Active Traffic Management Strategies Research Articles

Utilizing angle-based outlier detection method with sliding window mechanism to identify real-time crash risk

Developing real-time crash risk models has been a hot research topic as it could identify crash precursors and thus triggering active traffic management strategies. Currently, crash risk identification models were mainly developed based upon supervised learning techniques, which requires large sample size of historical crash data. However, crashes are rare events in the real world, where the performance of supervised learning methods can be severely degraded to deal with the imbalanced sample. Besides, the data heterogeneity issue is another critical challenge. In this study, the unsupervised learning approach has been introduced to address unbalanced samples and data heterogeneity issues, and the experimental results has verified the effectiveness of the method. Data from the Shanghai urban expressway system were utilized for the empirical analyses. Several unsupervised learning methods were tested, among which, Angle-Based Outlier Detection (ABOD) model showed the best performance with 80.4% sensitivity and 25.4% false alarm rate (FAR). Considering the varying traffic flow distribution, dynamic ABOD with sliding window is further proposed, which improves the sensitivity by 6.3% and reduces the FAR by 8.1%. Finally, the proposed model is used to construct personalized road-level models, which achieve good performance despite the small sample size and severe sample imbalance.

Active traffic management strategies for expressways based on crash risk prediction of moving vehicle groups

Active traffic management (ATM) strategies are useful methods to reduce crash risk and improve safety on expressways. Although there are some studies on ATM strategies, few studies take the moving vehicle group as the object of analysis. Based on the crash risk prediction of moving vehicle groups in a connected vehicle (CV) environment, this study developed various ATM safety strategies, that is, variable speed limits (VSLs), ramp metering (RM), and coordinated VSL and RM (VSL-RM) strategies. VSLs were updated to minimize the crash risk of multiple moving vehicle groups in the next time interval, which is 1 min, and the updated speed limits were sent directly to the CVs in the moving vehicle group. The metering rate and RM opening time were determined using mainline occupancy, the crash risk of upcoming moving vehicle groups, and the predicted time at which moving vehicle groups arrived at the on-ramp. The VSL-RM strategy was used to simultaneously control and coordinate traffic flow on the mainline and ramps. These strategies were tested in a well-calibrated and validated micro-simulation network. The crash risk index and conflict count were utilized to evaluate the safety effects of these strategies. The results indicate that the ATM strategies improved the expressway safety benefits by 2.84–15.92%. The increase in CV penetration rate would promote the safety benefits of VSL and VSL-RM. Moreover, VSL-RM was superior to VSL and RM in reducing crash risk and conflict count.

Evaluation of a variable speed limit system using a hierarchical framework under a connected vehicles environment: a case study of a field test in Seattle

Variable speed limit (VSL) system has been recognised as an active traffic management strategy to improve traffic safety and operational efficiency. The development of connected vehicle (CV) technologies have a potential to improve VSL systems because CVs can provide continuous and precise traffic condition as the input of VSL algorithm as well as real-time recommended speed to drivers. To demonstrate the improvement of the VSL performance from CV technologies, this study presents the evaluation of a VSL system's safety and operational impact on CVs, non-CVs, and overall traffic conditions, based on a field test in interstate 5 corridor, Seattle. A hierarchical evaluation framework was proposed to explore the effectiveness of the implemented algorithm. The framework consists of three parts, i.e. manoeuvring, safety, and operation. Each part conducts data analysis and statistical tests from traffic sensors and CVs. The findings indicate that the VSL system is able to inform drivers to follow the recommended speed and improve traffic operation even at a low penetration rate. However, the stability of traffic flow is decreased in certain sections. Therefore, the potential improvement of the VSL algorithm is presented based on the findings.

