Basic Freeway Segments Research Articles

Modeling and Estimating the Effect of a Mix of Varying Levels of Automated Vehicles on Operational Performance of Urban Freeways

Automated vehicles (AVs) will likely influence various aspects of the existing transportation system in the next few decades. Existing literature documents the operational benefits of AVs at different penetration rates. However, estimating the effect of the mix of different levels of AVs on the operational performance of traffic flow has not been explored in the past. This study employs a microsimulation approach to model and estimate the potential effect of varying levels of AVs on the operational performance of freeways. The proposed approach involves a two-step process for calibrating the simulation model. In the first step, the model is calibrated from an operational perspective. In the second step, the calibration is performed from a safety perspective. Thirteen scenarios were generated considering varying penetration rates of different levels of AVs. Congestion index (CI), buffer time (BT), travel time uncertainty (TTU), and delay were computed for each scenario and segment type. The estimations indicate that increasing the penetration of level 3 to level 5 AVs significantly reduces CI, BT, TTU, and delay. In contrast, the effect of level 1 and 2 AVs on freeway operational performance is negligible. Notably, the operational benefits are more pronounced with higher penetration of level 3 and higher AVs. Additionally, the reduction in delay is greater on interchange segments compared with basic freeway segments. The study findings are valuable insights concerning the operational performance of freeways under varying penetration of different levels of AVs, assisting practitioners in formulating AVs-inclusive policies.

Impacts of Different Types of Automated Vehicles on Traffic Flow Characteristics and Emissions: A Microscopic Traffic Simulation of Different Freeway Segments

Different types of automated vehicles (AVs) have emerged promptly in recent years, each of which might have different potential impacts on traffic flow and emissions. In this paper, the impacts of autonomous automated vehicles (AAVs) and cooperative automated vehicles (CAVs) on capacity, average traffic speed, average travel time per vehicle, and average delay per vehicle, as well as traffic emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter (PM10) have been investigated through a microsimulation study in VISSIM. Moreover, the moderating effects of different AV market penetration, and different freeway segments on AV’s impacts have been studied. The simulation results show that CAVs have a higher impact on capacity improvement regardless of the type of freeway segment. Compared to other scenarios, CAVs at 100% market penetration in basic freeway segments have a greater capacity improvement than AAVs. Furthermore, merging, diverging, and weaving segments showed a moderating effect on capacity improvements, particularly on CAVs’ impact, with merging and weaving having the highest moderating effect on CAVs’ capacity improvement potential. Taking average delay per vehicle, average traffic speed, and average travel time per vehicle into account, simulation results were diverse across the investigated scenarios. The emission estimation results show that 100% AAV scenarios had the best performance in emission reductions in basic freeway and merging sections, while other scenarios increased emissions in diverging and weaving sections.

Weather-Based Lane-Change Microsimulation Parameters for Safety and Operational Performance Evaluation of Weaving and Basic Freeway Segments

It is well recognized that adverse weather conditions have significant negative impacts on safety and mobility of transportation systems. Microsimulation modeling has emerged as a cost-effective tool to quantify the safety and operational effects arising from adverse weather. Developing a realistic microsimulation model necessitates adjusting driving behavior models through trajectory-level data. This study contributes a methodology to update lane-change parameters to develop weather-specific microsimulation models based on different freeway facilities, and ultimately evaluate the safety and operational performance of the roadways. Representative parameters in various weather and facility types were extracted using an automated process. As part of the comprehensive assessment of the adjusted parameters, a weaving section and a basic freeway segment on Interstate 80 in Wyoming were identified as potential candidates. The safety and operational analyses were conducted using VISSIM. Various simulation scenarios were designed based on the field traffic flow data. The safety analysis using three surrogate measures of safety including time-to-collision, deceleration rate to avoid collision, and post encroachment time revealed that adverse weather generated a higher number of conflicts than did clear weather for both facilities. The operational analysis suggested that adverse weather produced lower average speed and higher total travel time and delay than clear weather. The demonstrated methodology could be used in assessing various connected vehicle applications associated with lane change in microsimulation from safety and operational perspectives and could be adopted by transportation agencies to develop weather-based microsimulation models.

