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-v... more One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian's crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian's crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian-vehicle collisions by capturing the pedestrian's crossing intention.
Spacing policy for autonomous vehicles is one of the important issues because it is highly relate... more Spacing policy for autonomous vehicles is one of the important issues because it is highly related to the safety on the roads, efficiency of vehicles, and user satisfaction of autonomous vehicles. Many researchers have developed several spacing policies for autonomous vehicle control. Most of the spacing policies are mainly focused on the control scheme such as string stability and safety. So, the issues related to user satisfaction and possible effect of autonomous vehicles on manual vehicles are less considered especially in the mixed traffic situation of autonomous and manual vehicles. In this study, we propose an Asymmetric Collision Risk(ACR)-based spacing policy based on the analysis on collision risk and driving behavior. The proposed spacing policy is compared with other spacing policies by simulating with trajectories of human drivers. Based on the simulation results, the performance of each spacing policy is compared in terms of vehicle operation, CO2 emission and safety. The results show that ACR spacing policy has similar pattern with the human driver with smoother trajectory and less acceleration/deceleration actions. In terms of road efficiency and environment, the proposed ACR spacing policy shows the second best performance next to Safety Spacing policy. However, in terms of safety in mixed situation of manual and autonomous vehicles, ACR spacing policy is superior to other spacing policies with zero occurrence of critical event. By considering overall performance, the proposed ACR spacing policy is expected to generally show good performance in the mixed traffic situation of manual and autonomous vehicles.
Based on an analysis of collision risk propagation, a vehicle Traffic Predictive Cruise Control (... more Based on an analysis of collision risk propagation, a vehicle Traffic Predictive Cruise Control (TPCC) system, responding to the change of downstream traffic situation, is proposed in this study to improve traffic operation, safety, and fuel efficiency of vehicle. The proposed TPCC system consists of four parts: (1) Collision risk calculator of a subject vehicle, which represent the state of the subject vehicle. (2) Vehicle Control algorithm only based on the collision risk of the subject vehicle, (3) Cooperative measure for representing downstream traffic state, which is based on the results of an analysis of collision risk propagation, (4) TPCC algorithm, which controls the vehicle by using both collision risk of the subject vehicle and cooperative measure. By using both collision risk of subject vehicle and cooperative measure, which represent the integrated collision risk of leader vehicles, TPCC is designed to proactively determine actuation of vehicle by adjusting parameters of vehicle control algorithm before high collision risk arisen from leader vehicles reaches to the subject vehicle. A simulation using the real vehicle trajectories from the NGSIM data validates the performance of TPCC algorithm with various market penetration rates. It is found that the proposed TPCC system can contribute to CO2 emission reduction, traffic flow stability, and safety improvement. Such results are due to the effects of suppression of the high collision risk generated from downstream traffic and removal of unnecessary fluctuation of speed.
Stop-and-go traffic is a frequently observed phenomenon in congested highway traffic, but it has ... more Stop-and-go traffic is a frequently observed phenomenon in congested highway traffic, but it has not been accurately modeled in existing traffic models. Car-following models based on kinematic flow theory cannot model stop-and-go traffic. Other approach assumed traffic states deviating from the equilibrium curve in the fundamental diagram, and the transitions between them, but no explanation was provided on the reason
Proceedings of the Eastern Asia Society for Transportation Studies The 9th International Conference of Eastern Asia Society for Transportation Studies, 2011, 2011
This paper investigates characteristics of the stop-and-go traffic wave that occurs frequently in... more This paper investigates characteristics of the stop-and-go traffic wave that occurs frequently in congested traffic, and describes its development and evolution in time and space. Using NGSIM trajectory dataset, we investigated the relationship between the development of stop-and-go waves and lane changing events which causes deceleration of vehicles and subsequent wave growth and dissipation. Asymmetric traffic theory assuming the separation between acceleration and deceleration behavior was used as a framework for interpretation and explanation of the observed results. And, reciprocal interactions between consecutive stop-and-go waves were studied. Finally, we concluded that the characteristics of stop-and-go waves are closely related to asymmetric driving behavior.
