Mobility as a Service (MaaS) and new mobility concepts mutually inspire each other, provide alternatives for the private car-oriented transport system as we know it, and will offer more mobility choices in a single journey than ever. This multitude of mobility choices however poses challenges in modeling the travelers’ mode choices in travel demand prediction models. To address these challenges, this paper develops a multimodal tour-based mode choice model as part of an activity-based demand model. By explicitly modeling access and egress modes, this choice model creates multimodal mode chain sets on a tour level based on restrictions with respect to personal vehicle ownership, MaaS subscription ownership and vehicle states, and subsequently makes mode choices for every traveler. For the creation of these mode chain sets, we introduce the concept of mode categorization. Seven mode categories are proposed, which include both private and shared mobility concepts. This categorization makes sure that modes are mutually sufficiently different in nature, so that reasonably unbiased mode chain choices can be made. Furthermore, the reduction to seven categories enables the study of large scenarios, while the introduced categories still represent new and already existing modes well. The potential of the model is illustrated by simulating travel demand in the Metropolitan region Rotterdam-The Hague. The results show that our model is capable of making plausible mode choices in the presence of MaaS and new mobility concepts, and can be used to assess the impact of mobility hubs where access and egress mode choice is important.

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Connected and Autonomous Vehicles (CAVs) may exhibit different driving and route choice behaviours compared to Human-Driven Vehicles (HDVs), which can result in a mixed traffic flow with multiple classes of route choice behaviour. Therefore, it is necessary to solve the Multiclass Traffic Assignment Problem (TAP) for mixed traffic flow. However, most existing studies have relied on analytical solutions. Furthermore, simulation-based methods have not fully considered all of CAVs’ potential capabilities. This study presents an open-source solution framework for the multiclass simulation-based TAP in mixed traffic of CAVs and HDVs. The proposed model assumes that CAVs follow system optimal with rerouting capabilities, while HDVs follow user equilibrium. It also considers the impact of CAVs on road capacity at both micro and meso scales. The proposed model is demonstrated through three case studies. This study provides a valuable tool that can consider several assumptions for better understanding the impact of CAVs on mixed traffic flow.

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Understanding preferences and behaviours in road freight transport is valuable for planning and analysis. This paper proposes a data-driven vehicle routing and scheduling approach for use as a descriptive tool to study road freight transport activities. The model developed seeks to capture planners’ or drivers’ preferences in order to reproduce observed road freight activities. The model is based on a parametrized time-dependent vehicle routing problem whose parameters can be estimated from a set of observed planned tours. We propose a Bayesian optimization technique for parameter estimation of the model. Empirical results show that the model can fit real-world data accurately and synthesize tour flows close to reality.

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Activity-based travel demand models provide a high level of detail when modeling complex travel behavior. Since stochastic simulation is used, however, this high level may induce large random fluctuations in the output, necessitating many model reruns to produce reliable output. This may become prohibitive in terms of computation time when comparing travel behavior between multiple scenarios, in which case each scenario requires its own simulation. To alleviate this issue, we study the use of common random numbers, which is a technique that reuses the same random numbers for choices made by travelers between scenarios. This ensures that any observed difference in output across scenarios cannot be attributed to mutual differences in drawn random numbers, eliminating an important source of random fluctuation. We demonstrate by a numerical study that common random numbers can greatly reduce the number of runs needed, and thus also the required computation time, to obtain reliable output.

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Nowadays, urban areas are exposed to various challenges such as climate change, social inequalities, and congestion. Shared mobility hubs present the opportunity to reshape our cities and mitigate the previously mentioned challenges by contributing to a more sustainable transport system. These are places where shared cars, mopeds, and e-bikes are offered to improve connectivity in urban areas. In this paper, we investigate the impact of efficiently allocating multimodal shared mobility hubs on modal split, service level, and environmental factors while assuring economic feasibility. Given a limited budget, cities would like to optimize the hubs’ locations to maximize the population's benefits. For that purpose, we introduce a multi-stage design algorithm model that distributes the hubs and allocates fleets of shared cars, mopeds, and e-bikes to maximize travel utility for all the population traveling using traditional and/or shared modes while accounting for multimodal trips. The model is divided into several modules: computational modules that calculate the demand for the hubs; an optimization module to optimize the hubs’ capacities, availability, and relocation of shared vehicles; and finally, a genetic algorithm to find the optimal hub distribution. Our proposed model is one of the first that optimizes the location and capacity of multimodal hubs by considering multimodal trips in a large network. Additionally, it allows to assess mobility, spatial, and environmental impact of shared modes. The model is applied to the case of Amsterdam, the capital of The Netherlands, with around 800,000 inhabitants. After running several scenarios with different budgets allocated to build the hubs, results show that having more hubs with a lower number of shared vehicles is more beneficial than having fewer hubs with higher capacity. That is because the travel time savings increase considerably when investments lead to complete coverage of the area by the hubs network. A modal split of 5% for the shared modes is expected when Amsterdam is covered by 288 hubs. From an environmental point of view, only 32% of the shared trips replace trips previously made by ICE and electric cars, leading to a limited CO2 emissions reduction of 1.27%. Hence, introducing shared modes and mobility hubs without push measures for the use of private cars appears to offer limited benefits to decrease the negative impacts of private car usage.

