Multi-modal transport is getting more popular due to the emergence of new traffic
modes. The increase of modes also adds complexity for the transport researchers.
This paper proposes an augmented link-based super-network approach for modeling
multi-modal transport networks, addressing the scalability and versatility issues of
conventional methods. This approach is used to calculate the user equilibrium for
urban transport networks traffic assignment with multiple traffic modes, a difficult
problem due to the intractable enumeration of feasible paths between origindestination
pairs and restricted transfers between different traffic modes. In the supernetwork
representation of multi-modal transport networks, the travel cost of any
feasible route between the origin and destination is formulated as the sum of cost
functions of the augmented links, thus avoiding the enumeration of feasible paths.
Additionally, restrictions on traffic mode transfers can be embedded in the link-based
model by excluding infeasible transfer links or adding penalties for undesired transfers.
The user equilibrium of the augmented link-based super-network model is formulated
as a variational inequality problem, solved using the extra-gradient algorithm. A multimodal
transport network is considered in the case study. Simulation results validate the
effectiveness of the proposed model, demonstrating its scalability and versatility in
addressing complex multi-modal transport networks with diverse traffic modes.
We anticipate that our method can serve as an efficient modeling approach for more
general and complex multi-modal transport networks, facilitating traffic management
and network design.@en

Last-mile delivery, one of the most polluting segments of the supply chain, is the focus of numerous studies. There are various innovative delivery methods aimed at mitigating its adverse effects. This study explores whether crowdshipping (CS) could serve as a sustainable urban logistics solution for Rome, assessing its environmental viability. It poses the question: Can Rome adopt CS, and if so, how sustainable would it be? Using real-world data, we employed the MASS-GT simulation tool to simulate parcel demand for various parcel companies in Rome’s urban areas. Additionally, we considered real data on trips made by employees to offices within the study area and their modes of transport. The analyses include predicting parcel demand and forming parcel schedules, both with and without CS.We also assessed employees’ willingness to make detours for parcel pickups. Our findings suggest that CS can reduce emissions depending on users’ willingness to adjust travel routes, which can be incentivized through monetization. Furthermore, by considering the fleet composition of parcel companies, we quantified the potential emissions savings achievable through CS. The results indicate that CS is applicable in Rome and could significantly reduce emissions by approximately 1.3 tonnes of CO2 per day, equivalent to 93 euros in the EU’s Emissions Trading System. This approach aligns with European emissions plans and validates the feasibility of CS in Rome through practical research. It offers valuable insights for policymakers, emphasizing the importance of encouraging user participation and supporting CS platforms.@en

This study investigates the potential of link-and path-based incentives to mitigate congestion in urban transportation networks. Both incentive schemes are formulated as non-linear optimisation problems with complementarity constraints. Mathematically, it is demonstrated that the feasible region of the link-based model is a subset of the feasible region of the path-based model. Consequently, path-based incentives exhibit greater potential for shifting the user equilibrium flow pattern toward system optimum compared to link incentives. A column generation-based iterative solution technique, which generates new paths at each iteration, is devised to efficiently solve both optimisation problems. Numerical experiments conducted for various transport networks also highlight the superiority of path-based incentives in reducing total travel time in urban transportation networks.@en

Among real-time traffic control methods, max-pressure (MP) control stands out due to its simplicity, decentralized nature, and robust theoretical foundation. Besides, advancements in connected vehicle (CV) technology have motivated a significant amount of research into traffic signal control based on CVs. Nevertheless, few studies have been dedicated to MP control in partially CV environments and meanwhile consider multi-modal traffic flows. To fill this research gap, this study proposes CV-based multi-modal MP control (CV-MMP), which calculates the pressure based on travel time information of CVs weighted by vehicle occupancy. Therefore, a hierarchical multi-modal traffic signal priority controller is achieved in a soft manner. Besides, adapting to the requirements of practical applications, CV-MMP is extended to fuse detector data and consider phase switching lost time and cyclic phase sequence. The evaluation results based on a toy network simulation demonstrate that CV-MMP can significantly reduce transit delay with a small increase in private vehicle delay, resulting in a significant reduction in average person delay. In addition, approximately 75% of CBs pass through the network without experiencing delays due to stopping. Therefore, our method can achieve effective transit signal priority and even transit signal coordination under single transit requests.@en

This paper presents a novel auction-based traffic signal control mechanism aimed at optimizing multimodal traffic flow at signalized intersections through \textcolor{red}{connected vehicles}. The proposed framework, which utilizes a second price sealed bid auction mechanism, allocates green time dynamically based on user bids, incorporating policy-oriented modal priority. This approach addresses the limitations of current signal control systems by providing a computationally fast and distributable method that considers the priority hierarchy of traffic modes, thereby enhancing the efficiency and equity of intersection management.

