Roadway Capacity Research Articles

A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data.

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of "last-mile" transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060-3297 bikes/(h·m) and a pure bicycle capacity of 1538-2460 bikes/(h·m). The density-flow model calculates a pure EV capacity of 2349-2897 bikes/(h·m) and a pure bicycle capacity of 1753-2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.

Use of Engineering-Educational-Empowerment Model to Improve the Integrated Traffic Impact Analysis and Environment Impact Analysis Results

In order to control road carriage way capacity and/or road environment degradation due to transport activities, the Traffic Impact Analysis TIA document has been directed to be integrated within the Environment Impact Analysis EIA document. However, only medium and high trip attractiveness was considered. Consequently, cumulative impact due to dense small scale concentrated in a certain corridor not only decrease road capacity but also increase road environment degradation. These unintended situations, especially at fast growth corridors, was recorded, including the parking index, on-street parking impacts due to inadequacy in existing parking space capacity. This activity aims to socialized and discussed the technical and environmental impacts due to on-street parking in order to improve better knowledge and transport policy decision. Therefore, an integrated approaches - using a Focus Group Discussion - based on engineering, educational and empowerment method was undertaken to identify a change in stake holder’s perception towards the correlation between transport and land use. It was found that, although within low trip attractiveness level category, but a dense small scale of activities produces great roadside friction and contribute negative impact on roadway capacity and air pollutant essence. This strongly indicate that a micro zoning regulation is required to control the type, number, scale and dense of social-economic activities, particularly in fast growth corridors.

Capacity Reduction for Urban Road Due to Curbside Bus Stop: A Case Study of Srijana Chowk, Nepal

Curbside bus stops have a significant impact on traffic characteristics, including speed and roadway capacity. Srijana Chowk, the urban area within Pokhara metropolitan city, was selected for the study. The objective of the study is to evaluate the reduction in roadway capacity at the curbside bus stop at Srijana Chowk. The primary data was collected using videographic techniques. The analysis indicated that Srijana Chowk’s curbside bus stop caused an average stream speed reduction of 6.23%. The capacity at the Srijana Chowk bus stop was 2532 PCU/hr and 2718 PCU/hr at the curbside and on the opposite side of the bus stop, respectively. Similarly, the capacity reduction was analyzed to be 16.62% and 6.84% by the Indonesian Highway Capacity Manual and Greenshield’s model, respectively

Examining the Land Use and Land Cover Impacts of Highway Capacity Expansions in California Using Remote Sensing Technology

Highway capacity expansion has wide impacts on land use and land cover (LULC). Cost–benefit analyses and environmental review processes for roadway capacity expansion and maintenance decisions do not comprehensively consider the LULC impacts. This study examined land cover changes directly associated with highway expansion in California, and the relationship between land use and the vegetation impacts of highway projects using satellite remote sensing data. The methodology involves a geospatial analysis of normalized difference vegetation index (NDVI) data in 18 sites across California before and after highway expansion project completion. We accounted for seasonality and included a set of control sites. Findings indicate that the impacts of highway expansion on changes in NDVI are diverse, stressing the importance of the environmental context around each individual project site. Sites that are located near less-developed areas with more extensive natural vegetation (e.g., sprawled areas or exurbs), show a significant decline in NDVI values. Virtually all sites with insignificant changes in NDVI after highway expansion are located in areas that already exhibit heavy urban development (e.g., Los Angeles, San José) or are otherwise located near large expanses of bare, non-vegetated earth. Also, project sites that experienced multiple types of construction (i.e., adding more lanes, widening sections, bridge renovation, etc.) were more likely to exhibit decreasing NDVI values compared with project sites that only experienced one type of construction. Decisions about highway construction and capacity expansion should consider the context and the full environmental impacts, including land use and land cover changes over time.

