Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.
The mobility landscape is experiencing a paradigm shift due to rapid advancements of the information and vehicular technologies. Among them, the connected and automated vehicle (CAV) technologies have been contributing to the adoption of next-generation vehicles that are equipped with connectivity (i.e., connected vehicles) and/or automation (i.e., automated vehicles). In spite of CAV’s immense benefits and potentials in reshaping the mobility landscape, the adoption of CAVs by consumers is still uncertain [
The near-term deployment of CAVs is characterized by mixed traffic conditions, where human-driven vehicles (HVs) and CAVs constantly interact with each other. The potential benefits from CAVs may be offset by the interactions among different types of vehicles. For example, the short following time gap (e.g., 0.6 s) is only feasible when a CAV follows another CAV. To overcome such shortcoming in near-term CAV deployment, managed lane strategies, such as CAV dedicated lane, are one of the promising solutions in order to facilitate the formation of the CAV strings. Practically, managed lane strategies are freeway lanes that are set aside and operated under various fixed and/or real-time strategies in response to certain objectives, such as improving traffic operation [
The goal of this study is to investigate the impact of different lane use strategies under mixed traffic conditions at vehicle trajectory, as well as lane, level. For clarity, we refer mixed traffic condition to the condition that CAVs and HVs operate on the same roadway network in the following discussions. The contributions of the paper include the following: The analysis of CAV-enhanced traffic flow characteristics at the lane level and vehicle level The investigation of traffic performance with gradual introduction of CAV platoons under difference managed lane strategies The implications of managed lane strategies from a dedicated short-range communication (DSRC) perspective
The remainder of the paper is organized as follows. Related work regarding the research of CAVs in mixed traffic and managed lanes is reviewed in Section
There have been numerous studies on the implementation and evaluation of CAVs in various traffic settings. Aligning with our research topic, we focused our literature search on two key aspects of CAV studies: (1) CAV evaluation in mixed traffic conditions at network level and (2) managed lane strategies for CAV. A list of abbrevations used can be found in Table
List of abbreviations.
Abbreviation | Definition |
---|---|
ADAS | Advanced driver-assistance systems |
ADS | Automated driving systems |
ACC | Adaptive cruise control |
AV | Automated vehicles |
API | Application programming interface |
BSM | Basic safety message |
CV | Connected vehicles |
CAV | Connected and automated vehicles |
CACC | Cooperative adaptive cruise control |
CAH | Constant-acceleration heuristic |
CDF | Cumulative probability function |
CHEM | Comprehensive modal emission model |
DSRC | Dedicated short-range communication |
DLL | Dynamic-link library |
DTG | Desired time gap |
E-IDM | Enhanced intelligent driver model |
GPL | General purpose lane |
HV | Human-driven vehicle |
HOV | High-occupancy vehicles |
IEEE | Institute of Electrical and Electronics Engineers |
MPR | Market penetration rate |
MOVES | Motor vehicle emission simulator |
PET | Postencroachment time |
SSAM | Surrogate safety assessment model |
SAE | Society of Automotive Engineers International |
SUMO | Simulation of urban mobility |
TTC | Time to collision |
VAD | Vehicle awareness device |
WAVE | Wireless access in vehicular environment |
Three main approaches have been used to assess the benefits of CAVs: (1) analytical study, (2) simulation evaluation, and (3) field test with equipped vehicles.