Key innovations include a dynamic bid distance determination method and a modified bidding scheme that prioritize certain traffic modes according to predefined policies. The effectiveness of these methods is demonstrated through a case study focusing on bicycle prioritization at a real-world intersection in Bordeaux, France.

Simulation results indicate significant improvements in service levels for prioritized modes without substantially increasing delays for other users. The methods' flexibility for adaptation to different intersection configurations and computational feasibility ensure their applicability to a wide range of intersection types and traffic conditions. Our findings suggest that the sealed bid second price auction mechanism can be a useful tool for policymakers aiming to implement multimodal traffic priorities, contributing to reduced travel delays and more effective control at intersections.@en

This study investigates the potential of path-based incentives to mitigate congestion and reduce emissions in urban transport networks using a multi-objective optimisation problem with a budget limit. A column generation-based solution technique is developed that finds a new path between each origin-destination pair at each iteration, and stops when the objective value does not change more than a threshold at two consecutive iterations. Three different scenarios are defined based on the objective function: minimising total travel time (TTT), total emissions (TE), and integrated minimisation of both. Numerical results in the Sioux Falls network show that TTT and TE are conflicting objectives under our modeling assumptions: improving one worsens the other. Nonetheless, the integrated scenario demonstrates the capacity to harmonize both objectives, thereby achieving a reduction in both TTT and TE.@en

Physical distancing has been an important asset in limiting the SARS-CoV-2 virus spread during the COVID-19 pandemic. This study aims to assess compliance with physical distancing and to evaluate the combination of observed and self-reported data used. This research shows that it is difficult to operationalize new rules, that context affects compliance, that there needs to be a need for compliance, and that rules require upkeep. From a methodological point of view, this study found that the combined methods provide a comprehensive picture of compliance behaviour, that it is challenging but essential to mitigate response fatigue in long-term monitoring studies, and that it would be interesting in future research to learn how actual behaviour is influenced by personal narratives.

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The increasing urbanization, combined with shrinking space for transport infrastructure and private parking, significantly challenges urban accessibility. Moreover, the rising number of vehicles exacerbates congestion in city centers, leading to longer commute times, increased noise levels, and greater air pollution. These issues underscore the urgent need for creating low-car urban zones. One promising approach is an integrated traffic management system that considers various modes of transportation—such as cycling, walking, shared mobility, and public transport. However, multi-modal traffic management typically involves diverse stakeholders with potentially conflicting interests, which necessitates a balance of these interests through multi-objective optimization. Traditional approaches often employ a weighted sum method to transform multiple objectives into a single objective. This method significantly constrains the solution space and complicates the assignment of appropriate weights to different objectives. Therefore, generating a Pareto front for multi-modal traffic management could provide decision-makers with a set of efficient solutions, enabling them to select the most suitable option. The ε-constraint method is recognized for its ability to generate a Pareto front. The question we discuss here is whether this method can be effectively applied to managing multi-objective, multi-modal traffic networks. In this study, we answer this question by proposing an augmented ε-constraint-based optimization framework for multi-objective multi-modal traffic management. This framework is bi-level and can accommodate various traffic models and objectives that reflect the diverse interests of multiple stakeholders. Thus the multi-modal traffic management problem can be formulated as a multi-objective nonlinear optimization problem. The augmented ε-constraint method (Mavrotas, 2009) is employed to efficiently address the multiple objectives, and the multi-start sequential quadratic programming method is used to solve the nonlinear optimization problems, such that the Pareto front is obtained. We validate the effectiveness of our framework through a case study, whose preliminary results show that our method improves the traffic performance and provides insights into the trade-off among different objectives.@en

This study investigates the potential of link-and path-based incentives to mitigate congestion and reduce emissions in urban transportation networks. Both incentive schemes are formulated as non-linear optimisation problems with complementarity constraints. Mathematically, it is demonstrated that the feasible region of the link-based model is a subset of the feasible region of the path-based model. Consequently, path-based incentives exhibit a higher potential in pushing the user equilibrium flow pattern toward system optimum, compared to link incentives. A column generation-based iterative solution technique, which generates new paths at each iteration, is devised to efficiently solve both optimisation problems. The numerical results in the Sioux Falls network also highlight the superiority of path-based incentives in reducing total travel time and emissions in urban transportation networks.@en

