Vehicle Headway Research Articles

Capacity modeling for mixed traffic with connected automated vehicles on minor roads at priority intersections

ABSTRACT As connectivity technology advances, connected automated vehicles (CAVs), connected vehicles (CVs), and regular vehicles (RVs) form mixed traffic traffic at priority intersections. This paper presents an analytical capacity model to evaluate the impacts of such mixed flow on minor roads. We define two platoon types based on the leading vehicle: CAV-led and CV/RV-led platoons. Assuming headway distribution on the major roads follows the M3 distribution, we derive probabilities of these platoons passing through the intersection during a single time gap on a major road. According to the expected probabilities of different vehicle types, the probabilities of these two types of platoon configurations are obtained. Based on this, we establish a mixed traffic capacity model. The capacity model’s effectiveness is validated through numerical experiments. The results demonstrate that the proposed model can analyze the impacts of key factors on mixed traffic capacity, including CAV penetration rate, CV proportion, traffic volume on major roads, and vehicle headway. HIGHLIGHTS Propose an analytical capacity model for mixed traffic on minor roads at priority intersections. Conduct numerical experiments to validate the effectiveness of the proposed mixed traffic capacity model. The proposed model can be applied to evaluate the capacity impacts of connected automated vehicle penetration rate, connected vehicle proportion, traffic volume on major roads, and vehicle headway at priority intersections.

Multiclass Traffic Assignment Model Considering Heterogeneous Stochastic Headways of Autonomous Vehicles and Human-Driven Vehicles

Multiclass Traffic Assignment Model Considering Heterogeneous Stochastic Headways of Autonomous Vehicles and Human-Driven Vehicles

Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.

Car-following model based on spatial expectation effect in connected vehicle environment: modeling, stability analysis and identification

In driving situations, if drivers have advanced access to downstream traffic conditions ahead of time, they may modify their driving behavior accordingly. This proactive approach can help reduce frequent acceleration and deceleration of vehicles and minimize traffic congestion. This paper considers the average headway of downstream vehicles to predict the traffic conditions of downstream roads. Based on the full velocity difference (FVD) model, we propose a car-following model that incorporates spatial expectation effect (SEFVD). Firstly, a linear stability analysis is performed on the SEFVD model, and the results indicate that the critical value of the SEFVD model is relatively small, and the stabilization region increases gradually with the number of vehicles downstream. Secondly, employing perturbation amplitudes on a virtual circular road, a numerical simulation of the SEFVD model is carried out and compared with the FVD model. The results demonstrate that the SEFVD model has the ability to suppress the formation of traffic oscillations and stabilize vehicle formation more quickly. Simulation results for the 11 cars starting from a traffic signal indicate that the delay time and kinematic wave speed of the SEFVD model are within reasonable limits. To evaluate the changes in energy use, we also carried out energy consumption experiments. The experimental findings show that the SEFVD model uses less energy compared to the FVD model. Finally, the parameters of the models are calibrated and verified by using NGSIM data. The total calibration error of the SEFVD model (m = 2) decreases by 42.97% compared to the FVD model, and the total validation error decreases by 29.3%. The results show that the proposed model can fit the actual data well, and the fitting error is smaller.

Large eddy simulation of the cavity shedding characteristics of ventilated cavitation around the underwater vehicle

Ventilated cavitation is utilized for reducing drag and improving the headway of underwater vehicles. The ventilated cavitating flow around the axisymmetric body is simulated using the Large Eddy Simulation (LES) method in this study. Three cavity morphologies with different ventilation rates (Cq) are observed: Foamy Cavity (FC), Continuous Transparent and Foamy Cavity (CTFC) and Super Cavity (SC). The cavity shedding characteristic of the three different cavity morphologies is revealed. For partial cavity, the periodic cavity shedding is dominated by the local high pressure, re-entrant jet and vortex. The distributions of the turbulent kinetic energy and the pulsation enstrophy exhibit different migration mechanisms during the cavity shedding. The energy characteristic is reflected by three terms of the entropy generation. And the entropy production rate by direct dissipation occupies a dominant position, accounting for about 80% of the total entropy production, while the entropy production rate by viscous dissipation only accounts for 4%. For super cavity, gas leakage is dominated by a re-entrant jet induced by the intense adverse pressure gradient. And the results also demonstrated the velocity and vorticity pulsations around the test body wrapped in a super cavity are not intense.

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.

