For decades, vehicle time-headway distribution models have been studied by many researchers and traffic engineers. A good time-headway model can be beneficial to traffic studies and management in many aspects; e.g. with a better understanding of road traffic patterns and road user behaviour, the researchers or engineers can give better estimations and predictions under certain road traffic conditions and hence make better decisions on traffic management and control. The models also help us to implement high-quality microscopic traffic simulation studies to seek good solutions to traffic problems with minimal interruption of the real traffic environment and minimum costs. Compared within previously studied models, the mixed (SPM and GQM) mod- els, especially using the gamma or lognormal distributions to describe followers headways, are probably the most recognized ones by researchers in statistical stud- ies of headway data. These mixed models are reported with good fitting results indicated by goodness-of-fit tests, and some of them are better than others in com- putational costs. The gamma-SPM and gamma-GQM models are often reported to have similar fitting qualities, and they often out-perform the lognormal-GQM model in terms of computational costs. A lognormal-SPM model cannot be formed analytically as no explicit Laplace transform is available with the lognormal dis- tribution. The major downsides of using mixed models are the difficulties and more flexibilities in fitting process as they have more parameters than those single models, and this sometimes leads to unsuccessful fitting or unreasonable fitted pa- rameters despite their success in passing GoF tests. Furthermore, it is difficult to know the connections between model parameters and realistic traffic situations or environments, and these parameters have to be estimated using headway samples. Hence, it is almost impossible to explain any traffic phenomena with the param- eters of a model. Moreover, with the gamma distribution as the only common well-known followers headway model, it is hard to justify whether it has described the headway process appropriately. This creates a barrier for better understanding the process of how drivers would follow their preceding vehicles. This study firstly proposes a framework developed using MATLAB, which would help researchers in quick implementations of any headway distributions of interest. This framework uses common methods to manage and prepare headway samples to meet those requirements in data analysis. It also provides common structures and methods on implementing existing or new models, fitting models, testing their performance hence reporting results. This will simplify the development work involved in headway analysis, avoid unnecessary repetitions of work done by others and provide results in formats that are more comparable with those reported by others. Secondly, this study focuses on the implementation of existing mixed models, i.e. the gamma-SPM, gamma-GQM and lognormal-GQM, using the proposed framework. The lognormal-SPM is also tested for the first time, with the recently developed approximation method of Laplace transform available for lognormal distributions. The parameters of these mixed models are specially discussed, as means of restrictions to simplify the fitting process of these models. Three ways of parameter pre-determinations are attempted over gamma-SPM and gamma-GQM models. A couple of response-time (RT) distributions are focused on in the later part of this study. Two RT models, i.e. Ex-Gaussian (EMG) and inverse Gaussian (IVG) are used, for first time, as single models to describe headway data. The fitting performances are greatly comparable to the best known lognormal single model. Further extending this work, these two models are tested as followers headway distributions in both SPM and GQM mixed models. The test results have shown excellent fitting performance. These now bring researchers more alternatives to use mixed models in headway analysis, and this will help to compare the be- haviours of different models when they are used to describe followers headway data. Again, similar parameter restrictions are attempted for these new mixed models, and the results show well-acceptable performance, and also corrections on some unreasonable fittings caused by the over flexibilities using 4- or 5- parameter models.

Med fler än en miljon omkomna i trafikolyckor världen över varje år är trafiksäkerhet ett ständigt aktuellt område. Studier på deprimerade patienter har visat att negativ sinnesstämning medför försämrad körförmåga. Dessa effekter är i hög grad outforskade och det är därför viktigt att undersöka om de förekommer även vid en mildare grad av nedstämdhet, så kallad dysfori, vilket i så fall skulle innebära att negativ sinnesstämning i likhet med trötthet och alkoholpåverkan utgör en allvarlig trafikfara. För att bättre förstå hur sinnesstämning påverkar körförmåga är det dock också relevant att undersöka om dysfori kan bidra till riskkompensation, det fenomen som inträffar när människor kompenserar för säkerhetsförändringar genom ett förändrat riskbeteende. I denna uppsats beskrivs en del av en körsimulatorstudie kring dysfori och bilkörning, där syftet var att undersöka om dysfori kan vara en orsak till riskkompensation. 15 studenter vid Linköpings universitet delades upp i en testgrupp med dysforiska försöksdeltagare (N = 5) och en kontrollgrupp (N = 10) med hjälp av Major Depression Inventory, ett instrument för att diagnostisera depression. Dessa fick sedan genomföra en körning i simulatorn Desktop T&D där time headway, time to collision, genomsnittshastighet och antal omkörningar mättes för de olika grupperna för att undersöka förekomsten av riskkompensation. Resultatet visade att inga signifikanta skillnader kunde observeras för något av måtten. Riskkompensation har i många studier visat sig vara ett komplext fenomen att undersöka och ett flertal metodologiska problem förelåg, särskilt på grund av svårigheten att mäta risk i en simulator med god validitet. Det är dock viktigt att fortsatta undersökningar görs för att bättre förstå riskkompensation, samt att fenomenet beaktas som en tänkbar inverkande faktor i framtida studier av körförmåga.

