Automated driving developments should be considered when making decisions about investments in physical and digital infrastructure. This paper proposes four scenarios for automated driving developments in the Netherlands in 2040 and 2060 taking into account uncertainties regarding future penetration rates, the level of connectivity, the operational design domain, and the expected impacts of automated driving: 1) Late transition, 2) Automated vehicles on main roads, 3) Car-topia, and 4) Share-topia. To derive these scenarios, an extended switchboard method is introduced in which multiple driving forces for automated driving can be varied. The main driving forces were identified based on expert surveys. For each scenario, a modelling approach is used to compute the impact of automated driving on vehicle kilometres driven and congestion. The extended switchboard method offered more flexibility than existing scenario methods. The model-based impact assessment provided more conservative and probably more accurate insights into the expected impacts of automated driving on vehicle kilometres driven and congestion than expert estimates from the literature. The results show that in all scenarios automation leads to an increase in the number of trips, vehicle kilometres driven and congestion. In the scenarios with autonomous vehicles, congestion is expected to increase up to 17%. The higher the penetration rates of connected automated vehicles, the smaller the increase in congestion (1.5%-11%). The results indicate that investments in digital infrastructure are needed to prevent capacity reduction due to autonomous driving. The scenarios “car-topia” and “share-topia” may require additional physical infrastructure on motorways and regional roads, and/or the implementation of demand management strategies.

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This paper presents a two-stage simheuristics-based framework for optimizing bridge maintenance scheduling strategies in a highway transportation network under uncertainty considering bridge life-cycle reliability and the effects of different maintenance interventions. The design variables of the optimal bridge maintenance scheduling problem are the preventive maintenance schedules for all bridges in the network, with the conflicting objectives of minimizing total maintenance cost and total travel time over the life cycle of bridges. The outcome of the first stage of the framework is a Pareto front of deterministic optimal solutions, which are then tested in the second stage to measure their performance under uncertainty.@en

This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function evaluations of a genetic algorithm (GA) to approximate solutions for NDPs. Using three test networks, two NDP variants and an exact solver as benchmark, we show that on average, our proposed framework can provide solutions within 1.5 % gap of the best results in less than 0.5 % of the time used by the exact solution procedure. Our framework can be utilized within an expert system for infrastructure planning to determine the best infrastructure planning and management decisions under different scenarios. Given the flexibility of the framework, it can easily be adapted to many other decision problems that can be modeled as bi-level problems on graphs. Moreover, we foreseen interesting future research directions, thus we also put forward a brief research agenda for this topic. The key observation from our research that can shape future research is that the fitness function evaluation time using the inferences made by the GNN model was in the order of milliseconds, which points to an opportunity and a need for novel heuristics that 1) can cope well with noisy fitness function values provided by deep learning models, and 2) can use the significantly enlarged efficiency of the evaluation step to explore the search space effectively (rather than efficiently). This opens a new avenue for a modern class of metaheuristics that are crafted for use with AI-powered predictors.

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Automated vehicles (AVs) are on the horizon, and they are expected to deliver traffic safety and efficiency benefits to transportation systems. There are different automation levels for AVs based on the functionalities of the automation systems and their operating design domain (i.e., on which conditions these functionalities can be realized). AVs with limited automation functions are already available on the market; however, fully automated vehicles with unlimited operational design domain (ODD) are not expected in the near future. Reaching a high market penetration rate of fully automated vehicles is a gradual process that can take several decades. Thus for a long time, a heterogeneous mix of traffic with AVs of different automation levels and regular vehicles on the roads will be inevitable. During this transition period with mixed traffic, relying on driving automation technology alone without infrastructure support might compromise the potential safety and efficiency gains of AVs. A proper infrastructure can support AVs’ functionalities, extend their ODD, and improve safety for all road users, while lack of proper infrastructure can negatively influence these factors. Besides, road infrastructure elements usually have a long lifetime and adjusting them can be costly. Hence, there is a strong need for research and planning to ensure that large infrastructure investments provide the highest societal benefits...@en

This study focuses on network configurations to accommodate automated vehicles (AVs) on road networks during the transition period to full automation. The literature suggests that dedicated infrastructure for AVs and enhanced infrastructure for mixed traffic (i.e., AVs on the same lanes with conventional vehicles) are the main alternatives so far. We utilize both alternatives and propose a unified mathematical framework for optimizing road networks for AVs by simultaneous deployment of AV-ready subnetworks for mixed traffic, dedicated AV links, and dedicated AV lanes. We model the problem as a bilevel network design problem where the upper level represents road infrastructure adjustment decisions to deploy these concepts and the lower level includes a network equilibrium model representing the flows as a result of the travelers’ response to new network topologies. An efficient heuristic solution method is introduced to solve the formulated problem and find coherent network topologies. Applicability of the model on real road networks is demonstrated using a large-scale case study of the Amsterdam metropolitan region. Our results indicate that for low AV market penetration rates (MPRs), AV-ready subnetworks, which accommodate AVs in mixed traffic, are the most efficient configuration. However, after 30% MPR, dedicated AV lanes prove to be more beneficial. Additionally, road types can dictate the viable deployment plan for certain parts of road networks. These insights can be used to guide planners in developing their strategies regarding road network infrastructure during the transition period to full automation.@en