On-road testing provides the utmost degree of realism with equipped automated driving systems (ADS) and real-world traffic environment. However, the safety and efficiency issues for testing CAV on public roads have been the major concern, especially after several severe CAV-involved accidents in recent years. Due to safety, technological, and budgetary limitations, the scale of a CAV field test at current stage tends to be small (e.g., with a handful of CAVs). As a result, the conclusions from these small-scale field tests may not be reliably generalized to a traffic flow level. Furthermore, it was estimated by Kalra and Paddock that billions of kilometers of road test would be required to achieve the desired level of confidence in terms of safety of an ADS [
The majority of the analytical models is based on macroscopic traffic flow models and may experience difficulty in faithfully capturing the complex phenomena in transportation networks, such as lane drop. Smith et al. proposed an analytical framework for assessing the benefits of CAV operations [
At the corridor level, a capacity of 4250 vph/pl (vehicle per hour per lane) was observed in [
Arnaout and Arnaout evaluated CAVs under moderate, saturated, and oversaturated demand levels on a hypothetical 4-lane highway under different market penetrations. They found that 9400 vehicles could be served within an hour when the CAV MPR reached 40% [
Liu et al. investigated the benefits of alleviating freeway merge bottleneck and compared the performance of CACC with ACC under full market penetration. The results showed that CACC yielded a 50% reduction in fuel consumption (as estimated with the EPA MOVES model) while increasing corridor capacity by 49%, compared to the ACC scenario [
The potential impact of the short following time headway of CAVs on HVs has also been studied in previous studies. Among them, the KONVOI project found the carry-over effect for CACC drivers in manual driving after the disengagement of the CACC system [
Managed lanes have been in practice over the years to improve target operation objectives, such as (1) promoting the adaptation of environment-friendly vehicles by offering priority usage to specific travel lanes (e.g., the California Clean Air Vehicle Decal [
The provision of a CAV-managed lane has two primary reasons. First, CAV-managed lanes can incentivize the adaptation of CAVs by offering priority usage to managed lanes, which typically provides better and more reliable travel because of active traffic management. More importantly and unique to CAVs, CAV-managed lanes can provide accommodations for the underlying operational characteristics of CAVs. A CAV is able to operate at a much closer headway than a human driver with the assistance of V2V wireless communication and the automated driving system (ADS) [
To mitigate CAV degradation, ad hoc coordination, local coordination, and global coordination are the three major strategies that outline the organization of CAV platoons [
To successfully form and maintain platoons, accurate and cost-effective localization of CAVs in a dynamic traffic environment remains one of the biggest challenges, especially for local coordination [
To assess the impact of CAV-managed lane strategies, Zhang et al. compared the performance of a managed lane and general propose lanes (GPL) based on average speed, throughput, and travel time [
Qom et al. proposed a multiresolution framework to study the mobility impact of CAV lanes. Traffic flow-based static traffic assignment and the mesoscopic simulation-based dynamic traffic assignment were adapted in the bilevel framework. The former yielded the MPR-based trends, whereas the latter refined the trend based on traffic congestion. The results indicated that it was not beneficial to provide toll incentive for CAVs at lower MPR due to the marginal increase in highway capacity [
The introduction of a CAV lane to a signalized corridor was reported in [
The vast majority of previous studies evaluated the benefits of CAVs at an aggregated level with the emphasis of overall traffic improvement. Analytic models are in macroscopic nature under overly ideal conditions, and they have difficulty in factoring the stochastic nature of human drivers in a mixed traffic environment. CAV-managed lane strategy could be instrumental in the near-term deployment of CAVs, but it is still an underexplored area, despite its increasing recognition.
This study focuses on analyzing mixed traffic flow characteristics at a corridor level considering different CAV MRPs and managed lane strategies. In this section, the integrated simulation test bed, transportation network, and simulation scenarios are discussed in detail.
The PTV Vissim [
Differences between HVs and CAVs in the simulation models.
Vehicle type | Longitudinal control | DTG | Stochasticity |
---|---|---|---|
HV | Wiedemann 99 | 1.4 s | Y |
CAV | E-IDM | 0.6, 1.2 s | N |
In this study, the E-IDM model is selected as the longitudinal control for the CAVs. Although without built-in multianticipative car-following function, as the literature shows, E-IDM is still a good simple car-following model for CAVs, as the stochastic nature of human driving is removed (i.e., automation property), and the acceleration of the preceding vehicle is taken into account in the driving model (i.e., connectivity property). As shown in Table
E-IDM vehicle control parameters.
Parameter | ||||||||
---|---|---|---|---|---|---|---|---|
Value | 0.6 s | 1.2 s | 1 | 2 | 2 | 0.99 | 4 | 105 |
To implement these two car-following models in Vissim, the subset of the human driving behavior is realized by adjusting car-following parameters of the Wiedemann car-following model, which is relatively straightforward. The E-IDM, on the other hand, is implemented via the external driver model application programming interface (API) and connected with Vissim through a dynamic link library (DLL). The DLL is invoked in each simulation time step such that the default car-following behavior will be overwritten for a specified vehicle type. The DSRC wireless communication module, discussed later in Section
One of the most prominent features in CAV behavior modeling is the short time headway during car-following, which is manifested by several key differences between a CAV and a HV. First, the stochasticity of the CAVs is significantly lower than that of human drivers. This is enabled by the on-board sensors that are able to continuously and accurately perceive the surrounding environment. However, the stochasticity cannot be completely eliminated due to sensor noise and communication delay/error. Second, a CAV has minimal reaction time due to its algorithmic decision-making process and computational power. Past studies have already identified the impact of the reaction time of human drivers in various traffic phenomena, including capacity drop [
In addition, human factor plays a crucial role in the resumption of control of a CAV when an ADS exits its operational domain (e.g., high risk of collision, sensor failure, and communication interference). Quantitative evidence regarding the transition of control from traffic psychology or human-machine interactions is still limited [
Calvert and van Arem developed a framework that encompasses the driving task demand and driver task saturation [
Another human factor is driver compliance to the ADS. Since, in lower or medium level of automation, the driver is ultimately responsible for his or her vehicle, which means overwriting, when deemed necessary, is possible by the human driver, this control authority, in extreme cases, could cancel out the benefits promised by the CAV technologies. In a recent study [
In this study, we represent the differences of a CAV and a HV with different desired time headways through separate car following models, with the following assumptions made for CAVs: (1) no error for the on-board sensors and the vehicle controller, that is, perfect perception; (2) no human factor modeling pertaining to the transition of authority; and (3) no behavior adaptation for CAVs for non-CAV drivers.