Transportation Research Board 88th Annual MeetingTransportation Research Board, 2009
ABSTRACT The paper presents a microscopic asymmetric traffic flow theory proposed based on the ob... more ABSTRACT The paper presents a microscopic asymmetric traffic flow theory proposed based on the observation of individual vehicle trajectories from the NGSIM database. The findings clearly show the asymmetry in vehicle’s acceleration and deceleration and define five traffic phases: free flow, acceleration, deceleration, coasting, and stationary. The proposed theory provides detailed description and mechanism of phase transitions. Extensions of the basic theory address common driver behavioral characteristics such as maneuvering error and anticipation. The application of the proposed theory provides reasonable and intuitive explanations verified with experimental data on common traffic phenomena that cannot to date satisfactorily be addressed by existing macroscopic or microscopic theories. These phenomena include traffic hysteresis, capacity drop, and relaxation after lane change.
A vehicle Predictive Cruise Control system has been developed to improve the fuel efficiency of v... more A vehicle Predictive Cruise Control system has been developed to improve the fuel efficiency of vehicle and traffic flow performance based on the asymmetric traffic theory. The Predictive Cruise Control system consists of four parts: (1) Deceleration based Safety Surrogate Measure, (2) Adaptive Cruise Control, (3) Multi-vehicle measurement, and (4) Predictive Cruise Control. Adaptive Cruise Control basically decides the acceleration/deceleration action based on the estimated deceleration-based safety surrogate measure of the first leader vehicle. Then, Predictive Cruise Control adjusts the acceleration/deceleration action based on the multi-vehicle measurements, which represent the future traffic condition of the subject vehicle. The developed system is tested by simulating the real vehicle trajectories from the NGSIM data and comparing the results with real following pattern. It was found that the newly proposed Predictive Cruise Control system can contribute to energy consumption and traffic flow performance, because it can effectively suppress the shockwave from the downstream and remove the unnecessary deceleration and acceleration action.
Transportation Research Part C-emerging Technologies, Dec 1, 2016
Abstract Safety warning systems generally operate based on information from sensors attached to i... more Abstract Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.
Computing in Civil and Building Engineering (2014), Jun 17, 2014
Current safety warning systems generally operate based on the information from sensors attached t... more Current safety warning systems generally operate based on the information from sensors attached to individual vehicles. This vehicle-sensor based system can only estimate the collision potential situation in close proximity of a subject vehicle, and it requires additional communication technologies such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies in order to obtain a wide range of information. The device requirements for such technologies will lead to the price increase of the collision warning system. So, in this study, we propose the collision warning system that utilizes the information from the fusion of loop detector data and smartphone data. The proposed collision warning system can be directly applied without any additional cost, because the databases of loop detector and smartphones are available to be used. The developed system is tested by simulating a real vehicle trip based on the NGSIM data and comparing its results to vehicle-sensor based system and infrastructure information based system. It was found that the newly proposed collision risk warning system can show the similar performance with the V2V-based collision warning system.
In the intelligent transportation system field, there has been a growing interest in developing c... more In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network-based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.
IEEE Transactions on Intelligent Transportation Systems, Jun 1, 2016
Missing data imputation is a critical step in data processing for intelligent transportation syst... more Missing data imputation is a critical step in data processing for intelligent transportation systems. This paper proposes a data-driven imputation method for sections of road based on their spatial and temporal correlation using a modified k- nearest neighbor method. This computing-distributable imputation method is different from the conventional algorithms in the fact that it attempts to impute missing data of a section with multiple sensors that have correlation to each other, at once. This increases computational efficiency greatly compared with other methods, whose imputation subject is individual sensors. In addition, the geometrical property of each section is conserved; in other words, the continuation of traffic properties that each sensor captures is conserved, therefore increasing accuracy of imputation. This paper shows results and analysis of comparison of the proposed method to others such as nearest historical data and expectation maximization by varying missing data type, missing ratio, traffic state, and day type. The results show that the proposed algorithm achieves better performance in almost all of the missing types, missing ratios, day types, and traffic states. When the missing data type cannot be identified or various missing types are mixed, the proposed algorithm shows accurate and stable imputation performance.
One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-v... more One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian's crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian's crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian-vehicle collisions by capturing the pedestrian's crossing intention.
