Within the area of mobility much discussion is going on about human well-being and how mobility can contribute to that. Human well-being is about everything what influences the well-being of people, in the broadest sense. It is about welfare beyond the Gross Domestic Product (GDP). Mobility makes a valuable contribution to the welfare and well-being of people. It provides means for them so that they can reach important destinations and participate in activities, like jobs, all kinds of facilities (cultural, shopping, recreation, etc.) and social contacts. In this way mobility has a positive effect on physical and mental health. However, mobility can also decrease well-being, for example due to traffic unsafety, noise and environmental pollution and climate change. Also, the pros and cons of mobility could be unevenly distributed among certain groups of people, regions or even generations. In this paper we look at the current contributions of traffic management to human well-being and we search for opportunities that could increase these contributions. The research consisted of a literature scan and two workshops. In the first workshop traffic management experts were connected with experts with a focus on human well-being. Together they discussed the possibilities of traffic management in relation to human well-being. In the second workshop the findings from literature and the first workshop were discussed with experts from the Dutch national road authority Rijkswaterstaat to test how these findings are aligned with practical experience. The literature scan showed that not many studies make a clear link between traffic management and human well-being. The current focus of traffic management is mainly on accessibility (travel times and delays) and traffic safety. Widening this perspective to the dimensions of health and the living environment is desirable and advisable. There is also a gap between traffic management, that is mainly focussing on the short-term solution of specific problems, and the more umbrella approach of well-being. The opportunities of (operational) traffic management to directly contribute to human well-being are currently limited, due to the way that traffic management is organised. Policy should make clear choices and give directions with respect to the goals that should be achieved with traffic management. Then traffic management can apply those directions in practice, using a network perspective and the experience with multimodal approaches and distribution of the scarce capacity in all kinds of networks, to find the desired balance between the various societal goals.@en

Momenteel wordt binnen het vakgebied mobiliteit veel gesproken over brede welvaart en hoe vanuit de mobiliteitssector hieraan bijgedragen kan worden. Van oudsher was het mobiliteitsbeleid vooral gericht op het verbeteren van de bereikbaarheid. Naast het oplossen van knelpunten door de uitbreiding van de infrastructuur speelde verkeersmanagement daarin een belangrijke rol. Dat leidt tot de vraag hoe verkeersmanagement bijdraagt aan brede welvaart en of er veranderingen nodig zijn om dit beter te doen. Om deze vragen te beantwoorden is er een literatuurscan gedaan en zijn er workshops met verschillende experts gehouden. Uit de literatuur blijkt dat verkeersmanagementmaatregelen gericht zijn op een bepaalde dimensie van brede welvaart (bereikbaarheid), zonder dat er een afweging met de andere dimensies (veiligheid, leefbaarheid en gezondheid) gemaakt wordt. Ook laat de literatuur zien dat gemotoriseerd verkeer onbedoelde negatieve effecten heeft op de dimensie gezondheid, van zowel reizigers als niet-reizigers. Daarnaast is er aandacht voor de verdeling van effecten in de gezondheids- en bereikbaarheidsdimensie. De workshops lieten zien dat de bijdrage van verkeersmanagement aan brede welvaart in de praktijk nog vrij smal is en dat het beleid de keuzes maakt en niet de verkeersmanager, zodat er voor hem weinig speelruimte is. Ook is duidelijk dat het geografisch schaalniveau van belang is, omdat het veel uitmaakt of je verkeersmanagement toepast op autosnelwegen of in de stad. Verbreding naar brede welvaart lijkt in ieder geval niet zo eenvoudig te zijn. Maar er zijn wel mogelijkheden, zoals meer sturing en het versterken van de interactie tussen verkeers- en mobiliteitsmanagement, de monitoring en evaluatie uitbreiden voor brede welvaart en een verandering van zienswijze om te komen tot een bredere denk- en werkwijze.@en

