As the demand for traffic simulation has shifted to large-scale urban areas, achieving both high accuracy and computational efficiency has become increasingly challenging. Among various traffic simulation levels, microscopic simulation provides the most detailed and accurate representation of traffic dynamics; however, its high computational cost prevents its applicability to large-scale urban networks. Consequently, the need to find a compromise between accuracy and computational cost leads to hybrid or multi-level traffic simulation. Multi-level traffic simulation integrates multiple simulation levels within a single framework, where micro-meso hybrids are generally suitable for urban networks and micro-macro hybrids for highways. To maintain constant simulation performance in terms of accuracy and computational cost, recent studies have emphasized the importance of dynamic properties that adjust the simulation levels of road segments in response to the traffic conditions over time. Accordingly, this study proposes a dynamic multi-level traffic simulation for urban networks by combining microscopic and mesoscopic traffic simulations. A unified simulation framework and data structure were proposed to ensure compatibility and consistency between different simulation levels. Temporal and spatial interfaces were designed and verified for proper functioning, including the preservation of vehicle information and consistency in traffic dynamics. The proposed simulation was evaluated in terms of computational cost and accuracy under various demand scenarios, using multiple methods for dynamically determining micro- and meso-level representations. The dynamic multi-level simulation consistently achieved higher accuracy than the mesoscopic model with reduced computational cost than the microscopic model across all demand scenarios. In addition, the proposed approach showed higher reliability than the fixed multi-level simulations, especially under an ROI-unfocused demand patterns. These outcomes were consistently observed across networks of varying sizes. Finally, an application to a real-world urban network demonstrated the potential of the proposed model for practical use in traffic management systems.

Recent advances in sensing and artificial intelligence technologies have laid the foundation for proactive pedestrian safety at non-signalized intersections. These technologies enable the prediction of future pedestrian-vehicle conflicts, allowing for timely intervention. Existing studies, however, overlook the uncertainty inherent in future prediction, which mainly stems from behavioral variability and observational inaccuracies. Probabilistic trajectory prediction can capture these uncertainties, yet it has rarely been applied to future conflict evaluation. To improve future conflict evaluation by accounting for uncertainty, this study introduces a novel method that integrates probabilistic trajectory prediction with a newly designed surrogate safety measure, Stochastic Predicted Time to Collision (SPTTC). SPTTC enables accurate conflict prediction, reliable scenario classification and evaluation of conflict severity progression, as demonstrated in quantitative results and case studies. Across different types of non-signalized intersections, SPTTC was separately integrated with four probabilistic trajectory prediction models, achieving F1 scores of 0.77–0.84 and consistently outperforming baseline methods. The proposed method also exhibits strong robustness under high-uncertainty conditions, such as unpredictable pedestrian behavior (e.g., children), low visibility (e.g., nighttime), and missing data. This proposed method can be integrated into proactive pedestrian protection systems, enhancing safety by providing timely alerts to pedestrians or activating vehicle brakes at critical moments.

Reinforcement learning (RL) has emerged as an effective approach for signal control, and several studies attempt to apply RL to network-wide signal control problems. Most existing methods for network-wide signal controls simply apply the pretrained model independently to the network or simply expand the observation range for the neighbors. While several recent studies have proposed cooperative signal control models using multi-agent reinforcement learning (MARL) approaches, they have not considered the transferability of the policy. This issue becomes more critical in treating a multi-agent signal control problem, since there exist innumerable possibilities of data distributions for the demand patterns. Hence, this research aims to develop a transferable and cooperative multi-agent traffic signal control model. As the key idea, we propose a decentralized and communication-based MARL approach that considers the spatial correlation of traffic dynamics in the model design and employs a graph representation for the state. Three experiments in simulated environments of the Gangnam district in Seoul, South Korea are conducted to 1) validate the model, 2) assess the policy’s transferability, and 3) evaluate the efficiency of the multi-agent cooperation. The experimental results show that the proposed model obtains a transferable policy so that it adapts to the unexperienced demand scenario. In addition, it is concluded that the joint action of the proposed RL model cooperatively rebalances the traffic demands so that it improves the efficiency of the network-wide signal controls.

Numerous car-following models have been developed since the 1950s. However, there still exist many traffic phenomena that cannot be demonstrated using the existing models. Therefore, this research proposed a new car-following model, the Asymmetric car-following (ACF) model based on the understanding of driver's asymmetric behavior, which can explain complex traffic phenomena. We established the asymmetric car-following (ACF) rule under the vehicle's safety constraints using eight parameters that indicate the driver's characteristics and vehicle's performances. To evaluate the ACF model, we performed the simulation for car-following pairs and conducted a comparison analysis with the existing models: Newell, Gipps, GM, and IDM. As a result, the proposed ACF model showed good fitness with the empirical trajectory and the apparent asymmetric behavior compared to others. For further investigation in the congested traffic stream, we simulated a group of vehicles by adding an error term to represent the driver's unexpected behavior. The simulation showed growth, propagation, and dissipation of the stop-and-go traffic. These results proved that the ACF model has the strength to elaborate on various traffic phenomena, such as traffic hysteresis and stop-and-go traffic.

