A New Safety Objective for the Calibration of the Intelligent Driver Model

The intelligent driver model (IDM) is one of the most widely used car-following (CF) models in recent years. The parameters of this model have been calibrated using real trajectories obtained from naturalistic driving ,driving simulator experiment and drone data. An important aspect of the model calibration process is defining the main objective of the calibration. This objective, influences the objective function and the performance measure for the calibration. For example, to calibrate CF models, the objective is usually to minimize the error in measured spacing or speed while important safety aspects of the models such as the collision avoidance mechanisms are ignored. For such models, there is no guarantee that the calibrated parameters will preserve the safety properties of the model since they are not explicitly taken into account. To explicitly account for the safety properties during calibration, this paper proposes a simple objective function which minimizes both the error in the actual measured spacing (as it is currently done) and the error in the dynamic safety spacing (desired minimum gap) derived from the collision free property of the IDM model. The proposed objective function is used to calibrate two variants of the IDM using vehicle trajectories obtained with drone from a Dutch highway. The calibration performance is then compared in terms of the error in actual spacing and time gap. The results show that the proposed safety objective 15 function leads to lower errors in spacing and time gap compared to when minimizing for only spacing and preserves collision property of the IDM.Comment: To be submitted to the Transportation Research Records Journa

PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation

Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk. As a result, they are only applicable in scenarios where those assumptions hold. To address this issue, we present a novel data-driven Probabilistic RISk Measure derivAtion (PRISMA) method. The PRISMA method is used to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). Because we adopt a data-driven approach to predict the possible future evolutions of traffic participant trajectories, less assumptions on these trajectories are needed. Since the PRISMA is not bound to specific assumptions, multiple SSMs for different types of scenarios can be derived. To calculate the probability of the specific event, the PRISMA method uses Monte Carlo simulations to estimate the occurrence probability of the specified event. We further introduce a statistical method that requires fewer simulations to estimate this probability. Combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. It is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.Comment: 26 pages, 4 figures, 1 tabl

Ανάπτυξη και εφαρμογή υβριδικών μεθόδων ασαφoύς λογικής, στατιστικής και τεχνικής νοημοσύνης στο σχεδιασμό, λειτουργία και διαχείρηση συστημάτων μεταφορών και υποδομών

