Analysis and Modeling Time Headway Distributions Under Heavy Traffic Flow Conditions in the Urban Highways: Case of Isfahan

Abtahi, Sayyed Mahdi; Tamannaei, Mohammad; Haghshenash, Hosein

doi:10.3846/16484142.2011.635694

Cited by 39 publications

(21 citation statements)

References 10 publications

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“…This attributes to high risk-ability of driver population which results reduction in safety as a consequence. Similar results were also obtained in a study on time headway distributions under heavy traffic flow conditions in urban highways of Isfahan [13]. A careful examination of Table 3 reveals that with the increase of flow rate the mean and standard deviation of headways at all flow scopes decrease; this is somewhat expected because of the change in headways from random to constant state.…”

Section: Descriptive Statistics Of the Headway Datasupporting

confidence: 81%

“…Riccardo and Massimiliano [8] analyzed headway distribution on rural two-lane two-way roads and suggested that the inverse Weibull distribution fits the headway data well. Abtahi et al [13] studied different headway distribution models in the passing and middle lanes in urban highways under heavy traffic condition and concluded that lognormal and gamma models with 0.24 s and 0.69 s shifts exhibit their aptness in passing and middle lane respectively. Ren and Qu [14] studied headway distribution type analysis selecting three distributions namely, exponential, inverse gaussian and lognormal distribution.…”

Section: Research Motivationsmentioning

confidence: 99%

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Headway distribution models of two-lane roads under mixed traffic conditions: a case study from India

Roy

Saha

2017

Eur. Transp. Res. Rev.

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Introduction The time headway of vehicles is of fundamental importance in traffic engineering applications like capacity, levelof-service and safety studies. Further, the performance of traffic simulation depends on inputs into the simulation process and 'accurate vehicle generation' is critical in this context. Thus, it is important to define headway distribution pattern for the purpose of analyzing traffic and subsequently, taking infrastructure related decisions. In so far, majority of the researches on this subject are based on homogeneous traffic and effects of mixed traffic especially on two-lane roads are yet to be culminated. The present study, thus, aimed at investigating headway distributions on such roads under mixed traffic situation. Methods Field study was conducted on two-lane highways in India that exhibits heterogeneity in its traffic composition. Contestant headway distribution models were evaluated and four distribution functions namely, log-logistic, lognormal, Pearson 5 and Pearson 6 were considered while modeling the headway data. The appropriate models were selected using a methodology based on K-S test and subsequent field validation. Results Log-logistic distribution was found appropriate at moderate flow whereas, at congested state of flow it was Pearson 5. However, at unstable flow nearing capacity, both, following and non-following components of headways were observed to follow different distributional characteristics. Nomographs are developed for calculating the headway probabilities at different flow levels considering the appropriate distribution models. 'Probability of headway less than't's' increases with the flow rate and rate of such increase is considerably high for headways 7.5 s or more. This attributes to the fact that at heavy flow more vehicles are entrapped inside platoons and they move in following with shorter headways. Further, a comparison was made between the headway probabilities obtained in the current study and road segments that exhibit more or less homogeneous traffic. It was found that at moderate flow level proportion of shorter headways are considerably high under mixed traffic. Conclusions The present paper demonstrates the effect of mixed traffic on distribution of time headways of two-lane roads. Presence of slower vehicles in such traffic leads to frequent formation of platoons, thereby, increases the risk taking behavior of drivers' while overtaking. As a result, proportion of shorter headways increases resulting in highly skewed observations. Thus, the study proposed Log-logistic distribution under medium flow since it can model events which have 'increased initial rate' and Pearson 5 under heavy flow to model 'highly skewed data'. The model outputs were accordingly compared with other studies and were found to explain the mixed traffic characteristics satisfactorily. The present study, thus, creates a starting point of further initiatives aimed at establishing a robust method of modeling headways on two-lane roads with mixed traffic.

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Section: Descriptive Statistics Of the Headway Datasupporting

confidence: 81%

Section: Research Motivationsmentioning

confidence: 99%

Headway distribution models of two-lane roads under mixed traffic conditions: a case study from India

Roy

Saha

2017

Eur. Transp. Res. Rev.