Data-Driven Computation of State-Dependent Passenger Car Equivalency for Multiple Truck Lengths

Passenger car equivalency (PCE) has been used for decades to convert mixed vehicle traffic into equivalent pure passenger car traffic streams for transportation planning, design, and operational analysis purposes. The term PCE was defined by the Highway Capacity Manual (HCM) in 1965 as an adjustment factor, and many approaches have continued to be developed for refining PCE values for different types of freeway geometry and vehicle distributions since then. This paper proposes an improved data-driven state-dependent passenger car equivalency (PCE–SD) approach, initially for level terrain basic freeway segments that consider average vehicle lengths for different types of vehicles. The term “state-dependent” refers to the space headway ratio at the same speed ranges between long vehicles (trucks), that fall into the same clustered category, and passenger cars. The relatively unique data stream used in this study comes from the Portland Oregon Regional Transportation Archive Listing (PORTAL) online datahub. This rich continuous data stream provides valuable information about traffic-related data at a 20-s aggregation level. Most importantly for this effort, ranges of categorical vehicle lengths are also available. According to analysis results, PCE–SD values estimate the actual passenger car distribution at different speeds and levels of operational performance (levels of service) more accurately compared with the HCM-6 PCEs on level terrain basic freeway segments. The authors propose that a state-dependent passenger car equivalency approach bridges the conversion gap from different types of trucks to passenger vehicles and could be used as an improved supplementary methodology.

Validating the Bayesian hierarchical extreme value model for traffic conflict-based crash estimation on freeway segments with site-level factors

This study validates the Bayesian hierarchical extreme value model that is developed for estimating crashes from traffic conflicts. The model consists of a generalized extreme value distribution that characterizes the behavior of block maxima extremes and a Bayesian hierarchical structure that incorporates the non-stationarity and unobserved heterogeneity into the extreme analysis. In addition to the block-level factors, the site-level factors are also included in the model development for the first time. The model was applied to data of lane change conflicts collected from 11 basic freeway segments in Guangdong Province, China. Block-level factors such as traffic volume per 10 min, number of lane change events per 10 min, and proportion of oversized vehicles per 10 min and site-level factors such as segment length, curvature, and grade were considered. Two types of Bayesian hierarchical extreme value models were developed, including models without site-level factors and models with site-level factors. These models were also compared to at-site models that were developed for 11 segments separately. The results show that Bayesian hierarchical extreme value models significantly outperform the at-site models in terms of crash estimation accuracy and precision. As well, including site-level factors further improves the model performance in terms of goodness-of-fit. This demonstrates the validity of the Bayesian hierarchical extreme value model. The results also show that number of lane change events, segment length, and grade are significant factors which have adverse effect on the safety of lane changes on freeway segments.

Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Freeway Segments

Connected and automated vehicles (CAVs) will undoubtedly transform many aspects of transportation systems in the future. In the meantime, transportation agencies must make investment and policy decisions to address the future needs of the transportation system. This research provides much-needed guidance for agencies about planning-level capacities in a CAV future and quantify Highway Capacity Manual (HCM) capacities as a function of CAV penetration rates and vehicle behaviors such as car-following, lane change, and merge. As a result of numerous uncertainties on CAV implementation policies, the study considers many scenarios including variations in parameters (including CAV gap/headway settings), roadway geometry, and traffic characteristics. More specifically, this study considers basic freeway, freeway merge, and freeway weaving segments in which various simulation scenarios are evaluated using two major CAV applications: cooperative adaptive cruise control and advanced merging. Data from microscopic traffic simulation are collected to develop capacity adjustment factors for CAVs. Results show that the existence of CAVs in the traffic stream can significantly enhance the roadway capacity (by as much as 35% to 40% under certain cases), not only on basic freeways but also on merge and weaving segments, as the CAV market penetration rate increases. The human driver behavior of baseline traffic also affects the capacity benefits, particularly at lower CAV market penetration rates. Finally, tables of capacity adjustment factors and corresponding regression models are developed for HCM implementation of the results of this study.

Safety evaluation of freeway interchange merging areas based on driver workload theory.