A previously developed real-time forward collision warning system (RCWS) using a multi-layer perceptron neural network (MLPNN) with a single hidden layer aims to be implemented with in-vehicle sensor and smartphone under cloud-based communication environment. However, several issues exist concerning the communication delay between the smartphone and the cloud server, especially when uploading massive traffic information to the cloud server simultaneously. In order to mitigate the impact of the delay, this research proposes two modified RCWSs using an advanced feed-forward neural network (F2N2). One of them involves MLPNN with two hidden layers and the other includes radial basis function network. The modified RCWSs are evaluated by the real-time warning accuracy under different market penetration rates (MPRs) and delays. The evaluation shows that the warning performances of each RCWS increase when the MPR increases or the delay decreases overall. In addition, the modified RCWSs outperform the original one in all conditions. Furthermore, the performance gap between the modified RCWSs increases as the MPR decreases and the delay increases. These findings suggest that the advanced F2N2 model can be an effective alternative for uprating the performance of the RCWS, particularly under a large delay with low MPR.

Originally, decision and control of the lane change of the vehicle is on the human driver. It is mainly used to increase the individual's benefit such as decreasing travel time. However, the selfish decision on the lane-changing behavior can sometimes make a negative impact on the overall traffic flow. As autonomous vehicle technology develops, modeling lane changing action as well as lane changing decision making falls within the control category of autonomous vehicles. In this study, we focused on decision making of lane change for autonomous vehicles considering traffic flow, and accordingly, we propose a lane change control system considering whole traffic flow. The lane change control system predicts the future traffic situation using Cell Transmission Model and determines the lane change probability for each lane that minimizes the total time delay through the genetic algorithm. The lane change control system then provides the lane change probability to the vehicles. Performance evaluation of the proposed system in macroscopic simulation shows reduction in the overall travel time delay. The performance of proposed system is also evaluated in microscopic traffic simulation, evaluating the potential performance when it is applied to the actual traffic system: The maximum traffic flow was increased, and the congestion area was greatly reduced and the time required for individual vehicles was reduced.

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.

Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the key technologies for future “e-Enabled” aircrafts. ADS-B uses avionics in the e-Enabled aircrafts to broadcast essential flight data such as call sign, altitude, heading, and other extra positioning information. On the one hand, ADS-B brings significant benefits to the aviation industry, but, on the other hand, it could pose security concerns as channels between ground controllers and aircrafts for the ADS-B communication are not secured, and ADS-B messages could be captured by random individuals who own ADS-B receivers. In certain situations, ADS-B messages contain sensitive information, particularly when communications occur among mission-critical civil airplanes. These messages need to be protected from any interruption and eavesdropping. The challenge here is to construct an encryption scheme that is fast enough for very frequent encryption and that is flexible enough for effective key management. In this paper, we propose a Staged Identity-Based Encryption (SIBE) scheme, which modifies Boneh and Franklin's original IBE scheme to address those challenges, that is, to construct an efficient and functional encryption scheme for ADS-B system. Based on the proposed SIBE scheme, we provide a confidentiality framework for future e-Enabled aircraft with ADS-B capability.

A cost-efficient network-on-chip is needed in a scalable many-core systems. Recent multicore processors have leveraged a ring topology and hierarchical ring can increase scalability but presents different challenges, including higher hop count and global ring bottleneck. In this work, we describe a hierarchical ring topology that we refer to as a transportation-network-inspired network-on-chip (tNoC) that leverages principles from transportation network systems. In particular, we propose a novel hybridflow control for hierarchical ring topology to scale the topology efficiently. The flow control is hybrid in that the channels are allocated on flit granularity while the buffers are allocated on packet granularity. The hybrid flow control enables a simplified router microarchitecture (to minimize per-hop latency) as router input buffers are minimized and buffers are pushed to the edges, either at the output ports or at the hub routers that interconnect the local rings to the global ring-while still supporting virtual channels to avoid protocol deadlock. We describe a packet-quota-system (PQS) and a separate credit network that provide congestion management, support prioritized arbitration in the network, and provide support for multiflit packets. We also provide alternative designs for the credit network and PQS architectures. A detailed evaluation of a 64-core CMP shows that the tNoC improves performance by up to 21 percent compared with a baseline, buffered hierarchical ring topology while reducing NoC energy by 51 percent.