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Nowadays, many cities are intending to reduce the use of private vehicles. Governments are incorporating new mobility services and are adapting their parking policies to promote a more sustainable mobility, as both strategies are believed to have the potential to reduce private vehicle use. To understand the effects of these strategies, one needs to be able to model complex travel behaviour up to a very high level of detail. Owing to their flexibility, robustness and ability to model travel activity behaviour on an individual level, activity based travel demand models (ABM) offer a highly suitable methodology for this purpose. In this paper, we employ this methodology to perform a case study in a metropolitan region in the Netherlands which surrounds and includes the cities of Rotterdam and The Hague. This region is of vital economic importance and has a very developed and dense road network. The population of this region is growing, which motivates the ambition to improve its accessibility and move towards sustainable mobility. Therefore, the findings of this study are important to similar regions seeking to do this as well. After setting up a suitable, calibrated ABM able to perform a comprehensive study on the effects of new mobility services and parking policy adaptations in the above-mentioned region, we design seven scenarios to give quantitative answers to policy-related questions on how altering features can reduce the extent to which private vehicles are used for travelling. These features include the availability of mobility hubs (hubs on neighbourhood level where sustainable travel modes are linked), the availability of car/bike sharing services, the availability of ‘Mobility as a Service’ (MaaS) subscriptions, the amount of parking capacity in the region and the parking costs. We also study what the impact would be of an improved public transport service with lowered public transport travel times to and from the city centers, and the impact of an improved cycling network infrastructure with significantly lowered travel times for bike and e-bike travellers. Based on the case study, we find that the introduction of mobility hubs alone has limited impact. However, combining this with making sharing services available to the public through MaaS subscriptions, there is a potential to reduce the number of car trips significantly, while the number of trips undertaken by a more sustainable (shared) e-bike increases as well as the number of so-called multi-modal mode trips (trips undertaken by a combination of various modes). Furthermore, improving the public transport service and micromobility network further increases the potential of mobility hubs in terms of making mobility more sustainable. The case study also shows that limiting parking capacity and increasing parking costs in the city centers is especially helpful for the reduction of vehicle use, leading to an improved car flow.

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This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional deep neural network that predicts traffic volume, speed, and vehicle loss hours in the system with high accuracy. The model allows us to quantify the benefits of different degrees of adjustment of truck departure times towards the off-peak hours. In our case, travel time reductions over the network are possible up to 10%. Freight demand management can build on the model to design departure time advisory schemes or incentive schemes for peak avoidance by freight traffic. These measures may improve the reliability of road freight operations as well as overall traffic conditions on the network.

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Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.@en

This paper introduces an advisory-based time slot management system (TSMS) to control truck arrivals at seaport terminals with the aim to reduce congestion at terminal gates. A modeling framework is proposed, developed, and applied to assess the impact of a truck arrival shift for a case study in the Port of Rotterdam. This system is designed to apply control policies on truck inflow while taking the behavioral aspect of truck operating companies (TOCs) into account. Discrete choice modeling is used to infer the time-of-day preferences of TOCs for container pick-ups from the exchange of information between port and hinterland stakeholders. These preferences are used to shift truck arrivals to the off-peak period which consequently reduces the high waiting time of trucks at terminals gates. To evaluate the effectiveness of the designed TSMS, a simulation platform that resembles terminal operations has been developed using discrete-event simulation. For the allocation of trucks to a certain time of day, a choice-based stochastic assignment heuristic is designed to approximate the optimum configuration of the truck arrival shift policy experiment. The optimum truck arrival shift design shows that significant gain can be obtained even at a low shift rate.

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This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large trajectory dataset, collected for the Netherlands, consisting of 3,647 trajectories of passenger car drivers, 1,080 trajectories of delivery van drivers, and 2,226 trajectories of truck drivers. We apply gated recurrent unit neural networks to separately model their gap selection processes. These three models can not only handle class imbalance but also capture long-term interdependencies. The models can predict gap selection of three vehicle classes with geometric mean accuracies of 84% or higher. To obtain insights into their gap selection processes, we apply a gradient-based technique to analyze what neural networks have learned. Our results suggest that there exist significant differences between vehicle classes in terms of the importance of historical information and features. Trucks seem to value a relatively long period, recent 6 seconds, of driving experience to select gaps compared to passenger cars and delivery vans. In addition, the perception of road topology seems to be a significant factor for delivery vans and trucks, contrary to passenger cars which highly value their kinematic features and interactions with surrounding vehicles, to select gaps. These insights offer a novel contribution towards better understanding and modeling of the driving behavior of multiple vehicle classes.