Our cities are growing at an unprecedented pace. The flexible use of metropolitan infrastructures is the key to maintaining, if not increasing, the current quality of life. The combined use of geo-fence technology and connected vehicles can be the tool to achieve this flexibility. In this paper, we take a first step in the evaluation of the benefits that dynamic geo-fencing could bring. In a simulation-based environment, we employ a computer vision approach to dynamically identify congested areas in a given transportation network. We then compare the performance of perimeter control based on dynamic geo-fencing vs conventional perimeter strategies, based on a fixed, pre-determined area-a scenario mimicking traffic management approaches currently deployed in large metropolitan areas worldwide. Simulation results highlight a reduction of more than 20% of the Total Time Spent in a regular Manhattan grid network, encouraging further efforts to validate the efficiency of dynamic geo-fencing in addressing externalities (congestion, pollution, noise, etc.) in more realistic scenarios.

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We introduce a Markov Modulated Process (MMP) to describe human mobility. We represent the mobility process as a time-varying graph, where a link specifies a connection between two nodes (humans) at any discrete time step. Each state of the Markov chain encodes a certain modification to the original graph. We show that our MMP model successfully captures the main features of a random mobility simulator, in which nodes moves in a square region. We apply our MMP model to human mobility, measured in a library.

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To use efficiently the infrastructure of transportation networks, control strategies have been developed with the aim to reduce negative externalities, such as congestion and pollutant emissions. Previous works demonstrated that the maximum performance achievable by traffic control policies depends on the number and location of controllers employed, which implies the need to determine a set of controllers capable of fully controlling the underlying transportation network. Various approaches have been explored in the literature to locate controllers on networks, however a gap remains in terms of scalability as the methods proposed often exhibit heavy computational complexity. In this paper we aim to propose an approach capable of locating pricing controllers on transportation networks that is scalable, such that it can be applied on large instances, such as city-sized or regional networks. For this purpose, we propose a topology-based approach, adapted from the sensor location problem, as both problems share similar characteristics. We validate our proposed approach by analyzing the performance of controller sets produced on a wide range of artificially generated network ensembles. The analysis we provide reveals that the method proposed, while being easily applicable on large instances, is capable to locate an efficient controller set, and to redirect flows on the network so as to reduce the total time spent by road users.

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Holding has been extensively used as control strategy to regulate public transport operations, especially to maintain even headways and prevent buses of the same line to bunch up. Applying holding to multiple lines requires however to deal with the transition between corridor and branching segments. In this study, we introduce a holding criterion for network configurations with lines that operate jointly along a common corridor and then diverge to individual branches serving different urban areas. The proposed holding decision rule accounts for all different passenger groups in the overlapping segment and considers the transition to individual line operation. The holding rule is evaluated using simulation for different demand levels and compositions and is compared with state-of-the-art control schemes for a real-world network. Results show that the proposed multi-line control yields performance improvements along the shared transit corridor as well as at the line level. The performance of the control scheme is affected by the demand composition and we provide indications regarding the conditions under which multi-line control is advisable.

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In this work we extend our previously proposed cascading Kalman filtering technique, applied to the problem of urban network flow estimation, to adopt heterogeneous traf- fic data sources. Both static infrastructure detection (double induction loops) and Floating Car Data are collected from a given transportation network, and employed within separate stages of the cascading technique. The proposed approach relies upon notions of traffic flow inference (observability) to both i) determine the optimal set of locations in which sensors should be installed and ii) provide enhanced covariance information within the estimation technique. The impact of both penetration rates of Floating Car Data and sensor selection procedure is evaluated empirically, through a microscopic simulation software (SUMO) generating experimental data on a simple grid-like network.
Test results showcase that the proposed extension to the cascading framework is indeed beneficial in reducing the overall estimation error on network segments where static infrastructure is unavailable. Furthermore, the importance of observability- based sensor locations is clearly demonstrated.@en

To be used efficiently, transportation networks require control strategies to optimize flow distribution. However, the location and type of controllers employed in a transportation network directly impact the level of performance reachable by traffic control policies. In order to guarantee that the highest level of performance is reachable, fully exploiting the available network capacity and reducing negative externalities, we explore methods to identify relevant controller locations in transportation networks while minimizing the number of controllers used. Previous work provided an exact approach to identify controller locations on complex networks. However, said approach exhibits complications when applied to transportation networks containing bi-directional links. We therefore propose simple heuristic algorithms relying only on topological information to solve this problem, while also avoiding heavy computation, thus being able to determine a solution to the minimal controller location set problem on large networks. Based on the existing framework, we aim to provide an experimental setup, with diverse network sizes and configurations to analyze the performances of different heuristic methods, in order to develop an efficient algorithm.

