Roadway Capacity Research Articles

Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic at Two-Way Stop-Controlled Intersections

Connected and automated vehicles (CAVs) hold great promise in enhancing transportation operations and roadway capacity. However, there is still limited guidance in the field of highway capacity analysis to help agencies account for the potential benefits. This paper investigates the impact of CAVs on the capacity of two-way-stop-controlled (TWSC) intersections and quantifies capacity adjustments under different CAV scenarios. An isolated TWSC intersection is considered in this research to analyze the CAV impacts on various intersection movement capacities under different CAV market penetration rates (MPRs) and conflict flow rates (CFRs). To model CAVs, cooperative adaptive cruise control (CACC) is considered because it directly impacts the vehicle’s longitudinal car-following behavior and therefore the capacity. Additionally, enhanced gap-acceptance behavior based on vehicle-to-vehicle (V2V) communication is included because it can benefit minor road movements via advanced notifications. This paper collects data from customized, well-calibrated TWSC simulation models constructed in commercially available software. The results show that with the increase in MPRs, CAVs can substantially enhance the capacity of the stop or yield controlled movements, and the improvement is more significant under low CFR scenarios. CAVs can also reduce the follow-up headway and thus improve the potential capacity based on the existing HCM TWSC capacity estimation method. Finally, based on the data collected from the simulation, a capacity adjustment factor (CAF) table is proposed for HCM implementation.

A Review of Connected and Automated Vehicle Platoon Merging and Splitting Operations

Connected and automated vehicle (CAV) platoons have drawn much attention in the past decades, given their potential to reduce fuel consumption, elevate roadway capacity, and enhance traffic safety. As two basic platoon operations, platoon merging and splitting have been widely investigated. This study provides an overview of theoretical models and field experiments of CAV platoon merging and splitting operations. A three-step framework, including protocol design, trajectory planning, and vehicle control, is proposed to unify existing representative studies. Methodological techniques in each step are summarized and discussed. Finally, future research directions are discussed. This study contributes to the literature by providing a framework that categorizes relevant literature and guides the successful development of platoon merging and splitting operations. More importantly, it offers researchers and practitioners a rich reference for further investigations.

NLS Based Hierarchical Anti-Disturbance Controller for Vehicle Platoons With Time-Varying Parameter Uncertainties

Cooperative adaptive cruise control (CACC) is a promising technology for vehicle platoons to increase roadway capacity. This paper proposes a networked Lagrange system (NLS) based CACC dynamic model based on which a hierarchical anti-disturbance controller is developed to solve the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology. First, a hierarchical anti-disturbance controller for NLS is constructed which comprises three layers, these are, the adaptive smooth estimator-control layer, the distributed classifying amplitude-related layer, and the anti-disturbance control layer, in which the parameter uncertainties are estimated in the adaptive smooth estimator-control layer, the disturbance classification and distribute control are executed in the distributed classifying amplitude-related layer and the anti-disturbance control layer respectively. In addition, the proposed controller is extended to address the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology conditions, which shows the versatility of the controller. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented controllers.

Mathematical Modeling Methods and Their Application in the Analysis of Complex Signal Systems

Mathematical models are effective means of answers established by humans to solve real-world problems. Complex wireless communication can establish information interaction between vehicles, in order to reduce the delay time of the coordination control optimization timing scheme in coordination delay time. For smart car driving, a complex signal system, this study first establishes a relevant mathematical model. It is used to compare three mathematical models commonly used today. The results obtained under the same conditions show that the mathematical model is better in dealing with the complex signal system in terms of transmission accuracy in all segments. A number of vehicles in different states of the traffic system are selected, and the relevant data are collected to plot ROC curves using the mathematical model. It can be concluded that the freer and more complex the movement behavior of the vehicle, the greater the load it imposes on the road and the system. The results of the confusion matrix show that the model can effectively reduce the pressure on the road and the signal system. With the starting objective of smooth operation of public transportation, the target values are optimized by layering, and finally, the regional roadway capacity is effectively converged. Then, the mathematical model optimization of complex wireless systems and intelligent transportation networks is quantitatively evaluated. The optimized timing scheme through coordinated control achieves the expected effect in coordinated control of delay time and also reduces the average delay time of all intersections of the road network.

