Entire Freeway Research Articles

Science Education for Public and Design of Infrastructure at Rest Area KM 260B Banjaratma

Rest areas on toll roads are an important public infrastructure of the entire freeway construction. Interesting innovations occur on this toll road section in Brebes district, Central Java. The purpose of this study is to describe how design education and digital promotion design at 260B Banjaratma rest area occur. The research method used in this study is descriptive qualitative with an empirical approach, namely with on the spot research. Data was extracted by observation in the field, in addition to data derived from books, journals and other sources compatible with the research theme. The results of the study found that there is public learning about science in the area, namely science about the chemical process of sugar making, architecture, spatial construction, and microeconomics, (2) there is public learning about the history of the area, the physics of materials, namely the construction of bricks that are strong for hundreds of years, and (3) elements of edutourism, namely the combination of public learning with tourism for visitors or toll road users

Coordinated Variable Speed Limit Control for Consecutive Bottlenecks on Freeways Using Multiagent Reinforcement Learning

Most of the current variable speed limit (VSL) strategies are designed to alleviate congestion in relatively short freeway segments with a single bottleneck. However, in reality, consecutive bottlenecks can occur simultaneously due to the merging flow from multiple ramps. In such situations, the existing strategies use multiple VSL controllers that operate independently, without considering the traffic flow interactions and speed limit differences. In this research, we introduced a multiagent reinforcement learning-based VSL (MARL-VSL) approach to enhance collaboration among VSL controllers. The MARL-VSL approach employed a centralized training with decentralized execution structure to achieve a joint optimal solution for a series of VSL controllers. The consecutive bottleneck scenarios were simulated in the modified cell transmission model to validate the effectiveness of the proposed strategy. An independent single-agent reinforcement learning-based VSL (ISARL-VSL) and a feedback-based VSL (feedback-VSL) were also applied for comparison. Time-varying heterogeneous traffic flow stemming from the mainline and ramps was loaded into the freeway network. The results demonstrated that the proposed MARL-VSL achieved superior performance compared to the baseline methods. The proposed approach reduced the total time spent by the vehicles by 18.01% and 17.07% in static and dynamic traffic scenarios, respectively. The control actions of the MARL-VSL were more appropriate in maintaining a smooth freeway traffic flow due to its superior collaboration performance. More specifically, the MARL-VSL significantly improved the average driving speed and speed homogeneity across the entire freeway.

Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data

Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper.

A two-stage trajectory planning model for cooperative truck platooning on freeways

Truck platooning is considered as a promising approach to improve traffic stability and to reduce fuel consumption and emissions on freeways. This article develops a two-stage trajectory planning model for cooperative and automated trucks (CATs) traveling on freeways to minimize overall fuel consumption considering platooning time and velocity change. In the first stage, five different strategies are proposed to determine the permutation of the platoon given the initial states and travel information of CATs. Once the feasibility is confirmed, the model is formulated as a mixed-integer programming to optimize the trajectories of all CATs on the entire freeway section. The results from the numerical experiments show that the platooning time optimization (PTO) strategy has superior performance in optimizing overall platooning time. Moreover, the velocity change under the condition of maximum platooning time is generally lower for the PTO and the passing time minimum strategies. These characteristics make the PTO strategy combined with the trajectory optimization program a suitable application for cooperative truck platooning management.

Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China

Introduction: With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors. Method: The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark. Results: The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.

Crash analysis of mountainous freeways with high bridge and tunnelratios using road scenario-based discretization.

Mountainous freeways with high bridge and tunnel ratios are a new type of road that rarely contain many special road sections formed by various structures. The crash characteristics of the road are still unclear, but it also provides conditions for studying how various road environments affect traffic. In view of the various structures and differences in the driving environments, a scenario-based discretization method for such a road was established. The traffic-influence areas of elementary and composite structures were proposed and defined. Actual data were analyzed to investigate the crash patterns in an entire freeway and in each special section through statistical and comparative research. The results demonstrate the applicability and validity of this method. The crash rates were found to be the highest in interchange and service areas, lower in ordinary sections, and the lowest in tunnels, being mostly attributed to collisions with fixtures. The crash severity on bridges and bridge groups was significantly higher than that on the other types of road sections, being mostly attributed to single-vehicle crashes. The annual average daily traffic and driving adaptability were found to be related to crashes. The findings shed some light on the road design and traffic management implications for strengthening the traffic safety of mountainous freeways.

