Multilane Highways Research Articles

Influence of horizontal alignment elements on operating speed of vehicles on two-lane highways

Earlier studies acknowledged that the design speed does not principally assure consistency in the design of highway geometric elements. Methodologies for estimating operating speeds are limited to specific or local traffic conditions and design standards, as in various studies conducted on two-lane and multi-lane highways. The present study develops operating speed models of the vehicle types observed on horizontal curves and tangent sections on two-lane highways in southern parts of India. Geometric features of the curve section and adjacent tangent sections, such as the radius of the curve, curve length, deflection angle, degree of curvature, gradient, carriageway width, and shoulder width, are obtained from the field. The speed data collected at 24 locations on the highway, including curved and tangent gradient sections, are used to analyze and model operating speed. The results indicate that the inverse of the square root of the radius appears to be an influencing parameter for modeling the operating speed of vehicles. The operating speed is sensitive for a curve radius of up to 600 m; thereafter, the operating speed appears insignificant. On tangent sections, the gradient has a larger influence on the operating speeds of all vehicle types. The operating speed models are validated and compared with models available in the literature. The proposed models developed in the present study are helpful to traffic engineers for predicting the operating speed on two-lane highways.

Commercial Adaptive Cruise control (ACC) and capacity drop at freeway bottlenecks

Vehicle automation has already become commercially available. Today’s mainstream vehicles are equipped with Adaptive Cruise Control (ACC) to automate longitudinal car-following. However, ACC’s limited detection range and delayed reaction hinder the vehicle’s ability to respond promptly to speed changes, and ACC could amplify minor disturbance into severe stop-and-go waves and form queues at bottlenecks. Consequently, the discharge flow could be lower than the maximum flow observed in absence of queues or congested conditions, also known as capacity drop. Microscopic simulation of freeway bottlenecks consisting of a single lane with reduced speed zone, multilane with an on-ramp merge, and multilane with an off-ramp diverge demonstrates that ACC vehicles lead to capacity drop at freeway bottlenecks. For the single lane reduced speed zone, the extent of capacity drop for ACC could be either less or more severe than that of human driven vehicles. However, the multilane freeway on-ramp and off-ramp bottlenecks are more susceptible to capacity drop than human driven vehicles, due to ACC significantly amplifying disturbances caused by lane changes, merging, and diverging.

Pass-by Noise Analysis of Heavy Duty Trucks

As part of the NCHRP Project 25-45, pass-by levels were measured for 1,289 heavy duty trucks operating on 20 sites including multi-lane highways and more rural two-lane roadways. These sites included uphill, downhill, level sites and accelerating and cruising conditions at speeds from below 45 mph to 70 mph as well as nine different pavement types. This research was designed to develop a more comprehensive understanding of truck noise emissions and noise sources. In this paper, the frequency spectra for these pass-bys are considered from 25 to 10,000 Hz under the measurement conditions specified in the AASHTO TP 389-20 procedure for statistical isolated pass-bys. These data are enhanced by results from the 2009 NCHRP Report 635 entitled Acoustic Beamforming: Mapping Sources of Truck Noise and from Caltrans research on truck tire noise. The findings of these various studies are presented in order to develop a more comprehensive description of heavy-duty truck noise emissions.

Classification of Roadway Context and Target Speed for Multilane Highways in Thailand Using Fuzzy Expert System

Classification of Roadway Context and Target Speed for Multilane Highways in Thailand Using Fuzzy Expert System

Transformer based composite network for autonomous driving trajectory prediction on multi-lane highways

Transformer based composite network for autonomous driving trajectory prediction on multi-lane highways

Coupled Vibration Analysis of a Beam-Arch Composite Continuous Rigid Structure with Parallel Traffic Flow

To investigate the structural dynamic characteristics of a beam-arch composite continuous rigid structure bridge under the action of parallel moving loads on a multi-lane highway, a joint simulation analysis program based on Easy Language and ANSYS was developed. This study was conducted using a six-span beam-arch composite continuous rigid structure bridge located in Shaanxi Province as the reference project. The research focused on the most adverse dynamic coupled responses of the main beam midspan, arch crown, and suspension rods caused by double-axle cars, double-axle trucks, and three-axle trucks on a single-direction three-lane road surface.