Machine Learning and Reverse Methods for a Deeper Understanding of Public Roadway Improvement Action Impacts during Execution

The execution of public roadway maintenance, rehabilitation, and restoration activities disturb normal traffic flows, resulting in roadway capacity reduction, inducing travel time delays, and promoting traffic safety concerns. While they improve public roadway performance once complete, the impacts endured in executing these actions is significant. This work seeks a deeper understanding of the effects of improvement actions on traffic by juxtaposing their effects against those arising from traffic incidents that cause similar capacity reductions and related negative externalities. This is accomplished through direct and reverse comparisons with traffic incident impacts. A measure of unit delay that uses observations to determine event location extent, duration, and propagation direction was computed at both facility and corridor-wide levels to establish the degree to which improvement actions and traffic incidents are similar or dissimilar. Alternative hybrid machine-learning methods are proposed to identify and contrast those traffic characteristics that contribute greatest to correct detection of each type of downtime event. These techniques can detect traffic events and accurately distinguish between event types (whether a collision or improvement activity). The techniques were applied on seven months of data obtained from 2019 along three corridors from northern, southern, and western regions of the Commonwealth of Virginia. Those traffic characteristics that contribute greatest to correct event detection of each event type were identified and their similarities and differences were studied. General linear, multivariate regression equations were also developed for more general application.

Optimal Trajectory Planning of Connected and Automated Vehicles at On-Ramp Merging Area

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

A reliability-based approach to evaluate the lateral safety of truck platoon under extreme weather conditions

The truck platoon is one of the most promising connected and autonomous vehicle (CAV) technologies that can reduce fuel consumption and emission, enhance traffic safety, and increase roadway capacity. It is predicted to become mainstream in the next decade. Therefore, it is imperative to fully investigate the safety issues of the truck platoon before its large-scale deployment. However, studies on the lateral safety of the truck platoon under extreme weather, especially crosswinds are still lacking. To fill such a research gap, the current study contributes to the literature by proposing a reliability-based framework to evaluate the safety of the truck platoon regarding incursion into neighboring lanes due to extreme weather, especially crosswinds. The proposed approach involved three main steps: (1) the computational fluid dynamics (CFD) simulation of the aerodynamics of the truck platoon; (2) the truck platoon driving simulation under crosswind; and (3) the advanced response surface model development and the reliability analysis. Four main factors regarding lateral safety of the platoon were considered: wind speed, road friction coefficients, driving speed, and inter-vehicle spacing. The maximum lateral displacement (MLD) was chosen as a measure of lateral safety. The results showed that there was a significant difference between the aerodynamics of a single truck and that of the truck platoon vehicles and the inter-vehicle spacing between trucks within the truck platoon barely influenced the MLD. The MLD was largest for the leading truck as compared to those of the following trucks. The inter-vehicle spacing didn’t have a significant influence on MLD when the inter-vehicle spacing is shorter than 1.5 times of the truck length, while the other factors impacted the MLD significantly. In addition, the support vector regression with the radial basis function outperformed the other response surface functions. Based on reliability analysis, the risk level of the truck platoon was quantified using the safety index, and the impact of contributing factors towards the safety index of the truck platoon was also evaluated. This study confirmed that the proposed framework could be applied to evaluate the lateral safety of the truck platoon. The findings provide important practical implications for the decision-making of transportation management agencies and tailored countermeasures in the CAV) environment.

Influence of asphalt thickness on performance of geosynthetic-reinforced asphalt: Full-scale field study

In this study, a series of controlled traffic loadings was conducted on unreinforced and geosynthetic-reinforced full-scale asphalt overlays. Unlike the common objective of using paving interlayers to mitigate the development of reflective cracks, the main purpose of adopting geosynthetics for this study was to render an increased roadway structural capacity. The project involved instrumented test sections constructed during the rehabilitation of an in-service roadway in Texas, USA. The rehabilitation involved repairing the pre-existing pavement, placing tack coat, installing a geosynthetic interlayer (except in the unreinforced section), and finally constructing a 75 mm-thick asphalt overlay. This overlay comprised a 50 mm-thick, dense-graded (TY-D) layer overlain by a 25 mm-thick, thin-overlay mixture (TOM) layer. Controlled traffic loadings were conducted, which involved driving standard and light axle loads directly above asphalt strain gauges that had been installed at mid-depth of the pre-existing asphalt layer. Comparison of tensile strains among the different test sections revealed significantly smaller tensile strains in the geosynthetic-reinforced sections compared to those obtained in the unreinforced section. Consequently, and even though geosynthetic interlayers have often been adopted to minimize reflective cracking in asphalt overlays, the field monitoring results generated in this study demonstrate that they also provide added roadway structural capacity.

