The Value of Passenger Car Equivalent using the Time Headway Method on Urban Roads

The main objective of this paper, based upon the extensive empirical research of free flow in local conditions, is to quantify the unfavourable impact of the flow structure on the road capacity using PCE (Passenger Car Equivalent) values as a function of longitudinal grade. Based on literature reviews and empirical research, it has been proved that the PCE value for all vehicle classes is directly correlated with the road gradient. The PCE values in free flow conditions have been determined for the approved vehicle classes. Based on the measured values, models for determining the average PCE value depending on the upward grade on two-lane roads have been developed. Comparison of the developed models in conditions of free traffic flow with the Highway Capacity Manual (HCM) models has shown lower PCE values in this research. Models for the percentage of PCE values PCE15%, PCE50% and PCE85% have also been established.

Existing passenger car equivalent (PCE) values do not necessarily serve the purposes of highway cost allocation well, since their derivation has followed from a need to determine equivalency for traffic operations purposes. Highway cost allocation demands better knowledge of equivalencies among vehicle classes, for a wide range of vehicle types, and under the full range of traffic conditions. There are several possible methods for PCE development and various suggested PCE values, but there is currently no information on the suitability of these methods and estimates for cost allocation purposes. A framework for the development of PCEs is set forth, and some final PCE values for the 20 vehicle types and 30 weight groups that could be used in the current Federal Highway Cost Allocation Study are provided. Using traffic simulation models, PCE values were calculated for each of the 12 facility types for various roadway segments (i.e., grades, length of grade, number of lanes). PCEs were also calculated for high and low traffic volumes for additional flexibility in assigning congestion-related costs. The PCEs obtained for each roadway and traffic condition were combined into a weighted-average PCE value for each vehicle type and highway facility type, reflective of the actual geometric conditions of the entire highway. Weighted-average PCEs were separately calculated for congested and uncongested conditions for two different vehicle percentages.

This paper investigates Passenger Car Equivalent (PCE) or Passenger Car Unit (PCU) values and traffic flow characteristics of Sri Lankan expressways: Southern (E01), Outer Circular (E02), and Katunayake (E03). The study highlights the importance of considering region-specific traffic parameters due to differences in road traffic systems, vehicle characteristics, and transport infrastructure across countries. PCE values vary based on vehicle dimensions, climatic conditions, and traffic flow. The calculated PCE values for passenger car - small category was found to range between 0.74-0.77, while heavy goods vehicles recorded significantly higher PCE values, ranging from 3.59-3.99. Research on PCE has been widely conducted globally, however limited studies exist in the context of Sri Lankan expressways. This study uses Chandra’s method to calculate PCE values for eight vehicle categories, and the accuracy of the results was validated using the R-squared method, with simulations run through PTV VISSIM software. Additionally, the research evaluates traffic flow characteristics such as speed, density, and volume to optimize traffic management and support sustainable transportation. Relationships between speed-density, volume-density, and speed-volume were analysed using the Greenshield model. The average traffic volumes were determined as 911.8, 833.2, and 1884.9 passenger car per hour (pcph) on the three selected expressways. Observed results with the design were compared and revealed lower observed density (36.4 pc/km – 76.8 pc/km) values than the design predictions (160 pc/km) in passenger cars per kilometres. This study provides insights into traffic dynamics on Sri Lankan expressways and contributes to sustainable transport planning by identifying gaps between design assumptions and actual traffic conditions.

Passenger car equivalent (PCE) values are necessary for representing a traffic stream composing of different vehicle types in terms of a passenger car equivalent stream. For no lane-based heterogeneous traffic streams observed in India, previous studies mostly have suggested the vehicle-specific PCE values that vary dynamically with the flow rate and the traffic composition. The objective of this paper is to estimate and analyze the implications of the vehicle-specific PCE values that do not vary with the LOS and certain range of traffic composition, for the four-lane and the six-lane divided rural roads. Besides, the present study has also estimated the aggregate PCEs for the four-lane and the six-lane divided roads. The PCE values were estimated based on the macroscopic relationships generated for the base and the heterogeneous traffic streams. For defining the level of service, a new performance measure termed as the speed drop was chosen and it was found that it provides a relatively more accurate PCEs. Results show that for a particular traffic mix, constant PCEs can be used across different levels of service without much loss of accuracy. Aggregate PCEs for a particular traffic mix vary with the speed drop markedly at lower flow rates.