Determination of Motorcycle Equivalent Unit and Road Capacity: A Case Study of Selected Road Sections of Kathmandu Valley

The research focused on road congestion in Kathmandu Valley, Nepal, caused by the prevalence of small motorized vehicles, especially motorcycles. The objective of the study was to quantify the impact of different vehicle classes on congestion by calculating Motorcycle Equivalent Units (MEU) and determining roadway capacity using speed and effective space parameters. The field data collected over two locations in Kathmandu Valley were analyzed using Speed Estimation from Video Data (SEV) software that revealed a strong correlation between vehicle speed and effective space, represented by a quadratic non-linear model. MEU values were determined for different vehicle classes, and fundamental speed-flow-density relationships were used to calculate roadway capacity. Vehicles like motorcycles, standard cars, big cars, utility, minibusses, buses, light commercial vehicles, two/three-axle trucks, and multi-axle trucks had MEU values of 1, 3.32, 4.91, 4.65, 9.22, 12.22, 6.72, 13.57 and 18.04 respectively. The study indicated peak capacities during the evening hours on specific road sections. For instance, the Gatthaghar-Kaushaltar and Balkumari-Gwarko sections reached their maximum capacities at 12,335 and 20,589 (motorcycles per hour per direction), respectively. The study emphasized the significant influence of speed on effective space and roadway capacity, suggesting further exploration of additional factors like driver characteristics, gender, age, income, road geometry, and level of service using MEU, along with the potential for developing a motorcycle simulation model in Kathmandu valley.

Determination of Passenger Car Unit and Capacity at Grades of Two Lane Undivided Highway: A Case Study of Khurkot–Dhulikhel Section of BP Highway

The majority of Nepal’s National Highway and feeder road system comprises two-lane highways with heterogeneous traffic conditions. Factors such as lane width, gradient, lateral clearance, and shoulder conditions can influence the passenger car unit (PCU) and roadway capacity. Recognizing that grade magnitude (%) is an important yet unexplored roadway factor in Nepal, and the present study primarily aims to estimate the passenger car unit on two-lane undivided hilly roads with gradients under prevailing heterogonous traffic conditions. This study aims to determine the PCU values and roadway capacity at grade under mixed nature traffic flow for a two-lane undivided highway. In this research, the necessary data were collected at five sections of BP highways using a digital video recorder, which was then analyzed to assess traffic characteristics and calculate PCU values. Using the PCU values, the road capacity was estimated using Greenshield’s model. For capacity estimation, flow and density were addressed using dynamic PCU values. This study reveals that the PCU of all categorized vehicles increases linearly with the increase in gradient while roadway capacity decreases with an increase in grade. It was observed that PCU values obtained for two-wheelers in all sections are smaller than the PCU values specified in NRS 2013, whereas for buses, trucks, and LCVs, they are higher than the PCUs given in NRS 2013. The study highlights the impact of road grade on the PCU for different vehicle categories on a highway. Additionally, the study demonstrates that the developed capacity values are realistic and consistent with the values presented in the Highway Capacity Manual (HCM) of developing countries such as Indonesia and China. These results are expected to be beneficial for practitioners and the ongoing effort to develop a highway capacity manual.

A study on the capacity assessment of turbo roundabouts and the applicability in Vietnam

A turbo roundabout (or Turbine) is an improvement from conventional roundabouts with lanes running in a spiral shape, helping vehicles to enter the intersection smoothly and eliminate conflicts, reducing traffic congestion as well as the risk of traffic accidents. The turbo roundabout is a modern intersection, newly developed around 1996, initially in the Netherlands, then expanded to many European countries, Russia, and North America. In Vietnam, this type of intersection is still a novelty and requires more scientific exploration. Based on research from developed countries and through the use of Vissim traffic simulation software, the paper presents an analysis and evaluation of the effectiveness of Turbo roundabouts compared to conventional roundabouts. The study also proposed and developed shifted exponential models, capable of well describing the nonlinear relationship between capacity of entry roadway with circular flow. Results show that the turbo roundabout is a potential solution for Vietnamese traffic. The study's evaluation using a basic layout of a two-lane turbo roundabout with only automobile traffic composition limits its applicability to mixed traffic environments. Initially, the study proposes the installation of turbo roundabouts on access roads of freeways, or motor highways with low-to-medium traffic volume

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

The role of information dissemination in sustaining a stable and resilient traffic network under disruption