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In this work we develop a kalman filtering approach for the problem of traffic state estimation in urban networks. The proposed approach employs concepts developed in the field of traffic flow observability, to derive both i) a minimal set of locations wherein traffic sensing infrastructure the network should be equipped and ii) topological relationships to be employed in the filtering technique's error covariance matrices, to improve the estimation process. A Linear Time-Variant formulation of first-order traffic flow theory is employed to model node-node vehicle propagation, allowing to predict the evolution of Cumulative Vehicle Numbers at intersections. This model is then embedded in the proposed cascading Kalman Filter framework. Validation of the proposed filtering approach is performed on a simple grid-like network, bearing considerable congestion, spillback and rerouting behaviour. We generate experimental data through a microscopic simulation software (SUMO).Test results showcase how the proposed approach successfully exploits observability-based information to reconstruct data in unmeasured segments of the network. Particular care should however be devoted to appropriate inference of turning fractions at intersections, in order to achieve the lowest possible estimation error.

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Traffic control policies aim at reducing the negative externalities that ever-growing demand is causing on transportation networks, such as congestion and pollutant emissions. To achieve these goals, strategies coordinating and aligning the effects of several individual traffic controllers have received increasing attention in research and development in the past decades. However, a considerable gap still exists between the desired and experienced performance of advanced dynamic traffic management systems, resulting in failure to completely prevent the increasing peak-hour congestion in main urban areas worldwide. In this work we contribute to assessing whether this gap might be tied to inefficient network design, rather than algorithmic prowess. Based upon our earlier work, we investigate whether a trend can be found between determining locations of controllers in a network following control theoretical insights, and try to confirm our earlier intuitions when dealing with dynamic traffic assignment, featuring accurate propagation and spillback dynamics. To achieve these goals, we extend an existing synthetic network generation tool to allow us to test this hypothesis on real-life-like road networks, and extend our previously developed algorithms for the controller location problems allowing for sufficient generalization. Test results are presented on a simpler deterministic scenario and on 240 randomly generated networks, showcasing that placing controllers following controllability-based principles is advantageous from the perspective of model-based dynamic traffic management applications.

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Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed Integer Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxembourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.

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Public transport services are currently executing or planning a fundamental transition from traditional buses to electric buses. During this transition phase, the public transport offering is fulfilled with a mixed fleet across multiple bus terminals, which poses operational challenges for optimal vehicle scheduling, a problem not yet addressed in literature. As researchers in Transport Engineering and Operational Research at the University of Luxembourg, in collaboration with the Roma Tre University, we support the Ministry of Transport of Luxembourg and Volvo buses by modelling and simulating this transition phase, to help them managing and solving such challenges. In this work we develop a mixed-integer linear programming (MILP) formulation of the problem and implement a time-based decomposition framework, through which we can optimize real-life daily instances. This method is tested on the main urban bus lines that connect Central Station, Luxembourg Airport and ten other major terminals within Luxembourg City, providing (near) optimal solutions that explicitly consider the energy constraints arising from electric bus operations, while establishing an advantageous trade-off between delaying trips, to implement quick-charging of electric buses, and performing the same trip with costlier traditional buses. The results show a consistent decrease of operational costs as the percentage of e-buses in the fleet increases.

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Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport, are increasingly becoming key objectives for policymakers worldwide. In order to jointly achieve these goals, careful consideration should be put on the operational cost and management of PT services, in order to promote the adoption of green mobility solutions and advanced management techniques by operators. In this work we develop a dynamic fleet management approach for next generation Public Transportation systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators, explicitly considering real-Time disturbances, including delays, service disruptions etc. We propose a Mixed Integer Linear Program to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, and employ it as predictor in a Model Predictive Control approach. Test results based upon a real-life scenario showcase how the proposed approach is indeed capable of yielding a sizable reduction in operational costs, even when considerable disturbances arise from the underlying system.

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