This diploma thesis deals with driver’s reaction time in regular road traffic. The theoretical part of this work focuses on reaction time, such as various definitions, structuring of reaction time, methods of measuring or publications made by other authors. The practical part of this diploma thesis includes proposal and realization of experiment, based on analysis of the current state by the author of this diploma thesis, which focuses on driver’s reaction time in regular road traffic (various traffic situations). At the end, the practical part presents the results evaluation based on various traffic situations or headway.

No description available.

Since the introduction of motor vehicles, the number of fatalities and accidents has been a concern for society.The number of fatalities on roads is amongst the most common causes of mortality worldwide (WHO, 2015).Even in industrialized countries the number of fatalities remains unacceptable. Therefore, in the last decades, anumber of approaches have emerged to support and boost traffic safety towards a system free from fatalities andserious impairment outcomes. ‘Sustainable Safety’ and ‘Vision Zero’ are well-known examples aiming to avoidfatalities within the traffic system and reduce injury severity when a traffic accident is inevitable. However, thenumber of fatalities and seriously injured accidents are still relatively high. More specifically, vulnerable roadusers remain involved in fatal and serious accidents even in industrialized countries. Therefore, further advancesin traffic safety studies are needed. This thesis aims at evaluating the impact of road characteristics, traffic rulesand information provision towards a safer traffic system. The thesis is composed of five scientific papers whichsummarizes the main contributions of this work. / <p>QC 20161109</p>

Η επιρροή του παράγοντα χρονοαπόσταση σε ένα σύστημα ακολουθίας οχημάτων μπορεί να προσδιορίσει την επικινδυνότητα του πλήγματος που υφίσταται το σύστημα. Με βάση μια παρ’ολίγον οπισθο-μετωπική σύγκρουση σε αυτοκινητόδρομο 3 λωρίδων, εξετάζεται ο ρόλος της χρονοαπόστασης μεταξύ των οχημάτων σε συνδυασμό με τους χρόνους αντίδρασης των οδηγών στην αντίληψη του επικείμενου κινδύνου. Το μοντέλο ακολουθίας οχημάτων κατά Brill, που συσχετίζει την χρονοαπόσταση, τον χρόνο αντίδρασης του οδηγού και την επιβράδυνση με τη συχνότητα των ατυχημάτων, χρησιμοποιείται ως κύριο εργαλείο για την εκτίμηση της ευαισθησίας της πιθανότητας ενός ατυχήματος. Μέσω της μικροσκοπικής ανάλυσης του βίντεο καταγραφής του ατυχήματος και της επεξεργασίας των δεδομένων και με πηγή έμπνευσης τα προγενέστερα επίμαχα ερωτήματα που θέτει και απαντά ο G. Davis και οι συνεργάτες του, προκύπτουν οι απαραίτητες πληροφορίες για την αριθμητική περιγραφή του ατυχήματος. Με τη χρήση έπειτα του λογισμικού προγράμματος OpenBUGS, το οποίο βασίζεται στη μέθοδο Monte Carlo Markov Chain, γίνεται προσομοίωση του προτύπου ατυχήματος και υπολογίζονται οι τιμές των παραμέτρων που επηρεάζουν τη μορφή του πλήγματος. Από τα αποτελέσματα προκύπτει ο βαθμός που ο συνδυαστικός παράγοντας χρονοαπόσταση και χρόνος αντίδρασης επηρεάζει το πλήγμα και αξιολογείται. Τέλος, με συγκεκριμένες επεμβάσεις επιχειρείται η βελτίωση ολόκληρου του συστήματος ακολουθίας οχημάτων. / The influence of time headway on a car-following system can determine the severity of a shockwave. Based on a near-miss rear-end collision on a 3-lane highway, this study examines the importance of time headway in combination with the driver’s reaction time upon perception of the upcoming hazard. The car-following model developed by Ed. Brill, relating driver’s reaction time, temporal headway and deceleration response to accident frequency, is used as a main tool for assessing the sensitivity of collision probability. Through a microscopic analysis of the video record and data processing and inspired by earlier critical questions that G. Davis and his associates have posed and answered, all the necessary information for the arithmetical description of the accident is extracted. Using the OpenBUGS software, and based on the Monte Carlo Markov Chain method, simulation of the collision prototype is achieved along with the calculation of other main parameters that affect the shockwave form. Simulation results, revealing the influence that the combined factor headway-reaction time has on a shockwave are derived and evaluated. Through certain modifications, the improvement of the whole car-following system is attempted.