Fuzzy logic and probability theory are both useful for dealing with uncertainty, various models have been built using these two paradigms. However, there has been difficulty in combining the benefits of these two types of models because of the difference in approach and model formulation. For example, various fuzzy versions of probabilistic models have been built but their use has been limited. The reason is that experts in probabilistic models may not have the required knowledge in fuzzy logic to understand and use such fuzzy models. Similarly, different ways have been suggested to include probability and statistics in fuzzy logic models, but such models have had very limited applications. This is also because experts in fuzzy logic may not possess the adequate statistical background to understand these types of models. The goal of this thesis was to solve this problem of methodological difference between statistical and fuzzy models.In contrast to most works which normally build new fuzzy or statistical versions of original models, this thesis proposes to build hybrid fuzzy-statistical models that use some important elements of one model in the other without significant change in the structure of the original models. The advantage of the proposed approach is that experts in any of the models (fuzzy or statistical) can continuing using such models without the need to acquire significant new knowledge in the other domain.It is shown that the proposed approach gives comparable or better results to the original models. In the various applications, the proposed models proved to be highly flexible since already existing models like linear regression, neural networks and fuzzy logic models can be easily transformed to fuzzy-statistical models. The need for additional knowledge is minimised and the proposed models can be used by anyone already familiar with linear regression, fuzzy logic or neural network models. Also, it shown that the proposed models can handle uncertainties related to randomness and fuzziness and are particularly suited to handling most transportation decision making problems which are usually made under high uncertainty.Πολλά συνηθισμένα προβλήματα που αντιμετωπίζονται στο σχεδιασμό, τη λειτουργία και τη διαχείριση συστημάτων και υποδομών μεταφορών έχουν λυθεί με τη χρήση γνωστών στατιστικών μεθόδων όπως η παλινδρόμηση, η ανάλυση παραγόντων, η ανάλυση χρονοσειρών, η διακριτή ανάλυση επιλογής. Αυτές οι μέθοδοι βασίζονται σε μεγάλο βαθμό στη θεωρία των πιθανοτήτων και της στατιστικής, προκειμένου να παριστάνει ή να χειριστεί την αβεβαιότητα που εμπλέκεται σε τέτοιου είδους προβλήματα. Σε αυτά τα είδη των μοντέλων, η αβεβαιότητα συνήθως οφείλεται στην τυχαιότητα και συχνά εκπροσωπείται μέσω των στατιστικών διαστημάτων, όπως διαστήματα εμπιστοσύνης και πρόβλεψης. Σε άλλες περιπτώσεις, αβεβαιότητες λόγω της ανακρίβειας των δεδομένων ή της έλλειψης των πληροφοριών, προκύπτουν συχνά και πρέπει να αντιμετωπίζονται επίσης. Η χρήση της ασαφούς λογικής έχει αναγνωριστεί ως ένας εναλλακτικός τρόπος για τη διαχείριση τέτοιας αβεβαιότητας, στην οποία εμπλέκονται προβλήματα μεταφοράς λόγω ανακρίβειας ή έλλειψης πληροφοριών. Αυτοί οι τύποι των αβεβαιότητας συχνά αντιπροσωπεύονται μέσω ασαφών αριθμών.Δεδομένου ότι η ασαφής λογικής και η θεωρία πιθανοτήτων είναι τόσο χρήσιμες για την αντιμετώπιση της αβεβαιότητας, έχουν αναπτυχθεί διάφορα μοντέλα με τη χρήση αυτών των δύο προσεγγίσεων. Η ενσωμάτωση της ασαφούς λογικής με την στατιστική είναι ιδιαίτερα χρήσιμη για συστήματα μεταφορών, επειδή είναι πολύπλοκα κοινωνικο-τεχνικά συστήματα, τα οποία συχνά απαιτούν τη λήψη αποφάσεων υπό συνθήκες αβεβαιότητας. Η ασαφής λογική, που βασίζεται στη θεωρία των ασαφών συνόλων, και η στατιστική, που βασίζεται στη θεωρία πιθανοτήτων, είναι εναλλακτικές προσεγγίσεις για τη λήψη τέτοιων αποφάσεων υπό αβεβαιότητα. Τα ασαφή σύνολα είναι καλά στο χειρισμό της ανθρωπογενούς αβεβαιότητας (ελλιπής κατανόηση φαινομένου, περιορισμένη εμπιστοσύνη στο μοντέλο, ανακρίβεια ή ανεπάρκεια δεδομένων), ενώ η θεωρία πιθανοτήτων είναι καλή για το χειρισμό της αβεβαιότητας που προκύπτει από τυχαιότητα. Σε αντίθεση με τα περισσότερα μοντέλα, τα οποία αναπτύσσουν εντελώς νέες μεθόδους ασαφούς λογικής ή στατιστικής, αυτή η διατριβή προτείνει την κατασκευή υβριδικών μοντέλων, που χρησιμοποιούν ορισμένα σημαντικά στοιχεία από το ένα μοντέλο στο άλλο χωρίς σημαντική αλλαγή στη δομή των αρχικών μοντέλων. Το πλεονέκτημα της προτεινόμενης προσέγγισης είναι ότι οι ειδικοί σε ένα είδος μοντέλων (ασαφή ή στατιστικά) μπορούν να συνεχίζουν τη χρήση τέτοιων μοντέλων, χωρίς να χρειάζεται να αποκτήσουν σημαντικές νέες γνώσεις στον άλλο τομέα. Δείχνεται ότι οι προτεινόμενες μέθοδοι δίνουν παρόμοια ή καλύτερα αποτελέσματα από τα αρχικά μοντέλα. Στις διάφορες εφαρμογές, αποδείχθηκε ότι τα προτεινόμενα υβριδικά μοντέλα είναι εξαιρετικά ευέλικτα, δεδομένου ότι τα ήδη υπάρχοντα μοντέλα, όπως η γραμμική παλινδρόμηση, τα νευρωνικά δίκτυα και τα μοντέλα ασαφής λογικής μπορούν εύκολα να μετατραπούν σε ασαφή-στατιστικά μοντέλα. Η ανάγκη για πρόσθετες γνώσεις ελαχιστοποιείται και τα προτεινόμενα μοντέλα μπορούν να χρησιμοποιηθούν από οποιονδήποτε ήδη εξοικειωμένο με γραμμική παλινδρόμηση, ασαφής λογική ή μοντέλα νευρωνικών δικτύων. Επίσης δείχνουμε ότι τα προτεινόμενα μοντέλα μπορούν να χειριστούν τις αβεβαιότητες που σχετίζονται με την τυχαιότητα και την ασάφεια και είναι ιδιαίτερα κατάλληλα για τη λήψη αποφάσεων στα διάφορα προβλήματα μεταφορών, που συνήθως γίνονται υπό υψηλή αβεβαιότητα και ασάφεια

The Monetary Value of a Pleasant and Productive Train Trip: Developing an experimental method for estimating the monetary value of activities performed during travel