With the advent of automated vehicles (AVs), new infrastructure planning concepts such as subnetworks of AV-ready roads have been proposed to improve the performance of transportation networks and to promote the adoption of AVs. However, these subnetworks should evolve over time in response to the growing AV demand, which necessitates a multi-stage modeling approach. In this study, we propose multi-stage deployment of AV-ready subnetworks and formulate it as a time-dependent network design problem, which is a bi-level mixed-integer programming problem. The lower level is a simultaneous travel mode and route choice equilibrium with continuous decision variables, and the upper level is a design problem including infrastructure investment decisions to determine which roads to upgrade and include in AV-ready subnetworks for mixed traffic. We use a case study of a real road network to demonstrate the concept. Since computational efficiency is a key factor for solving such large-scale problems, we develop two efficient and tailored evolutionary heuristics to solve the problem, and compare their performance to a computationally demanding Genetic-algorithm-based solution method. The results indicate that the proposed algorithms can efficiently solve this large-scale problem while satisfying constraints in all scenarios, and outperform Genetic algorithm, particularly in the scenario with larger number of stages. Moreover, in all scenarios, deployment of AV-ready subnetworks leads to improvements in network performance in terms of total travel time and cost. However, the improvements are always accompanied with increased total travel distance. The extent of changes depends on AV market penetration rate, AV-ready subnetwork density and timing of densification.

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This paper presents a bi‐level model to optimize automated‐vehicle‐friendly subnetworks in urban road networks and an efficient algorithm to solve the model, which is relevant for the transition period with vehicles of different automation levels. We formulate the problem as a network design problem, define solution requirements, present an effective solution method that meets those requirements, and compare its performance with two other solution algorithms. Numerical examples for network of Delft are presented to demonstrate the concept and solution algorithm performances. Results indicate that our proposed solution outperforms competing ones in all criteria considered. Furthermore, our findings show that the optimal configuration of these subnetworks depends on the level of demand; lower penetration rates of automated vehicles call for less dense subnetworks, and thereby less investments. Nonetheless, a large proportion of benefits are already achievable with low‐density subnetworks. Denser subnetworks can deliver higher benefits with higher penetration rates.@en

Globalization and subsequent increase in seaborne trade have necessitated efficient planning and management of world's anchorage areas. These areas serve as a temporary stay area for commercial vessels for various reasons such as waiting for passage or port, fuel services, and bad weather conditions. The research question we consider in this study is how to place these vessels inside a polygon-shaped anchorage area in a dynamic fashion as they arrive and depart, which seems to be the first of its kind in the literature. We specifically take into account the objectives of (1) anchorage area utilization, (2) risk of vessel collisions, and (3) fuel consumption performance. These three objectives define our objective function in a weighted sum scheme. We present a spatio-temporal methodology for this multi-objective anchorage planning problem where we use Monte Carlo simulations to measure the effect of any particular combination of planning metrics (measured in real time for an incoming vessel) on the objective function (measured in steady state). We resort to the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm for identifying the linear combination of the planning metrics that optimizes the objective function. We present computational experiments on a major Istanbul Straight anchorage, which is one of the busiest in the world, as well as synthetic anchorages. Our results indicate that our methodology significantly outperforms comparable algorithms in the literature for daily anchorage planning. For the Istanbul Straight anchorage, for instance, reduction in risk was 42% whereas reduction in fuel costs was 45% when compared the best of the current state-of-the-art methods. Our methodology can be utilized within a planning expert system that intelligently places incoming vessels inside the anchorage so as to optimize multiple strategic goals. Given the flexibility of our approach in terms of the planning objectives, it can easily be adapted to more general variants of multi-objective spatio-temporal planning problems where certain objects need to be dynamically placed inside two or even-three dimensional spaces in an intelligent manner.