Dynamic Planning of Optimally Safe Lane-change Trajectory for Autonomous Driving on Multi-lane Highways Using a Fuzzy Logic–based Collision Estimator

The collision avoidance system in an autonomous vehicle, intended to address traffic safety issues, has a crucial function called collision estimation. It accomplishes this by identifying potential dangers and notifying the drivers in advance or by using autonomous control to navigate safely. In this work, a novel approach is proposed for generating and selecting a lane-change trajectory for the vehicle in a driving scenario where two vehicles are simultaneously executing lane-change processes on highways and approaching the same target lane. Moreover, a novel fuzzy logic estimator based on time-to-collision (TTC) and time-to-gap (TTG) is designed to estimate the collision risk. In the collision-avoidance process, the proposed estimator is utilized to determine the risk of a collision with polynomial function-based generation of possible lane-change trajectories. The safest lane-change trajectory is then provided to the motion controller so it can navigate the vehicle safely through such a challenging lane-change scenario. This work also investigates Stanley and Pure Pursuit controllers to follow the optimized trajectory. The simulation experiment results demonstrate that the proposed approach for dynamic trajectory generation during the lane-change process can successfully handle this type of challenging situation and prevent a potential collision. Experimental results also indicate that monitoring the movement of the nearby lane-changing vehicle is crucial for safe lane-change execution and that the proposed approach successfully handles the challenging situation, preventing potential collision.

Multi-Agent Deep Reinforcement Learning for Multi-Lane Freeways Differential Variable Speed Limit Control in Mixed Traffic Environment

In advanced freeway traffic management systems, variable speed limit control (VSLC) is frequently discussed as one of the control measures. However, in a mixed traffic environment where connected and automated vehicles (CAVs) and human-driven vehicles coexist, the existing VSLC strategies for multi-lane freeways have two major shortcomings: the lack of precise control at the individual vehicle level, and the implementation of uniform VSLC across all lanes. This paper proposes a novel differential variable speed limit control (DVSLC) strategy based on multi-agent reinforcement learning (MARL) in a mixed traffic environment (abbreviated as MARL-DVSLC). The proposed MARL-DVSLC approach utilized a centralized training with decentralized execution paradigm to learn the joint actions of variable speed limit controllers across all lanes, thereby setting different speed limits for each lane. The reward function takes into account the total time spent (TTS) on freeways to improve traffic mobility. Note that MARL-DVSLC disseminates speed limit information to CAVs via infrastructure-to-vehicle (I2V) communication. The effectiveness of MARL-DVSLC is verified under different simulation scenarios. Moreover, its performance is compared with the feedback-based VSLC method, the DVSLC method based on deep deterministic policy gradient (DDPG) (abbreviated as DDPG-DVSLC), and the no-control case in relation to performance. The results indicate that the proposed strategy can effectively improve traffic efficiency and reduce the spatiotemporal range of traffic congestion at a 30% penetration rate of CAVs. Compared with the suboptimal DDPG-DVSLC method, the proposed strategy can improve TTS by 12.88% with stable traffic demand and 10.24% with fluctuating traffic demand.

Merging Maneuver Technique for an Adaptive Autonomous Platoon in Multi-lane Highways

Merging Maneuver Technique for an Adaptive Autonomous Platoon in Multi-lane Highways

Diffractors on a low screen: a valid alternative for conventional noise screens along multi lane motorways?

Diffractors are an innovative way of shielding noise from e.g. motorways by bending the propagation direction of sound waves upwards and hence creating an acoustic shadow area along the road. The bending upwards is done by the interaction of the road noise with a series of slits, all parallel to the road but with varying depths for varying resonance frequencies. This principle has been invented about 10 years ago and has since then been tested with temporary and permanent constructions. The slits can be integrated in the ground, but can also be put on a low (0,5 - 1 m) screen for enhancing the performance at short distance. The Belgian Road Research Centre has carried out several measurement campaigns at different diffractor set ups on low screens. The measurements were of the "Controlled Pass-By" type yielding consistent results. An overview is given of the findings since the first measurement, including the new results obtained along a ring road in the Dutch town of Amersfoort in 2022, where for the first time such a device was constructed along a 2 x 2 lane, showing the performance in case the noise source is at a larger distance from the diffractor.