Real‐time detection of end‐of‐queue shockwaves on freeways using probe vehicles with spacing equipment

The identification of a traffic shockwave traditionally is conducted offline, which inhibits its implementation for active traffic management strategies. This study aims to develop an online approach to detect traffic shockwaves on freeways, particularly the end-of-queue shockwaves, using spacing-based probe vehicles (SPVs) that can obtain the trajectories of its leading and/or following vehicles. The proposed framework consists of four stages: (i) local shockwave (LSW) position detection, (ii) LSW speed estimation, (iii) grouping of LSWs into a whole shockwave (WSW) and (iv) WSW speed estimation. In particular, two alternatives, namely the line connection-based method and the Lighthill–Whitham–Richards model-based method (LWRM), are proposed for stage 2, and other two alternatives, namely the simple averaging method and the hybrid method (HM), are proposed for stage 4. A set of next generation simulation data are utilised to evaluate the performance of the proposed method. The results demonstrate that the combination of LWRM + HM outperforms among the four combined methods. A series of the analysis indicate that the proposed method is computationally efficient, accurate and more importantly, it is applicable to sensor data from SPVs with real-world noise.

A Risk-Based Systematic Method for Identifying Fog-Related Crash Prone Locations

Fog is one of the most influential factors in fatal crashes because of reduced visibility. This study aims to propose a systematic safety analysis framework for selecting fog-crash-prone areas on freeways. To achieve these goals, the spatial analysis in ArcGIS was combined with the latent class cluster-based crash severity estimation models. Nine latent class cluster-based crash severity estimation models were built. Fog events led to a statistically significant increase in the likelihood of fatal crashes in two of the nine models. Comparing the ArcGIS spatial clusters of fog-related exposure with the fatal crash-prone freeway segments, 28 freeway segments were found to be fog-crash-prone areas where safety improvements are required, particularly in foggy weather. Based on the spatial patterns of the fog-crash-prone freeway segments, this study concludes that the current standard for fog-crash-prone area selection should be modified to apply spatially different standards over the Korean freeway network. This study is the first data-driven study to comprehensively examine the effects of fog visibility levels and frequencies on fatal crashes in the entire Korean freeway system. The findings provide meaningful insights to the policy decision making for fog-related policy changes, highway safety enhancement and active traffic management strategies.

Implementation of Active Traffic Management Strategies for Safety on Congested Expressway Weaving Segments

In weaving segments, traffic merges, diverges, and weaves in a limited space. These traffic maneuvers might result in high crash hazards. To improve the safety of a congested expressway weaving segment, this study tested various active traffic management (ATM) strategies in microsimulations. Crash odds and the Surrogate Safety Assessment Model were used to evaluate the impact of ATM strategies on traffic safety. The crash odds were calculated based on the real-time safety analysis model for weaving segments. The strategies included ramp metering (RM), variable speed limit (VSL), and integrated RM and VSL (RM-VSL). Overall, the results showed that the ATM strategies improved the safety of the studied weaving segment. The modified ALINEA RM algorithms, which took lane occupancy and safety into consideration, outperformed the traditional ALINEA algorithm from a safety perspective. The 45 mph VSLs, which were located at the upstream of the studied weaving segment, significantly enhanced safety without notably increasing average travel time. A consolidated RM-VSL strategy was also proposed with the aim of improving traffic safety by implementing RM and VSL. In the consolidated RM-VSL strategy, the modified ALINEA RM was adjusted according to the queue length to prevent long queues on ramps. The results proved that the consolidated RM-VSL strategy reduced the number of conflicts by 16.8% and decreased the crash odds by 6.0%.

Calibration of a micro-traffic simulation model with respect to the spatial-temporal evolution of expressway on-ramp bottlenecks