Prior safety evaluations of interchange merging areas have mostly focused on traffic conflicts and operating speeds, without considering how these factors can influence the driver workload. Researches regarding the level of driver workload have largely concentrated on urban roads, tunnel sections, and basic freeway segments, without considering the impact of merging traffic flows on safety. Therefore, this study has investigated how merging vehicles can impact through-driver workload and safety. Three independent field experiments were conducted on freeways, involving 18 drivers and 17 interchanges. The results showed that the vehicles merging onto freeway impact driver workload and driving performance, generating potential risk that cannot be ignored. Merging into the mainline traffic flow increases the through-driver workload to a higher level or even exceeds the safety threshold, despite there being better geometrical conditions of interchanges than those of basic freeway segments. When the volume of merging vehicles exceeds 564 pcu/h or the traffic saturation is above 0.485, driver workload rises above the safety thresholds and the driving risk is elevated, which potentially would lead to crashes. This study offers insights for more effective segment division of operating speed prediction, and dynamic risk management with regard to interchange safety.

Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model

ABSTRACTConnected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.

Modelling Travel Time After Incidents on Freeway Segments in China

The reduction in incident-induced delays on freeways is a main objective of transportation management. The use of travel time estimation model for freeway segments is an important method for estimating delays resulting from incidents on freeways. In this study, freeways with temporary partial lane closures were considered to simulate traffic accidents occupying lanes. Travel time, traffic volumes, and speeds under various traffic conditions on a few typical Chinese freeway segments under regular and simulated accident conditions were investigated through field experiments. The collected traffic data collected were used to establish travel time models based on the Bureau of Public Roads (BPR) function for basic freeway segments under both regular and accident conditions, and to obtain the model parameters. The results demonstrate that the calibrated BPR models established in this study fit the data well. In addition, this study proposes an application method for the established travel time models by which variations in travel time can be estimated rapidly and easily. The results of this study can be used to reduce travel time for road users and contribute to decision making of transportation management systems to improve traffic efficiency after incidents.

Capacity Predictions and Capacity Passenger Car Equivalents of Platooning Vehicles on Basic Segments

Vehicle platooning is an innovative strategy that uses automated driving technology and communication to enable a more efficient use of transportation networks. By controlling tighter gaps between vehicles, vehicle platooning will increase freeway capacity. However, it is important to quantify the extent of the increase of capacity so that highway engineers can plan for this technology. This Federal Highway Administration research develops an analytical model to predict the capacity of basic freeway segments based on the market penetration and the maximum number of vehicles allowed in a platoon. It uses these predictions to calculate passenger car equivalents, which may be required for planning purposes. Simulations show that capacity can be predicted analytically to within 2% of simulated values. In addition, for the parameters used in the analysis, the capacity of freeways that restrict platoons to no more than 5 vehicles is comparable to the capacity of freeways that allow larger platoons (e.g., 6.1% maximum difference in capacity between maximum 5 and 12 vehicle platoons); consideration of limiting platoon size is important to ensure maneuverability.

Comparison of modelling methods accounting for temporal correlation in crash counts

ABSTRACTTemporal correlation in crash counts is an important issue that road safety researchers face when developing crash prediction models. Several modelling methods have been proposed to date to address this issue. Random effects negative binomial (RENB) model, random parameters negative binomial (RPNB) model, multilevel negative binomial (MLNB) model, negative multinomial (NM) model, and generalized estimating equation (GEE) model are the most frequently used ones. Although those models have been applied successfully, it is not clear to researchers what their differences are and how to make a choice among them. This study attempts to answer these questions through describing the main features of the five models, showing their connections, illustrating their applications to predict the crash frequency on basic freeway segments, and discussing their differences that can guide the choice making. The application results show that the RENB, MLNB, and RPNB models are superior to others in terms of predictability (for the current data set), capability, usability, and interpretability. Three fundamental differences, including the ways to handle temporal correlation, whether to account for other heterogeneities, and the interpretation of estimated coefficients, provide some guidance on the model choice.

Crash Prediction Model for Basic Freeway Segments Incorporating Influence of Road Geometrics and Traffic Signs

AbstractDividing a freeway into segments is a fundamental step in establishing its crash prediction model. Instead of using the common segmentation criteria that defines short segments as homogeneo...

