An Introduction to Traffic Flow Theory

Elefteriadou, Lily

doi:10.1007/978-1-4614-8435-6

Cited by 140 publications

(123 citation statements)

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“…The main reason we would suggest for such a result is that at higher speeds 8 drivers' aggressiveness tends to produce greater effects: the more aggressive a driver is at higher speeds, the closer the 9 spacing he/she adopts with respect to non-aggressive drivers. This effect is less evident at lower speeds, where 10 aggressive and non-aggressive drivers tend to adopt more similar spacing (Elefteriadou, 2014). As regards the 11 comparison across the three different datasets, the average influence of speed on equilibrium conditions is similar 12 (piecewise linear) and in line with previous studies (Figure 6): it all falls in quite a narrow band (except for the curve 13 obtained by Chandler which seems very conservative).…”

supporting

confidence: 73%

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

Pariota

Bifulco

Galante

et al. 2016

Accident Analysis & Prevention

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5This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected 6 during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to 7 be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. 8In particular, identification of differences and similarities in observed car-following behaviours for different samples of 9 drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active 10 safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or 11 customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this 12 direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, 13 and a promising approach for safety improvement is the progressive introduction of increasing levels of driving 14 automation in next-generation vehicles, according to the active/preventive safety approach. However, the more 15 advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may 16 sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, 17 implementation of these systems will require the interaction of human driving logics with automation logics and then an 18 enhanced ability in modelling drivers' behaviour. This will allow both higher active-safety levels and higher user 19 acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to 20 supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal 21 driving. This is required in various driving conditions, among which car-following assumes key importance because of 22 the huge number of rear-end crashes. 23The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the 24 possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from 25 three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments 26 involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. 27Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the 28 adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases 29 with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway 30 thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. 31Three different car-following paradig...

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supporting

confidence: 73%

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

Pariota

Bifulco

Galante

et al. 2016

Accident Analysis & Prevention

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“…The chapter includes a brief overview of macroscopic flow models used to model highway traffic. For a more in depth description of macroscopic traffic flow theory the reader is referred to Treiber and Kesting (2013) or Elefteriadou (2014).…”

Section: Macroscopic Traffic Flow Theorymentioning

confidence: 99%

Highway Traffic State Estimation and Short-term Prediction

Allström¹

2016

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Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors.The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with a n E ns e m bl e K a l ma n F i l t e r ( E nK F ) i t c a n be u s e d t o f u s e d i f f e r e n t t y pe s of poi n t s p e e d measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors.In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.

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“…A gap (Fig. 1) between two vehicles is the distance between the rear bumper of the first vehicle and the front bumper of the following vehicle and is usually measured in seconds [1]. Lag (Fig.…”

Section: Introductionmentioning

confidence: 99%

Gap acceptance behavior of drivers at uncontrolled T-intersections under mixed traffic conditions

Dutta

Ahmed

2017

J. Mod. Transport.

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Explicit traffic control measures are absent in uncontrolled intersections which make them susceptible to frequent conflicts and resulting collisions between vehicles. In developing countries like India, drivers at such intersections do not yield to higher priority movements which cause more crashes between vehicles. The objective of this study is to analyze and model the gap acceptance behavior of minor street drivers at uncontrolled T-intersections considering their aggressive nature. Three intersections in the northeast region of India have been selected as the case study area. Preliminary analysis of the data revealed that drivers behave aggressively, not because they have to wait for a long time at the stop line, but because of their lack of respect for traffic rules. Binary logit models are developed for minor road right turning vehicles which show that gap acceptance behavior is influenced by gap duration, clearing time and aggressive nature of drivers. The equations obtained were used to estimate the critical gaps for aggressive and non-aggressive drivers. Critical gaps are also calculated using an existing method called clearing behavior approach. It is also shown that the estimation of critical gap is more realistic if clearing time and aggressive behavior of drivers are considered.

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An Introduction to Traffic Flow Theory

Cited by 140 publications

References 0 publications

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

Highway Traffic State Estimation and Short-term Prediction

Gap acceptance behavior of drivers at uncontrolled T-intersections under mixed traffic conditions

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