For the different traffic compositions on the four-lane road, the total PCEs range from 1.5–3.0 with the change in HMV composition from 25%–50% for both speed drop and area occupancy. For the six-lane divided road, the total PCEs came around 1.5 with the change in HMV composition in case of land cover.
81 Figure 5.2 Velocity-flow relationships for PCE estimation of HMV cont. a) Four-lane roads and (b) Six-lane divided roads. 82 Figure 5.4 Velocity-flow relationships for PCE estimation of MTW contg. a) Four-lane roads and (b) Six-lane divided roads.
List of Tables
Symbols
Sm Average speed of passenger car in the mixed flow PCEi Passenger car equivalent of the ith vehicle. P Number of cells in all lanes (sub-lanes) vs(t) Average speed in cell length/sec on time step t vj,i (t) Speed of the ith vehicle in the jth sub-lane 𝜗𝑖 SV's speed in cells/sec.
ANALYSIS AND MODELING OF PASSENGER CAR EQUIVALENTS FOR HETEROGENEOUS TRAFFIC
CONDITIONS
DOCTOR OF PHILOSOPHY In
Civil Engineering
Syed Omar Ballari (10610415)
CERTIFICATE
Acknowledgements
I also express my gratitude to my siblings and all other family members for their immense love and encouragement. I wish to express my great love and gratitude to my wife, Fouzia Shaheen, who supported me through her enlightened heart and sacrifice during our stay at IIT Guwahati.
Abstract
Introduction
Literature Review
Selection of Equivalency Criterion for PCE Estimation
Results and Analysis
Conclusions
56 Figure 3.22 Comparison of capacity values for the six-lane divided road 56 Figure 3.23 Regression line fit to three points (Source: Van Aerde and . Rakha, 1995). 81 Figure 5.1(b) Speed-density relationships for several runs moving on the four track. and six-lane roads.
Introduction
- General
- Need for the Study
- Research Objectives
- Organization of the Thesis
Estimation of the PCE values is therefore essential for the design and operational analysis of roadways. The objective of this study is to estimate the PCE values of different vehicle types to represent the heterogeneous traffic flows moving on the multilane rural highways in India.
Literature Review
- General
- Individual PCE and Aggregate PCE
- PCE Estimation Methods
- Summary of PCE Estimation Methods
- Review of the Performance Measures
- Summary of Literature Review
Furthermore, the PCE value of heavy vehicles increases with the increasing proportion of heavy vehicles. But the PCE value of the motorized two-wheeler decreases as the percentage of two-wheelers increases. For estimating the PCE value of a vehicle type (subject vehicle), a few passenger cars were removed and the subject vehicles were added to the mixed traffic flow.
They found that the PCE value of a vehicle type increases with increasing road width. They also found that the PCE value of a vehicle type increases with the increase of its share in the traffic flow.
Data Collection and Analysis of Macroscopic Relations for No-Lane Based Heterogeneous Traffic Stream
General
Data Collection and Extraction Methodology
The length of the observation period used to collect the data is another aspect that affects the macroscopic data. The researchers used 1- to 15-minute observation periods and found that increasing the length of the observation period reduced the dispersion of the data. The basic guideline for the selection of the road section was that at least 100 m should be fairly straight and level, without the influence of a nearby intersection.
The majority of vehicles in the traffic flow consisted of cars, motorized two-wheelers and three-wheelers. It provides output in the form of vehicle trajectories that are tracked over most of the road section visible in the video film.
Macroscopic Traffic characteristics
Average free-flowing speed was taken as the weighted average of the average speed of the free-flowing vehicles corresponding to four vehicle types. Free flow densities are estimated based on the average traffic flow composition observed on this stretch of road. The distributions of observed and the theoretical speeds (resulting from the normality assumption) of different types of vehicles are compared and are shown in Figure 3.2.
The statistics corresponding to the free flow velocities observed at the study site are shown in Table 3.2. To determine the blocking density of the road, different frames with jammed conditions were identified (Figure 3.1).
Review of the Studies on Macroscopic Relations
The underestimation is due to the temporal nature of the flow data used to estimate the density. Regardless of the functional form considered for modeling the velocity-density relationship, the above problems are common. Most times, field data are limited to free flow conditions and very few researchers have captured the cool traffic conditions (also only for urban traffic).
Velocity-density relations are used to obtain the congested branch of the velocity-flow relation (Dhamaniya and Chandra 2014). In this context, it is necessary to understand the consequences of the collected field data on conditions of restricted traffic.
Analysis of the Macroscopic Traffic characteristics .1 Background
- Development of Macroscopic Models for the Different Traffic States
When such data are used in speed-density model estimation, it can be expected that the resulting parameters do not represent road characteristics. The conventional data collection approach considers selected data sets that mainly contain free traffic states. From this figure, it can be seen that there is a slight improvement in the estimated parameters based on the speed-density data.