Impact of driving prediction on headway and velocity in car-following model under V2X environment

The car-following model holds significant importance within the realm of the microscopic approach for examining the dynamic behavior of traffic and creating safe collaborative driving systems. Information sharing among vehicles, individuals and the surrounding environment can be made available to drivers with the help of V2X technology. Additionally, to optimize the traffic flow, it becomes essential to accurately predict the headway and velocity of leading vehicles. In this study, a car-following model is developed to analyze the impact of driving prediction on headway and velocity in V2X communication. According to the stability criteria which is discovered through linear analysis, it is observed that the stable region is more for the proposed model as compared to OV and FVD models. Nonlinear analysis is applied to derive Burger’s and mKdV equations, allowing for the exploration of triangular and kink–antikink waves in stable and unstable regions, respectively. Moreover, the stability interval of the model is determined by using the bifurcation analysis. The simulation results revealed that the stable zone enhances with the increase in the value of the prediction coefficient of headway and velocity of vehicles. Also, the numerical results are in accordance with the theoretical study. Therefore, we can depict that the proposed model is more effective in improving traffic stability due to its capability to predict future vehicle’s headway and speed.

Evaluation of Car-Following Model for Mixed Autonomous and Human Driven Vehicles on Road Facilities.

Driverless cars are emerging slowly but bear the opportunity to improve the traffic system efficiency and user comfort. For the near future, a mix of human-driven and driver-less vehicle co- existence is inevitable. The quest to integrate autonomous and human-driven vehicles has created numerous critical questions in the road traffic system: How can autonomous and human- driven vehicles co-exist efficiently? What are the prospects of breaching the existing car-following model widely adopted to control the longitudinal movement of vehicles and control the driver’s maintenance of a safe distance? There is no doubt, that the emergence of autonomous vehicles and their integration is altering the existing traffic theories and the idea behind the car-following model. Currently, researchers are working hard to efficiently describe the physics behind the traffic flow theory of car-following models in mixed traffic. This work investigated a homogeneous car-following model sensitivity with varying human drivers’ reaction time in mixed traffic. The ability to predict the optimal vehicle response time in a platoon to the behaviour of its predecessor is vital in predicting the effect a change in the driving environment of mixed traffic has on traffic flow. The major complexities surrounding the car- following model in a mixed traffic flow environment are how the traffic participants share the road intersection space and the car-following behavior of human-driven vehicles. Investigation in microscopic traffic flow describes the way a single car be- haves and then introduces the impact another car has on a car’s behavior to address the mixed traffic flow. The traffic speed performance under the different traffic conditions suggests that the proposed mixed traffic car-following model outperforms the existing car-following model for human-driven vehicles with an average of 22.59mph. There is a significant throughput improvement in a mixed traffic environment when the distance headway of autonomous vehicles is adjusted to support the research hypothesis.

Characteristics of naturally formed semitruck platoons on interstate highways and their implications for truck platooning field deployment

This paper presents an effort to address urgent issues associated with semitruck platooning. The weigh-in-motion data were first examined to determine the characteristics of naturally formed semitruck platoons. A safety-based two-vehicle platoon model was then utilized to determine the critical headways for assessing the safety of semitruck platooning in various driving scenarios. The possibilities for passenger vehicles to cut into semitruck platoons were assessed using actual vehicle headway data. Finally, the potential impacts of semitruck platooning on typical interstate highway pavements were assessed using finite element analysis. Most semitruck platoons consist of two to three vehicles. An optimal platoon gap of 0.94 s was identified for connected and automated semitruck platooning. The potential impacts of truck platooning on concrete pavement were negligible for a platoon gap greater than 0.2 s. The maximum possible impact was less than 4% in terms of the service life reduction for interstate asphalt pavement.

Assessing the impacts of connected-and-autonomous vehicle management strategy on the environmental sustainability of urban expressway system

Urban expressway network has become essential for over 30% of road trips in urban areas, making them significant in shaping environmental sustainability. Recently connected-and-autonomous vehicle (CAV) technology is expected to significantly transform the urban expressway systems, but little attention has been attributed to quantitatively assessing the impacts of managing CAV in mixed traffic flow on urban expressway environmental sustainability. To address this research gap, this study proposes a comprehensive simulation-optimization framework. The simulation component involves multiclass cell transmission model that simulates and evaluates the traffic flow of mixed human-driven vehicles and CAVs on the expressway, whereas the optimization component proposes a novel management strategy that jointly considers the dedicated lanes and speed management. An analytical tool is developed and validated in North-South Elevated Expressway in Shanghai, China, and further investigated the sensitivity of environmental impacts to vehicle headway, travel demand, and CAV market penetration rate. The results indicate that use of the proposed tool can reduce the total fuel consumption and exhaust emissions compared to baseline scenario. Overall, the insights from this study are valuable for policymakers of transportation in future cities, who develop management strategies on urban expressways that enhance the potential impact of CAVs on environmental sustainability.