The field of Artificial Intelligence (AI) seems promising for traffic and transport. All kinds of possibilities and applications are suggested, but are these suggestions feasible and when will they become available? To address this question for traffic management, a picture of the field and its latest, state-of-the-art innovations is painted and opportunities for the future are investigated. Applications that have already been implemented or tested as pilots are described, as well as those applications that domain experts expect to be developed within one to five years, with a focus on applications that generate the greatest improvements in terms of traffic flow, safety, and sustainability. Also, the study looks at what the possible pitfalls and challenges could be during development and implementation. The research method consisted of several elements. Interviews were conducted with experts in the field of AI and traffic management and the interviewees were asked about possible opportunities and obstacles. In addition to the interviews, relevant and current sources describing applications of AI in traffic management were studied. The focus was on the added value of applications that have already been implemented. Based on the information gathered, a selection of the most promising future applications was made and these applications were discussed in a workshop. The current applications of AI in traffic management show that the focus is now on performing one specific task, using a limited number of data sources. It also shows there is great future potential for AI-based applications that combine multiple data sources or address multiple complex tasks in a combined fashion. This could, for example, lead to new insights about traffic being derived from data; insights that are not readily apparent with existing methods and a single data source.@en

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.@en

An increasing amount of vehicles are equipped with driver assistance systems; many of the vehicles currently on the market can be optionally equipped with adaptive cruise control and lane centering systems. Using both systems at the same time brings the vehicle to SAE level-2 automation . This means a driver does not need to perform longitudinal and lateral operational driving, although the driver should be ready to intervene at any time. While this can provide comfort, the interaction between vehicles operated by these systems might cause some undesired effects. This becomes particularly relevant with increasing market penetration rates. This paper describes an experiment with seven SAE level-2 vehicles driven as a platoon on public roads for a trip of almost 500 km. The paper discusses how the experiment was organized and the equipment of the vehicles. It also discusses the interaction of the platoon in traffic, as well as, in basic terms, the interaction between the automated vehicles. The experiences can be useful for other studies setting up field tests. The conclusion from this platoon test is: intentionally creating platoons on public roads is difficult in busy traffic conditions. Moreover, interactions between the vehicles in the platoon show that the current SAE level-2 systems are not suitable for driving as platoons of more than typically three to four vehicles, because of instabilities in the car-following behavior.@en

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.@en

Het gaat niet goed met de verkeersveiligheid in Nederland. Het aantal zwaargewonden is al een paar jaar stijgende en het aantal doden stabiliseert –
en het ziet er niet naar uit dat de trend spontaan weer dalend zal worden. Wat
te doen? ‘De’ oplossing bestaat niet, maar slim verkeersmanagement en (C-) ITS
kunnen wellicht een bijdrage leveren, zo concludeerde TrafficQuest op basis
van een challenge onder verkeersveiligheidsexperts.@en

De verkeersveiligheid in Nederland gaat een ongewenste kant op. Deze verandering was de aanleiding om extra aandacht te besteden aan verkeersveiligheid en alle mogelijke maatregelen om de verkeersveiligheid te verbeteren. Eén van de opties is de inzet van verkeersmanagement. Deze maatregelen worden meestal ontworpen voor een betere doorstroming, maar kunnen eventueel ook ingezet worden voor verkeersveiligheid. Daartoe zijn eerst de statistieken met betrekking tot de verkeersveiligheid en aanknopingspunten die verkeersmanagement biedt, verkend en daarna zijn hierover een aantal experts geïnterviewd. Het onderzoek laat zien dat dat de relatie tussen verkeersmanagement en verkeersveiligheid niet zomaar gelegd wordt en dat het potentieel van verkeersmanagement beter belicht en in sommige gevallen verder onderzocht moet worden. Op dit moment lijken vooral doorstroming en duurzaamheid aandacht te krijgen. Het verbeteren van de verkeersveiligheid ontbreekt nog wel eens bij de doelstellingen, of is alleen aanwezig in de vorm van een randvoorwaarde. Het is daarom wenselijk dat zoveel mogelijk expliciete doelstellingen met betrekking tot de verkeersveiligheid geformuleerd worden.@en

