Evaluation of the 2010 Highway Capacity Manual Urban Street Free-Flow Speed Prediction Model

2014 ◽  
Vol 2461 (1) ◽  
pp. 1-8
Author(s):  
Ren Moses ◽  
Enock T. Mtoi ◽  
Eren E. Ozguven
Keyword(s):  
Prediction Model ◽  
Flow Speed ◽  
Free Flow ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Street ◽  
Free Flow Speed ◽  
Speed Prediction

Application of Moving Car Observer Method for Measuring Free Flow Speed on Two-lane Highways

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
Othman Che Puan ◽  
Muttaka Na’iya Ibrahim ◽  
Usman Tasiu Abdurrahman
Keyword(s):  
Field Measurement ◽  
Flow Speed ◽  
Operating Conditions ◽  
Free Flow ◽  
Geometric Features ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Free Flow Speed ◽  
The Subject ◽  
Low Traffic

There exists a need to evaluate the performance indicator that reflects the current level of service (LOS) of the subject facility to justify any decision making on expenditures to be made for improving the performance level of a road facility. Free-flow speed (FFS) is one of the key parameters associated with LOS assessment for two-lane highways. Application of a more realistic approach for assessing road’s performance indicators would result in better estimates which could in turn suggest the most appropriate decision to be made (for situations where upgrading is needed); especially, in terms of finance, materials and human resources. FFS is the driver’s desired speed at low traffic volume condition and in the absence of traffic control devices. Its estimation is significant in the analysis of two-lane highways through which average travel speed (ATS); an LOS indicator for the subject road class is determined. The Highway Capacity Manual (HCM) 2010 offers an indirect method for field estimation of FSS based on the highway operating conditions in terms of base-free-flow-speed (BFFS). It is however, recommended by the same manual that direct field FSS measurement approach is most preferred. The Malaysian Highway Capacity Manual (MHCM) established a model for estimating FFS based on BFFS, the geometric features of the highway and proportion of motorcycles in the traffic stream. Estimating FFS based on BFFS is regarded as an indirect approach which is only resorted to, if direct field measurement proved difficult or not feasible. This paper presents the application of moving car observer (MCO) method for direct field measurement of FFS. Data for the study were collected on six segments of two-lane highways with varying geometric features. FFS estimates from MCO method were compared with those based on MHCM model. Findings from the study revealed that FFS values from MCO method seem to be consistently lower than those based on MHCM model. To ascertain the extent of the difference between the FFS values from the two approaches, student t-statistics was used. The t-statistics revealed a P–value of less than 0.05 (P < 0.05) which implies that there is a statistically significant difference between the two sets of data. Since MCO method was conducted under low traffic flow (most desired condition for field observation), it can be suggested that MCO estimates of FFS represent the actual scenario. A relationship was therefore developed between the estimates from the two methods. Thus, if the MHCM model is to be applied, the measured value needs to be adjusted based on the relationship developed between the two approaches.

Estimating Passenger Car Equivalent using the HCM-6 PCE Methodology on Four-Lane Level Freeway Segments in Western U.S.

2019 ◽  
Vol 2673 (11) ◽  
pp. 529-545 ◽  
Author(s):  
Jianan Zhou ◽  
Laurence Rilett ◽  
Elizabeth Jones
Keyword(s):  
Flow Speed ◽  
Free Flow ◽  
Passenger Cars ◽  
Highway Capacity ◽  
Passenger Car ◽  
Highway Capacity Manual ◽  
Free Flow Speed ◽  
Passenger Car Equivalent ◽  
Equal Capacity ◽  
Using Data

