Stochastic macroscopic analysis and modelling for traffic management

When congestion becomes a problem on a road or road network, there are generally three main solution areas available to tackle it: construction, pricing or traffic management. Traffic management became an increasingly preferred option towards the end of the twentieth century as an alternative to construction in many cases. Traffic management proves a more efficient alternative and focusses on influencing traffic flows such that the existing road and network capacity is more effectively utilised resulting in a reduction in congestion. The effectiveness of traffic management is dependent on the ability to influence traffic flow. However, traffic contains a relatively large amount of stochastic behaviour, which is connected to human driving behaviour. The fluctuations that occur in traffic flow due to this stochastic behaviour have a large effect on the effectiveness of traffic management. Furthermore, uncertainty between time dependant scenarios has also shown to have a large influence on the outcome of the analysis of traffic management measures. In the past, little attention has been paid to these effects. Therefore, the main objective of this thesis is to give insight into the stochastic fluctuations and uncertainty in traffic flow for the application of traffic management measures and to propose tools that allow these effects to be analysed and subsequently modelled in aggregated macroscopic flows. In doing this, the necessity to consider uncertainty and fluctuations for traffic management is also demonstrated. Stochastic processes are considered as uncertainty, which describes day-to-day uncertainties between traffic flows, and fluctuations, which describes microscopic variability in the traffic flow. Three main areas are focussed on: the analysis of variations in traffic, modelling fluctuations and uncertainty in traffic, and the visual communication of uncertainty from traffic models.TRAIL Thesis Series no. T2016/6Transport and Plannin

Opkomende technologie met impact

Automatische voertuigen bieden grote kansen op het gebiedvan toegankelijkheid, mobiliteit en verkeersveiligheid, als wede risico’s op tijd onder ogen zien, zegt Simeon Calvert.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 Plannin

A-Priori Travel Time Predictor for Long Term Roadworks on Motorways

Road users have never had as much travel information as is available today. However the extent of congestion on major roads has also never been as critical as it is now. For this reason road authorities, including Rijkswaterstaat1, aim to inform road users as best they can in an effort to allow the road user to make a more educated decision on travel and to increase the confidence they have in travel times. In a bid to improve traffic flow on motorways, many roadworks are carried out yearly with a large number planned for the coming years. This contributes to congestion and delays in the short term however and leads to a greater uncertainty in travel times. Many techniques and models already exist to predict travel times under ‘irregular’ traffic conditions. For situations where roadworks are due to be carried out in the future however, no models or methods explicitly exist which allow travel times to be predicted in advance. It is this problem that this research project attempts to tackle. The main objective for this research is to develop a methodology incorporated in a model, which is capable of predicting travel times on motorway corridors for situations during roadworks that are to be carried out in the future. To achieve this objective the research question is posed: How can a-priori travel times be predicted on motorway corridors for situations during roadworks, prior to the commencement of the roadworks? The objective is achieved by firstly consulting external research on the topics of travel time estimation with models and the influence of roadworks on travel times. Using the acquired knowledge a modelling approach is developed which makes use of the basic principles of traffic flow based on the conservation of vehicles and first order traffic flow theory. The developed model makes use of traffic flow profiles and capacity profiles, which are processed by an LWR-model using a Godunov scheme. Traffic is numerically fed through the model and where it exceeds capacity, congestion occurs and propagates backwards in space according to first order traffic flow theory and in keeping with the general characteristics of real traffic flow. From the modelled data, speeds are derived for each iterated section. This allows for travel times per section and total travel times along a certain trajectory starting at a specific time of day to be calculated. These travel times form the prediction for the corresponding motorway corridor. The effects of roadworks are incorporated in the model through a reduction of the road capacity in the capacity profile. This is performed by applying a capacity reduction factor to the available capacity. This reduction factor is determined using characteristics of the roadworks which correspond to certain reduction values taken from extensive research preformed externally. The traffic flow profile is also adjusted for the effects of mobility management, which is commonly applied during roadworks in the Netherlands. Mobility management is an organised attempt to reduce the level of traffic demand on routes where road capacity is not expected to be able to cope with traffic demand, such as during roadworks. A mobility management factor is therefore applied to the traffic demand profile to reduce demand as a consequence of this. The model is evaluated using a roadworks study case on the A12 between The Hague and Gouda. The results of the model, in which a base capacity2 of 2100 veh/hr/ln is applied, show a good likeness to the recorded travel times during the performed roadworks. An absolute relative error of less than 5% is recorded for the travel times during the main peak periods. These results are produced with the application of a mobility management factor of 6-7%, which corresponds to the expected values for this specific case. The performance requirement for the error of travel times during the entire day is also achieved in the case study. The research shows that predicting travel times for future roadworks is possible and moreover can be performed in a relatively accurate fashion without the necessity of an overcomplicated model. Producing traffic flow demands is achievable, however estimating the extent of mobility management and the indirect reduction of traffic demand is more complicated. Road capacity during roadworks is affected and estimates are made of the reduced workzone capacity. The capacities found show a good likeness to recorded data, however small adjustments in the capacity reduction have the potential for large travel times variations. For this reason the application of confidence bandwidths, as applied, is valuable. Further difficulties in determining capacities stem from the inability to produce operational capacity estimations where no congestion occurs. The application of a base capacity solves this, however the applied value cannot be generically validated with great ease. The application of the model is most suited to implementation for road user information through a website or incorporated in a route planner. The use of the model in roadwork planning is also possible, but will require alterations to model. The case study results are encouraging, however the model requires further validation over a wider range of roadworks as varying locations and roadwork characteristics may lead to differing results. Further research is recommended into a simple capacity reduction method for roadworks. Research on the effect of mobility management and an effective method to estimate the effect of it is also recommended. The implementation of these as well a generic manner of determining a base capacity in the model are further recommended as possible adjustments to improve the model.Transport & PlanningCivil Engineering and Geoscience