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“…As will be discussed below, researchers have studied the headway in many traffic studies [147,148,149].…”

Section: More On the Headway Distancementioning

confidence: 99%

Friend: A cyber-physical system for traffic flow related information aggregation and dissemination

El-Tawab

Olariu

Almalag

2012

2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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A cyber-physical system for traffic Flow-Related Information aggrEgatioN and Dissemination. By integrating resources and capabilities at the nexus between the cyber and physical worlds, FRIEND will contribute to aggregating traffic flow data collected by the huge fleet of vehicles on our roads into a comprehensive, near real-time synopsis of traffic flow conditions. We anticipate providing drivers with a meaningful, color-coded, at-a-glance view of flow conditions ahead, alerting them to congested traffic.FRIEND can be used to provide accurate information about traffic flow and can be used to propagate this information. The workhorse of FRIEND is the ubiquitous lane delimiters (a.k.a. cat's eyes) on our roadways that, at the moment, are used simply as dumb reflectors.Our main vision is that by endowing cat's eyes with a modest power source, detection and communication capabilities they will play an important role in collecting, aggregating and disseminating traffic flow conditions to the driving public. We envision the cat's eyes system to be supplemented by road-side units (RSU) deployed at regular intervals (e.g. every kilometer or so). The RSUs placed on opposite sides of the roadway constitute a logical unit and are connected by optical fiber under the median. Unlike inductive loop detectors, adjacent RSUs along the roadway are not connected with each other, thus avoiding the huge cost of optical fiber. Each RSU contains a GPS device (for time synchronization), an active Radio Frequency Identification (RFID) tag for communication with passing cars, a radio transceiver for RSU to RSU communication and a laptop-class computing device.The physical components of FRIEND collect traffic flow-related data from passing vehicles. The collected data is used by FRIEND's inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic flow-related information along the roadway. The second contribution of this thesis is the development of an incident classification and detection algorithm that can be used to classify different types of traffic incident. Then, it can notify the necessary target of the incident. We also compare our incident detection technique with other VANET techniques.Our third contribution is a novel strategy for information dissemination on highways. First, we aim to prevent secondary accidents. Second, we notify drivers far away from the accident of an expected delay that gives them the option to continue or exit before reaching the incident location. A new mechanism tracks the source of the incident while notifying drivers away from the accident. The more time the incident stays, the further the information needs to be propagated. Furthermore, the denser the traffic, the faster it will backup. In high density highways, an incident may form a backup of vehicles faster than low density highways. In order to satisfy this point, we need to propagate information as a function of density and time.iv

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“…There are some restrictions in the use of these distribution models because they are affected by many factors such as traffic volume, percentage of trucks, road infrastructure, weather, time, and so on. 12 Mei and Bullen 13 measured the headway in a four-lane highway and found the lognormal distribution is suitable. Luttinen 14 has found that M3 model was effective for larger vehicle headways.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction

Kong

Guo

2016

Advances in Mechanical Engineering

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The primary objective of this study is to identify the characteristics of vehicle headway on multi-lane freeway under lane management in China considering car-truck interaction. More specifically, the study focused on answering the following two questions: (1) whether the car-truck interaction has impact on headway, that is, headway varies by different leading and following vehicle types and (2) what is the best-fitted distribution model for particular headway type under lane management. The team collected traffic data, including traffic flow rates, percentage of trucks, speeds, headways, and so on from four segments of Shanghai-Nanjing freeway, Jiangsu Province in China. Then, some statistical methods were used to analyze the vehicle headway. It was found that car-car, car/truck, and truck-truck headways are significantly different from each other. Also, the traffic flow rate, percentage of trucks, and lane position were found to have an influence on the vehicle headway through the tests. Using the maximum-likelihood estimation, Kolmogorov-Smirnov test, and chisquare test techniques, the distribution models and parameter functions for each headway type were built and validated. The results showed that lognormal model is suitable for car-car and truck-truck headway types, and inverse Gaussian model fits the car/truck headway type well.

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Analysis and Modeling Time Headway Distributions Under Heavy Traffic Flow Conditions in the Urban Highways: Case of Isfahan

Cited by 39 publications

References 10 publications

Headway distribution models of two-lane roads under mixed traffic conditions: a case study from India

Headway distribution models of two-lane roads under mixed traffic conditions: a case study from India

Friend: A cyber-physical system for traffic flow related information aggregation and dissemination

Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction

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