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.

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.

Performance Measures to Characterize Corridor Travel Time Delay Based on Probe Vehicle Data

2015 ◽  
Vol 2526 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Thomas M. Brennan ◽  
Stephen M. Remias ◽  
Lucas Manili
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
The United States ◽  
Flow Speed ◽  
Performance Measure ◽  
Vehicle Speed ◽  
Travel Time Reliability ◽  
Highway Capacity Manual ◽  
Probe Vehicle ◽  
Vehicle Data

Anonymous probe vehicle data are being collected on roadways throughout the United States. These data are incorporated into local and statewide mobility reports to measure the performance of highways and arterial systems. Predefined spatially located segments, known as traffic message channels (TMCs), are spatially and temporally joined with probe vehicle speed data. Through the analysis of these data, transportation agencies have been developing agencywide travel time performance measures. One widely accepted performance measure is travel time reliability, which is calculated along a series of TMCs. When reliable travel times are not achieved because of incidents and recurring congestion, it is desirable to understand the time and the location of these occurrences so that the corridor can be proactively managed. This research emphasizes a visually intuitive methodology that aggregates a series of TMC segments based on a cursory review of congestion hotspots within a corridor. Instead of a fixed congestion speed threshold, each TMC is assigned a congestion threshold based on the 70th percentile of the 15-min average speeds between 02:00 and 06:00. An analysis of approximately 90 million speed records collected in 2013 along I-80 in northern New Jersey was performed for this project. Travel time inflation, the time exceeding the expected travel time at 70% of measured free-flow speed, was used to evaluate each of the 166 directional TMC segments along 70 mi of I-80. This performance measure accounts for speed variability caused by roadway geometry and other Highway Capacity Manual speed-reducing friction factors associated with each TMC.

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.

Using high resolution data to reveal depth-dependent mechanisms that drive land subsidence: The Venice coast, Italy

2009 ◽  
Vol 474 (1-2) ◽  
pp. 271-284 ◽  
Author(s):  
L. Tosi ◽  
P. Teatini ◽  
L. Carbognin ◽  
G. Brancolini
Keyword(s):  
High Resolution ◽  
Land Subsidence ◽  
High Resolution Data ◽  
Resolution Data

Estimating maize lethal necrosis (MLN) severity in Kenya using multispectral high-resolution data

2021 ◽  
Author(s):  
Kyalo Richard ◽  
Elfatih M. Abdel-Rahman ◽  
Sevgan Subramanian ◽  
Johnson O. Nyasani ◽  
Michael Thiel ◽  
...  
Keyword(s):  
High Resolution ◽  
High Resolution Data ◽  
Resolution Data
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