Although at times traffic flow can be complicated to understand and predict, our current understanding is nevertheless pretty good and allows predictions of current and future situations to be made. However, this is about to change with the emergence of (partially) automated vehicles1on roads. This emergence will change the way people drive (speed and acceleration choices, following behaviour, lane change behaviour) and will change the dynamics present in traffic flow and with that the ability of traffic to move effectively. The first generation of partially automated vehicles is already on the road in the form of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA), as the so-called SAE level 1 vehicle automation (SAE 2014). Practical and technical tests are already widespread on higher levels of automation, with their mass-produced introduction expected in advance of 2020 (Merat et al. 2014). According to Shladover (2016) fully automated (SAE level 5) vehicles will not be here until 2075 at the earliest. At this point, no statement has been made on the point if these changes are going to be positive or negative. The reason for this is that there are too many uncertainties in relation to the effects that automated vehicles will have on traffic. Many enthusiasts have made incredible claims that automated vehicles will solve many of our traffic problems and can have an overwhelming positive effect on mobility, traffic flow and safety. However, these claims are rarely substantiated and are often based on questionable assumptions regarding the future. Furthermore, even if these claims are correct, they are applicable to a situation that lies many decades in the future, i.e. 100% penetration rate, and do not consider the route leading towards this future (the so called transition period). The main consensus within the traffic and mobility community is that traffic flow will initially worsen with the introduction of automated vehicles (Le Vine, Zolfaghari, and Polak 2015, SBD and HERE, 2016). This has a lot to do with conservative settings of automated vehicles, unfamiliarity with automated vehicles from both drivers and other road users, initial teething problems with the technology and a limited ability and penetration for cooperation between vehicles (Shladover, Su, and Lu 2012). Furthermore, there are predictions that traffic volume may eventually increase due to a steep uptake in automated vehicles (Wadud, MacKenzie, and Leiby 2016). However, again there is much uncertainty in relation to these statements, even if they are well substantiated Next steps in describing possible effects of automated driving on traffic flow. Available from: https://www.researchgate.net/publication/312377387_Next_steps_in_describing_possible_effects_of_automated_driving_on_traffic_flow?channel=doi&linkId=587c8ba108ae9a860fed444b&showFulltext=true [accessed Jul 18 2018].@en

Road authorities and transport planners play a role in facilitating a smooth introduction of (partially) automated vehicles1 on their roads. They need to take their possible effects and requirements into account, as well as regulation that may be needed. It is expected that a mix of conventional and automated vehicles will be present on the roads for many decades to come. The increasing levels of automation influence the performance of traffic flow. There will also be safety and environmental impacts. These aspects are considered in the accompanying document ‘Next steps in describing possible effects of automated driving on traffic flow’ by TrafficQuest (Calvert, Wilmink, and Farah 2016). In this memo, a closer look is taken at the challenges that exist on a regulatory and strategic level. These are issues that refer to required and desired preparations for, consequences of, and challenges involving existing resources and infrastructure for automated vehicles on roads in mixed traffic. The described challenges are considered for the case in the Netherlands and mainly focus on areas in which there are significant unanswered questions and on which decisions still need to be made by policy makers or road authorities. (Questions related to standardisation have not been included here.) The challenges will require (facilitative) action from road planners and operators. The list is a summary of the main challenges that are relevant for the situation in the Netherlands, grouped in the following categories: · physical infrastructure · road regulations · digital infrastructure · approach to network operations and traffic management · vehicle capabilities @en

Overheden en marktpartijen investeren momenteel veel in Smart Mobility toepassingen en dit roept vragen op over de effecten van deze innovatieve mobiliteitsoplossingen. SimSmartMobility is een platform voor simulaties van Smart Mobility en richt zich op het steeds beter kunnen beantwoorden van deze vragen. In dit discussiepaper geven we een korte toelichting op SimSmartMobility. Vervolgens bespreken we waarom simulatie van Smart Mobility een belangrijke stap is in het verkrijgen van meer inzicht in de effecten van Smart Mobility en welke type vragen met SimSmartMobility beantwoord kunnen worden. Tot slot poneren we een aantal stellingen voor een discussie zodat we tijdens het NVC met u hierover in gesprek kunnen gaan en de inzichten uit de discussie kunnen gebruiken om de toepassing en doorontwikkeling SimSmartMobility zo goed mogelijk aan te sluiten bij uw wensen.@en