Improving Simulation Models of Unsignalized Intersections on Rural Multilane Highways through Calibration Using Naturalistic Driving Study Data from Strategic Highway Research Program 2

The aim of this study is to create a calibration method for traffic simulation models of two crucial movements at unsignalized intersections on rural divided highways. This analysis draws on data from the Naturalistic Driving Study, specifically direct left turn (DLT) movements at conventional intersections and right turn followed by U-turn (RTUT) movements at restricted crossing U-turn (RCUT) intersections. Using 453 naturalistic trips from 79 drivers, the speed trajectory, stopping behavior, lane utilization, and acceleration and deceleration rates were analyzed. Three-step and four-step calibration methods were then developed for the Vissim simulation models for DLT and RTUT movements, respectively, which were verified by comparison with naturalistic driving data. The calibrated Vissim models were verified by comparing the naturalistic driving speed trajectories of DLT and RTUT movements. The results show that calibrated models can improve the operational analysis for overall unsignalized intersections on rural multilane divided highways.

Research on Cooperative Control for On-Ramp Merging with Multiple Lanes in Connected and Automated Environments

This paper addresses the issue of on-ramp merging in multi-lane freeways and proposes a cooperative control method based on connected and automated vehicles. Focusing on a two-main-lane freeway scenario, the method consists of two key models: a merging sequence decision model and a motion planning model. The merging sequence decision model prioritizes collision avoidance by predicting the motion state and lane-changing trajectory of vehicles in the merging area. The motion planning model utilizes longitudinal and lateral cooperation to control the main lane vehicles to generate the merging gap through coordinated adjustments in the longitudinal speed or by performing lane changes. The optimal merging trajectory is determined using the entropy weight method, and a fast optimization method based on neural networks is employed. Through simulations considering different traffic density combinations, the proposed method is compared with traditional control schemes. Results demonstrate its superiority in improving traffic flow stability, rapidity, and overall efficiency.

Cooperative On-Ramp Merging Control Model for Mixed Traffic on Multi-Lane Freeways

This paper proposes a hierarchical model for cooperative on-ramp merging control (CORMC) in mixed traffic with both connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). The upper-layer of the CORMC model employs an anticipatory position searching (APS) algorithm to determine the anticipatory positions at which merging vehicles (MVs) should merge from the on-ramp lane to the adjacent mainline lane, and to assign cooperative vehicles (CVs) for each MV. A collaborative utility choice (CUC) model is presented to determine the optimal maneuver of CVs to create proper gaps for MVs. The driver compliance rate is introduced to account for CHVs’ unwillingness to follow the instructions given by the CUC model. The lower-layer comprises a cooperative merging control (CMC) model that ensures safe and smooth merging execution for MVs. Longitudinal and lane changing models are developed for mainline vehicles to facilitate an efficient and safe merging process. Simulation results show that the performance benefits of the CUC model are marginal when the CHV compliance rate is relatively low. However, the performance improvement is significant at higher compliance rates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 50%). Furthermore, the CORMC model has the potential to increase merging and mainline throughput by over 75% at sufficiently high CAV penetration rates. Comparison of three control strategies shows that the APS algorithm plays an important role in the CORMC model. A comparison with the Simulation of Urban Mobility (SUMO) indicates that the CORMC model significantly mitigates the propagation of congestion waves across varying levels of CAV penetration and on-ramp flow rates.

Application of Unsupervised Learning in Implementation of Joint Power and Index Modulation Access in V2X Systems

The implementation of joint Power-and-Index Modulation Access (PIMA), which is an effective way of realizing non-orthogonal multiple access (NOMA), requires a process of user ordering over the available subcarriers, before multiplexing their data streams for power and index modulation. The conventional way of user ordering in NOMA with respect to the channel state information can be impractical and expensive given the large numbers of users and subcarriers, in Vehicle-to-Everything (V2X) communications. Therefore, this paper proposes a novel approach that leverages K-Means clustering to learn the pattern of multi-user multi-carrier energy variations and use them to effectively order the users. For this, we develop several learning algorithms and an analytical model for the Symbol Error Probability (SEP) of PIMA, which is used to quickly generate part of the training dataset (transfer learning). The results of the comprehensive evaluations that have been carried out in this study show that the proposed approach outperforms the benchmark techniques in terms of SEP. It is also shown that the proposed scheme provides improved performance in terms of SEP as compared to distance-based ordering in realistic V2X scenarios using synthetic mobility traces for the locations of vehicles along multi-lane highways.

