Hwasoo Yeo

Oversaturated Freeway Flow Algorithm for Use in Next Generation Simulation

Existing simulation models have difficulty in accurately modeling oversaturated traffic conditions on freeways. A new behavioral algorithm for oversaturated freeway flow can be used in microscopic simulation models; it was developed as part of the Next Generation Simulation (NGSIM) project, sponsored by FHWA. The proposed algorithm is an integrated car-following and lane-changing modeling framework that is consistent with the kinematic wave theory. The algorithm can explicitly model mandatory and discretionary lane changing, including cooperation during lane changing. As an extension of the algorithm, a new on-ramp merging model was developed and incorporated into the model. The proposed algorithm also accounts for the relaxation process following lane changing. The proposed model includes a small number of parameters that can be readily measured in the field. The proposed model was implemented into a microscopic simulator, and it was validated with both vehicle trajectory data and macroscopic detector data from an NGSIM test site. The results show good agreement with real-world traffic behavior. The study products, including an algorithm description, analysis results, and computer code, are documented and made available to developers and users of traffic simulation tools.

Algorithms for bottom-up maintenance optimisation for heterogeneous infrastructure systems

This paper presents a methodology for maintenance optimisation for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials, and deterioration processes. We present a bottom-up approach: facility-level optimal maintenance policies are first found; these policies are then combined with budget constraints in the system-level optimisation. In the first step, optimal and near-optimal maintenance policies for each facility are found and used as inputs for the system-level optimisation. In the second step, the problem is formulated as a constrained combinatorial optimisation problem, where the best combination of facility-level optimal and near-optimal solutions is identified. Two heuristics, pattern search heuristic (PSH) and evolutionary algorithm (EA), are adopted to solve the combinatorial optimisation problem. Their performance is evaluated using a hypothetical system of pavement sections. Comparison result with real optimal solutions for 20 facilities showed that both algorithms give near-optimal solutions (within less than 0.1% difference from the optimal solution) in 978 (PSH) and 966 (EA) cases out of 1000 executions. The EA performs better in terms of processing time than the PSH. Numerical experiments show the potential of the proposed algorithms to solve the maintenance optimisation problem for realistic heterogeneous systems.

Estimation of Capacity Drop in Highway Merging Sections

Capacity drop, which is defined as discharge flow drop after bottleneck activation, has been frequently observed on urban highways, especially in merging sections. Maintaining high capacity on roadways is a main concern for traffic operators, theorists, and transportation modelers. Accordingly, many researchers have investigated capacity drop, yet highway capacity and discharge flow measurement methods vary, and results are not comparable. A systematic methodology is introduced for finding capacity drops by using detector data to estimate roadway capacity and discharge flow. The impact of the number of lanes on capacity drops at merging sections on highways is investigated. Results show that capacity drop is negatively related to the number of lanes. Detailed information is analyzed for individual lanes and off-ramp effect on capacity drop. Individual lane analysis supports the negative relationship between the amount of capacity drop and the number of lanes. A decrease in capacity drop is observed when the flow ratio of the off-ramp increases.

Evaluating the performance of network screening methods for detecting high collision concentration locations on highways

This paper documents findings from evaluating performances of three different methods for segmenting freeway sites for the purpose of identifying high collision concentration locations: Sliding Moving Window (SMW), Peak Searching (PS) and Continuous Risk Profile (CRP). The traffic collision data from sites segmented in each method under two different roadway definitions were used to estimate excess expected average crash frequency with Empirical Bayes adjustment with respect to two different sets of Safety Performance Functions (SPFs). The estimates from each of the methods were then used to prioritize the detected sites for safety investigation and these lists were compared with previously confirmed high collision concentration locations (or hot spots). The input requirements for each of three methods were identical, yet their performance markedly varied. The findings revealed that CRP method has the lowest false positive (i.e., requiring a site for safety investigation while it is not needed) rate. The performances of SMW and PS significantly varied when different sets of SPFs were used while that of CRP was less affected.

Microscopic Traffic Simulation of Vehicle-to-Vehicle Hazard Alerts on Freeway

Vehicle-to-vehicle (V2V) cooperative systems that alert drivers to roadway hazards are increasingly being studied to improve freeway safety and should be implemented by automakers in the near future. However, the impact of these systems on traffic operations has not been assessed. A methodology is described for modeling driver response to lane-blocking freeway incidents with and without the use of V2V hazard alerts. Driver response to assumed incidents and V2V safety messages is modeled microscopically with the NGSIM oversaturated freeway flow algorithm. The simulation results show how the V2V alerts could influence freeway traffic and provide initial estimates of their benefits.

An Analytical Planning Model to Estimate the Optimal Density of Charging Stations for Electric Vehicles.

The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station’s density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric vehicles.

Development of a Deceleration-Based Surrogate Safety Measure for Rear-End Collision Risk

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).

Transportation-network-inspired network-on-chip

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%.

Method for estimating highway collision rate that considers state of traffic flow

Among traffic-related variables, vehicle speed is thought to be a major one closely related to collision occurrence. This study aimed to develop a methodology to estimate a collision rate that considers the traffic state in a freeway section and focuses on speed variables. To define the traffic condition of a section, four section-based traffic states related to the speed of upstream and downstream locations were used. The collisions were classified into three types (rear-end, sideswipe, and other), and two variables (speed difference between upstream and downstream and average speed of upstream and downstream) were considered as independent for standard linear regression analysis. The findings showed that both speed difference and average speed had an influencing effect on the collision rate for each type of collision and traffic state. Rear-end and sideswipe collisions showed a different collision rate pattern than the others, and the influence of the speed difference between upstream and downstream changed with the traffic state. Through regression analysis, rear-end and sideswipe collision rates were expressed by speed difference while other types of collisions were described by average speed. This research can provide a methodology for understanding the collision potential of a highway as it relates to the traffic state and can be widely used for roadway traffic safety improvement.

A comparative study of time-based maintenance and condition-based maintenance for optimal choice of maintenance policy

Abstract Cost-effective maintenance of infrastructure systems within an acceptable level of safety and performance is the major concern of managing agencies. Recent maintenance approaches have offered two distinct maintenance policies: time-based maintenance (TBM) and condition-based maintenance (CBM). This paper compares the two policies under different cost environments for stochastically deteriorating infrastructures. The performance of TBM and CBM is evaluated from the viewpoint of condition transition and life cycle cost. We found the optimal maintenance solutions for TBM and CBM using dynamic programming and performed a simulation study. The simulation study showed that TBM causes some unexpected deterioration that leads to high cost, while CBM maintains a certain level of condition steadily under consistent inspection, which enables steady spending at the management level. The life cycle cost under CBM is relatively symmetric and has a more concentrated distribution than TBM, which has a large number of outliers from unexpected deteriorations. Finally, we evaluated the life cycle cost with a change in the inspection–repair cost ratio to find the most appropriate cost environment for each maintenance policy. While CBM needs periodic inspections, it still has more advantages than TBM when the inspection cost is relatively low.