For improvement of road safety, many collision-warning systems are developed. In this study, we p... more For improvement of road safety, many collision-warning systems are developed. In this study, we propose Sampling-based Collision Warning System (SCWS) that overcomes the limitations of existing collision warning systems such as high installation cost, requirement of high market penetration rate, and the lack of consideration of traffic dynamics. SCWS gathers vehicle operation data though smartphones of drivers on the road and shares the information of surrounding vehicles' movement through a cloud server. From the pool of information on the cloud, SCWS uses sampled data, which indirectly represents the traffic state and traffic changes in the perspective of the leader vehicle. Therefore, SCWS can effectively replace the leader vehicle's information with the average behavior of sampled surrounding vehicles. The performance of SCWS is evaluated with comparison to Vehicle-to-Vehicle communication based Collision Warning System (VCWS) and Infrastructure based Collision Warning System (ICWS), where VCWS is considered the most similar measure to the actual collision risk in theory, but in practice very difficult to achieve due many limitations, such as high installation cost and market penetration. The result shows that in both aggregation and disaggregation level analysis the proposed SCWS exhibits a similar collision risk trend to the VCWS. Furthermore, the SCWS shows a high potential for practical application because it has the acceptable performance even with a low sampling ratio (40%), requiring a low market penetration rate and low installation cost by using the wide spread smartphone.
The modeling of walking behavior and design of walk-friendly urban pathways have been of interest... more The modeling of walking behavior and design of walk-friendly urban pathways have been of interest to many researchers over the past decades. One of the major issues in pedestrian modeling is path planning decision-making in a dynamic walking environment with different pedestrian flows. While previous studies have agreed that pedestrian flow influences path planning, only a few studies have dealt with the empirical data to show the relationship between pedestrian flow and path planning behavior. This study introduces a new methodology for analyzing pedestrian trajectory data to find the dynamic walking conditions that influence the path planning decision. The comparison of the pedestrians' path shows that the higher proportion of opposite flows are, the greater they influence the path selection decision. In this study, we investigate the relationship between the opposite flow changes and path planning behavior and find the spatial and temporal ranges of the opposite flow that affects the path planning behavior. Lastly, we find the ratio of pedestrians that update their paths with respect to the opposite flow rate.
Procedia - Social and Behavioral Sciences, May 1, 2016
Since the beginning of the new millennium, various ideas and methods of measuring road network vu... more Since the beginning of the new millennium, various ideas and methods of measuring road network vulnerability have been proposed in the field of transportation and infrastructure engineering. However, most of the existing measures have been focusing on large road networks like highways between cities, even though urban networks are equally important. Also, the measures require full microscopic simulations for deriving the value of travel time increase, which is used for predicting network performance in case a disaster situation occurs. In order to overcome such issue, this study provides a new evaluation method for vulnerability of urban road network based on the concept of macroscopic fundamental diagram (MFD). We call such measure as MFD-based Vulnerability Index (MVI), which is derived by comparing the MFD behaviours in normal condition and event condition, in which one or more links of a road network are failed by a disaster. For testing the measure, a simulation study based on the road network of Gangnam district, Seoul, South Korea is provided. The results show that the measure can help us investigate the different patterns of network performance loss by different traffic states. This work provides a different perspective of investigating road network vulnerability.
One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-v... more One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian-vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian's crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian's crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian-vehicle collisions by capturing the pedestrian's crossing intention.
Spacing policy for autonomous vehicles is one of the important issues because it is highly relate... more Spacing policy for autonomous vehicles is one of the important issues because it is highly related to the safety on the roads, efficiency of vehicles, and user satisfaction of autonomous vehicles. Many researchers have developed several spacing policies for autonomous vehicle control. Most of the spacing policies are mainly focused on the control scheme such as string stability and safety. So, the issues related to user satisfaction and possible effect of autonomous vehicles on manual vehicles are less considered especially in the mixed traffic situation of autonomous and manual vehicles. In this study, we propose an Asymmetric Collision Risk(ACR)-based spacing policy based on the analysis on collision risk and driving behavior. The proposed spacing policy is compared with other spacing policies by simulating with trajectories of human drivers. Based on the simulation results, the performance of each spacing policy is compared in terms of vehicle operation, CO2 emission and safety. The results show that ACR spacing policy has similar pattern with the human driver with smoother trajectory and less acceleration/deceleration actions. In terms of road efficiency and environment, the proposed ACR spacing policy shows the second best performance next to Safety Spacing policy. However, in terms of safety in mixed situation of manual and autonomous vehicles, ACR spacing policy is superior to other spacing policies with zero occurrence of critical event. By considering overall performance, the proposed ACR spacing policy is expected to generally show good performance in the mixed traffic situation of manual and autonomous vehicles.