The existing rear-end collision warning systems (CWS) that involve the variable perception-reaction time (PRT) have some negative effects on the collision warning performance due to the poor adaptive capability for the influence of different PRTs. To deal with the related problems, several studies have been conducted based on nonparametric approaches. However, the previous nonparametric methods are of doubtful validity with different PRTs. Moreover, there is a lack of consideration for the criterion to split the real-time data into training and testing sets in terms of enhancing the algorithm performance. In this paper, we propose multilayer perceptron neural-network-based rear-end collision warning algorithm (MCWA) to develop a real-time CWS without any influence of human PRTs. Through a sensitivity analysis, the optimal criterion for splitting real-time data into training and prediction is found in terms of a tradeoff between training time and algorithm accuracy. Comparison study demonstrates that the proposed algorithm outperforms other previous algorithms for predicting the potential rear-end collision by detecting severe deceleration in advance.

Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.

A vehicle traffic predictive cruise control (TPCC) system, responding to the change of downstream traffic situation, has been proposed to improve traffic operation and the fuel efficiency of vehicle based on the asymmetric traffic theory. The proposed predictive cruise control system consists of four parts: 1) deceleration-based safety surrogate measure (DSSM); 2) single-vehicle control algorithm; 3) multivehicle safety measurement (Co-DSSM); and 4) TPCC. The single-vehicle control algorithm basically decides the acceleration action based on estimated safety state between a subject vehicle and the immediate preceding vehicle, and the control strategy is determined by asymmetric driving behavior. Then, TPCC adjusts the amount of acceleration based on the Co-DSSM from multiple downstream vehicles, which contains the information on future traffic condition of the subject vehicle. A simulation using the real vehicle trajectories from the Next Generation Simulation (NGSIM) data validates the proposed TPCC system, and we compare the results with the real vehicles' car-following patterns. It is found that the proposed TPCC system can contribute to both the energy consumption and traffic flow operation by obtaining a higher level of traffic stability. Such results are due to the effects of suppressing the shockwave from downstream traffic and removing the unnecessary deceleration and acceleration actions.

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.

A rear-end Collision Warning System (CWS) is applied for mitigating collision risk to the frontal motor vehicle under the traffic conditions. Most of the previous studies have been performed to address the braking behavior related problems based on the deterministic or stochastic parametric methods. However, these algorithms are of doubtful validity in the context of individual driving characteristics such as Perception-Reaction Time (PRT). This paper proposes a framework on Rear-end CWS to take into consideration of PRT effects based on the Artificial Neural Network (ANN). Multi-layer perceptron neural network based rear-end collision warning algorithm (MCWA) is developed and evaluated through a comparison between the conventional algorithms such as Time To Collision (TTC) and Stopping Distance Algorithm (SDA). The comparison study demonstrates that the proposed algorithm outperforms other traditional algorithms for detecting and predicting the rear-end collision risks. The proposed algorithm could be used for rear-end collision warning in car-following case without the influence of different human PRT.

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.

A surrogate safety measure can be used for preventing hazardous roadway events by evaluating the potential safety risk by using information on the driving environment gathered from vehicles. In this paper, the deceleration-based surrogate safety measure (DSSM) is proposed as a safety indicator for rear-end collision risk evaluation based on the safety conditions and the decision-making process during human driving. The DSSM shows how drivers deal with collision risk differently in acceleration and deceleration phases. The proposed surrogate safety model has been validated for severe deceleration behavior, which is a driver-critical behavior in high-risk situations of collision based on microscopic vehicle trajectory data. The results indicate that there is a strong relationship between the proposed surrogate safety measures and crash potential. The measure could be used for collision warning and collision avoidance systems. It has a merit in that it reflects the characteristics of both vehicle (e.g., mechanical braking capability) and driver (e.g., preference for certain acceleration rates).

A cost-efficient network-on-chip is needed in a scalable many-core systems. Recent multicore processors have leveraged a ring topology and hierarchical ring can increase scalability but presents different challenges, including higher hop count and global ring bottleneck. In this work, we describe a hierarchical ring topology that we refer to as a transportation-network-inspired network-on-chip (tNoC) that leverages principles from transportation network systems. In particular, we propose a novel hybrid flow control for hierarchical ring topology to scale the topology efficiently. The flow control is hybrid in that the channels are allocated on flit granularity while the buffers are allocated on packet granularity. The hybrid flow control enables a simplified router microarchitecture (to minimize per-hop latency) as router input buffers are minimized and buffers are pushed to the edges, either at the output ports or at the hub routers that interconnect the local rings to the global ring - while still supporting virtual channels to avoid protocol deadlock. We also describe a packet-quota-system (PQS) and a separate credit network that provide congestion management, support prioritized arbitration in the network, and provide support for multiflit packets. A detailed evaluation of a 64-core CMP shows that the tNoC improves performance by up to 21% compared with a baseline, buffered hierarchical ring topology while reducing NoC energy by 51%.