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Current road infrastructure is designed for human drivers, and may be unable to deal with the integration of highly automated vehicles. The aim of this paper is to explore potential infrastructure requirements for automated driving SAE Level 4. To cope with the wide range of uncertainties involved, a scenario-based approach was used to define two plausible future scenarios. Subsequently, two rounds of interviews with manufacturers, road operators, and academicians were conducted. The first round aimed at specifying potential requirements while the second round aimed at mapping these requirements into the scenarios. The main finding is that under both plausible scenarios, clear and harmonized road signs and lane markings, High-definition maps and vehicle-to-infrastructure communication technology are key requirements while dedicated lanes can only be considered under the advanced scenario. Additionally, the results show that a transition from physical infrastructure to digital infrastructure will happen after roadside wireless technology is available.@en

This study explores a network configuration concept for vehicle automation levels 3–4 (according to SAE classifications) in an urban road network having mixed traffic and demonstrates its potential impacts. We assume automated driving will be allowed on a selection of roads. For the remaining roads, manual driving (although supported by assisting driving automation systems) will be compulsory. Accordingly, we introduce an approach for road selection and present relevant operational concepts. To evaluate the impacts of this configuration and model different vehicles’ route choice behavior in mixed traffic, a static multi-class stochastic user equilibrium traffic assignment with a path-size logit route choice model and a Monte Carlo labeling route-set generation is adapted. Two user-classes are distinguished: vehicles with automation levels 0–2 and vehicles with automation levels 3–4 having a different passenger car unit to account for lower driving headways, lower value of time, and higher fuel efficiency. The results indicate a decrease in total travel cost with the increase in market penetration rate of higher automation levels, a decrease in total travel time, and a minor increase in total travel distance. Although in most cases vehicles with higher automation levels benefit more from the improvements, no deterioration in travel conditions is observed for the rest of the vehicles in any scenario. Furthermore, a noticeable shift of traffic from roads with access function to roads with flow function and distributors is observed. Sensitivity analysis shows that the extent of changes in the impacts is not strongly dependent on the input parameters.@en

This study explores a network configuration concept for vehicle automation levels 3-4 (according to SAE classifications) in an urban road network having mixed traffic and demonstrates its potential impacts. The authors assume automated driving will be allowed on a selection of roads. For the remaining roads, manual driving (although supported by assisting driving automation systems) will be compulsory. A static Multi-Class Stochastic User Equilibrium traffic assignment based on the Path-Size logit and a Monte Carlo-Labeling combination approach for route-set generation is adapted to model the behavioral differences of vehicles in mixed traffic. Two user-classes are distinguished: vehicles with automation levels 0-2 and vehicles with automation levels 3-4 having a different Passenger Car Unit to account for lower driving headways, lower Value of Time, and higher fuel efficiency. The results indicate a decrease in total travel cost with the increase in market penetration rate of higher automation levels, a decrease in total travel time, and a minor increase in total travel distance. Although in most cases vehicles with higher automation levels benefit more from the improvements, the rest of the vehicles do not suffer deterioration in their travel conditions in any scenario. Furthermore, a noticeable shift of traffic from roads with access function to roads with flow function and distributors is observed. Sensitivity analysis shows that the extent of changes in the impacts is not strongly dependent on the input parameters. Finally, a steady decline in total travel cost is observed with increase in market penetration rate of higher automation levels.@en

Automated Driving (AD) is expected to deliver various benefits beyond those possible with manual driving for transport systems and the environment, yet there are many uncertainties with respect to the development path of AD to full automation. (SAE International, 2016) defines five levels of vehicle automation summarized in Figure 1. Automated driving system (ADS) can take over more driving tasks at higher automation levels until finally at level 5, ADS can handle the full range of driving complexity and it is feasible in all driving modes. However, the transition period to full automation might be long and full of uncertainties. Two incremental paths toward full automation have been observed so far. (CPBR, 2015) describes them as “something everywhere” and “everything somewhere”. Most traditional car manufacturers are embracing “something everywhere” path, i.e., gradually improving ADS in existing vehicles and shifting more driving tasks from the driver to ADS over time. Then the user is responsible for using the ADS wisely. This is also consistent with SAE automation levels. The other alternative, which was recently adapted by Google, involves aiming at full automation within a limited domain (e.g., only certain road types) and expanding this domain to more road types and more complex driving situations. This means the absolute ADS autonomy can only be realized in specific conditions. Then the challenge is to define those conditions specifically. For both paths, infrastructure is a defining factor. It can either facilitate or prevent higher automation capabilities. During the transition period to full automation, safe operation of levels 3-4 at their full automation capacity will highly depend on the type of infrastructure they encounter. For road authorities it is important to know how ready the road infrastructure is for safe automated driving. However, the academic literature and the field reports do not offer sufficient information to answer this question. (Farah et al., 2018) point out numerous knowledge gaps regarding infrastructure for AD. Therefore, we embarked on providing some insight into the matter via an expert workshop.@en