Assessment of Basic Freeway Segments in the German Highway Capacity Manual HBS 2015 and Beyond

The second edition of the German Highway Capacity Manual HBS was published in 2015. The paper presents the HBS procedure for the assessment of basic freeway segments and discusses challenges for the future development of the methodology. As in the former edition of the HBS, the volume-to-capacity ratio is used as measure of effectiveness for basic freeway segments. The design capacities and speed-flow diagrams for basic freeway segments were completely revised and supplemented by new design values for segments with four-lane carriageways and segments with hard shoulder running. Besides the provision of specific design capacities, the reduced capacity variance on segments with variable speed limits, which was ascertained in recent investigations, is considered by adjusting the lower threshold value of LOS E. The new edition of the HBS also provides a framework for the use of specific parameters and the application of alternative methods for the assessment of traffic flow quality. Overall, the revised procedure for the analysis of basic freeway segments includes major enhancements and covers a larger number of segment types, but the main concept still follows the tradition of using deterministic capacities and providing rather simple analytical procedures to assess the traffic flow in one specific peak hour. For the future development of the HBS, the application of computer-based simulation models as well as the use of stochastic traffic flow parameters that better represent traffic reliability will increasingly emerge.

Autonomous and Connected Cars: HCM Estimates for Freeways with Various Market Penetration Rates

Major IT companies and vehicle manufacturers have announced their plans for autonomous driving technology. Autonomous light duty vehicles are often referred to as “driverless cars” (DLC). These technologies intend to partly or fully replace driving by combining navigation systems, artificial intelligence, in-vehicle sensors, roadside ITS and traffic monitoring data, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The absence of perception errors and the minimal perception and reaction time of DLC enable them to maintain shorter headways, to apply consistent acceleration and deceleration rates, and to optimize the use of gaps. In theory, freeway operations and level of service (LOS) can be impacted to a substantial but yet unknown degree by the DLC. The Highway Capacity Manual (HCM).is a static and macroscopic methodology for assessing various traffic flow facilities. Its 2010 edition is not adjusted for the presence of DLC on highways, however, several of its parameters are sensitive to differences in perception and reaction times, headways, etc. An investigation was conducted to assess the likely changes to traffic flow characteristics that may result from the introduction of DLC. The focus of this paper is on expressways, that is, on HCM analyses of basic freeway segment and freeway weaving segment. DLC is able to increase capacity (c), the maximum service flow rate (MSFi), the adjustment factor for unfamiliar driver population (fP), passenger-car equivalent (PCE), and the proportion of heavy vehicles (PT). The combined benefits improve LOS. The results from the case studies show that the impacts of DLC on HCM parameters are tiny if the DLC have a very small market share. Two types of DLC are considered in this paper: Autonomous DLC, and Connected DLC with V2V and V2I. On a basic freeway segment the Autonomous DLC improves LOS from D to C when its share in traffic reaches 7%. The same case study shows that the Connected DLC improves LOS from D to C when its share in traffic reaches 3%.

The Contribution of Ramp Demand in the Capacity of Merge Bottleneck Locations

Transportation engineers rely on the Highway Capacity Manual (HCM) for estimating capacity at freeway segments. According to the HCM 2010, the capacity of the basic freeway segments is a function of the free-flow speed and it ranges from 2,400 passenger cars per hour per lane (pc/h/ln) for FFS 70 or 75 mi/h, to 2,250 pc/h/ln for FFS 55 mi/h. The freeway merge segments methodology in the HCM 2010 Update uses these same capacity values in the analysis procedure, although research has shown that capacities at these bottleneck locations are considerably lower. In addition to that, researchers have also observed that capacity varies significantly from day to day and from one site to the other. Researchers acknowledge that driver behavior and frequent interactions between mainline and ramp vehicles at these junctions are the causal factor of these variations in capacity and the low capacity values; however, this has not been reflected in the updated version of the HCM 2010. Furthermore, the HCM 2010 Update does not account for the conflicting movements and the contribution of the ramp vehicles on the overall merge junction capacity. This paper investigates the relationship between freeway and ramp demand and capacity at merge junctions. For the purposes of this research, historic data at merge bottleneck locations across North America with different geometric and operational characteristics were analyzed. The results of the analysis show that, there is a clear correlation between ramp demand, freeway demand and freeway capacity. More specifically, higher demand on the on-ramps produces lower overall capacity values. In addition, this paper proposes new capacity values for merge junctions as a function of the freeway and ramp demand and number of lanes.