Optimum Volume of Freeway Corridors

Conventional methods to assess the quality of service on freeways are based on the comparison of a specific peak hour traffic demand to the capacity of the facility, which is usually measured at a single uniform bottleneck section. However, estimating the quality of service of one bottleneck section may not be sufficient to assess the performance of an entire freeway facility. A driver traveling along a freeway corridor may actually encounter multiple flow breakdowns at independent bottleneck sections, which affect the overall quality of service. This paper introduces a comprehensive approach that considers an entire freeway corridor as a system consisting of successive independent bottlenecks with different characteristics, and can be used to estimate the optimum sustainable volume. The methodology is based on the sustained flow index, which is defined as the product of traffic volume and the probability of survival at this volume. Optimum volumes of two real-world corridors are estimated based on the new derivations. The empirical results reveal that the optimum volume and the capacity of an entire corridor is less than those of its most restrictive bottleneck.

Method for Estimating Vehicle-to-Vehicle Travel Time Variability Models at the Link and Network Levels of Freeways/Expressways through Censoring Mechanism

A method was proposed to estimate vehicle-to-vehicle travel time variability (TTV) at the link and network levels of the entire freeway network. Standard deviation (SD) of travel time rate (TTR) was selected for the TTV. Models estimating the TTV were developed through a Tobit model using a left-censored limit. For the analysis of impact factors on TTV including day-to-day, the model included various types of variables: density, occupancy, traffic flow, link lengths, lane count, speed limits, rainfall amount, crash indicator, weekend indicator, and holiday indicator. According to the exploration and modeling results, TTR and its SD (vehicle-to-vehicle and day-to-day) have a statistical positive significant relationship at the link and network levels. Furthermore, it was confirmed that there is network fundamental diagram (NFD) at the network level. According to the modeling results, an increase in the number of lanes and speed limits, and crash occurrence, raises vehicle-to-vehicle and day-to-day TTV. However, TTV decreases if the link length is long. A high rainfall amount would reduce vehicle-to-vehicle TTV, but raise day-to-day TTV. Weekends and holidays increase vehicle-to-vehicle TTV but diminish day-to-day TTV. Finally, a linear regression model between TTV and TTR at the network level was developed. Through the relationship between the linear regression model and NFD, it is possible to develop new traffic management strategies and algorithms optimizing the vehicle-to-vehicle TTV at the network level. The developed vehicle-to-vehicle TTV models can be applied to validate the mobility improvement potential of vehicle-to-everything (V2X) communication applications on a segment, corridor, and regional scale.

Evaluation of Ramp Metering Impacts on Travel Time Reliability and Traffic Operations through Simulation

Ramp metering has been found to improve traffic conditions on the freeway mainline by breaking the platoons of ramp vehicles minimizing turbulence at the merge locations. The majority of the ramp metering evaluation studies have examined traffic performance under specific demand conditions, whereas travel time reliability and variability aspects have not been adequately addressed. This paper focuses on evaluating two well-known ramp metering algorithms in terms of travel time reliability as well as other performance measures such as queue lengths, throughput, and congestion duration, looking at a wide range of traffic demands throughout a calendar year. The evaluation was done through simulating an 8-mile corridor in Kansas City, KS. The results showed localized improvements due to ramp metering at the northern section of the facility, in terms of travel time reliability, throughput, and congestion duration. It was also shown that ramp metering may cause a new (possibly “hidden”) bottleneck to occur downstream, thus diluting its overall benefits when looking at an entire freeway facility. It is further noted that although ALINEA performed better than HERO on the mainline, traffic operations on the on-ramps significantly deteriorated using isolated control.

Life cycle assessment for carbon dioxide emissions from freeway construction in mountainous area: Primary source, cut-off determination of system boundary

Carbon dioxide (CO2) emissions mitigation from transportation infrastructure construction activities is regarded as one of the potential pathways to deal with climate change. Aiming to assess the magnitude of CO2 emissions associated with freeway infrastructure construction activities and identify primary emissions sources in mountainous area, this study firstly assessed life cycle CO2 emissions of an entire freeway project in mountainous area in China, including pavement, bridge, tunnel, intersection, traffic safety facilities and temporary works into the system boundary. The results show that CO2 emissions related to road segment are one magnitude lower than that of bridge and tunnel. In addition, negligence of intersection, traffic safety facilities and temporary works could underestimate 4% of total CO2 emissions of the freeway project. CO2 emissions distribution of the different types of materials and equipment distribute as the similar Pareto Principle, which reminds that definition of system boundary could focus on the few of inputs that matter on the aspects of data limited, rough estimation and comprehensive understanding of environmental impact. Findings of this study could offer transport agencies more comprehensive references to understand the contribution of freeway infrastructure construction activities in the mountainous area to climate change, and provide decision makers insights to take measures in the planning phase, particularly in the construction phase in terms of equipment selection, construction design, and construction materials design.

Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information.

To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads.

Microsimulation Analysis of Practical Aspects of Traffic Control With Variable Speed Limits

Mainstream traffic flow control (MTFC) with variable speed limits (VSLs) is a freeway traffic control method that aims to maximize throughput by regulating the mainstream flow upstream from a bottleneck. Previous studies in a macroscopic simulator have shown optimal and feedback MTFC potential to improve traffic conditions. In this paper, local feedback MTFC is applied in microscopic simulation for an on-ramp merge bottleneck. Traffic behavior reveals important aspects that had not been previously captured in macroscopic simulation. Mainly, the more realistic VSL application at specific points instead of along an entire freeway section produces a slower traffic response to speed limit changes. In addition, the nonlinear capacity flow/speed limit relation observed in the microscopic model is more pronounced than what was observed at the macroscopic level. After appropriate modifications in the control law, significant improvements in traffic conditions are obtained.

Comparative analysis of probe‐based ramp metering with detector‐based and pretimed ramp metering

SUMMARYIn this paper, a probe‐based adaptive ramp metering approach has been developed and compared with a detector‐based and pretimed ramp metering approaches using PARAMICS microsimulation model made by Quadstone Limited. Both examined probe‐based and detector‐based ramp metering approaches adopt the same problem formulation; however, they differ by the data source that provides estimates of their input parameters. The probe‐based approach takes as main input space mean speed extracted from vehicle probes moving constantly on the entire freeway. The detector‐based approach takes the density readings directly from point detectors placed upstream and downstream of each on‐ramp. The developed approach formulates the ramp metering rates to simultaneously reduce freeway travel time and decrease delays on ramps. A dynamic weighting scheme is adopted in this formulation to give different weighting schemes to the various freeway sections and the on‐ramps. These weights are able to adjust dynamically based on prevailing and forecasted traffic conditions to give priority in each step to the sections of the freeways or/and on‐ramps experiencing critical traffic conditions. The comparative analysis of the three ramp metering approaches was conducted on an 8‐km freeway section on Highway 2 in Calgary, Alberta, Canada. The results of the analysis indicate that the probe‐based algorithm consistently outperformed the two other approaches in terms of all performance measures. The probe‐based approach is shown to exceed the performance of the detector‐based approach with a market share of vehicle probes as low as 3%. The results of the analysis are promising and show the effectiveness of developing ramp metering control algorithms taking the main input parameters from vehicle probes on the freeway. Copyright © 2012 John Wiley & Sons, Ltd.

Probabilistic Graphical Models of Fundamental Diagram Parameters for Simulations of Freeway Traffic

Freeway traffic simulations must account for the probabilistic nature of model parameters to capture observed variations in traffic behavior. Fundamental diagrams specify freeway section parameters describing the flow–density relationship in macroscopic simulation models. A triangular fundamental diagram—specified with the free-flow speed, congestion wave speed, and capacity—is commonly adopted in first-order cell transmission models. Capacity (defined as the maximum flow observed in a given freeway section over a particular day) exhibits significant day-today variation, and capacity variations across different sections of the freeway are significantly correlated. Free-flow speeds do not exhibit significant variation, but congestion wave speeds exhibit variation uncorrelated with section capacities or parameters from other sections. A probabilistic graphical approach is presented to model the probabilistic distribution of fundamental diagram parameters of an entire freeway section chosen for simulation. More than 1 year of data from dozens of loop detectors along a 25-mi section of the I-210 freeway westbound in Los Angeles, California, are used for demonstration. The parameters of the distribution are estimated with the expectation–maximization algorithm to account for missing observations. Model selection from among plausible models indicates that a first-order spatial Markov model is appropriate to capture the capacity distribution, which is the joint probability distribution of freeway section capacities. Stochastic simulations with sampled parameters demonstrate that capacity variations can lead to significant variations in congestion patterns and freeway performance.

Assessing Safety Benefits of Variable Speed Limits

A method is suggested for evaluating the effectiveness of variable speed limits in reducing freeway crash potential. The real-time crash prediction model that was developed in earlier studies was used to estimate crash potential for different control strategies of variable speed limits. To mimic realistic responses of drivers to changes in speed limits, a microscopic traffic simulation model was used. The simulation results indicate that total crash potential over the entire freeway segment could be significantly reduced under variable speed limit control with a minimal increase in travel time compared to the fixed speed limit. The methodology for assessing safety benefits of variable speed limits is illustrated, and findings from the experiment that used a simple freeway segment are presented.