Passenger Car Equivalent (PCE) is essential for transportation engineering to assess heavy vehicles’ (HV) impact on highway operations and capacity planning. Highway Capacity Manual 2010 (HCM 2010) used PCE values and percent of heavy vehicles to account the impacts on both highway planning and operation, however, PCE values in the latest version of HCM derived based on the steady and balanced two-lane-two-way (TLTW) traffic flows. The objective of the study is to identify PCE values for TLTW highway at various traffic volume with an emphasis on congestion conditions. This study introduces an analytical model, combining a headway-based and a delay-based algorithms, for estimating PCEs of HV on a TLTW highway. This study contributes to the literature by providing relationships among PCE, the traffic volume level (TVL) of both lanes, and the TVL duration on a TLTW highway. Traffic volume was categorized into five levels: TVL A (<250 pc/h), TVL B (250–375 pc/h), TVL C (375–600 pc/h), TVL D (600–850 pc/h), and TVL E (>850 pc/h). The results indicate that on a TLTW highway, the TVLs of both lanes and their durations have significant impact on PCE values. In general, PCE values increase as TVL duration increases. Trucks have much higher impacts on operation under unbalanced conditions of TVL A with D, TVL B with C, and TVL D with B, when duration time is greater than one hour. When both lanes are saturated, trucks’ effect on capacity diminishes over time, and PCE values are approaching to 1.0.

PURPOSES : The Toll Collection System (TCS) operated by the Korea Expressway Corporation provides accurate traffic counts between tollgates within the expressway network under the closed-type toll collection system. However, although origin-destination (OD) matrices for a travel demand model can be constructed using these traffic counts, these matrices cannot be directly applied because it is technically difficult to determine appropriate passenger car equivalent (PCE) values for the vehicle types used in TCS. Therefore, this study was initiated to systematically determine the appropriate PCE values of TCS vehicle types for the travel demand model. METHODS : To search for the appropriate PCE values of TCS vehicle types, a traffic demand model based on TCS-based OD matrices and the expressway network was developed. Using the traffic demand model and a genetic algorithm, the appropriate PCE values were optimized through an approach that minimizes errors between actual link counts and estimated link volumes. RESULTS : As a result of the optimization, the optimal PCE values of TCS vehicle types 1 and 5 were determined to be 1 and 3.7, respectively. Those of TCS vehicle types 2 through 4 are found in the manual for the preliminary feasibility study. CONCLUSIONS : Based on the given vehicle delay functions and network properties (i.e., speeds and capacities), the travel demand model with the optimized PCE values produced a MAPE value of 37.7%, RMSE value of 17124.14, and correlation coefficient of 0.9506. Conclusively, the optimized PCE values were revealed to produce estimates of expressway link volumes sufficiently close to actual link counts.

The significant impact of heavy vehicles (HVs) on freeway operations has been identified since the first edition of the Highway Capacity Manual (HCM). The HCM 2010 used passenger car equivalent (PCE) values and percentages of trucks, buses, and recreational vehicles to account for the effect of HVs on roadway performance. Unfortunately, the PCE values in the HCM 2010 relied on a limited field database and simulation runs that were calibrated for steady-flow traffic conditions, although the effect of HVs on traffic flow could reasonably have been expected to have varied with traffic conditions. Few studies have been conducted with extensive field data to examine impacts on traffic characteristics by HV presence under congested and forced-flow conditions. This paper presents such an effort by using urban freeway data containing 1.2 million individual vehicle observations. The results indicated a significant difference in lagging–leading behavior between vehicle pairs related to HV presence. Passenger car and HV headways were found to increase with HV presence in the traffic stream. A similar pattern was found for the PCE factor. The PCE value under congested conditions and more than 9% HV presence was found to be 1.76, which was higher than the 1.5 value recommended by the HCM 2010 for level freeway sections. The maximum throughput of the freeway was found to be affected by HV presence. The maximum throughput was observed at a truck presence of 3%, after which it became progressively lower. The results of this paper can be used as input for future simulation runs of congested freeway flow conditions.