Estimation of Passenger Car Equivalents for Basic Freeway Sections at Different Traffic Conditions

Passenger car equivalent (PCE) is an important factor which is used to convert traffic volumes containing proportions of heavy good vehicles (HGVs) to a unify measure containing only passenger cars units (PCU). This paper uses large data base of real traffic raw data extracted from loop detector before being aggregated to estimate PCEs. These detectors are located on the M25 and the M42 motorway sites in the United Kingdom. The selected sites represent basic freeway segments as they are far away from the influence of entrance (on ramp) and exit (off ramp) sections. The data are filtered properly so as to estimate passenger car equivalents (PCEs) using lagging headway method for close following situations at different speed ranges. The results suggest that for the same location, the equivalency factors are varies significantly based traffic speed. However, it is proved that such variation with traffic speed is influenced by the differences in lengths between HGVs and cars. Regression models have also been developed linking the PCEs with traffic speed.

Freeway Speed Limits under Inclement Weather Conditions

Over the past decade, over speeding has been identified to be the most dominant factor contributing to the occurrence of severe crashes and injuries on freeways, and thus speed limit performs the most popular countermeasure. However, few studies have focused on the problem of freeway speed limits in bad weather due to reduction of visibility and pavement friction. Therefore, the primary purpose of this research is to develop a prototype of speed limits recommendations for basic freeway segments under the inclement weather conditions. If the leading vehicle makes a sudden stop, the travelling distance of the following vehicle is divided into four phases. Assuming the visibility is no less than the safe following distance between consecutive vehicles, consequently, a parabolic equation is constructed to describe the relation between the vehicle’s maximum safe speed, pavement conditions, segment slope and visibility, and then maximum safe speed is rounded down to the nearest multiple of 5 as the proposed speed limit under the fogy, rainy or snowy conditions.

Planning-Level Methodology for Freeway Facilities

This paper presents a planning-level methodology for the analysis of freeway facilities. The proposed approach is based on and compatible with the operational method of the Highway Capacity Manual 2010 (HCM 2010). The approach is specifically constructed with the intent to minimize input data requirements. The method covers both under- and oversaturated flow conditions and produces estimates of travel time, speed, density, and level of service. The underlying methodology relies on developing a relationship between a basic freeway segment's delay rate per unit distance and its demand-to-capacity ratio. For weaving segments, the study develops capacity adjustment factors on the basis of volume ratio and segment length. With these factors, demand-to-capacity ratios on weave segments were adjusted and the segment was treated similarly to a basic freeway segment. For merge and diverge segments, a novel methodology is proposed to estimate their capacity on the basis of demand level, free-flow speed, and space mean speed. Subsequently, capacity adjustment factors are calculated on those segments and their demand-to-capacity ratios are adjusted accordingly. The proposed approach is applied to two examples in the HCM 2010 and produced very promising results. For undersaturated flow conditions, facility travel time is at most 3.4% and density was at most 1.1% at variance from the results found by applying the HCM 2010 operational methodology. The corresponding differences for oversaturated conditions are 6.7% and 13.0%, respectively.