Communicatie van model onzekerheden: Meer of minder?

Het is al langer bekend dat onzekerheid een belangrijke rol speelt in het verkeer net als in de wereld om ons heen. Dit geldt zowel op een strategische lange termijn basis als op de operationele basis in verkeersdynamiek. Er geldt echter een onbekendheid en misschien zelfs een onwil om voldoende rekening te houden met onzekerheden. Een belangrijke drempel om er goed mee om te gaan ligt bij de kwantiteit, maar ook de kwaliteit van de communicatie over onzekerheden. In dit debat paper wordt het probleem aangekaart dat het verkeerskundigen niet goed lukt om de belangrijkste onzekerheden te communiceren met beleidsmakers. Er worden zowel voor- als tegenargumenten gegeven, waarna er stelling wordt genomen dat het noodzakelijk is om bij modelstudies meer onzekerheid te communiceren richting beleidsmakers.Transport & PlanningCivil Engineering and Geoscience

A methodology for road traffic resilience analysis and review of related concepts

Major and minor disturbances can have a considerable impact on the performance of road networks. In this respect, resilience is considered as the ability of a road section to resist and to recover from disturbances in traffic flow. In this contribution, an indicator is presented, the Link Performance Index for Resilience (LPIR), which evaluates the resilience level of individual road sections in relation to a wider road network. The indicator can be used to detect poorly resilient road sections and to analyse which underlying road and traffic characteristics cause this non-resilience. The method adds to related concepts such as robustness and vulnerability by also considering recovery from congestion events explicitly and by focussing on everyday operational traffic situations rather than just on disasters or major events. The LPIR is demonstrated in an experimental case on a real network in which the effectiveness of the method is demonstrated.Transport and Plannin

A Conceptual Control System Description of Cooperative and Automated Driving in Mixed Urban Traffic With Meaningful Human Control for Design and Evaluation

The introduction of automated vehicles means that some or all operational control over these vehicles is diverted away from a human driver to a technological system. The concept of Meaningful Human Control (MHC) was derived to address control issues over automated systems, allowing a system to explicitly consider human intentions and reasons. Applying MHC to technological systems, such as automated driving is a real challenge, and the main focus of this article. An approach with mathematical elaboration has been developed that offers a first quantifiable operationalisation of MHC for the traffic domain and for use with automated vehicles. A major contribution lies in the taxonomification of control for MHC in the broader traffic environment, including consideration of the driver, the vehicle, the traffic environment, considering behaviour, moral standards and societal values, which are considered in a case study. The demonstration case shows the validity of the developed approach for an automated vehicle overtaking a cyclist on an urban street. This article is one of the first to operationalise MHC to such a level of detail and opens the door to further development of the concept for technological implementation.Transport and Plannin

De noodzaak van probabilistisch modelleren voor tactische en operationele analyses

Dit artikel gaat in op de noodzaak van Probabilistisch Modelleren voor Tactische en Operationele Analyses. Het artikel laat aan de hand van literatuur en een case study naar DRIP’s de risico’s van het negeren van variaties in het verkeer bij de verkeersmodellering zien.Transport & PlanningCivil Engineering and Geoscience