PRINCIPLES OF STREET AND ROAD SPACE REDISTRIBUTION IN ACCORDANCE WITH MODERN URBAN PLANNING TRENDS

. The article analyzes the modern urban trends of street and road space redistribution, which in recent years are reflected in the regulations of Ukraine. The basic options for such redistribution are: narrowing of lanes for private transport (allowed by updated regulations in Ukraine); reducing the number of lanes for private transport (which does not always reduce the roadway capacity). As a result of foreign experience analysis the stages in which redistribution of street and road space can be carried out are revealed: pilot actions (temporary marking, road barriers and change of traffic organization); intermediate steps of reconstruction (application of new markings, installation of stationary and sliding columns, mobile flower beds, containers with young trees or bushes, large natural stones, lanterns, benches, etc.); comprehensive reorganization of streets and spaces (sidewalks widening, curbs moving, bike lanes allocating or separating, installing of elements to slow down traffic, organizational measures to optimize traffic lights, relocation of utilities, drainage and storm sewer systems, etc.). As a result of the analysis of modern tendencies in designing settlements’ streets and roads and world experience of these tendencies realization, the following principles of street and road space redistribution are formulated: the principle of equality in the right of use, based on space availability for people of any social class, age and state of health; the principle of security, which is to give citizens the opportunity to feel protected on the streets of their city; the principle of diversity of activities, which aims to provide all social groups with accessible and diverse types of recreation and leisure in public spaces; the principle of comfortable microclimate, which is to ensure the comfort of residents on the settlements streets, and the principle of aesthetics, ensuring an attractive appearance of urban spaces.

Synergistic resin anchoring technology of rebar bolts in coal mine roadways

The resin anchoring process is a critical aspect of coal mine roadway support, but presently faces several serious challenges. For example, resin s cannot be properly broken and mixed, often leak, and their density and circumferential thickness values frequently deviate from the design values. This results in a reduced effective anchorage length and prevents the correct application of a high pretension force. To address these issues, this study presents a systematic analysis of the anchoring characteristics of roadway bolts including theoretical calculations, a detailed analysis on the influence of mixing density, mixing parameters, and pretension force on the anchoring performance, and a primary focus on the components and mechanism within synergistic anchoring technology. Numerical simulations are performed following the orthogonal experiment method to optimize and determine the synergistic component structural parameters, and laboratory tests are conducted to compare and analyze the modes of the combined synergistic component use. The working performance of the synergistic anchoring technology is comprehensively verified using field tests. The results show that the anchoring system’s bearing strength, yield displacement, bearing time, and energy absorption capacity are greatly improved when using a combination of the synergistic components. Bolts applied in the field using the proposed synergistic anchoring technology produce significantly higher anchoring force and pretension moment values than ordinary bolts, exceed the engineering requirements, and produce a good overall anchoring effect. A high pretension force can be applied to enhance the support capacity, ensure the bolt resin anchor quality for coal mine roadways, and improve the roadway bearing capacity under static and dynamic loading, which have high practical significance and application value.

Examining deterministic and probabilistic capacity estimation methods under mixed traffic using empirical data

The present research primarily investigates the applicability of different roadway capacity estimation methods under mixed-traffic conditions, operating with weak-lane discipline. The study also explores the feasibility and advantages of stochastic capacity estimation methods for mixed traffic scenarios on urban roads in India. Three methods, namely Traffic-stream models (calibrated empirically), Product limit method (PLM), and Sustained Flow Index (SFI), are used to estimate the capacity for five different categories of road sections. The Van-Aerde model is found to be the best representative for estimating deterministic roadway capacity. The PLM method is observed to provide the freedom of selecting desirable operational quality. And the SFI method provides the maximum sustained flow for the roadway infrastructure. As a practical outcome, a novel Level of Service monitoring framework is developed by augmenting the traditional volume-to-capacity ratio with the breakdown probabilities.

Determination of PCU values for mixed traffic conditions along the hilly road of East Sikkim

The road space in India is shared by different travel modes with different physical dimensions. On top of this, lane disciple is also not followed in India. All these factors influence the evaluation of traffic characteristics, roadway capacity, defining model for traffic flow and other transportation design factors. Passenger car unit (PCU) is a frequently used unit for calculating and expression of heterogeneous traffic volume to a simpler equivalent traffic unit where a passenger car is taken as a standard vehicle and all other classes of vehicle are expressed in terms of the standard car. For the present study, 11 km stretch of NH-10 in East Sikkim was taken into consideration. Data collection was done by video graphic survey. PCUs of different classes of vehicle are estimated using the speed area method by using traffic volume data. The PCU is calculated by multiplying the speed and area ratios of standard cars by the PCU of the subject vehicle type. The methodology adopted in this study is very much suitable for making proper PCU model under different conditions of heterogeneous traffic and road conditions of the study area..