Modeling Speed Mean and Variance for Different Enforcement Conditions on Multilane Highways

Two major characteristics that can impact the frequency and severity of traffic crashes are average speed and speed variance. This paper investigated these characteristics on 49 multilane highway segments in Jordan and developed prediction models for three different conditions: free (no enforcement), camera, and police enforcement. Speed data were collected during off-peak periods with fair weather conditions. Multiple linear regressions and nonlinear regressions were used to develop speed variance and average speed models. For the free condition, speed variance mainly depends on the difference between design speed and speed limit (DS-SL), with the speed variance decreasing as the difference reduces, while the average speed was positively correlated with the design speed. Camera enforcement causes speed variance and average speed to be positively correlated and linearly dependent with the speed limit. A quadratic relationship was found between speed variance during police enforcement and DS-SL, while the average speed during police enforcement depended on the speed limit and design speed. These modeling results, along with analysis of the collected data, can help operating agencies and roadway designers determine how various enforcement strategies affect average speed and speed variance and better set speed limits on existing and future roadways to improve safety.

Assessing the Performance of Highway Safety Manual (HSM) Predictive Models for Brazilian Multilane Highways

This paper assesses the performance of Highway Safety Manual (HSM) predictive models when applied to Brazilian highways. The study evaluates five rural multilane highways and calculates calibration factors (Cx) of 2.62 for all types of crashes and 2.35 for Fatal or Injury (FI) crashes. The Goodness of Fit measures show that models for all types of crashes perform better than FI crashes. Additionally, the paper assesses the application of the calibrated prediction model to the atypical year of 2020, in which the COVID-19 pandemic altered traffic patterns worldwide. The HSM method was applied to 2020 using the Cx obtained from the four previous years. Results show that for 2020, the observed counts were about 10% lower than the calibrated predictive model estimate of crash frequency for all types of crashes, while the calibrated prediction of FI crashes was very close to the observed counts. The findings of this study demonstrate the usefulness of HSM predictive models in identifying high-risk areas or situations and improving road safety, contributing to making investment decisions in infrastructure and road safety more sustainable.

Research on left hard shoulder width of super multilane highway based on PTSU operation.

Part-time shoulder use (PTSU) is a traffic strategy that temporarily uses the shoulder as a lane when necessary. Research has shown that, when a hard shoulder is required to set the traffic function, the left hard shoulder is preferable. Super multilane highways are usually equipped with left hard shoulders of sufficient width, but the wide cross-sectional characteristics make it difficult for vehicles to turn into the emergency parking lane to avoid a breakdown or accident in the lane, which is an ideal implementation object of PTSU. In this study, two virtual simulation scenarios for PTSU were created: one with the left hard shoulder open and used as a travel lane, and the other with the left hard shoulder closed and its original function restored. Vehicle driving data were collected through driving simulation experiments to reveal the influence of the left hard shoulder on vehicle handling stability. The optimal width of the left hard shoulder was determined by ANOVA and comparison of the mean and standard deviation. The purpose of this study was to quantify the effect of the width of the left hard shoulder on the driving stability of vehicles in the inside lane under PTSU and determine the ideal shoulder width by comparing the stability parameters of vehicles.

Two-Level Full Factorial Design Approach for the Analysis of Multi-Lane Highway Section under Saturated and Unsaturated Traffic Flow Conditions

Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The “significance and effects” of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the “length-lanes” significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors.

Multilane freeway merging control via trajectory optimization in a mixed traffic environment

AbstractMerging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.

Impact of Pavement Defects on Traffic Operational Performance

The present research investigates the influence of pavement distresses on traffic operational performance in terms of speed and capacity. The pavement distresses are expressed by the pavement condition index (PCI). Three roads of multi-lane highways located on the road network of Kafrelsheikh governorate, Egypt, were chosen as a case study. Road geometry, pavement condition characteristics, and traffic volume were collected manually from 23 sections for field data surveys. For speed measurements, spot speed data were gathered using the stopwatch method under the free-flow conditions, and the percentile speeds (V50% and V85%) have been calculated for different vehicles classes. Traffic capacity was determined via extrapolation of the quadratic function. Regression analysis models were created to express the relationships of both the percentile speeds and traffic capacity as dependent variables, and three independent variables (PCI, lane, and shoulder widths). These empirical mathematical relations help in developing road design and maintenance works on multi-lane highways. The main conclusion of the study demonstrated that traffic operational performance was more significantly affected by pavement distresses than road geometry. Moreover, the results indicated that pavement distresses have a significant impact on light vehicles more than heavy ones.