ABSTRACT A functioning roadway system is key to any society and economy. Traffic events such as maintenance and accidents reduce roadway capacity and can have immediate, large and lasting impact on traffic conditions. This paper investigates the role of information dissemination as a control tool for reducing the negative effects of a traffic event. It examines how timely information can prevent unnecessary route changes and counteract misinformation, restoring traffic equilibrium faster. Systems that recover equilibrium more rapidly are considered more resilient. A traffic flow evolution model is applied to analyze network behavior post-disruption, with a model parameter interpreted as an information dissemination penetration rate adjustable by operators. Numerical experiments investigate the effects of the penetration rate on travel times, revealing a critical rate for quickly re-establishing a new equilibrium. Beyond this rate, greater traffic oscillations can be expected. The study determines the best information penetration rate to maximize network resilience while ensuring stability.

Analysis of roadway capacity for heterogeneous traffic flows considering the degree of trust of drivers of HVs in CAVs

Future traffic flows will be driven by a mixture of CAVs and HVs. Under heterogeneous traffic flow conditions, drivers of HVs will change their following distance when driving behind CAVs due to psychological factors, which will affect road capacity. This study builds a heterogeneous traffic flow model based on the Markov chain theory, which can derive the proportions of different car-following types under different vehicle distributions. The roadway capacity model is constructed by comprehensively considering the penetration rate of CAVs, platoon intensity, maximum platoon size, and the driver's trust degree of CAVs, and the effects of the four factors on the roadway capacity are analyzed through numerical experiments. Finally, the theoretical model was validated using python-sumo joint simulation experiments. The results show that increasing the degree of trust that HV drivers have in CAVs can further enhance roadway capacity, and the effect of the degree of trust on capacity becomes progressively smaller as the platoon intensity increases. It was also found that platoon intensity can be harmful to the improvement of capacity when the degree of trust is high and the maximum platoon size is small.

Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts.

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.

Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use

Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing literature, which could impact the occurrence of crashes on freeways. This study contributes to the safety literature by analyzing various potential crash contributing factors related to PTSU operation and design elements through the development of short-term Safety Performance Functions (SPFs). A comparison of the estimated models demonstrated that by utilizing the mixed distribution and allowing the posterior parameter estimates of explanatory variables to vary from one observation to another, the Random Parameters Negative Binomial-Lindley (RPNB-L) model outperformed the traditional NB and fixed coefficient NB-L models. The results of the proposed RPNB-L model indicated that the PTSU implemented sections experienced a lower number of traffic crashes compared to the non-PTSU freeway sections. Among the attributes related to PTSU operation and design elements, the usage of the leftmost shoulder lane as PTSU, the presence of emergency rest areas for damaged vehicles, and adequate shoulder width would significantly reduce crash frequency for the PTSU implemented freeways. Moreover, investigation of the identified hotspots revealed that the transition areas (start/end locations of PTSU) are the most critical sections. The findings from this research could assist transportation agencies to take appropriate countermeasures for preventing and reducing crash occurrences on PTSU implemented freeways.

Capacity drop at the bus stop on multilane divided urban roads under mixed traffic conditions

Time headway characteristics are of great importance for planning and design of roadway. The present study determines time headway distribution pattern of vehicular stream at various urban roads and evaluate drop in capacity of roadway section having curb-side bus stops. Field data for present study was collected at 8 different locations on four-lane and six-lane divided sections in the cities of Warangal and Hyderabad in India. The mean time headway of heterogeneous vehicles fitted to various headway distributions for estimating capacity of road section. The factors affecting the capacity have been identified as bus frequency, dwell time, and the average roadway width lost. Percentage reduction in capacity (PRC) is determined to develop regression model. Further, speed-flow diagrams were developed using field observed traffic flow data for determining parameters free-speed, critical speed, and roadway capacity on selected section. After comparing capacity values obtained from two methods, the study determined avg. time headway of vehicular stream under the traffic flow at capacity and free flow conditions. The study also finds capacity value of bus stop section using methodology provided in Indo-HCM (2017). The PRC values estimated based on Indo-HCM guidelines are found to be significantly higher than those estimated by the proposed PRC model. It is due to the Indo-HCM guideline considering only one parameter i.e. bus frequency. The study finds, in addition to bus frequency the dwell time, roadway width lost, and presence of an extra lane also influences the drop in capacity of bus stop section on multilane divided urban roads.