Empirical investigation of shared space traffic: A comparison to conventional urban road environment

Shared space is an unconventional concept that is not based on formal rules and standards, as it encourages road users to share the same road space with little physical or visual separation. Consequently, this concept creates intriguing research questions that have not been fully answered yet, i.e., a) can a shared space road section produce more pedestrian crossings? b) what is the relationship between pedestrian crossings and traffic speeds? and c) what are the differences with a conventional road when motorizing traffic dominates in shared space? This study examines traffic conditions in shared space by answering these research questions. More specifically, it uses Amalias Street in Nafplio Greece as a case study. This road is divided into two main sections, namely: the conventional road section and the shared space road section, allowing meaningful comparisons. The collected data are further analyzed by developing multiple linear regression models that predict pedestrian crossings and mean car speeds in both sections. This study discusses model outputs with the literature to export valid conclusions. The results show that pedestrian crossings were increased in shared space when vehicle headways were high. Shared space results in a significant drop in car speeds that is confirmed by previous studies; surprisingly, the variance of car speeds was also reduced, leading to a more homogenous driving behavior. Pedestrian crossing rate significantly influences car speeds in shared space, while this relationship was not significant in the conventional road section. Shared space seems to calm traffic speed and allow coexistence even when motorizing traffic dominates.

Microscopic Analysis of Time Headway of Vehicles on Two-Lane Highways with Mixed Traffic

Microscopic Analysis of Time Headway of Vehicles on Two-Lane Highways with Mixed Traffic

Adaptive Headway Control Algorithm for Mixed-Traffic Stabilization and Optimization with Automated Cars and Trucks

Automated vehicle (AV) technology provides an opportunity to increase capacity and reduce congestion while maintaining stability and flow rate by allowing AVs to control their movements using references such as vehicle headways. Studies have been conducted on advancing the control algorithm of automated cars, and recent research has focused on different AV market penetration ratios. However, a straightforward way to control AVs using model parameters considering different AV types (i.e., cars and trucks) has seldom been both investigated and implemented. A method is developed to show the feasibility of controlling AVs using the desired headway as a variable to ensure stability and satisfy demand. The proposed adaptive headway control method for automated vehicles can stabilize flow and optimize flow rate in a mixed-traffic environment with four vehicle types: (1) AV cars, (2) non-AV cars, (3) AV trucks, and (4) non-AV trucks. At various AV market penetration ratios and truck percentages, the relationship between AV headways and stability is investigated. The model is implemented and tested in a simulation environment based on real-time interactions of the External Driver Model and VISSIM. Numerical analyses demonstrate that if the truck percentage is 80% or less, traffic is stable with the proposed control algorithm. The proposed algorithm is evaluated in microscopic simulation. Compared with the baseline case with no headway control, the proposed adaptive headway control algorithm can lead to less oscillation in traffic flow and can also reduce the delay by 23.19% while increasing the average travel speed up to 9.09%.

Investigation of the discharge flow rate patterns at real-time traffic signal control intersections

This study investigates the effects of variable queue lengths and green times on discharge flow rates at real-time managed intersections in Mersin, Turkey. For this purpose, traffic flow data were collected at two different signalized intersections during morning peak hours for two days. The traffic data including the time headways, queue lengths for each cycle were derived from video records via MATLAB coding while the signal timing data were obtained from Mersin Metropolitan Municipality. The impact of variable queue lengths and green times on discharge flow rate were evaluated separately via analysis of variance (ANOVA) tests. The results indicated that time headways of the first vehicles in the queue were statistically larger than the time headways of the remaining vehicles in the queue (p-value<0.05). On the other hand, the time headways of the remaining vehicles in the queue were not found statistically different at 95 confidence level (p-value>0.05). Furthermore, the effect of the variable green time on discharge flow rate revealed that the significant difference was only observed for the first twelve seconds of the green time.

The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data

Most existing studies on queue length estimation based on license plate recognition (LPR) data require multisection LPR data. Studies based on single-section LPR data cannot ensure the accuracy and stability of the estimates when missed detections occur, which greatly limits the practicability of existing studies. Therefore, using single-section LPR data, this study proposes a lane-based queue length estimation method based on a two-dimensional Gaussian mixture model. First, the LPR data were processed to obtain the departure times and time headway of vehicles. Then, the two-dimensional Gaussian distributions of queued vehicles and nonqueued vehicles were fitted, and the expectation-maximization algorithm was adopted to solve the distribution parameters. Finally, the queuing status of each vehicle was determined, and the lane-based queue length was estimated based on the last identified queued vehicle in the cycle. The empirical results showed that the mean absolute errors (MAEs) of the proposed method were just 1.3 veh/cycle under no missed detections and 2 veh/cycle under a 20% missed detection rate, outperforming existing methods. The simulation results indicate that the proposed method can achieve accurate estimates under various traffic demands. In addition, the proposed method can be extended to real-time applications and multisection LPR systems.