The value of travel time saving(VOTTS) is very important in cost benefit analysis(CBA) as it is used to calculate the monetary gain in case of an improvement in transport that leads to reduction in travel time. Although it is easy to directly calculate monetary gains due to a speed improvement, monetary gains due to transport service improvement like comfort or ability to perform useful activities during a trip are far more difficult to include in CBA. This is because the total travel time stays the same before and after such service improvement since there is no visible reduction in total travel time. This is of course not true because travelers who travel comfortably and are able to engage in productive and pleasant activities during their trip can derive some benefits from the trip. These benefits can have an impact on the traveler’s VOTTS e.g. a reduction in the VOTTS of the traveler. In order to measure this impact and calculate the monetary value of activities, a new methodology is proposed. The intuition behind the method is that the same traveler might have two different VOTTS. One VOTTS when he/she is able to make productive or pleasant use of the travel time by engaging in a preferred activity and another VOTTS when he/she cannot engage in the preferred activity during the journey. If the VOTTS without the activity is higher than the VOTTS with the activity, then the difference between these two VOTTS can be conceptualized as the monetary value of the activity. The proposed methodology was translated into a hypothetical stated choice experiment with in-vehicle time and cost attributes. Respondents were recruited from train travellers on Netherlands Railways(NS) panel. The VOTTS of respondents with and without the ability to perform their preferred activity was then calculated. The results show that the ability to perform certain preferred activities like reading, working and listening to music during train trips, reduces the VOTTS especially for commuters by about 5euros/hour. For leisure travellers, reading was found to reduce the VOTTS by about 3euros/hr. The research concludes with a discussion on the general implication of results for transport project evaluations and investments. Based on this, some recommendations are made for NS and also for the Netherlands institute for transport policy analysis(KiM) regarding future VOTTS calculations that will be used in transport project evaluations. Transport, Infrastructure and Logistic

Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings

The value of travel time savings (VOTTS) is one of the most important variables for calculating the benefits of transportation projects. However, the way it is currently calculated (usually via discrete choice models) is complex, tedious and subject to a reasonable level of uncertainty. Furthermore, the method is not easily understood by government officials who use the VOTTS for appraisal and the citizens are not fully aware how such values are calculated. This lack of understanding and transparency in methodology may lead to misuse of the VOTTS during transport project appraisals which in turn can result in unfair transport decisions for citizens, government and the environment. To solve these problems, a fuzzy logic rule-based approach is proposed. With this approach, the rules can be made based on economic and behavioral theories by experts, government officials and citizens (via surveys). This approach makes it understandable to everyone how values are calculated. To test the applicability of the approach, a simple numerical example is presented by estimating the VOTTS of various countries using their gross domestic product-purchasing power parity (GDP-PPP) and the traffic congestion level. Results are then compared to values obtained from a recent metanalysis on VOTTS in Europe and some official VOTTS. </b

A Fuzzy-Statistical Tolerance Interval from Residuals of Crisp Linear Regression Models

Linear regression is a simple but powerful tool for prediction. However, it still suffers from some deficiencies, which are related to the assumptions made when using a model like normality of residuals, uncorrelated errors, where the mean of residuals should be zero. Sometimes these assumptions are violated or partially violated, thereby leading to uncertainties or unreliability in the predictions. This paper introduces a new method to account for uncertainty in the residuals of a linear regression model. First, the error in the estimation of the dependent variable is calculated and transformed to a fuzzy number, and this fuzzy error is then added to the original crisp prediction, thereby resulting in a fuzzy prediction. The results are compared to a fuzzy linear regression with crisp input and fuzzy output, in terms of their ability to represent uncertainty in prediction

Does conducting activities while traveling reduce the value of time? Evidence from a within-subjects choice experiment

Many studies about conducting activities while traveling start from the hypothesis that conducting onboard activities reduces the value of time (VoT). However, surprisingly limited empirical evidence is provided for this hypothesis. The few studies that aim at providing this evidence face methodological problems in the sense that effects attributed to conducting onboard activities are confounded with differences between groups. This paper further develops and applies a solution for this problem proposed by Wardman and Lyons (2016). In essence, this method includes constructing a within-person choice experiment, which involves that the same respondents make choices in a context that enables conducting activities, as well as in a context that does not enable conducting activities. This method is applied in a study that collected data from 820 train travelers in the Netherlands. The results show that as expected, the VoT in the activity context is significantly lower than the VoT in the non-activity context, which thus supports the hypothesis. Reduction in VoT due to conducting onboard activities is around 30% for commuters, while leisure travelers who prefer to read lose almost half their VoT value. In addition, this paper discusses how the estimated VoT reduction values can be interpreted as the Value of Activity (VoA), which can be used for appraising investments aimed at reducing the disutility of travel other than by means of reducing travel time, such as improving Internet connections.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and LogisticsTransport and Plannin