Car-Following Headways on Freeways Interpreted by the Semi-Poisson Headway Distribution Model

1979 ◽  
Vol 13 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Paul Wasielewski
Keyword(s):  
Distribution Model ◽  
Car Following ◽  
Headway Distribution

New Estimation Technique for Vehicle-Type-Specific Headway Distributions

1998 ◽  
Vol 1646 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Piet H. L. Bovy
Keyword(s):  
Real Life ◽  
Estimation Procedure ◽  
Distribution Model ◽  
Parameter Estimates ◽  
Passenger Cars ◽  
Car Following ◽  
Vehicle Type ◽  
Real Life Data ◽  
Headway Distribution ◽  
Parameter Values

Recently, a new statistical procedure was developed that enables fast, accurate, and robust estimation of composite headway distributions, such as Branston’s generalized queueing model (GQM). Until now, the new procedure had only been applied to aggregate vehicular flow. In this paper, the estimation procedure is extended to headway observations segregated according to vehicle type and period of the day. Consequently, the parameters of a new mixed-vehicle-type headway distribution model based on Branston’s headway model can be estimated. Distinction of vehicle type and sample periods provides additional insight into the plausibility of the headway distributions and parameter values, as well as into the car-following behavior of the distinct vehicle classes varying across the different periods. The estimation procedure was applied to traffic data collected on a two-lane rural road in the Netherlands. Comparison of the estimated headway distributions with real-life data shows that headway distributions can be realistically replicated with the Pearson-III-based mixed-vehicle-type GQM. Inter-pretable differences between the morning, noon, and evening sample periods and between passenger cars, unarticulated trucks, and articulated trucks are found. In addition, passenger-car equivalents for both articulated trucks and unarticulated trucks were determined from the parameter estimates.

scholarly journals TIME HEADWAY ANALYSIS TO DETERMINE THE ROAD CAPACITY

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
I Wayan Suweda
Keyword(s):  
Developed Countries ◽  
Distribution Model ◽  
Analysis Model ◽  
Time Headway ◽  
The Road ◽  
Traffic Conditions ◽  
Traffic Characteristics ◽  
Road Capacity ◽  
Headway Distribution ◽  
The City

ABSTRACT: In developed countries, road capacity values derived from time headway is in accordance to their local traffic characteristics. In theory, time headway standards are developed using statistics models. These standards however, are not necessarily relevant to use in Indonesia. This is because of the differences in traffic conditions and motorists behaviours between those in developed countries and Indonesia. This study is to develop the time headway distribution model and subsequently to determine lionk-road capacity in the city of Denpasar, Bali Province. The study consists of time headway data analysis, model calibration and validation and road capacity values??determination. The study found that normal distribution model fitted the local traffic conditions. Road capacity values are of  2,466 pcus/hour and 2,900 pcus/hour obtained from time headway model and the Indonesian Road Capacity Manual (MKJI) respectively.

ANALYSIS OF CAR FOLLOWING HEADWAY ALONG MULTILANE HIGHWAY

2016 ◽  
Vol 78 (4) ◽  
Author(s):  
Mohd Erwan Sanik ◽  
Joewono Prasetijo ◽  
Ahmad Hakimi Mat Nor ◽  
Nor Baizura Hamid ◽  
Ismail Yusof ◽  
...  
Keyword(s):  
Regression Models ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Linear Regression Models ◽  
Car Following ◽  
Vehicle Type ◽  
Field Data Collection ◽  
Headway Distribution ◽  
Heavy Goods Vehicles ◽  
Linear Regressions

This study describes driver’s car following headway on multilane highways.  The aim of this study is to analyse the driver’s car following headway along multilane highway at four selected locations.  The objectives of this study were to determine car headway at Jalan Batu Pahat – Ayer Hitam multilane highway and to develop linear regression models to present the relationships between headway and speed.  Videotaping method was used in field data collection during peak hours.  Data were extracted from recorded video by using the image processing technique software.  The distance headways and associated vehicles speeds were classified into vehicle following category by vehicle type: car following car, car following heavy goods vehicle, heavy goods vehicle following heavy goods vehicle and heavy goods vehicle following car categories.  Linear regressions models were used to develop the relationships between headway and speed. Based on all headway distribution, it is found that patterns of the vehicle headways at four study locations were similar, which shown a significant number of the vehicles travel at headways less than 5 seconds.  Furthermore, it can be concluded that many drivers tend to follow the vehicles ahead closely on multilane highways.  The regression models were significantly reliable based on their R-square values which are ranging between 0.80 and 0.95.  From the analysis, cars were found to maintain larger headways when following heavy goods vehicles compare to when following other cars.

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