Lane management for mixed traffic flow on roadways considering the car-following behaviors of human-driven vehicles to follow connected and automated vehicles

The widespread adoption of emerging connected and automated vehicles (CAVs) highlights the need for identifying the roadway capacity of mixed traffic flow with CAVs and human-driven vehicles (HDVs) for future traffic management. Previous studies focus on analyzing the impacts of CAV technologies on the mixed traffic capacity. However, research on how different car-following behaviors of HDVs to follow CAVs affect the mixed traffic capacity is still lacking. This study proposes an analytical formulation of the mixed traffic capacity based on the probability distributions of HDVs and CAVs and three types of behavior willingness (i.e., believer, neutral, and skeptic) of HDV-following-CAV in a mixed traffic environment. The sufficient conditions are derived for mixed traffic capacity to increase or decrease with the behavior willingness and CAV penetration rate. Based on that, an analytical lane management (LM) model is further proposed to determine the optimal number of CAV dedicated lanes (CDLs) and CAV non-dedicated lane (CNL) strategies that maximize the mixed traffic throughput, considering the mixed traffic demand, behavior willingness, and CAV penetration rate. The results from numerical experiments show that the proposed LM strategies can significantly improve mixed traffic throughputs by deploying optimal CDLs and CNL strategies, compared to one without the LM strategy. The mixed traffic throughputs improve and reduce as the trust willingness and skeptic willingness increase and decrease, respectively. Moreover, the increment in CAV penetration rate does not certainly correspond to the high mixed traffic throughputs. The behavior willingness and heterogeneous headways could be jointly considered to guarantee the positive influences of CAV penetration rate on the mixed traffic throughputs when implementing the LM strategy for the mixed traffic flow.

Study on deformation and instability characteristics and control technology of deep soft rock roadway with strong mining

With the development of deep mining of coal resources, the problem of deep roadway management is becoming increasingly prominent. This study aims to analyze the deformation and failure mechanism of deep-buried soft rock roadway under the influence of mining. Taking the material roadway of 3205 working face in Xin’an Coal Mine as the engineering background, this study analyzed the characteristics of roadway deformation and failure and its influencing factors through field investigation. This study firstly expounded the principles of graded continuous anchorage for roadway roof and then proposed a new support scheme whose feasibility was verified by numerical simulation. Field verification shows that the optimized support scheme can effectively control the deformation of roadway surrounding rock. Specifically, the deformation of roof and two sides is reduced by 91% and 50% respectively; the maximum crack depth decreases from 9.56 to 3.26 m, indicating that the roof crack development has been significantly inhibited. The new support scheme builds a solid anchored rock beam structure with thick roof layer, which can maintain the long-term stable bearing capacity of deep-buried composite roof roadway. This study provides a solution for the surrounding rock control of deep soft rock roadway.

Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks

Autonomous vehicles (AVs) show promise for increasing roadway safety and capacity. During the AV transition era, which will be characterized by a mixed fleet of AVs and human-driven vehicles (HDVs), it is expected that the allure of these prospective benefits will motivate road agencies to allocate AV-dedicated lanes. This paper proposes a sustainability-driven AV-dedicated lane and pricing policy (SALP) framework that addresses the three pillars of sustainable development—social, environmental, and economic. The framework is formulated as a bi-level problem where the upper-level model yields decisions on the timing, location, and quantity of AV-dedicated lanes and tolling levels to minimize total travel time, emissions, and electricity consumption costs (that is, the economic and environmental pillars). To alleviate potential inequity (the social pillar), two considerations are proposed: revenue neutrality to compensate for the increase in travel costs of travelers and an equity constraint to limit the exacerbation of HDV travel costs. At the lower level, travelers react to the decisions made at the upper level by choosing their vehicle types (AV vs. HDV) and routes. The SALP is solved using Genetic and Frank-Wolfe algorithms. The results of the numerical experiments suggest that the proposed SALP addresses all three pillars, as it yields significant reductions in total travel time, emissions, and electricity costs.