The effect of trucks on the level of service is determined by considering passenger car equivalents (PCE) of trucks. The Highway Capacity Manual (HCM) uses a single PCE value for all tucks combined. However, the composition of truck traffic varies from location to location; therefore a single PCE-value for all trucks may not correctly represent the impact of truck traffic at specific locations. Consequently, the Indiana Department of Transportation wanted to develop separate PCE values for single-unit and combination trucks to replace the single value provided in the HCM. Traditionally, equivalent delay and microscopic simulations have been used to estimate PCE values. In order to facilitate the development of site specific PCE values, an alternative PCE-estimation methodology was explored in the present study on the basis of lagging headways measured from field traffic data. The study used data from four locations on a single urban freeway and three different rural freeways in Indiana. Three-stage-least-squares (3SLS) regression techniques were used to generate models that predict lagging headways for passenger cars, single unit trucks, and combination trucks. The estimated PCE values for single-unit and combination truck for basic urban freeways (level terrain) were 1.35 and 1.60, respectively. For rural freeways, the estimated PCE values for single-unit and combination truck were 1.30 and 1.45, respectively. However, due to the lack of sufficient quality data for rural freeways, the estimated PCE values for rural freeways are not recommended for use. As expected, traffic variables such as vehicle flow rates and speed have significant impacts on vehicle headways. The use of separate PCE values can have significant influence on the LOS estimation. This study also explored regional variation of PCE values. The results of the likelihood ratio test indicated that it is appropriate to combine data from similar locations (freeway sections at different geographical locations) for the PCE analysis.

The study worked on direction-wise dynamic Passenger Car Equivalent (PCE) model to optimize delay at signalized intersection under heterogeneous traffic condition. Static PCE values are usually practiced to transform heterogeneous traffic into homogeneous stream. However, the PCE values are dynamic due to variation in vehicle composition. Video sensors were used to count direction-wise classified vehicle at various signalized intersections for this research. The adaptive PCE values were compared with the PCE values used in the manual of Roads and Highway Department (RHD), Bangladesh. Furthermore, Synchronous regression method was performed to estimate saturation flow. Field total delay was estimated from queuing diagram considering residual queue, arrival and saturation flow rate. After that, signal timing was adjusted by optimization of total delay. RHD manual based PCE was observed to be underestimated comparing to direction-wise dynamic PCE in case of field delay and optimized delay by 6.79% and 27% respectively. Turning vehicles occupy more space and time and influence operation of signalized intersection. Hence, conventional PCE estimation failed to comply with actual scenario, and consequently, could optimize signal timing inaccurately. This study can be used as a framework to calibrate practiced PCE values in the road capacity manual and design traffic signal.

Environmental and traffic assessment of adjusting Triple Highway Capacity Manual (HCM) factors at signalized intersections

Passenger Car Equivalent (PCE) is expected to be very accurate because it is very important in determining the capacity, degree of saturation, and handling of unsignalized intersections. Several methods have been used to estimate the PCE value, but based on the literature review, it is stated that the occupancy time method and the speed method are the most applicable for the unsignalized intersection conditions. This study aims to determine the most appropriate PCE value by comparing the occupancy time method and the speed method. To determine the most appropriate method in calculating the PCE of the unsignalized intersection, the calculation of the value of capacity and degree of saturation based on the method of speed and occupancy time is compared with the PCE value of MKJI. The result shows that the most suitable PCE value for unsigned intersections is the speed method. The recommended changes in the value of PCE are unmotorized vehicle PCE = 0.45, motorcycle PCE = 0.17, and large vehicle PCE = 2.2. Determining the correct PCE value is expected to provide accurate intersection performance results and determination of intersection problems.

In the Highway Capacity Manual (HCM), the passenger car equivalent (PCE) of a truck is used to account for the impacts of trucks on traffic flow. The 2010 HCM PCE values were estimated by the equal-density method using a FRESIM simulation. It was determined that the truck PCE for level freeway segments was 1.5 for all conditions. In the 2016 HCM, the PCE values were based on VISSIM simulation output at 1 min intervals along a three-lane, 13 mile (8 mile level and 5 mile graded) section of a roadway. It was determined that the truck PCE for level freeway segments was 2.0. It is hypothesized in this paper that the HCM PCE values are not appropriate for the western United States, which consistently experiences truck percentages higher than 25%, the maximum truck percentage published in the HCM PCE table. The HCM PCE procedure assumes that truck and passenger cars travel at the same average free-flow speed on level terrain. However, many heavy trucks in the western United States have speed limiters to improve fuel economy, and therefore travel slower than the speed limit. The interaction of high truck percentages and large speed differences may result in moving bottlenecks when trucks pass other trucks at low speed differentials. This may lead to an increased delay for the following passenger car vehicles. The 2016 HCM PCEs are based on three-lane simulations where the PCE is calculated based on near-capacity flows. This approach might not be appropriate for western states where these conditions rarely exist. This paper examines these effects using data from I-80 in western Nebraska. The paper develops new PCE values based on the 2010 HCM equal-density approach using calibrated CORSIM and VISSIM simulation models. It was found that the PCE values in the HCM 2010 and HCM 2016 underestimate the effect of heavy trucks on level terrain freeways that experience high truck percentages, and where different vehicle types have large differences in average free-flow speeds.

