Browsing by Author "Roncoli, Claudio"

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  • Achieving social routing via navigation apps : User acceptance of travel time sacrifice
    (2024-03) Vosough, Shaghayegh; Roncoli, Claudio
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Trip information and navigation systems are expected to become key components of future traffic management strategies, which, if properly exploited, may contribute to the mitigation of car usage externalities. In this study, we investigate social routing recommendations, which could be associated with nudges, and delivered via a navigation app, aiming at promoting sustainable routing behavior, where some drivers are asked to take longer routes and make travel time sacrifices (TTS). In particular, we propose a framework including data collection and behavioral modeling to identify the impacts of various types of information delivered to drivers, goals of the detour, and personal characteristics on drivers’ TTS behavior. The methodology includes stated choice and revealed choice experiments in two European cities, Amsterdam and Helsinki, and a mixed ordered-response logit model to provide insights into TTS behavior. Our analyses show that delivering different information and nudges results in different levels of TTS. However, regardless of the goal of the detour, offering incentives to drivers enables achieving a higher level of TTS. Comparing the stated and revealed data, regarding TTS and compliance rate, also clarifies significant differences between these two types of data.
  • Adaptive framework with policy setting tool for fuzzy logic based traffic signal controller
    (2018-06-11) Toivio, Tuomas
     Insinööritieteiden korkeakoulu | Master's thesis
    Fuzzy logic applications for traffic signal control have gained continual attention in academic research in the last decades and their performance have been recognized to be competitive against conventional control models as well as other control paradigms advancing artificial intelligence. Fuzzy logic inference systems advance usually much expert knowledge, which yields that they are more often designed for certain installation. This work proposes a method to let the fuzzy signal controller system to adapt itself according to the junction design and traffic demand levels incorporating a policy setting tool. The adaptive optimisation framework is built on top of the existing fuzzy logic signal controller called FITS, which is a part of the so-called Simon Traffic Management System that contains a micro simulation software used to evaluate and control junctions. Simon is currently installed in a big urban junction in San Jose, which is applied as a case study in the evaluation of the adaptive capabilities of the proposed framework. Output decision of the FITS is the extension of the green signal phase, and the framework is built to adjust the membership functions of the fuzzy sets used by the inference rules. Performance of the proposed framework is evaluated through several scenarios defining the adaptive procedure for traffic demand changes throughout the day. Especially, its capability to adapt according to desired policy structures is emphasized in the experiments done with microscopic simulations and real traffic model. Results indicate that proposed method is successful tool to fine-tune the FITS fuzzy logic control system.
  • Adaptive traffic control at motorway bottlenecks with time-varying Fundamental Diagram
    (2021) Tajdari, Farzam; Roncoli, Claudio
     A4 Artikkeli konferenssijulkaisussa
    This paper deals with the problem of controlling traffic at motorways bottlenecks in presence of an unknown, time-varying, Fundamental Diagram (FD). The FD may change over time due to traffic composition or to the presence of Connected and Automated Vehicles (CAVs) with varying driving characteristics and penetration rates. A novel methodology, based on Model Reference Adaptive Control, is presented to robustly estimate the time-varying set-points that maximise the bottleneck throughput. The proposed approach is integrated in a control scheme that includes a linear quadratic integral regulator designed to control traffic which comprises a percentage of CAVs. Simulation experiments, based on a first-order multi-lane macroscopic traffic flow model that also considers for the capacity drop phenomenon, are presented to illustrate the effectiveness of the proposed approach. Copyright (C) 2021 The Authors
  • Analysing the Environmental and Social Impacts of a Novel User-Based Transit Signal Priority Strategy in a Connected Vehicle Environment
    (2024-10-15) Mohammadi, Roozbeh; Vosough, Shaghayegh; Roncoli, Claudio
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Transit signal priority (TSP) is a traffic control strategy aiming at prioritising public transit vehicles at signalised intersections. The emergence of connected vehicles (CVs) provides the opportunity to enhance TSP operation, mitigating challenges such as the negative impact on nontransit users and the management of conflicting priority requests. Furthermore, traffic control policies produce environmental impacts, whilst TSP strategies are typically evaluated based on common traffic flow indicators, such as average vehicle speed, delay and/or the number of stops. In light of the recent progress made in CV technology, we propose and assess two user-based TSP strategies. The first approach aims to minimise total user delay at a signalised intersection, whilst the second considers both reducing bus schedule delay and total user delay. We also measure the environmental effects of these TSP strategies. A microscopic simulation environment is used to compare the proposed methods’ performance against a conventional TSP ring-and-barrier controller in a case study involving two adjacent signalised intersections in Helsinki, Finland. The findings indicate that implementing the proposed strategies effectively enhances TSP performance whilst also lowering adverse environmental impacts.
  • Application of cyclists' route choice preferences in travel demand modelling
    (2022-03-21) Tarkkala, Konsta
     Insinööritieteiden korkeakoulu | Master's thesis
    Factors that influence cyclists’ route choice preferences have been studied widely in international literature as a part of coherent cycling network development. This has not been the case in Finland where the number of related studies has been few. