Freeway Off-ramp Research Articles

Game-theory based truck platoon avoidance modes selection near the highway off-ramp in mixed traffic environment

Game-theory based truck platoon avoidance modes selection near the highway off-ramp in mixed traffic environment

Assessment of automated vehicles’ freeway exit distances in mixed and managed lane traffic environments

Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate ( qQ). The results indicated an increase in qQ as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates.

Learning-based MPC for Autonomous Motion Planning at Freeway Off-ramp Diverging

Learning-based MPC for Autonomous Motion Planning at Freeway Off-ramp Diverging

A Field Test of Police Vehicle Presence on Speed(ing): Parked, Unoccupied, and Effective

As part of the present research, we employed a field study paradigm to test the effects of an unoccupied police vehicle on speed(ing) along a voluminous highway offramp in Western Canada. During the intervention period, a randomly assigned, marked police vehicle was parked nightly at the target location while radar-recording devices captured the speed of passing vehicles. Our analyses of speed data collected before, during, and after the intervention period, both before and at the target location, indicated that the average speed of vehicles and the proportion of speeding vehicles were significantly lower in the presence of the unoccupied police vehicle. We discuss the results of our police-directed field study with respect to theory, research, and practice.

Analysis of Carbon Emissions in Heterogeneous Traffic Flow within the Influence Area of Highway Off-Ramps

With the continuous advancements in electrification, connectivity, and intelligence in the automotive industry, the mixed traffic of vehicles with different levels of driving automation is changing the carbon emission characteristics in the impact areas of off-ramps on highways. Considering the insufficient research on the carbon emission characteristics of heterogeneous traffic flow in the downstream influence areas of highway off-ramps, this study applied a scenario analysis method. Furthermore, considering factors such as vehicle composition, road control, and platoon management, it establishes and calibrates measurement models for carbon emissions from conventional vehicles, intelligent vehicles, the platoon driving of electric vehicles, and the mixed platoon driving of conventional vehicles and electric vehicles. This study also provides a simulation scenario for a three-lane highway off-ramp based on the actual conditions of the Xi’an Ring Expressway. Finally, by applying the constructed carbon emission calculation models for heterogeneous traffic flow in the intelligent vehicle mixed traffic scenario, a quantitative analysis was conducted to assess the impacts of the intelligent vehicle infiltration rate, off-ramp vehicle proportion, smart-vehicle-dedicated lanes, and platoon driving on carbon emissions in the downstream influence area of off-ramps. The results revealed the impact of intelligent vehicle integration and platoon driving on carbon emission characteristics in the downstream influence areas of highway off-ramps.

Optimal Differential Variable Speed Limit Control in a Connected and Autonomous Vehicle Environment for Freeway Off-Ramp Bottlenecks

When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway off-ramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment.

An Evolutionary Learning Framework of Lane-Changing Control for Autonomous Vehicles at Freeway Off-Ramps

This paper proposes a lateral control strategy for autonomous vehicles (AVs) and develops an evolutionary learning framework for off-ramps. Random forest (RF) and back-propagation neural network (BPNN) integrated with model predictive control (MPC) algorithm are respectively used to capture the decision-making and trajectory characteristics during the lane-changing maneuver based on the Next Generation Simulation (NGSIM) dataset. Then, a running cost function is calculated to optimize the trajectory dataset. Finally, the numerical simulation is conducted to investigate the characteristics of the proposed framework. Simulation results indicate that the performance of our method is much better than some other methods in lane-changing gap choice and trajectory execution. Moreover, the traffic system controlled by the evolutionary algorithms reaches the highest capacity and safest level when all vehicles are equipped with cooperative adaptive cruise control (CACC) systems. On the contrary, the scenario with 50% CACC vehicles shows the lowest travel efficiency and the worst safety because of the CACC vehicles’ degradation. Furthermore, three iterations and 500 vehicle trajectories at each optimization cycle are recommended for the application in the off-ramp traffic control.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road