Optimizing Energy Savings for a Fleet of Commercial Autonomous Trucks

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

MODIFIED EFFECTIVE AREA APPROACH TO ESTIMATE MOTORCYCLE EQUIVALENT UNITS IN AHMEDABAD CITY

This paper presents a methodology for estimation of Motorcycle Equivalent Units (MEU) in mixed traffic flow for motorcycle dominated traffic with increased accuracy by considering dynamic characteristics of subject vehicles, like speed and effective area. Besides, this increased accuracy is the result of the inclusion of speed of adjacent motorcycles in the form of speed ratios to estimate the effective area required by the subject vehicle at a particular speed. The effective area for each sample is computed with consideration of the effective dimensions and speed of that subject vehicle and its adjacent motorcycles on both sides in the proposed methodology. Two mid-block sections of urban roads in Ahmedabad city were selected for field data collection by videography method in this case study. The collected field data was analysed through Speed Estimation from Video Data (SEV) software. A table of classified speed ratios is also presented to derive an idea regarding the magnitude of change in lateral clearances of subject vehicles. The MEU values obtained for cars, motorcycles, rickshaws, buses, Light Commercial Vehicles (LCV), and bicycles were 3.02, 1.00, 1.84, 9.82, 6.2, and 1.9 respectively. Further, the proposed model was compared with a previously developed model to justify the increase in accuracy and to observe the variations in MEUs. The values estimated can be used to establish speed-flow relations, measure roadway capacity in urban roads, analyse the level of service in order to plan suitable traffic control and regulatory measures.

Review of Methods for Estimating Construction Work Zone Capacity

Road reconstruction and the resulting work zones are considered as a major source of traffic congestion and delays on freeways. The roadway capacity is decreased as a result of a reduced number of traffic lanes, narrower lanes, and work zone speed limits. Accurate prediction of construction work zone capacity helps traffic engineers to have a better estimation of the traffic flow characteristics. To this end, multiple methodologies have been developed to quantify the impacts of work zones on traffic flow. This paper presents a critical review of the three types of approaches to estimating construction work zone capacities, including parametric, non-parametric, and simulation. Then the most commonly considered factors and their frequency are presented. It also performs a detailed review of the approaches, their objectives, and weaknesses. Lastly, it provides recommendations for future research. The presented work could help researchers in the area of work zone capacity estimation by presenting all the previous methodologies in one place.

Multi-objective framework for optimum configuration of human-driven and shared or privately owned autonomous vehicles

Private Autonomous Vehicles (PAV) and Shared Autonomous Vehicles (SAV) are known to improve safety, mobility, roadway capacity, driver productivity and reduce parking costs (due to better utilization of space and self-parking to less expensive spots). However, the increased vehicle miles traveled (VMT), might increase overall emission production, system travel time, and operating costs. Also, the purchase price of autonomous vehicle (AV) is expected to be higher than that of human-driven (conventional) vehicles. A multi-objective mathematical model is proposed to minimize the purchase and operating costs, time spent, and emission production. The proposed model captures the tradeoff between the benefits of increased mobility, reduction in the value of travel of time (VOTT), efficient driving pattern, and the negative impacts of increased VMT, ownership cost due to the adoption of AVs. The proposed framework assists with the development of simplified adoption models that can be used by the policymakers and/or investors. SAVs would be the optimal solution if the replacement rate or CO2 costs are significantly low, or CO costs are sufficiently high. SAVs can also be the optimal solution if the travel time is used efficiently, or the purchase price is below certain relative threshold while minimizing system cost. Considering the private mobility system, PAVs can be the optimal solution only if the on-board amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and is being adopted by people with high VOTT.