In an early study, we implemented a packet-level communication module through Vissim API [
A 9.3 km 4-lane hypothetical network was constructed in Vissim with two interchanges located at mile markers 2 (km) and 6 (km), respectively. An abstract geometry of the network along with vehicle demand of the origins and destination is shown in Figure
Network geometry and demand.
Three cases of CAV lanes, as shown in Table No managed lane (NML): This scenario serves as the base condition of the study. There is no priority lane use for CAVs, and they are mixed with HVs throughout the network; One CAV lane (CAV-1): In this strategy, one CAV lane is implemented in the left-most lane (the fourth lane from the right); Two CAV lanes (CAV-2): An additional CAV lane is added to the CAV-1 case, making two CAV lanes available at the left-most lane and the second-left-most lane in the roadway segment. It aims to investigate the duel managed lane configuration.
Managed lane evaluation plan.
Policy | No managed lane | Managed lane #1 | Managed lane #2 |
---|---|---|---|
ID | NML | CAV-1 | CAV-2 |
1st lane | HV + CAV | HV + CAV | HV + CAV |
2nd lane | HV + CAV | HV + CAV | HV + CAV |
3rd lane | HV + CAV | HV + CAV | CAV |
4th lane | HV + CAV | CAV | CAV |
MPR | 0%–100% | 30%–100% | 40%–100% |
As revealed in previous studies [
Five replications are run for each combination of managed lane policies and MPRs. Aggregated data are collected at 5-minute intervals, and the raw data are collected at each simulation time step. The analysis is performed on five aspects: (1) traffic flow characteristics, (2) headway distribution, (3) fuel consumption, (4) wireless communication, and (5) overall network performance.
Figure
Speed-flow curves.
The simulation collects raw data from the data collector, an equivalent of real-world detectors (e.g., loop detectors, video cameras, and microwave sensors). By analyzing the high-resolution raw data (collected every 0.1 s), the headway distribution in CAV lanes can be obtained. Recall that the collectors are placed in three sections of the roadway segment, as shown in Figure
The cumulative probability function (CDF) curves are displayed in Figure
Cumulative distribution for headway distribution among travel lanes.
Two-sample Kolmogorov-Smirnov (K-S) test is adopted to analyze the CDFs to check whether two random samples are from the same population [
K-S statistics for CDF comparison.
The average headway for HVs and CAVs in every travel lane is shown in Figure
Average headway.
Figure
Headway distributions in the left-most lane. (a) 40% MPR. (b) 50% MPR. (c) 60% MPR. (d) 70% MPR.
The VT-Micro model [
The vehicle data was derived from the raw data from the detectors in three locations marked in Figure
Instantaneous fuel consumption for all vehicles.
We then isolate the CDF curve for both CAVs and HVs, when they operate on the left-most lane under homogeneous flow condition. More specifically, the separated CDF curves represent the observations of HVs from the 0% MPR in NML case and the observations for CAVs from the 100% MPR for CAV-1 case. The CDF curves in Figure
In the GPLs, the MPR plays a role as an indicator for the dominance of each traffic flow. The higher the MPR is, the closer the CDF curves approach the pattern of managed lane that is used by CAV exclusively. In the managed lane, the CAV traffic is the sole dominating traffic. Therefore, the fuel consumption curve exhibits only CAV traffic characteristics, regardless of the MPR. We include the fuel consumption rate CDF curves for HVs and CAVs in Appendix C, Figures
Instantaneous fuel consumption for HVs.
Instantaneous fuel consumption for CAVs.
Instantaneous fuel consumption curve for homogeneous flow.
Figure
V2V communication performance measure. (a) Vehicle density. (b) Packet perception rate.