A Novel Spatio-Temporal Synchronization Method of Roadside Asynchronous MMW Radar-Camera for Sensor Fusion

Roadside sensors, such as camera and millimeter-wave (MMW) radar, provide traffic information beyond the visual range of intelligent vehicles in cooperative vehicle-infrastructure systems. Unlike onboard equipment, roadside sensors are affiliated with different systems and lack synchronization in both space and time. In this paper, we propose a novel spatio-temporal synchronization method of asynchronous roadside MMW radar–camera for sensor fusion, which utilizes features of the scenario to extract lane line corner points to pre-calibrate the camera. Based on the consistent time flow rate of the separate sensors, multiple virtual detection lines are set up to match the time headway of successive vehicles and conduct objective matching to track data. Finally, a synchronization optimization model is formulated and a constrained nonlinear minimization solver is applied to tune the parameters. Measure data from Donghai Bridge in Shanghai is applied to verify the feasibility and effectiveness of the method. The results determine that there are 33 frames (33*40 ms) of temporal deviation between the camera and the radar in this case. After the synchronization, the average spatial deviation is reduced from 2.47 m to 0.42 m in the X-direction and 64.06 m to 2.34 m in the Y-direction, respectively. This study provides an economical and effective way to solve the problem of spatio-temporal synchronization of roadside sensors.

A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics

Autonomous driving technology and vehicle-to-vehicle communication technology make the hybrid driving of connected and automated vehicles (CAVs) and regular vehicles (RVs) a long-existing phenomenon in the coming future. Among the existing studies, IDM models are mostly used to study the performance of homogeneous traffic flow. To explore the stability of mixed traffic flow, an extended intelligent driver model (IDM) based car-following model was proposed for mixed traffic flow (MTF) with both CAVs and RVs, considering the headway, the speed and acceleration of multiple front vehicles, as well as the response characteristics of RV drivers. Through the linear stability analysis, the criterion for the stability of MTFs was derived, and the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF are discussed. Based on the above theoretical model, a numerical simulation was conducted in two typical scenarios of starting and braking. The results showed that, at the microscopic scale, the vehicle in the Cooperative Adaptive Cruise Control (CACC) mode could significantly decelerate in response to the interference from other vehicles in the same traffic environment. At the macroscopic scale, as the penetration rate of CAVs increased, the overall acceleration fluctuation of the traffic flow decreased. At the same penetration rate of CAVs, the higher density of CAVs coincided with the higher stability of the MTF. When the penetration rate of CAVs was 50%, the degree of distribution had the greatest impact on the MTF. When the penetration rate of CAVs exceeded 70%, the degree of distribution had little impact on the MTF. This research can provide basic theoretical support for the management and control of MTF in the future.

Characterization of heavy vehicle headways in oversaturated interrupted conditions: Towards development of passenger car equivalency factors

Passenger car equivalency (PCE) of heavy trucks is often studied using filed observation and microscopic simulation models, especially for signalized intersections. While the Highway Capacity Manual recommends a single value regardless of the percentage of those heavy vehicles, literatures have shown that this equivalency is affected by different factors, including the trucks percentage. This research aims to examine the PCE under different level of traffic and heavy trucks demands. First, field measurements were collected and used to examine the characteristics of heavy vehicle and passenger car headways in oversaturated interrupted flow conditions. Field observations were then used to calibrate a microscopic simulation model. The model was then used to evaluate impact on headways of different levels of congestion and heavy vehicle percentages. Field results show that truck headways are about 2.3 those of passenger cars. The results also show that trucks are 1.5 more likely to be first in a queue when compared to passenger cars and 1.7 times more likely to be in first four vehicles in a standing queue. Passenger cars immediately behind trucks had longer than average headway. The simulation results suggest that PCE increases nonlinearly with increase in congestion level and with percentage of trucks; PCE's increase becomes less marked once sever congestion (stop-and-go with increasing queue lengths) conditions set in.

Analysis of the Effect of Human-Machine Co-Driving Vehicle on Pedestrian Crossing Speed at Uncontrolled Mid-Block Road Sections: A VR-Based Case Study.

The current study investigates the effects of speed and time headway of human-machine co-driving vehicles on pedestrian crossing speed at uncontrolled mid-block road sections. A VR-based simulation study is conducted to study pedestrian crossing behaviour when facing human-machine co-driving vehicles. A total of 30 college students are recruited, and each participant is required to complete 5 street-crossing simulator trials facing human-machine co-driving vehicles with varying time headway levels and speeds. The correlations and differences between demographic information, time headway, vehicle speed, and pedestrian crossing speed are analyzed. The results show that gender and pedestrian’s trust in human-machine co-driving vehicles are significantly correlated with pedestrian crossing speed. The pedestrian crossing speed increases with the increase in vehicle speed and decreases with the increase in vehicle time headway. In addition, the time headway has a stronger correlation with the pedestrian crossing speed than the vehicle speed. The findings will provide theoretical and methodological support for the formulation of pedestrian crossing control measures in the stage of human-machine co-driving.