The value of congestion density increased slightly compared to the parameter estimated on the basis of data corresponding only to free traffic conditions. Underestimated densities corresponding to some queuing traffic states affect the model, and the estimated parameters differ from the observed roadway parameters.
Selection of stationary periods using the Cassidy’s Method
Then, the time periods corresponding to the nearly linear slope were visually identified using the N (x, t) – qot curve (Figure 3.11). As shown in Figure 3.14, the density values corresponding to some of the queued states are underestimated, and these density values were observed by replaying the video clip. The velocity density model was estimated using the revised density data and is shown in Figure 3.15.
The notation ND refers to the parameters estimated using the commonly used data collection approaches and the notation SDC refers to the parameters estimated using the stationary data collected using the Cassidy approach. It can be seen from Table 3.3 that the road parameters corresponding to the stationary data, with the revised congestion density values, are consistent with those of the actual values.
Cellular Automata (CA) Model
The vehicle inputs include the acceleration, deceleration, mean and standard deviation of the maximum speed, length, width and maximum lateral clearance of each type of vehicle present in the traffic flow. The randomization parameter (p) indicates the random acceleration delay of different vehicles associated with a certain probability. Where li is the length of SV, ai (vi, li) is the acceleration of SV and vmax is the maximum allowable speed.
Wi is the width of SV, lgil is the lateral space required for SV; (t + 1/3) and (t + 2/3) indicate different stages where the velocity values are updated within each update step. The important inputs of the CA model are warm-up time, data extraction time, edge conditions and the length of the simulated road section.
Calibration of Speed-Density Models
The shape of the speed density model is highly influenced by the parameters such as free flow speed (vf), jam density (kj) and characteristic wave speed (cj). Therefore, it is better to use this approach for calibrating the speed density model under heterogeneous traffic conditions. Furthermore, most of the times, the presence of wide scatter in the speed-density relation complicates the calibration process.
The present study used the calibration method described by Van Aerde and Rakha (1995) for velocity-density models. The velocity-density model of Del Castillo and Benitez provided the best fit to the data.
Selection of Equivalency Criterion for PCE Estimation
- Background
- PCE Values Using Stream Speed as the Equivalency Criterion
- PCE Values Using Speed of Passenger Car as the Equivalency Criterion
- PCE Values Using Density as the Equivalency Criterion
- PCE values using area occupancy as the equivalency criterion
The MAPE for a six-lane divided road was 11.07% using flow velocity as the equivalence criterion. The MAPE was found to be 34.93% in the case of a six-lane divided road using the speed of passenger cars as the equivalence criterion. PCE values and errors for four-lane and six-lane divided roads using speed drop as equivalence criteria are presented in Table 4.7 and Table 4.8, respectively.
The variation of Error values with decreasing speed for four-lane and six-lane roads is shown in Figure 4.9. The variation of error values with area occupancy in the case of four-lane and six-lane roads is presented in Figure 4.11.
Results and Analysis
- General
- CA model Parameters
- Individual PCE Values using Speed Drop and Area Occupancy
- Constant PCE Values Using Speed Drop and Area Occupancy
- Aggregate PCE Values Using Speed Drop and Area Occupancy
Similarly, the PCE values for equal area coverage are estimated for four-lane and six-lane roads. The variation of the PCE values of HMV, MThW and MTW with speed drop and area coverage for the four- and six-lane roads is shown in Figure 6.8. The PCE value for HMV increases with the increase in speed drop or area coverage for both four-lane and six-lane roads.
The PCE value of MThW also increases with the increase in speed drop or area coverage (increase in flow rate) for four- and six-lane roads. The PCE values of HMV, MThW and MTW were estimated for the different traffic mixes.
Conclusions
- Summary
- Contributions to the Field of Research
- Recommendations for Further Research
For a given traffic composition, individual PCEs for various vehicle types were found to vary with the speed drop and area occupancy. This may be due to the fact that the extent of area occupancy (upper limit is 5%) falls within the speed drop range of ten percent. For the six-lane divided road, with the area occupancy, the total PCEs remain 1.5 and do not depend on the HMV composition.
But with the velocity decrease, the total PCEs range from 1.0-3.0 with the changing HMV composition from 10%-25%. The effect of the other important factors on the overall PCEs needs to be studied.
Implementing the Concept of Reliability for Highway Capacity Analysis.” Transportation Research Record 2027, Transportation Research Board, Washington, D.C., 1-8. Development of passenger car equivalents for highways, two-lane highways and arterial roads.” Transportation Research Record 1572, Transportation Research Board, Washington, DC, 51-58. Estimation of passenger car equivalents based on capacity variability.” Transportation Research Record 2130, Transportation Research Board, Washington, D.C., 1-6.
The equivalent of a passenger car for trucks on straight stretches of highway.” Transportation Research Record 1091, Transportation Research Board, Washington, D.C., 10–17. Level of Service on Rural Highways Based on Traveler Perception.” Transportation Research Record 1988, Transportation Research Board, Washington, D.C., 31-37.
Appendix A
Publications