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Worldwide, cities and urbanised areas attract more people and economic activities. In addition, new ways of working, shopping and recreation, mobility and ‘ownership’ are introduced– all influenced by digitalisation. The United Nations (2018) expect the world-wide population living in urbanised regions to grow from 55% to 68% by 2050. However, concentration of residential, industrial, commercial, and recreational activities leads to an increasing pressure on land use and accessibility, potentially causing adverse environmental, health and liveability effects. Increasing housing density and decreasing space for transport infrastructure and parking call for new smart mobility approaches to ensure sustainable and inclusive accessibility.@en

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.

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Cooperative intelligent transportation systems (C-ITS) support the exchange of information between vehicles and infrastructure (V2I or I2V). This paper presents an in-vehicle C-ITS application to improve traffic efficiency around a merging section. The application balances the distribution of traffic over the available lanes of a freeway, by issuing targeted lane-changing advice to a selection of vehicles. We add to existing research by embedding multiple vehicle classes in the lane-changing advisory framework. We use a multi-class multi-lane macroscopic traffic flow model to design a feedback-feedforward control law that is based on a linear quadratic regulator (LQR). The weights of the LQR controller are fine-tuned using a response surface method. The performance of the proposed system is evaluated using a microscopic traffic simulator. The results indicate that the multi-class lane-changing advisory system is able to suppress shockwaves in traffic flow and can significantly alleviate congestion. Besides bringing substantial travel time benefits around merging sections of up to nearly 21%, the system dramatically reduces the variance of travel time losses in the system. The proposed system also seems to improve travel times for mainline and ramp vehicles by nearly 20% and 42%, respectively.

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Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways. Hence, their prediction is important to help manage traffic operations. However, The link between short-term dynamics of logistics activities and the generation of truck traffic has not yet been properly explored. This paper aims to develop a model that predicts short-term changes in truck volumes, generated from major container terminals in maritime ports. We develop, test, and demonstrate the model for the port of Rotterdam. Our input data are derived from exchanges of operational logistics messages between terminal operators, carriers and shippers, via the local Port Community System. We propose a feed-forward neural network to predict the next one hour of outbound truck traffic. To extract hidden features from the input data and select a model with appropriate features, we employ an evolutionary algorithm in accordance with the neural network model. Our model predicts outbound truck volumes with high accuracy. We formulate 2 scenarios to evaluate the forecasting abilities of the model. The model predicts lag and non-proportional responses of truck flows to changes in container turnover at terminals. The findings are relevant for traffic management agencies to help improve the efficiency and reliability of transport networks, in particular around major freight hubs.

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Cooperative intelligent transportation systems (C-ITS) support the exchange of information between vehicles and infrastructure (V2I or I2V). This paper presents an in-vehicle C-ITS application to improve traffic efficiency around a merging section. This application balances the distribution of traffic over the available lanes of a freeway, by issuing targeted lane-changing advice to a selection of vehicles. We add to existing research by embedding multiple vehicle classes in the lane-changing advisory framework. We use a multi-class multi-lane macroscopic traffic flow model to design a feedback-feedforward control law that is based on a linear quadratic regulator (LQR). The performance of the proposed system is evaluated using a microscopic traffic simulator. The results indicate that the lane-changing advisory system is able to suppress Shockwaves in traffic flow and can significantly alleviate congestion. Besides bringing substantial travel time benefits around merging sections of up to nearly 21%, the system dramatically reduces the variance of travel time losses in the system.

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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 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

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

A Field-Operational-Test (FOT) with CACC vehicles and wireless communication with intelligent intersections on arterial roads was performed in autumn 2018, initiated by the Province of Noord-Holland in The Netherlands. The goal of the pilot was to demonstrate the feasibility of CACC platooning and V2I/I2V (infrastructure-to-vehicle) communication and investigate the potential effects on traffic flow and safety in such an urban environment. During the pilot, seven CACC-enabled vehicles traversed a provincial road corridor, crossing five intelligent intersections, sending Cooperative Awareness Message (CAM) and receiving time-to-green information from Intelligent Traffic Signals (iTS). A simulation model was calibrated using the data from the pilot to give indications of the potential future effects of CACC and I2V communication. The results showed that full CACC penetration can lead to 5% lower travel times on average with an enhanced improvement when intelligent intersections with V2I/I2V technology is applied (11% lower). If scaled up to the provincial road network, this could lead to a reduction of 12% in the total experienced delay for road users. As for previous tests on motorways, a reduction in effectiveness was also found for lower CACC- penetration rates with penetration rates of 10% no longer showing any substantial gains.

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