Verschuiving naar probabilistische modellering is geen keuze

Verkeersmodellen spelen een belangrijke rol als nuttige instrumenten bij plan- en evaluatiestudies en het begeleiden van processen rondom onbekende of onzekere verkeerssituaties. Recentelijke ontwikkelingen in verkeersmodellering zijn gaande op het gebied van het modelleren van variatie in verkeersomstandigheden. Dergelijke modellen worden vaak probabilistisch genoemd, in verband met het gebruik van kansen op bepaalde variaties. Variaties zijn aanwezig in het verkeer in de vorm van wisselende verkeersdrukte, weersomstandigheden, of (tijdelijke) blokkades om maar een paar te noemen. Het is in verschillende gevallen aangetoond dat het modelleren met variaties vaak noodzakelijk is om juiste beoordelingen te maken, terwijl dit in de praktijk vaak niet bekend is. In deze bijdrage zijn enkele risico’s aangetoond van het negeren van variaties in het verkeer bij verkeersmodellering door middel van een experimentele casus en uit de literatuur. Daarnaast is de noodzaak beargumenteerd om in bepaalde gevallen te moeten modelleren gebruikmakend van in de werkelijkheid aanwezige variaties. In de experimentele casus wordt de noodzaak van een probabilistische aanpak bewezen voor een scenario waarin DRIP’s worden ingezet om verkeer te geleiden over een alternatieve route in geval van een incident. In deze casus werd gevonden dat de standaard deterministische aanpak het positieve effect van de DRIP met 9% onderschatte. Daarnaast werd voor de verdeling over de twee mogelijke routes een waarde gevonden dat een absolute ondergrens aangaf, terwijl de probabilistische aanpak liet zien dat deze routeverdeling slechts in een deel van de tijd het geval zal zijn en vaak een stuk hoger zou liggen. Het is niet in alle gevallen nodig gebruik te maken van een probabilistisch aanpak. Wanneer het niet noodzakelijk is, kan men beter gebruik maken van een simpeler deterministische verkeersmodel, omdat deze minder rekenintensief zijn. Om op hoofdlijnen aan te geven in welke gevallen een deterministische of probabilistische aanpak de voorkeur dient, is er een korte analyse uitgevoerd waarin adviezen hierover zijn gegeven. Onlangs het feit dat het vaak nodig is om variaties op het verkeer mee te nemen in een modelstudie, bestaat op het gebied van probabilistische verkeersmodellering weinig tot geen beleid. Het is sterk aan te bevelen dat de overheid hier meer op in zet, bijvoorbeeld door de ontwikkeling van een modellenarchitectuur. Probabilistisch modelleren zou daarbinnen een belangrijke plaats in moeten nemen.Transport and PlanningCivil Engineering and Geoscience

Quantifying the impact of adverse weather conditions on road network performance

Adverse weather conditions regularly lead to severe congestion and large travel time delays on road networks all over the world. Different climate scenarios indicate that in the future adverse weather conditions are likely to become more frequent, last longer and will be more extreme. Although climate mitigation measures are being taken, it remains important to investigate how adverse weather events will affect the performance of the road network in the future. The main objective of this paper is to give an overview of how the impact of adverse weather conditions and adaptation measures on road network performance can be quantified. A literature review has been performed to show what is empirically known about the impact of adverse weather conditions on the road network performance. Furthermore, available methods to quantify the impact of adverse weather conditions and adaptation measures on the road network performance for future situations are reviewed. As an example, a case study for the municipality of Rotterdam has been carried out that shows how a combination of models can be used to analyse which links in the road network are most vulnerable for increasingly severe local weather related disturbances. The results of the case study allow local authorities to decide whether or not they need to take adaptation measures

A methodology for prediction accuracy assessment of intelligent traffic signal control algorithms with SPaT messages

New smart traffic signal control algorithms are capable of predicting the traffic signal state (red, green, or amber) changes, which can be provided to users to achieve more efficient traffic flow. However, these predictions pose an uncertain impact on the traffic flow and safety depending upon the quality of prediction. The information regarding the current state as well as the predicted residual time of state is communicated to other users in the form of Signal Phase and Timing (SPaT) data. In this paper, the SPaT message data is analyzed from an on-road pilot of different traffic signal control algorithms on provincial roads in the Province of North Holland. For analysis, new methods and indicators for quantification of prediction accuracy and quality of algorithm are proposed. These indicators can either be used for correction of state change prediction in real-time or for comparative analysis of the performance of different traffic signal control algorithms. This paper presents three main findings. First, it is found that a half fixed algorithm has very high prediction accuracy up to 99% for optimized directions. Second, the prediction accuracy of Time to Amber predictions improved by around 30% with this algorithm. Third, the overall reliability of prediction always increased with the use of the algorithmAccepted Author ManuscriptTransport and Plannin