On-ramp bottlenecks are usually the direct cause of traffic breakdowns on expressways, affecting the overall traffic operation of the expressway. A high-precision simulation model is the basis of an on-ramp bottleneck active traffic management strategy design and evaluation. This paper presents a practical and effective calibration method with respect to the congestion spatial-temporal evolution and two-capacity phenomenon of bottlenecks. An integrated criterion is proposed to evaluate how well the micro-simulation model can replicate the breakdown process. The widely used micro-traffic simulation model VISSIM is selected to simulate two on-ramp bottlenecks on an expressway in Shanghai, China. Firstly, 28 parameters of car-following and lane-changing behaviors are analyzed, and nine significant parameters of the operation at on-ramp bottlenecks are selected using sensitive analysis. Then, an orthogonal experiment is conducted to seek the best parameter combination. Finally, the calibrated simulation model is validated using a dataset of other days. Moreover, the transferability of the calibrated model is tested at Wuning bottleneck. The results show that the calibrated model can not only replicate the congestion evolution process and the special early onset breakdown phenomenon, but it can also apply to other similar on-ramp bottlenecks directly. The proposed calibration procedure, the integrated criterion for precision evaluation, and the selected significant parameters are meaningful to inspire rapid and effective calibration for other simulators.

State of the Non-operations Based Research of Hard Shoulder Running

Hard Shoulder Running (HSR) is an operationally focused active traffic management strategy to optimize the utilization of the roadway, specifically the shoulder portion of the roadway geometry. Roadway networks needs to be more sustainable by utilizing its full capacity before using more materials to construct an expanded roadway. The understanding of the operational benefits has been the focus of much HSR research as it is an operational focused Active Traffic Management (ATM) strategy. Before HSR can be utilized, its effects on safety and the pavement structure on the shoulder need to be understood. While it has been found to improve the road operation, it must be safe and structurally sound. This paper provides a literature review of the HSR research that analyzed the safety effects and the pavement performance effects. HSR is a quality strategy that can reduce congestion, car emissions, and paved lane use requirements through improved traffic flow.

Assessing Longitudinal Arterial Performance and Traffic Signal Retiming Outcomes

Agencies often find it difficult to justify investments in active traffic management. Historically, it has been a challenge to obtain data that would help make the case for those investments. Although new data sources have emerged recently, there remains very little documentation of the potential long-term benefits from signal retiming using associated performance measures. This paper presents a use case for an active traffic management strategy on a signalized corridor over a 5-year period, during which traffic volumes increased by approximately 36% and offset optimization was performed every 2 to 3 years. Despite the considerable volume growth, the number of vehicles arriving on green increased by more than 41%, and the percentage of vehicles arriving on green increased by 10%, a gain of 6 percentage points. Furthermore, drivers experienced an average of 5% reduction in travel time and travel time reliability costs after each optimization. The retiming resulted in a 5-year net present value of approximately $3.7 million and a benefit–cost ratio of 52. Agencies can use these strategies to quantitatively assess how traffic performance and signal timing degrade over time, in a manner similar to physical infrastructure assets. The results highlight the benefits and associated business case of adopting a long-term active traffic management strategy that is based on data-driven performance monitoring and decision making.

Real-time queue estimation model development for uninterrupted freeway flow based on shockwave analysis

In this study, the authors developed a time-space discrete macroscopic model based on the shockwave theory for real-time queue estimation in uninterrupted freeway flow, using fixed-location loop detector data. After investigating the queue characteristics both at an active bottleneck and within a variable speed limit control case, the proposed model was applied to these two cases on Whitemud Drive, a major freeway corridor in Edmonton, Alberta, Canada. Modified Highway Capacity Manual–based methods were used to determine queue density in uninterrupted freeway flow. The effect of time interval size on queue estimation was studied, as loop detector data acquisition frequencies may differ. It was found that the proposed model accurately estimates real-time queue length independent of the time interval. Multiple single queues were implemented in a calibrated VISSIM 5.3 micro-simulation model to perform the validation task. The study is a helpful foundation for future active traffic management strategy development and improvement.