The passenger car equivalent (PCE) of a truck is used to account for the presence of trucks in the Highway Capacity Manual (HCM). The HCM-6 employed an equivalency capacity methodology to estimate PCE. It is hypothesized in this paper that the HCM-6 PCE values are not appropriate for the western U.S., which consistently experiences truck percentages higher than 25%. Furthermore, the HCM PCE procedure assumes that truck and passenger cars travel at the same desired free-flow speed on level terrain. However, many heavy trucks in the western U.S. are governed through the use of speed limiters so that their speeds are considerably less than the speed limit. Thirdly, the HCM-6 PCEs are based on the freeways having three lanes per direction, which might not be appropriate for the freeways with two lanes per direction that predominate in the rural sections of the western U.S. Lastly, the trucks used in the HCM-6 simulation might not be representative of the empirical trucks observed on rural freeways in western states. This paper examines these effects on PCEs using data from I-80 in western Nebraska. The PCEs were estimated using the HCM-6 equal-capacity method and VISSIM 9.0 simulation data under (1) the HCM-6 conditions and (2) the Nebraska empirical conditions. It was found that the PCEs recommended in HCM-6 underestimate the effects of trucks on four-lane level freeway segments that experience high truck percentages having large differences in free-flow speed distributions, and which have different truck lengths.

scholarly journals Self-Organizing Tree Algorithm (SOTA) Clustering for Defining Level of Service (LOS) Criteria of Urban Streets

2018 ◽  
Vol 47 (4) ◽  
pp. 309-317
Author(s):  
Amit Kumar Das ◽  
Prasanta Kumar Bhuyan
Keyword(s):  
Travel Speed ◽  
Flow Speed ◽  
Free Flow ◽  
Level Of Service ◽  
Highway Capacity ◽  
Tree Algorithm ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Flow Speeds ◽  
Self Organizing

This study is intended to define the Free Flow Speed (FFS) ranges of urban street classes and speed ranges of Level of Service (LOS) categories. In order to accomplish the study FFS data and average travel speed data were collected on five urban road corridors in the city of Mumbai, India. Mid-sized vehicle (car) mounted with Global Positioning System (GPS) device was used for the collection of large number of speed data. Self-Organizing Tree Algorithm (SOTA) clustering method and five cluster validation measures were used to classify the urban streets and LOS categories. The above study divulges that the speed ranges for different LOS categories are lower than that suggested by Highway Capacity Manual (HCM) 2000. Also it has been observed that average travel speed of LOS categories expressed in percentage of free flow speeds closely resembles the percentages mentioned in HCM 2010.

Application of High-Resolution Vehicle Data for Free-Flow Speed Estimation

2017 ◽  
Vol 2615 (1) ◽  
pp. 105-112 ◽  
Author(s):  
Nagui M. Rouphail ◽  
SangKey Kim ◽  
Seyedbehzad Aghdashi
Keyword(s):  
High Resolution ◽  
Flow Speed ◽  
Highway Capacity ◽  
High Resolution Data ◽  
Highway Capacity Manual ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Free Flow Speed ◽  
Research Questions ◽  
Resolution Data

The use of probe vehicle data for highway performance monitoring is increasingly being adopted in many countries. In the United States, third-party data provider entities such as Google, INRIX, HERE, and TomTom are delivering products to state and local transportation agencies that are enabling them to identify bottlenecks, incidents, and other key operational events on the basis of probe vehicle speed and travel time. However, the capacity analysis methods in the U.S. Highway Capacity Manual continue, for the most part, to rely on the analyst’s ability to gather data at fixed points, whether manually or from fixed point sensors. This paper explores the use of intelligence to drive (i2D) high-resolution vehicle data to assess several research questions related to free-flow speed (FFS) estimation, a key parameter in freeway segment analyses. On the basis of 1 year of high-resolution data collected from a local fleet of about 20 vehicles driven by volunteer drivers, researchers accumulated more than 20 million s of driving, which when filtered were used to evaluate research questions and develop enhanced predictive models for FFS. Speed limit and section ramp density (i.e., those ramps within the segment proper only) were found to have had a strong effect on the value of FFS. Driver familiarity was found to have an effect also, although this effect was not conclusive across 10 study sites. Finally, an FFS predictive model that incorporates speed limit and section ramp density was found to fit the high-resolution data quite well, generating an absolute error of only 1.3% across all sites. That finding compares with an error of 6.6% with the current Highway Capacity Manual 2010 model predictions.

