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.

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.

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.

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.

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.

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.

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.

This paper seeks traffic signal control policies which maximise long-run network throughput when demand is within network capacity and also when demand is beyond network capacity. The paper considers smooth versions of two well-known responsive traffic signal control policies: the P₀ policy (see Smith, 1979b, 1980 or Smith et al., 2015) and the maxpressure or MP policy (see Varaiya, 2013a). The paper gives two simple example networks where both of these smooth responsive traffic control policies give rise to decreasing throughput for increasing demands; when these demands are beyond the network capacity. The paper then suggests a simple modification of the two responsive traffic control policies and shows that the two modified policies do maximise the long run throughput of the two signal-controlled networks; both when a steady input demand D is within network capacity and also when a steady demand exceeds network capacity. The modified version of P₀ is as follows: for a certain positive number K, forD⩽Kuse responsive policyP₀,and forD>Kuse the green-times generated byP₀whenD=K. Thus in this modification the green-time proportions generated by responsive P₀ are frozen as demand passes through K; and remain fixed for all demands D which exceed K. The paper shows that the above modification when applied to a continuous version of MP also maximises throughput in both networks for all values of the steady demand D. Finally the paper considers a signal controlled network with route choice. It is shown that if routes are chosen by travellers in their own best interests then the continuous version of MP does not always maximise throughput, even if the constant input demand D is within the network capacity, but P₀ does always maximise throughput, even when the constant input demand D is beyond the network capacity.

Network-wide traffic control policies determine the optimal values for the different kinds of controllers equipped on a transportation network, with the objective of reducing delays and congestion, improving safety and reaching a target Level of Service. While models and algorithms for these problems have been extensively studied in literature, little attention has been devoted to investigating whether/how different locations, kinds (pricing controllers, traffic lights, …) and amounts of controllers in a network affect the overall performance of network-wide schemes. In this work, we adapt the control-theoretical approach of controllability of complex networks to the specific instance of transportation networks, considering both propagation/spillback dynamics and users’ behavior in terms of route choice. Thanks to the newly developed methodology, we then provide exact solutions to the Full Controllability Pricing Controller Location Problem for transportation networks. Comparing different pricing controller location policies through two artificial test cases, we empirically demonstrate how indeed the amount and kind of controllers in a network strongly affect the level of performance reachable by network-wide control policies, specifically in terms of Total Cost minimization.

Traffic control performance on networks depends on the flow response to the policy adopted, which in turn contributes to determine the optimal signal settings. This paper focuses on the relationship between local and network wide traffic control policies within the combined traffic control and assignment problem. Through a full exploration of the solution space, an in depth cross comparison is performed between the well-known local policies P0 and Equisaturation, versus the global policies Maximum Throughput and Minimum Delay, to verify how the two local policies approximate the optimal settings for signalized intersections. Realistic traffic dynamics, such as congestion, multiple controllers and spillback are considered, to empirically determine the conditions under which the local policies are able to approximate global performances. After presenting the different local and global control policies, experiments are performed on simple toy networks. The complexity of the underlying network and, therefore, of the problems' boundary conditions is then increased, allowing us to showcase how the different metrics perform in different situations. Finally, conclusions on the results are drawn.