The probability of successful reception of BSM from a leading vehicle to a subject vehicle is shown in Figure
The measures used in this section gauge the overall performance of the simulation network at an aggregated level. The throughput represents the total number of vehicles that have arrived at their destinations, shown in Figure
Network throughput.
The average delay experienced by vehicles (plotted in Figure
Average speed and delay. (a) Average delay. (b) Average speed.
In this section, we highlight the findings from the previous section and discuss the study in a boarder context.
The analysis results indicate that the introduction of CAV could increase the throughput of the overall system, even when no managed lane policy is in place. The congestion region in the speed-flow diagram disappears as the MPR of the CAVs increases. This is an indication of the improvement of roadway capacity owing to CAVs, which is consistent with the findings of previous studies. More importantly, the congestion region first disappears in the CAV lane in CAV-1 case, illustrating that the homogeneity of CAV traffic results in a more stable traffic flow with a high throughput. A CAV lane, with an MPR as low as 40%, is able to accommodate more traffic compared to a GP lane and it helps to alleviate the overall congestion of the network. The average vehicle delay exhibits a decreasing trend, even after the network throughput levels after 70% MPR. This is an indicator that the network is able to carry more traffic than the high demand specified in Figure
The individual headways among consecutive vehicles are measured for each lane. From the headway distribution, one can measure not only the compactness of the traffic but also the stability of the traffic flow. Both HVs and CAVs have a predominate headway as shown in Table
The VT-Micro model, which produces instantaneous fuel consumption for individual vehicles, was employed to estimate the environmental impact of the CAV lane. The vehicle speed and acceleration were collected as inputs and the relative fuel consumption, instead of the absolute one, was examined. Again, distinct patterns for a GPL and a CAV lane were observed. The average instantaneous fuel consumption for CAV lane has a narrower distribution.
Lastly, the DSRC communication was measured using an analytical communication model that is derived from a package-level network simulator. It simulates the physical layer of the DSRC communication that is an integral element of CAVs. We found a lower communication density in CAV lane, as the CAVs were more evenly distributed longitudinally. A lower communication density indicates a less congested communication channel, which increases the performance of the V2V communication. Compared to CAV-1 and CAV-2 scenarios, it is more likely under NML scenario to generate pockets of traffic with CAVs across multiple lanes, which could introduce higher localized transmission activity and increase the loss of BSM packets.
The overall results show that a single CAV lane in a four-lane highway network is able to provide the necessary technical accommodation efficiently in the mixed traffic conditions with a wide range of MPR. A CAV dedicated lane is helpful to guarantee the benefits of CAVs, as it creates a homogeneous CAV flow. Implementing two CAV lanes, however, may adversely affect the overall traffic, especially when the MPR of CAV does not warrant an additional CAV lane.
While the paper demonstrates the benefits of managed lane for CAV at lane level and vehicle level, we should note that there are limitations in this study and the benefits are realized in a controlled environment under certain assumptions. First of all, although the Wiedemann model is behaviorally sound and has been adopted by numerous researchers for simulating human drivers, the complexity of a human driver under dynamic traffic conditions is difficult, if possible at all, to be captured by simulation models. In addition, the behavioral adaptation for human drivers in the presence of CAV is not known yet, due to the lack of empirical evidence in the public domain. Preliminary results revealed that a smaller time headway was adopted by a HV when driving along side closed platooned CAVs [
In addition, there are several salient issues regarding the low-level automation and its modeling as well. For a CAV, the drivers’ acceptance of short following headway (e.g., 0.6 s) is still an open question [
Another crucial issue is the transition of control from the ADS back to the human driver. As per the definition of vehicle automation by the SAE, the level 3 automation (and below) requires a fallback receptive driver when the ADS exits its designed operational domain. As studies have shown, such fallback process is way more complicated than merely retaking the steering wheel. First, a driver needs to regain situational awareness of the traffic environment from the disengagement of driving. The surge in cognitive demand during the initial period of reengaging in driving tasks could result in deterioration in driver’s performance (e.g., increased reaction time and inadequate situational awareness). This aspect rarely exists in current CAV models, and much likely it will require an endogenous cognitive model that is able to take into account the driving task demand and the cognitive capacity of human drivers [
The future research would focus on relaxing the assumptions in this study. The first direction is the CAV behavior modeling. Researchers have recently started the incorporation of human factor aspect, such as an extension module in IDM to model driver’s responses to advanced traffic information [
The list of abbreviation used is provided in Table
The coefficients obtained from the polynomial function
DSRC model coefficients and PDF. (a) Coefficient
Instantaneous fuel consumption for HV and CAV is shown in Figures
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported in part by the National Science Foundation under Grant No. CMMI-1844238.