Calibration and Validation of a Swedish Space-time Analytical Model

The present capacity models for freeways descended from the early 70th, and were partly using the HCM 1965 Highway Research Board (1965) as background. Not especially valid for the todays freeway network. During the last decade one large project, Traffic Performance on Major Arterials (TPMA) Carlsson et al. (2000a), Carlsson et al. (2000b) and Carlsson et al. (2002) has been implemented. New models for basic freeway segment, merging and weaving segment was developed. A new model for weaving were developed 2010 with new empirical data Strömgren (2011). In addition to the manual calculation method for not oversaturated condition a time-space model that also handled oversaturated conditions has been included.The development of the model started with analysis of video and aerial photo to be able to calculate the jam density, the last point in the oversaturated regime curve. The oversaturated flow-density curve has been developed using the density at capacity for a certain cross section and speed limit based on empirical data of flow and density in oversaturated conditions including jam density.The development of the Swedish space-time model used FREEVAL Transport research Board (2010). The resulting computer model, called CALMAR, CALculation of performance for Motorway ARterial facilities, was built in VB.NET and VBA. Calibration of the model was done by using the Swedish capacity models for link, merging, diverging and weaving but also flow-density estimations. All other country specific parameters as speed limits etc. was set to Swedish conditions. A traffic environmental factor was implemented describing the environment where the freeway is situated, and divided into rural or urban. Urban conditions has an interchange density higher or equal to 0.5 (interchanges/km), rural conditions has a density lower than 0.5 (interchanges/km).The model was calibrated for four and six lane freeways with merging or weaving lanes. The speed limit range from 70 kph to 120 kph in step of 10 kph. The model has its limitations. Lane width, shoulder width, distance to obstacles, gradients for ramps and length of merging was not taken into account, since no relationship could be found in the empirical data.The validation of the model was carried out for three different cases on the A4 freeway south of Stockholm, one accident, one vehicle break down and one at oversaturated condition due to high demand flow. Recorded flows from the motorway control system (MCS) as well as the automatic incident detection (AID) data from this system were used to identify queue increases and decreases. Reports from the roadside assistance team were also used to identify the degree and duration of bottleneck blockade.A careful collection of flows including bottleneck throughput and upstream demand were collected in the cases of accidents. The throughput and the upstream flow (demand) on the upstream not affected link before the end of the queue were registered for each time step (60minutes). The throughput flow was used as the capacity value in CALMAR, and the demand flow upstream as the segment demand. This was repeated for each time step of 60minutes. In the case with oversaturated condition no explicit change of the capacity in the bottleneck was done. The capacity was indirectly changed by the related interchange, where the number of lanes was changed from 3 to 2 lanes and the speed limit decreased from 100 kph to 80 kph. The demand flow was captured as in the two first cases.To estimate the queue length AID data was collected from the MCS database and converted from binary to decimal format. Data from 12 MCS gantries for each accident in the southbound direction, and 18 gantries in the northbound direction were collected. The average gantry spacing was 200-300 m.For each time step the end of the queue was registered as a length from the bottleneck where the AID alarm was registered. The AID gives a sign of 30 kph without flashing lamps and has a threshold of 22 kph. This means that the speed is in the range of 0 to 22 kph. A check is also done in the speed flow data from the MCS, if speed less than approximately 10 kph the density is too high for the radar detectors and no data will be recorded.The result from the validation showed a good fit. The CALMAR model gives in case 1, southbound accident, a maximum queue length of 2472 m and the maximum empirical queue length was 2635 m. Case 2, northbound vehicle breakdown gave a CALMAR queue length result of 3608 m compared to a maximum empirical queue length of 3530 m. Case 3, northbound oversaturated condition, gave a CALMAR result of 4149 m queue length and an maximum empirical queue length of 4310 m.

Generic Speed–Flow Models for Basic Freeway Segments on General-Purpose and Managed Lanes in Undersaturated Flow Conditions

This paper presents a generic set of undersaturated speed–flow models for basic freeway segments on general purpose and managed lanes (MLs) consistent with the Highway Capacity Manual 2010 (HCM 2010). The proposed models predict segment space mean speed under a wide set of freeway operational conditions that can affect its free-flow speed (FFS) and capacity. Furthermore, the proposed models allow quantifying the impacts of nonrecurring events, such as severe weather conditions, incidents, and work zones on the speed–flow relationship. In addition, the model allows calibration of real-world facilities through adjustments to FFS and capacity. The incorporation of analyses of reliability and active traffic and demand management in the HCM context requires a set of speed–flow models capable of accounting for the effect of nonrecurring sources of congestion. Currently, the HCM 2010 provides a set of speed–flow models to predict space mean speed and consequently other freeway performance measures. This family of equations provides a limited adjustment to FFS. With guidance from NCHRP Project 3-96, separate speed–flow models are proposed for MLs through use of a different form from that in the HCM. The proposed generic equations describing the speed–flow relationship provide consistency between speed–flow relationships of managed and general purpose lanes and can incorporate any capacity or FFS adjustments to predict segment speed under different circumstances. The proposed generic equations are wholly consistent with the speed–flow models in the HCM 2010 and predict the same speed under any flow rate.