Incentives to Encourage the Adoption of Connected and Automated Vehicles: Lessons Learned from Hybrid-Electric Vehicle Incentive Programs

Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types.

Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns

Roadway congestion leads to wasted time and money and environmental damage. Since adding more roadway capacity is often not possible in urban environments, it is becoming more important to use existing road networks more efficiently. Toward this goal, recent research in real-time, schedule-driven intersection control has shown an ability to significantly reduce the delays incurred at signalized intersections. Such approaches operate by building a predictive model of when locally sensed approaching traffic is expected to arrive at a given intersection, and then using the model to generate a signal timing plan (a phase schedule) that minimizes the cumulative delay of this traffic as it moves through the intersection. In this paper, we consider whether further reduction in delay could be gained by applying this predictive model to dynamically reroute some portion of vehicles willing to share their destinations along less congested paths. We developed an algorithm that simulates the current traffic state forward at each vehicle decision point, based on knowledge of other vehicles’ current routes and traffic signals’ control algorithms, and evaluated it using the SUMO microscopic simulator on different road networks (one as a simple synthetic example and the other taken from the real world) using different traffic signal control algorithms (fixed-timing plans and schedule-driven intersection control). Experiments carried out on combinations of networks and traffic signal control algorithms show that our rerouting protocol reduces delay for both vehicles participating in route guidance (adopters) and those that do not (non-adopters) and that the reduction in delay generally increases as the proportion of adopters does.

Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels

Crash injuries not only result in huge property damages, physical distress, and loss of lives, but arouse a reduction in roadway capacity and delay the recovery progress of traffic to normality. To assess the resilience of post-crash tunnel traffic, two novel concepts, i.e., surrogate resilience measure (SRM) and injury-based resilience (IR), were proposed in this study. As a special kind of semi-closed infrastructure, urban tunnels are more vulnerable to traffic crashes and injuries than regular roadways. To assess the IR of the post-crash roadway tunnel traffic system, an over-one-year accident dataset comprising 8621 crashes in urban roadway tunnels in Shanghai, China was utilized. A total of 34 variables from 11 factors were selected to establish the IR assessment indicator system. Methodologically, to tackle the skewness issue in the dataset, a binary skewed logit (Scobit) model was found to be superior to a conventional logistic model and subsequently adopted for further analysis. The estimated results showed that 15 variables were identified to be significant in assessing the IR of the roadway tunnels in Shanghai. Finally, the formula for calculating the IR levels of post-crash traffic systems in tunnels was given and would be a helpful tool to mitigate potential trends in crash-related resilience deterioration. The findings of this study have implications for bridging the gap between conventional traffic safety research and system resilience modeling.

Impact of Autonomous Vehicles on the Car-Following Behavior of Human Drivers

The past few years have been witness to an increase in autonomous vehicle (AV) development and testing. However, even with a fully developed and comprehensively tested AV technology, AVs are anticipated to share the roadway network with human drivers for the unforeseeable future. In such a mixed environment, we use naturalistic driving data from the next generation simulation (NGSIM) and Lyft Level 5 (Lyft L5) prediction data sets to investigate whether the existence of AVs influences the car-following behavior of human drivers. We use time headway time series as a proxy to capture the car-following behavior of human drivers. We then develop a nested fixed model to find possible changes in behavior when human drivers are following different types of vehicles (i.e., human-driven vehicles or AVs). The factors included in this model are the platoon structure (a legacy vehicle following a legacy vehicle, and a legacy vehicle following an autonomous vehicle), road type (freeway and urban), time period (morning and afternoon), lane (right, middle, and left), and the source of the data (NGSIM and Lyft L5). Results indicate a statistically significant difference between the car-following behavior of drivers when they follow a human-driven vehicle compared to an AV. This change in the car-following behavior of drivers is manifested in the form of a reduction in the mean and variance of time headways when human drivers follow an AV. These findings can bridge the gap between anticipated and real-world impacts of AVs on traffic streams and roadway capacity, providing meaningful insights on the influence of AVs on the driving behavior of humans in a naturalistic driving environment.