Passenger car equivalents (PCEs) represent the effects of heavy vehicles on traffic operations. PCE estimation is based on equating the passenger-car-only flow rate to the mix-fleet flow rate such that it results in the same performance for the selected measure. Most existing methods rely on simulation to generate PCE values. However, this approach generates PCEs using the truck characteristics of the simulation rather than local field conditions and heavy vehicle types. Most existing methods estimate the marginal effect of adding one specific truck in the traffic stream assuming relatively high volumes, which results in higher PCE values. This study proposes a new method for estimating PCEs using field-observed traffic data considering the impact of each truck type for a broad set of flows, not just their marginal effect at high flows. Based on density equivalency, the maximum likelihood estimation is used to estimate the means and the variances of the PCE values from traffic data collected at 10 sites on the national motorways of Thailand. When aggregated across all observed percentages of heavy vehicles and flow rates, the PCE values are 1.46 (trucks with length between 5.2 and 13.0 m) and 2.08 (trucks with length of 13.1 m or longer). For low to medium percentage of trucks, the PCEs become lower after the onset of oversaturation. However, for high percentages of trucks the PCEs tend to be higher after the onset of oversaturation. The proposed methodology can be applied using data from other locations to estimate the corresponding PCEs from field-observed data.

Passenger Car Equivalent (PCE) is a unit used to represent the impact of a large vehicle on a road by expressing it as the number of equivalent passenger vehicles. This paper focuses on estimating the PCE of various sized heavy vehicles in roundabouts with respect to different entry flow rates. A single-lane roundabout was tested under predefined mixed traffic and demand scenarios in VISSIM micro-simulation environments. The individual and group behavior of four separate heavy-vehicle types were tested: single-unit trucks, buses, small semitrailers, and large semitrailers. The obtained PCE values were found to be on average lower than those suggested in the United States guidelines for roundabouts. The estimated PCE values for heavy vehicles in mixed traffic conditions are 1.30 for single unit trucks, 1.40 for small semitrailers, 1.60 for buses, and 1.70 for large semitrailers. Additional factors such as varying inflow (balanced, unbalanced, and congested traffic) show direct influences on the PCE values. The PCE value under these conditions ranged from 1.25 to 1.75 for smaller vehicles (single-unit trucks, buses, and small semitrailers) and 1.45–2.10 for larger heavy vehicles (large semitrailers). A general equation was developed based on the data to relate vehicle proportions and heavy-vehicle reduction factors that would be useful for professionals to analyze the operational performance of roundabouts with better accuracy.

Passenger Car Equivalent (PCE) is a conversion factor to make equal the various types of vehicles that operating on the road section into one type of vehicles i.e. passenger cars. Indonesian Highway Capacity Manual (MKJI) 1997 has set the PCE values for various types of vehicle groups either motorized. PCE values of various types of vehicles are not absolute because many factors that affect can change over time and development of automotive technology. This study aimed to find out the changes of PCE value that occurs. As for the purpose of research to determine the current number of PCE values due to the change of operational characteristics of vehicles on a highway especially for urban highways. Data analysis for the determination of PCE values used: time headway method, speed method, capacity method, and vehicle dimension method. The conclusion of this study: PCE of light vehicles (LV) = 1 still in accordance with MJKI 1997; PCE of heavy vehicles (HV) varied depending on the road types. PCE of heavy vehicles that according to MKJI 1997 is used as a median; PCE of motorcycles (MC) of MKJI 1997 need to be adjusted to 0.4 or more, particularly in the calculation of actualy traffic flow.