Capacity Estimation for Weaving Segments Using a Lane-Changing Model

During peak periods, freeway bottlenecks can be activated by intensive lane changing at weaving segments, where merging and diverging areas are in close proximity. This weaving phenomenon has a major impact on capacity. Much research has been devoted to investigating capacity estimation models for weaving segments. However, because of the model parameters, estimation models are difficult to adopt directly in active traffic management strategies to estimate real-time maximum discharge flow. To this end, this study defined a linear optimization problem to solve weaving capacity and then established a lane-changing model to constrain the weaving flows. The proposed method was evaluated and analyzed for sensitivity with field data from two weaving segments on Whitemud Drive, Edmonton, Alberta, Canada. The capacity estimates from the proposed model were consistent with that from the Highway Capacity Manual 2010 model and with field observations. Moreover, it was also observed that the weaving capacity was sensitive to weaving maneuvers. Finally, the proposed method was applied to estimate the real-time maximum discharge flow rate; the estimates matched field measurements. These findings could lead to implementations in designing optimal traffic control strategies.

Assessing Mobility and Safety Impacts of a Variable Speed Limit Control Strategy

With the recent advances in active transportation and demand management, variable speed limits (VSLs) have been identified as an active traffic management strategy for improving freeway mobility and safety. Several heuristic VSL strategies have been proposed and evaluated. This paper proposes a model predictive VSL control strategy and evaluates its safety and mobility impacts. The strategy uses second-order traffic flow models to predict the traffic state and to provide a speed for optimizing corridor operational performance. A sensitivity analysis of the VSL update frequency and the safety constraints for the VSL strategy was performed to determine the best scenario in terms of safety and mobility. A stretch of Whitemud Drive, an urban freeway corridor in Edmonton, Alberta, Canada, was selected as the study area. The proposed VSL strategy was implemented in the microsimulation platform with a special software module. A real-time collision prediction model was developed for the same study area by using a matched case-control logistic regression technique to estimate the collision probability for each scenario. The results indicated that the proposed VSL control strategy can improve safety by approximately 50% and mobility by approximately 30%. A VSL update frequency of 5 min and a maximum speed difference of 10 km/h between successive time steps yielded the best performances. This finding can be useful for field implementation of VSL control.

A simulation study of using active traffic management strategies on congested freeways

The main objective of this study is to evaluate the effectiveness of using active traffic management (ATM) strategies on freeways in terms of the driver’s behavior and operational impacts. A few test beds were selected to evaluate the impacts of ATM such as speed harmonization, shoulder utilization, and ramp metering. Test beds used in this study were at places where an ATM is either proposed or previously implemented, i.e., where data exists for conditions prior to and after the implementation of ATM. Data collected from the test beds were used in a simulation model to evaluate the impacts of each ATM strategy on speed, travel time, and crash rates. Simulation results indicated that the implementation of speed harmonization on US 90 showed a 14% reduction in crashes and a 2%–3% increase in freeway speed; the implementation of hard shoulders on US 90 showed a 39% increase in travel time, 22% increase in freeway capacity and 60% decrease in delays; and the implementation of ramp metering on US 59 between Bissonnet St. and Fondern road showed a decrease of 23 % in freeway travel time, a 14% increase in freeway speed and 11% decrease in accident rates.

Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers’ behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on I-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes.

Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data

This study investigated the effect of the interaction between roadway geometric features and real-time weather and traffic data on the occurrence of crashes on a mountainous freeway. The Bayesian logistic regression technique was used to link a total of 301 crash occurrences on I-70 in Colorado with the space mean speed collected in real time from an automatic vehicle identification (AVI) system and real-time weather and roadway geometry data. The results suggested that the inclusion of roadway geometrics and real-time weather with data from an AV I system in the context of active traffic management systems was essential, in particular with roadway sections characterized by mountainous terrain and adverse weather. The modeling results showed that the geometric factors were significant in the dry and the snowy seasons and that the likelihood of a crash could double during the snowy season because of the interaction between the pavement condition and steep grades. The 6-min average speed at the crash segment during the 6 to 12 min before the crash and the visibility 1 h before the crash were found to be significant during the dry season, whereas the logarithms of the coefficient of variation in speed at the crash segment during the 6 to 12 min before the crash, the visibility 1 h before the crash, as well as the precipitation 10 min before the crash were found to be significant during the snowy season. The results from the two models suggest that different active traffic management strategies should be in place during these two distinct seasons.