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On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.

The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.

Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.

Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment.

The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions.

The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.

This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Freeway work zones have been traffic bottlenecks that lead to a series of problems, including long travel time, high-speed variation, driver’s dissatisfaction and traffic congestion. This research aims to develop a collaborative component of connected and automated vehicles (CAVs) to alleviate negative effects caused by work zones.

The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations.

Simulation results show that, with the proposed component and penetration of CAVs, the average performances (travel time, safety and emission) can all be improved and the stochasticity of performances will be minimized too.

To the best of the authors’ knowledge, this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle (CV) Pilot Deployment Program (WYDOT Pilot).

This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot. An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data.

This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications. Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects. Analyses were conducted on data collected during the baseline period, and pre-deployment conditions were established for 1 performance measures. Additionally, microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system, particularly when evaluating system performance under various CV penetration rates and/or CV strategies.

The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities.

To the best of the authors’ knowledge, this is the first research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions. This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.

This paper aims to examine the time it would take to provide medical prophylaxis for a large urban population in the wake of an airborne anthrax attack and the effect that various parameters have on the total logistical time.

A mathematical model that evaluates key parameters and suggests alternatives for improvement is formulated. The objective of the model is to minimize the total logistical time required for prophylaxis by balancing three cycles as follows: the loading cycle, the shipping cycle and the service cycle.

Applying the model to two representative cases reveals the effect of various parameters on the process. For example, the number of distribution centers and the number of servers in each center are key parameters, whereas the number of central depots and the local shipping method is less important.

Various psychological factors such as mass panic are not included in the model.

There are few papers analyzing the logistical response to an anthrax attack, and most focus mainly on the strategic level. The study deals with the tactical logistical level. The authors focus on the distribution process of prophylaxis and other medical supplies during the crisis, analyze it and identify the parameters that influence the time between the detection of the attack and the provision of effective medical treatment to the exposed population.

WIth limited research on the effects of variable message sign (VMS) message content and verbiage on revealed driver behavior, this study aims to investigate how different verbiage of crash-related messages are related to the diversion rate.

Using ordered logit models, the associations of message verbiage with diversion rates during crash incidents were assessed using five years of VMS message history within a section of I-15 in the state of Utah.

A significant impact of message verbiage on the diversion rate was observed. Based on the analysis results, the crash message verbiage with the highest diversion was found to be miles to crash + “prepare to stop,” followed by crash location + delay information, miles to crash + “use caution” + lane of the crash, etc. In addition, the diversion rate was found to be correlated to some roadway characteristics (e.g. occupancy in mainline, weather condition and light condition) along with the temporal variations.

These findings could be used by transportation agencies (e.g. state department of transportation [DOTs]) to make informed decisions about choosing the message verbiage during future crash incidents. This study also revealed that higher diversion rates are associated with a shorter distance between the crash location and VMS device location, recommending increasing the number of VMS devices, particularly in crash-prone areas.