An investigation of freeway standstill distance, headway, and time gap data in heterogeneous traffic in Iowa

Microsimulation models have been growing in popularity in traffic engineering in recent years, and are often used as an important tool in the decision making process on large roadway design projects. In order to get valid results, it is necessary to calibrate such microsimulation models to local conditions. This is frequently achieved through a trial and error process of adjusting model parameters to get simulation results to match real world calibration data. Rarely is data collected on the model parameters themselves to provide a physical basis for the selection of their value. Two of the most important microsimulation model parameters for freeway models are standstill distance (the distance between stopped vehicles) and preferred time headway or time gap (the time between successive vehicles). Many simulation models treat these values as constants for all drivers and do not allow them to be set separately for different vehicle classes. This study presents a repeatable methodology for collecting standstill distance and headway/time gap values on freeways (mostly urban, with one rural location). It applies that methodology to locations throughout the state of Iowa. It continues by analyzing that data and comparing it for different locations and conditions. It finds that standstill distances vary by location and vehicle pair type. Headways/time gaps are found to be consistent within the same driver population and across different driver populations when the conditions are similar. An initial comparison between headways/time gaps at three urban areas to one rural location indicates a potential difference in driver behavior between those two conditions. Both standstill distance and headway/time gap are found to follow fairly disperse and skewed distributions. As a result of these findings, it is recommended that microsimulation models are modified to include the option for standstill distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes. Additionally, the standstill distances and headway/time gaps found in this study may be used as a starting point for future microsimulation calibration efforts on urban freeways in Iowa

Measurement and Analysis of Heterogenous Vehicle Following Behavior on Urban Freeways: Time Headways and Standstill Distances

Microscopic traffic modelling is a popular tool in the transportation field, but using such models comes with significant data needs in order to properly calibrate them. Two important driver behavior parameters in these models are the preferred time headways and standstill distances. In this paper, an economical method for collecting headways and standstill distances is presented and applied to urban freeways in Iowa, USA. The following time headways and standstill distances were categorized into four combinations of car and truck pairs. It was found that headway values largely depend on the following vehicle type-when a car was following the average headway was around 2 seconds, compared to around 3 seconds when a truck was following. Additionally, the car-car combination leaves much less space when stopped than when a pair involves trucks. In particular, the average standstill distance of a car following a car was found to be around 9 feet, while the average standstill distances are around 12 feet when a truck is involved. However, both headways and standstill distances follow fairly disperse distributions, due to the heterogeneity in driver behavior. Thus, microsimulation software should be improved to allow these parameters to follow distributions

Incorporating the standstill distance and time headway distributions into freeway car-following models and an application to estimating freeway travel time reliability

Standstill distances and following time headways are two important microsimulation model parameters associated with driver aggression. This paper investigates the distributions of standstill distances and time headways and incorporates these distributions into car-following models to estimate travel time reliability. By incorporating standstill distance and following headway into car-following models as stochastic parameters, a speed-density region can be generated, based on which various travel-time-reliability measures can be calculated. Key findings of this study are as follows: (1) Both standstill distances and time headways follow fairly dispersed distributions. Therefore, it is suggested that microsimulation models should include the option of allowing standstill distances and time headways to follow distributions as well as to be specified separately for different vehicle classes. (2) By incorporating stochastic standstill distance and time headway parameters in car-following models, travel-time-reliability measures can be estimated more precisely and faster compared with using VISSIM

VISSIM Calibration for Urban Freeways

In urban areas, interchange spacing and the adequacy of design for weaving, merge, and diverge areas can significantly influence available capacity. Traffic microsimulation tools allow detailed analyses of these critical areas in complex locations that often yield results that differ from the generalized approach of the Highway Capacity Manual. In order to obtain valid results, various inputs should be calibrated to local conditions. This project investigated basic calibration factors for the simulation of traffic conditions within an urban freeway merge/diverge environment. By collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. In particular, a repeatable methodology for collecting standstill distance and headway/time gap data on urban freeways was applied to locations throughout the state of Iowa. This collection process relies on the manual processing of video for standstill distances and individual vehicle data from radar detectors to measure the headways/time gaps. By comparing the data collected from different locations, it was found that standstill distances vary by location and lead-follow vehicle types. Headways and time gaps were found to be consistent within the same driver population and across different driver populations when the conditions were similar. Both standstill distance and headway/time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended that microsimulation models be modified to include the option for standstill distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes. In addition, for the driving behavior parameters that cannot be easily collected, a sensitivity analysis was conducted to examine the impact of these parameters on the capacity of the facility. The sensitivity analysis results can be used as a reference to manually adjust parameters to match the simulation results to the observed traffic conditions. A well-calibrated microsimulation model can enable a higher level of fidelity in modeling traffic behavior and serve to improve decision making in balancing need with investment

Investigation of factors influencing the hydrolytic degradation of single PLGA microparticles

Abstract Poly lactide-co-glycolide (PLGA) is an important polymer matrix used to provide sustained release across a range of active pharmaceutical ingredients (APIs) and works by hydrolytic degradation within the body, thereby releasing entrapped drug. Processing and sterilisation can impact on the morphology and chemistry of PLGA therefore influencing the hydrolysis rate and in turn the release rate of any entrapped API. This paper has looked at the effect of supercritical carbon dioxide (scCO2) processing, gamma irradiation, comonomer ratio and temperature on the hydrolysis of individual PLGA microparticles, using a combination of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) imaging, Scanning Electron Microscopy (SEM), Differential Scanning Calorimetery (DSC) and Gel Permeation chromatography (GPC) to facilitate a better understanding of the physiochemical factors affecting the hydrolysis rate. This work has shown that scCO2 processing influences hydrolysis rates by increasing the porosity of the PLGA microparticles, increasing the lactide comonomer ratio decreases hydrolysis rates by reducing the hydrophilicity of the PLGA microparticles and increasing the gamma irradiation dose systematically increases the rate of hydrolysis due to reducing the overall molecular weight of the polymer matrix via a chain scission mechanism. Moreover this work shows that ATR-FTIR imaging facilitates the determination of a range of physicochemical parameters during the hydrolysis of a single PLGA microparticle including water ingress, water/polymer interface dimensions, degradation product distribution and hydrolysis rates for both lactide and glycolide copolymer units from the same experimen

An investigation of freeway standstill distance, headway, and time gap data in heterogeneous traffic in Iowa

Microsimulation models have been growing in popularity in traffic engineering in recent years, and are often used as an important tool in the decision making process on large roadway design projects. In order to get valid results, it is necessary to calibrate such microsimulation models to local conditions. This is frequently achieved through a trial and error process of adjusting model parameters to get simulation results to match real world calibration data. Rarely is data collected on the model parameters themselves to provide a physical basis for the selection of their value. Two of the most important microsimulation model parameters for freeway models are standstill distance (the distance between stopped vehicles) and preferred time headway or time gap (the time between successive vehicles). Many simulation models treat these values as constants for all drivers and do not allow them to be set separately for different vehicle classes. This study presents a repeatable methodology for collecting standstill distance and headway/time gap values on freeways (mostly urban, with one rural location). It applies that methodology to locations throughout the state of Iowa. It continues by analyzing that data and comparing it for different locations and conditions. It finds that standstill distances vary by location and vehicle pair type. Headways/time gaps are found to be consistent within the same driver population and across different driver populations when the conditions are similar. An initial comparison between headways/time gaps at three urban areas to one rural location indicates a potential difference in driver behavior between those two conditions. Both standstill distance and headway/time gap are found to follow fairly disperse and skewed distributions. As a result of these findings, it is recommended that microsimulation models are modified to include the option for standstill distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes. Additionally, the standstill distances and headway/time gaps found in this study may be used as a starting point for future microsimulation calibration efforts on urban freeways in Iowa.</p

An investigation of freeway standstill distance, headway, and time gap data in heterogeneous traffic in Iowa

Microsimulation models have been growing in popularity in traffic engineering in recent years, and are often used as an important tool in the decision making process on large roadway design projects. In order to get valid results, it is necessary to calibrate such microsimulation models to local conditions. This is frequently achieved through a trial and error process of adjusting model parameters to get simulation results to match real world calibration data. Rarely is data collected on the model parameters themselves to provide a physical basis for the selection of their value. Two of the most important microsimulation model parameters for freeway models are standstill distance (the distance between stopped vehicles) and preferred time headway or time gap (the time between successive vehicles). Many simulation models treat these values as constants for all drivers and do not allow them to be set separately for different vehicle classes. This study presents a repeatable methodology for collecting standstill distance and headway/time gap values on freeways (mostly urban, with one rural location). It applies that methodology to locations throughout the state of Iowa. It continues by analyzing that data and comparing it for different locations and conditions. It finds that standstill distances vary by location and vehicle pair type. Headways/time gaps are found to be consistent within the same driver population and across different driver populations when the conditions are similar. An initial comparison between headways/time gaps at three urban areas to one rural location indicates a potential difference in driver behavior between those two conditions. Both standstill distance and headway/time gap are found to follow fairly disperse and skewed distributions. As a result of these findings, it

Incorporating the standstill distance and time headway distributions into freeway car-following models and an application to estimating freeway travel time reliability

Standstill distances and following time headways are two important microsimulation model parameters associated with driver aggression. This paper investigates the distributions of standstill distances and time headways and incorporates these distributions into car-following models to estimate travel time reliability. By incorporating standstill distance and following headway into car-following models as stochastic parameters, a speed-density region can be generated, based on which various travel-time-reliability measures can be calculated. Key findings of this study are as follows: (1) Both standstill distances and time headways follow fairly dispersed distributions. Therefore, it is suggested that microsimulation models should include the option of allowing standstill distances and time headways to follow distributions as well as to be specified separately for different vehicle classes. (2) By incorporating stochastic standstill distance and time headway parameters in car-following models, travel-time-reliability measures can be estimated more precisely and faster compared with using VISSIM.This article is published as Lu, Chaoru, Jing Dong, Andrew Houchin, and Chenhui Liu. "Incorporating the standstill distance and time headway distributions into freeway car-following models and an application to estimating freeway travel time reliability." Journal of Intelligent Transportation Systems 25, no. 1 (2021): 21-40. DOI: 10.1080/15472450.2019.1683450.</p