The phenomenon of stop-and-go traffic and its environmental impact has become a crucial issue that needs to be tackled, in terms of the junctions between freeway and urban road networks, which consist of freeway off-ramps, downstream intersections, and the junction section. The development of Connected and Automated Vehicles (CAVs) has provided promising solutions to tackle the difficulties that arise along intersections and freeway off-ramps separately. However, several problems still exist that need to be handled in terms of junction structure, including vehicle merging trajectory optimization, vehicle crossing trajectory optimization, and heterogeneous decision-making. In this paper, a two-stage CAV trajectory optimization strategy is presented to improve fuel economy and to reduce delays through a joint framework. The first stage considers an approach to determine travel time considering the different topological structures of each subarea to ensure maximum capacity. In the second stage, Pontryagin’s Minimum Principle (PMP) is employed to construct Hamiltonian equations to smooth vehicle trajectory under the requirements of vehicle dynamics and safety. Targeted methods are devised to avoid driving backwards and to ensure an optimal vehicle gap, which make up for the shortcomings of the PMP theory. Finally, simulation experiments are designed to verify the effectiveness of the proposed strategy. The evaluation results show that our strategy could effectively militate travel delays and fuel consumption.

A Deep Learning Model for Off-Ramp Hourly Traffic Volume Estimation

This paper addresses estimation of traffic volume of freeway off-ramps. Freeways are the transportation network’s main corridors, serving a large portion of the traffic volume. This traffic passes into the lower-level roads through off-ramps. Therefore, the traffic condition of the off-ramps is an essential factor affecting the operation of the transportation network. The continuous collection of volume data is impractical, and transportation authorities install vehicle detectors permanently on only a few off-ramps and temporarily (e.g., a week) on some others. Thus, traffic volume is the most challenging to estimate among various traffic measures. Moreover, the existing literature on volume estimation is mainly concerned with evaluating traffic on the main road segments. However, the distinct characteristics of the connection links, such as off-ramps, demands specified modeling. This study estimates the hourly traffic volume of off-ramps using a deep learning model. It evaluates the advantages of inputting the connected lower-level road features to the model, and explores various detector installation strategies on the model training process. The primary data sources are volume counts, probe speeds, and road segment infrastructure characteristics. The model results indicate that the incorporation of traffic flow characteristics and infrastructure attributes of the lower-level road connected to the freeway significantly improves the accuracy of estimation off-ramp traffic volume. Further, analysis illustrated that the model trained with data from temporarily installed detectors on all interchanges outperformed models trained with permanently installed detectors on 90% of the interchanges, indicating the model’s ability in extracting temporal correlations significantly more than spatial correlations.

Modeling impacts of driving automation system on mixed traffic flow at off-ramp freeway facilities

This study aims to model a mixed traffic flow consisting of vehicles controlled by a driving automation system in the highway off-ramp system. The driving automation system includes no automation (NA), driver assistance (DA), partial automation (PA), and conditional automation (CA). Developing this model requires specifying the longitudinal and lateral driving characteristics of vehicles with different driving automation systems and then selecting appropriate car-following and lane-changing models. The proposed cooperative lane change model applied by CA considers the lane utility and car-following models of the following vehicles in adjacent lanes that can be obtained by vehicle-to-vehicle communication. The impacts of the market penetrations of PA and CA on traffic flow are investigated, and it is found that both PA and CA can improve traffic capability. Compared to PA, CA is more effective in improving the highway capacity near the off-ramp. The impact of CA on the headway indicates that CA enables vehicles that require changing lanes to find suitable gaps and changing lanes in a timely manner, which improves the capacity of the off-ramp and the overall freeway. By adjusting the lane change adventure factors, the contribution of CA to capacity can be further enhanced without compromising driving comfort and safety.

Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps

Crashes are occurred frequently at freeway off-ramps due to improper lane-changing (LC) behaviours. The LC behaviour is the main cause of freeway off-ramp crashes. It is important to warn the LC to reduce potential crashes. The uncertainty of LC behaviour increases the difficulties of predicting in advance. The off-ramps at Xi’an Raocheng freeway were chosen for investigation. The datasets were collected by the UAV. There was a total of 637 LC images extracted from the 200 minutes’ video stream. All LC behaviours were divided into twelve categories according to the changing direction and the influence of other vehicles in the target-lane or ego-lane. The machine learning technology is efficient in the image recognition. Thus, the vision technology was applied to devised a lane-changing recognition (LCR) model with the two-level convolutional neural network. A novel convolutional neural network based on the AlexNet was also proposed to compare with the LCR model. All samples were divided into a training dataset and a testing dataset for two models. The performance of two machines networks was compared. The training average accuracy was above 94.6% with the LCR model. The LCR model outperformed the model based on the AlexNet which was only 73.97% on average.

A queue length estimation and prediction model for long freeway off-ramps

Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main lanes of freeways is one of the traffic issues caused by vehicular queues that can be efficiently managed using dynamic queue information. In this paper, a case-based reasoning algorithm combined with a Kalman filter is developed to provide real-time queue length estimations and predictions on freeway off-ramps. The estimations are based on occupancy readings from three loop detectors installed on a ramp. The proposed method is examined using a micro-simulation model of an off-ramp with a length of 650 meters and a traffic signal downstream of the ramp. The simulation results show an accuracy of ±3.15 vehicles in the queue in 60-second time intervals. In addition, a rigorous sensitivity analysis is conducted to examine the performance of the algorithm under various demand loading scenarios, time intervals, number of detectors used, and errors in prior estimations. The results show that the model performs well in terms of estimating and predicting the length of long queues on freeway off-ramps at various congestion levels. The outcomes of this study can be utilized to activate dynamic, responsive, and proactive queue management and traffic control measures.

Impacts of Directional Rumble Strips on Vehicle Speeds and Driver Behavior at Freeway Off-Ramps

AbstractDrivers have an increased crash or incident risk when driving on freeway interchange ramps compared with the other interchange-related segments. Directional rumble strips (DRS), a pattern s...

Modeling Lane-Change Risk in Urban Expressway Off-Ramp Area Based on Naturalistic Driving Data

Abstract Off-ramp areas are considered the critical sections of urban expressways where the exiting vehicles and straight-through vehicles merge. Therefore, lane-change behaviors frequently occur at the upstream of the urban expressway off-ramp, which lead to high chance of traffic crashes. This study looks at the risk of lane-change behaviors in the multilane urban expressway off-ramp areas. First, lane-change process information of exit vehicles in urban expressway off-ramp area was extracted from the Shanghai Naturalistic Driving Study (SH-NDS) database. Second, for each lane-change movements of exit vehicles, a risk evaluation indicator (risk perception, RP) was adopted to quantify the lane-change risk. Based on the RP, the study proposed a four-rank risk classification criterion using K-means clustering to define the risk rank of each lane-change movement. Finally, a lane-change risk rank classification model was developed for traffic in the off-ramp areas of multilane expressways using four distinctive influencing factors. Four influencing factors, namely, traffic congestion level, demand lane change times, lane-change direction, and relative distance between vehicle and exit, were used to describe the traffic flow characteristics and exiting lane-change route for the modeling purpose. The risk model was developed using two support vector machine models, which were based on the partial binary tree structure and the directed acyclic graph structure, respectively. The results showed that the overall accuracy of the partial binary tree structure classifier was 65.71 % and the average AUC value was 0.9004, both of which shows a better performance of the partial binary tree structure classifier, compared with the directed acyclic graph structure classifier.

Cooperative Lane Changing Strategies to Improve Traffic Operation and Safety Nearby Freeway Off-Ramps in a Connected and Automated Vehicles Environment

The study proposes a cooperative lane changing strategy to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). The cooperative strategy was implemented by the coordination of behaviors between the diverging vehicle and its cooperative vehicle on the target lane. The Minimizing Overall Braking Induced by Lane Changes Model (MOBIL) and Intelligent Driver Model (IDM) were modified to develop a simulation platform for a CAV environment. The optimal cooperative lane changing zones were firstly calculated by a heuristic algorithm, and then were applied in the simulation platform to implement the cooperative strategy. Various metrics were considered to evaluate the proposed strategy, including: total travel time, surrogate safety measures and traffic waves in the system. The experimental results showed that the length of the optimal cooperative zones obtained in our strategy were smaller than the fixed zone required in modified MOBIL strategy. Moreover, the results indicated that the cooperative strategy with the optimal zones, could improve traffic operation, traffic safety and traffic oscillation as compared to the modified MOBIL strategy with the fixed zone. The cooperative strategy can be potentially implemented nearby highway off-ramps by vehicle-based control, with the applications of the aforementioned cooperative zones.