Based on an analysis of collision risk propagation, a vehicle Traffic Predictive Cruise Control (... more Based on an analysis of collision risk propagation, a vehicle Traffic Predictive Cruise Control (TPCC) system, responding to the change of downstream traffic situation, is proposed in this study to improve traffic operation, safety, and fuel efficiency of vehicle. The proposed TPCC system consists of four parts: (1) Collision risk calculator of a subject vehicle, which represent the state of the subject vehicle. (2) Vehicle Control algorithm only based on the collision risk of the subject vehicle, (3) Cooperative measure for representing downstream traffic state, which is based on the results of an analysis of collision risk propagation, (4) TPCC algorithm, which controls the vehicle by using both collision risk of the subject vehicle and cooperative measure. By using both collision risk of subject vehicle and cooperative measure, which represent the integrated collision risk of leader vehicles, TPCC is designed to proactively determine actuation of vehicle by adjusting parameters of vehicle control algorithm before high collision risk arisen from leader vehicles reaches to the subject vehicle. A simulation using the real vehicle trajectories from the NGSIM data validates the performance of TPCC algorithm with various market penetration rates. It is found that the proposed TPCC system can contribute to CO2 emission reduction, traffic flow stability, and safety improvement. Such results are due to the effects of suppression of the high collision risk generated from downstream traffic and removal of unnecessary fluctuation of speed.
Maintenance optimization based on life cycle cost (LCC) assessment is applied to many civil infra... more Maintenance optimization based on life cycle cost (LCC) assessment is applied to many civil infrastructure systems such as pavements and bridges to reduce operating and socioeconomic cost. However, due to the large size of infrastructure networks, maintaining such networks is challenging, especially when many constraints exist and the budget is limited. So, it is important to develop new strategies for managing public infrastructures in a way that ensures long-term sustainability under constrained budgets. This chapter reviews metaheuristic methods for infrastructure maintenance optimization, followed by current critical issues such as deterministic and stochastic problems, single-and multi-facility problems, and infrastructure interdependencies.
This chapter presents a methodology for maintenance optimization for heterogeneous infrastructure... more This chapter presents a methodology for maintenance optimization for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials and deterioration processes. We present a two-stage bottom-up approach. In the first step, optimal and near-optimal maintenance policies for each facility are found and used as inputs for the system-level optimization. In the second step, the problem is formulated as a constrained combinatorial optimization problem, where the best combination of facility-level optimal and near-optimal solutions is identified. An Evolutionary Algorithm (EA) is adopted to solve the combinatorial optimization problem. Its performance is evaluated using a hypothetical system of pavement sections. We find that a near-optimal solution (within less than 0.1% difference from the optimal solution) can be obtained in most cases. Numerical experiments show the potential of the proposed algorithm to solve the maintenance optimiza- tion problem for realistic heterogeneous systems.
Safety performance measurement of an airline company is the objective evidence providing how well... more Safety performance measurement of an airline company is the objective evidence providing how well the organization is executing its own safety management system (SMS), which should cooperate deeply with State safety program (SSP). In order to appropriately measure the safety performances of airline companies, studies on building an appropriate structure of safety performance indicators (SPIs) is required as the first step. This study reviews on the definition and required characteristics of SPIs, structures of SPIs guided in some fields other than aviation, and recommendations from the ICAO about safety performance measurement. Then, a structure of SPIs expressing safety performance in flight operation of an airline is proposed. It has a hierarchical structure that is composed with "event level SPIs," "flight phase level SPIs," and an "organizational level SPI." Some recommendations on selecting the lists of both event and flight phase level indicators are provided as well.
This paper describes the parameters estimation of the NGSIM oversaturated freeway flow model whic... more This paper describes the parameters estimation of the NGSIM oversaturated freeway flow model which is developed as part of the Next Generation Simulation (NGSIM) Project, sponsored by the US Federal Highway Administration. The model, based on the KW theory with added vehicle performance and safety constraints, has 5 basic car-following parameters (free flow speed, jam gap, wave travel time and maximum acceleration and deceleration) and 3 lane changing parameters (discretionary lane changing parameter, mandatory lane changing parameters). The distributions of the parameters are estimated using microscopic trajectories data which has 0.1sec time resolution for 2 freeway sites, and the results are suggested. Two freeway sites show similar distributions in spite of the difference in traffic characteristics in each site.
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Papers by Hwasoo Yeo