Estimating the Effects of Urban Street Incidents on Capacity

2017 ◽  
Vol 2615 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Aidin Massahi ◽  
Mohammed Hadi ◽  
Maria Adriana Cutillo ◽  
Yan Xiao
Keyword(s):  
Network Performance ◽  
Field Measurements ◽  
Signalized Intersection ◽  
Limited Information ◽  
Microscopic Simulation ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Urban Street ◽  
Incident Duration

The effect of incidents on capacity is the most critical parameter in estimating the influence of incidents on network performance. The Highway Capacity Manual 2010 (HCM 2010) provides estimates of the drop in capacity resulting from incidents as a function of the number of blocked lanes and the total number of lanes in the freeway section. However, there is limited information on the effects of incidents on the capacity of urban streets. This study investigated the effects on capacity of the interaction between the drop in capacity below demand at a midblock urban street segment location and upstream and downstream of signalized intersection operations. A model was developed to estimate the drop in capacity at the incident location as a function of the number of blocked lanes, the distance from the downstream intersection, and the green time–to–cycle length (g:C) ratio of the downstream signal. A second model was developed to estimate the reduction in the upstream intersection capacity resulting from the drop in capacity at the midblock incident location as estimated by the first model. The second model estimated the drop in capacity of the upstream links feeding the incident locations as a function of incident duration time, the volume-to-capacity (V/C) ratio at the incident location, and distance from an upstream signalized intersection. The models were developed on the basis of data generated with the use of a microscopic simulation model calibrated by comparison with parameters suggested in HCM 2010 for incident and no-incident conditions and by comparison with field measurements.

scholarly journals An Efficient Scheduling of Flyover at Grade Intersection under Mixed Traffic Environment

2021 ◽  
Vol 9 (VI) ◽  
pp. 824-833
Author(s):  
Shradha S. Zanjad
Keyword(s):  
Queue Length ◽  
Fixed Time ◽  
Flow Speed ◽  
Free Flow ◽  
Mixed Traffic ◽  
Time Control ◽  
Traffic Environment ◽  
Free Flow Speed ◽  
Big Cities

A flyover is a bridge constructed along an intersecting highway over an at-grade intersection. It allows two –direction traffic to flow at free flow speed on the bridge. The flyover is one of the methods for solving traffic problems at at-grade junctions on highways including capacity, congestion, long delay and queue length. Traffic signalization at the upgraded intersection often uses the same fixed time control plans, even after the installation of a flyover over the intersection. Most of the flyovers in India are constructed at the junctions on highway bypasses of big cities. The present work deals with a efficient scheduling of flyover at the grade intersection under the mixed traffic environment. From the results and the modeling carried out in the “SIDRA Intersection” software different points are observed. The present work consists of the Proposed Intersection at Rajkamal Square, Amravati.

scholarly journals The Effect of Congestion Pricing Scheme on the Generalized Cost and Speed of a Motorcycle

2016 ◽  
Vol 15 (1) ◽  
pp. 95-106
Author(s):  
Gito SUGIYANTO
Keyword(s):  
Traffic Congestion ◽  
Demand Management ◽  
Congestion Pricing ◽  
Flow Speed ◽  
Free Flow ◽  
Vehicle Speed ◽  
Traffic Jams ◽  
Pricing Scheme ◽  
Free Flow Speed ◽  
Generalized Cost

Traffic congestion is one of the significant transport problems in many cities in developing countries. Increased economic growth and motorization have created more traffic congestion. The application of transportation demand management like congestion pricing can reduce congestion, pollution and increase road safety. The aim of this research is to estimate the congestion pricing of motorcycles and the effect of a congestion pricing scheme on the generalized cost and speed of a motorcycle. The amount of congestion pricing is the difference between actual generalized cost in traffic jams and in free-flow speed conditions. The analysis approach using 3 components of generalized costs of motorcycle: vehicle operating, travel time and externality cost (pollution cost). The approach to analyze the pollution cost is marginal-health cost and fuel consumption in traffic jams and free-flow speed conditions. The value of time based on Gross Regional Domestic Product per capita in Yogyakarta City in October 2012. The simulation to estimate the effect of congestion pricing using Equilibre Multimodal, Multimodal Equilibrium-2 (EMME-2) software. The results of this study show that while the free-flow speed of a motorcycle to the city of Yogyakarta is 42.42 km/h, with corresponding generalized cost of IDR1098 per trip, the actual speed in traffic jams is 10.77 km/h producing a generalized cost of IDR2767 per trip, giving a congestion pricing for a motorcycle of IDR1669 per trip. Based on the simulation by using EMME-2, the effect of congestion pricing will increase on vehicle speed by 0.72 to 8.11 %. The highest increase of vehicle speed occurred in Malioboro Street at 2.26 km/h, while the largest decrease occurred in Mayor Suryotomo Street at north-south direction at 1.07 km/h. Another effect of this application for motorcycles users will decrease the generalized cost by 1.09 to 6.63 %.