Developing the guidelines for managing autonomous vehicle flows on congested highways: A case study of MD-100

Recognizing the need for responsible highway agencies to effectively manage emerging autonomous vehicles (AV) flows in contending with daily recurrent congestion, this study presents a systematic procedure for understanding the impacts of AV flows on traffic conditions under different AV behavioral mechanisms (i.e., car-following and lane-changing), and different penetration rates. Research results show that the presence of AV flows, depending on their adopted behavioral mechanisms, may have significant (either positive or negative) impacts on the overall traffic conditions. Hence, it is essential for responsible highway agencies to have proper operational guidelines to manage and coordinate AV flows. To demonstrate the proposed methodology, this study has carried out extensive simulation experiments using a congested segment of the MD-100 network (a multilane highway segment located in Maryland) under various AV penetration rates and observable behavioral patterns. The collected Measures of Effectiveness highlight that at each AV penetration level there exists a set of optimal behavioral patterns for the AV flows to coordinate with non-AV flows via the Vehicle to Infrastructure or Vehicle to Vehicle infrastructure so as to maximize the roadway capacity and minimize the resulting highway congestion.

Modeling multi-class disordered traffic flow subject to varying vehicle composition using the concept of traversable distance

Disordered traffic stream at the microscopic level can be described as a permeable medium. Each vehicle is considered to traverse through a series of lateral gaps created by other vehicles. We develop a multi-class traffic flow model that considers such viable and accessible gaps for individual vehicle classes to traverse downstream. The model accounts for the varying shares of different vehicle classes. The concept of traversable distance and modified equilibrium speed functions are used to model the interplay among multiple vehicle classes. Using a higher order finite volume approximation method, evolution for a two-class traffic stream is shown. This model replicates prominent empirical characteristics exhibited by multi-class disordered traffic such as overtaking and creeping. Varying shares of smaller vehicles affect queue formation and discharge characteristics, and have significant impacts on roadway capacity. The model also could compute dynamic class-specific travel times at different vehicular compositions more realistically.

Real-time deep reinforcement learning based vehicle navigation

Traffic congestion has become one of the most serious contemporary city issues as it leads to unnecessary high energy consumption, air pollution and extra traveling time. During the past decade, many optimization algorithms have been designed to achieve the optimal usage of existing roadway capacity in cities to leverage the problem. However, it is still a challenging task for the vehicles to interact with the complex city environment in a real time manner. In this paper, we propose a deep reinforcement learning (DRL) method to build a real-time intelligent vehicle routing and navigation system by formulating the task as a sequence of decisions. In addition, an integrated framework is provided to facilitate the intelligent vehicle navigation research by embedding smart agents into the SUMO simulator. Nine realistic traffic scenarios are simulated to test the proposed navigation method. The experimental results have demonstrated the efficient convergence of the vehicle navigation agents and their effectiveness to make optimal decisions under the volatile traffic conditions. The results also show that the proposed method provides a better navigation solution comparing to the benchmark routing optimization algorithms. The performance has been further validated by using the Wilcoxon test. It is found that the achieved improvement of our proposed method becomes more significant under the maps with more edges (roads) and more complicated traffics comparing to the state-of-the-art navigation methods.

Anticipating land-use impacts of self-driving vehicles in the Austin, Texas, region

This paper used an implementation of the land-use model SILO in Austin, Texas, over a 27-year period with an aim to understand the impacts of the full adoption of self-driving vehicles on the region’s residential land use. SILO was integrated with MATSim for the Austin region. Land-use and travel results were generated for a business-as-usual case (BAU) of 0% self-driving or “autonomous” vehicles (AVs) over the model timeframe versus a scenario in which households’ value of travel time savings (VTTS) was reduced by 50% to reflect the travel-burden reductions of no longer having to drive. A third scenario was also compared and examined against BAU to understand the impacts of rising vehicle occupancy (VO) and/or higher roadway capacities due to dynamic ride-sharing (DRS) options in shared AV (SAV) fleets. Results suggested an 8.1% increase in average work-trip times when VTTS fell by 50% and VO remained unaffected (the 100% AV scenario) and a 33.3% increase in the number of households with “extreme work-trips” (over 1 hour, each way) in the final model year (versus BAU of 0% AVs). When VO was raised to 2.0 and VTTS fell instead by 25% (the “Hi-DRS” SAV scenario), average work-trip times increased by 3.5% and the number of households with “extreme work-trips” increased by 16.4% in the final model year (versus BAU of 0% AVs). The model also predicted 5.3% fewer households and 19.1% more available, developable land in the city of Austin in the 100% AV scenario in the final model year relative to the BAU scenario’s final year, with 5.6% more households and 10.2% less developable land outside the city. In addition, the model results predicted 5.6% fewer households and 62.9% more available developable land in the city of Austin in the Hi-DRS SAV scenario in the final model year relative to the BAU scenario’s final year, with 6.2% more households and 9.9% less developable land outside the city.