Field Verification of Directional Rumble Strips to Deter Wrong-Way Freeway Driving

AbstractThis paper summarizes the results of Phase II of a research project developing a novel design of directional rumble strips (DRSs) to deter wrong-way driving (WWD) onto freeway off-ramps. In...

The Hybrid Model for Lane-Changing Detection at Freeway Off-Ramps Using Naturalistic Driving Trajectories

In order to promote traffic safety at freeway off-ramps, this paper designed a hybrid model to identify a lane-changing with vision technology. The unmanned aerial vehicle was used to collect video stream data at five off-ramps for Xi'an Raocheng freeway during weekdays. The positional information-of-an individual vehicle is recorded at a frequency of 5 Hz. Each trajectory is composed of 30 positional records and all trajectories are divided into lane-changing and lane-keeping units. Features such as lateral driving speed, lane departure, and the lane deviation angle extracted from trajectory records are related to the lane-changing behaviors. We develop a hybrid model of the Gaussian Mixture Model and the Continuous Hidden Markov Model to identify lane-changing behaviors at off-ramps with these features. Basing on test set, we conduct a test for the hybrid model and the result shows that the prediction accuracy of the proposed model is as high as 94.4% for lane-changing behavior and 93.6% for lane-keeping behavior.

Access Separation at Interchanges: Examining Crash Rates on the Cross-Street and in the Transition Areas from Full to Partial Access Control

Access control remains an important concern to roadway agencies as the spacing of at-grade access points significantly affects both the safety and operational performance of highways. Significant variability exists with respect to policies dictating where the first access points may occur in the vicinity of interchanges. This study examines two important spacing criteria, which include: (1) the distance from the freeway off-ramp to the first access on the interchanging arterial roadway; and (2) the distance from a freeway on-ramp to the first at-grade intersection in areas with partial access control. To investigate these relationships, a series of crash prediction models were estimated that examined how crash risk changed with respect to access spacing while controlling for the effects of traffic volume and other pertinent characteristics. The results illustrate that at cross-streets near ramp terminal intersections, the crash rate generally increases as the distance to the nearest access point decreases. The same trend was also true of freeway transition areas. Ultimately, this research illustrates a complex relationship that exists among the proximity of the exit point of the controlled access facility and the adjacent access point, the volume of traffic along the roadway, and the volume of traffic at the access point.

A Novel Method to Integrate Speed and Emissions Characteristics in Pavement Marking Design

Pavement marking is a popular method to reduce freeway off-ramp speed. The objective of this study is to develop a methodology for selecting the optimal pavement marking design that will improve traffic safety and reduce emission levels in the freeway off-ramp. Six pavement marking pattern scenarios on a freeway off-ramp were examined through the following sub-tasks. The first step focused on the testing of six selected scenarios for pavement marking design using a VR driving simulator to obtain driving behavior patterns. Next, the driving behavior pattern for each scenario was implemented in VISSIM, a microscopic traffic flow simulation, to extract the speed reduction for each scenario considering vehicle interactions. Then, the vehicle emission level of each scenario was evaluated by a Motor Vehicle Emission Simulator (MOVES). Through a multiple-criteria decision making model, the best pavement marking design scenario that was both eco-friendly and effective in reducing vehicle speed was selected. Study results showed that transverse line type is the optimal design of pavement marking that improves traffic safety while decreasing emission at a freeway off-ramp. The proposed methodology in this study would quantitatively support the selection of optimal pavement marking design that will promote both traffic safety and traffic environment.