scholarly journals Modelling Free Flow Speed on Two-Lane Rural Highways in Bosnia and Herzegovina

2014 ◽  
Vol 26 (2) ◽  
pp. 121-127 ◽  
Author(s):  
Ivan Lovrić ◽  
Dražen Cvitanić ◽  
Deana Breški
Keyword(s):  
Field Survey ◽  
Evaluation Model ◽  
Practical Method ◽  
Flow Speed ◽  
Free Flow ◽  
Rural Highways ◽  
Speed Flow ◽  
Free Flow Speed ◽  
Flow Diagrams ◽  
Analysis Models

Free flow speed is used as a parameter in transportation planning and capacity analysis models, as well as speed-flow diagrams. Many of these models suggest estimating free flow speed according to measurements from similar highways, which is not a practical method for use in B&H. This paper first discusses problems with using these methodologies in conditions prevailing in B&H and then presents a free flow speed evaluation model developed from a comprehensive field survey conducted on nine homogeneous sections of state and regional roads.

scholarly journals Freeway Free Flow Speed: a Case Study in Italy

2012 ◽  
Vol 54 ◽  
pp. 628-636 ◽  
Author(s):  
Mario De Luca ◽  
Renato Lamberti ◽  
Gianluca Dell’Acqua
Keyword(s):  
Flow Speed ◽  
Free Flow ◽  
Free Flow Speed

Impact of Rainfall Condition on Traffic Flow and Speed: A Case Study in Johor and Terengganu

2014 ◽  
Vol 70 (4) ◽  
Author(s):  
Nordiana Mashros ◽  
Johnnie Ben- Edigbe ◽  
Sitti Asmah Hassan ◽  
Norhidayah Abdul Hassan ◽  
Nor Zurairahetty Mohd Yunus
Keyword(s):  
Traffic Flow ◽  
Monsoon Season ◽  
Flow Speed ◽  
Free Flow ◽  
Road User ◽  
Traffic Data ◽  
Average Volume ◽  
Rainfall Condition ◽  
Free Flow Speed ◽  
The Impact

This paper explores the impact of various rainfall conditions on traffic flow and speed at selected location in Terengganu and Johor using data collected on two-lane highway. The study aims to quantify the effect of rainfall on average volume, capacity, mean speed, free-flow speed and speed at capacity. This study is important to come out with recommendation for managing traffic under rainfall condition. Traffic data were generated using automatic traffic counters for about three months during the monsoon season. Rainfall data were obtained from nearest surface rain gauge station. Detailed vehicular information logged by the counters were retrieved and processed into dry and various rainfall conditions. Only daylight traffic data have been used in this paper. The effect of rain on traffic flow and speed for each condition were then analysed separately and compared. The results indicated that average volumes shows no pronounce effect under rainfall condition compared to those under dry condition. Other parameters, however, show a decrease under rainfall condition. Capacity dropped by 2-32%, mean speed, free-flow speed and speed at capacity reduced by 3-14%, 1-14% and 3-17%, respectively. The paper recommends that findings from the study can be incorporated with variable message sign, local radio and television, and variable speed limit sign which should help traffic management to provide safer and more proactive driving experiences to the road user. The paper concluded that rainfall irrespective of their intensities have impact on traffic flow and speed except average volume.

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