Many car-following models predict a stable car-following behavior with a very small fluctuation around an equilibrium value g* of the net headway g with zero speed-difference Δv between the following and the lead vehicle. However, it is well-known and additionally demonstrated by data in this paper, that the fluctuations are much larger than these models predict. Typically, the fluctuation in speed difference is around ±2m/s, while the fluctuation in the net time headway T=g/v can be as big as one or even two seconds, which is as large as the mean time headway itself. By analyzing data from loop detectors as well as data from vehicle trajectories, evidence is provided that this randomness is not due to driver heterogeneity, but can be attributed to an internal stochasticity of the driver itself. A final model-based analysis supports the hypothesis, that the preferred headway of the driver is the parameter that is not kept constant but fluctuates strongly, thus causing the even macroscopically observable randomness in traffic flow.

Temps Inter-véhiculaire (TIV) est une variable microscopique fondamentale dans la théorie du trafic, et a été étudié depuis le début du développement de cette théorie, vers 1930. La distribution de probabilité du TIV décrit la répartition des arrivées des véhicules en un point donné et reflète dans une certaine mesure le comportement de conduite. Beaucoup d'applications en ingénierie du trafic viennent de la connaissance fine de cette variable. La thèse a pour but d'approfondir cette connaissance en modélisant la distribution du TIV dans différents contextes selon différents points de vue. Tout d'abord, deux méthodes d'échantillonnage, la méthode de groupement et la méthode de raffinement sont considérées. L'application numérique concerne deux bases de données, celle de la route nationale RN118 et celle de l'autoroute A6. Ensuite, trois types de modèles probabilistes sont analysés et classifiés. Une comparaison exhaustive des modèles et des méthodes d'estimation est réalisée ce qui conduit à considérer que le modèle gamma-GQM est supérieur aux autres modèles en matière de performance statistique et en efficacité de calcul. Différentes procédures d'estimation sont testées, celle qui est proposée et retenue favorise la stabilité des paramètres estimés. Six nouveaux modèles de TIV sont proposés, calibrés, analysés. Mis à part deux modèles de performance inférieure aux autres et au modèle gamma-GQM, quatre modèles sont équivalents voire meilleurs que le modèle gamma-GQM. Pour une raison pratique, le modèle Double Gamma est choisi à côté du modèle gamma-GQM, comme modèle de comparaison, dans toute la modélisation des TIV. Le calibrage des modèles et l'analyse des paramètres des modèles sont menés, à partir des données réelles, en considérant trois dimensions d'étude du trafic: les échelles macroscopique, mésoscopique et microscopique. Une quatrième dimension d'étude des TIV est constituée des facteurs exogènes au trafic. La prise en compte de ces facteurs exogènes, à chaque échelle macroscopique entraîne la distinction de deux types de facteur exogène : « empêchant » et « impulsant». Finalement, différentes approches de validation sont testées. L'approche proposée par « enveloppe des distributions » semble prometteuse pour le futur / Time Headway (TH) is a microscopic variable in traffic flow theories that has been studied since the 1930s. Distribution of this fundamental variable describes the arrival pattern of vehicles in traffic flow, so probabilistic modeling is the main approach to study TH and represent driving behaviour. The applications of the variable in traffic engineering are varied; include capacity calculation, microscopic simulation, traffic safety analysis, etc. This dissertation aims at modeling the TH distribution in different contexts. Firstly, the short-time sampling method and long-time sampling method are applied to obtain TH samples from the two data bases (the RN118 national roadway and the A6 motorway). Then, three probabilistic TH model types are analyzed and classified. An exhaustive comparison between the existing models and between the corresponding estimation methods lead to consider that the gamma-GQM is the best TH model in the literature. An estimation process is also proposed in order to obtain good and stable estimated results of the parameters. After that, the TH probabilistic modeling is developed by six new models. Except for the two ones which are worse, the four other models are statistically equivalent and/or better than the gamma-GQM. For practical reason, the Double Gamma model is selected, as a comparison model, with the gamma-GQM to calibrate all TH samples. Three traffic levels are considered: macroscopic, mesoscopic and microscopic. The effects of exogenous factors are also examined. Examining this factor in each macroscopic variable level leads to distinguish two following factor types: impeding factor and propulsive factor. Finally, different approaches for TH validation are tested. The proposed approach of “envelope of distributions” seems to be promising for future applications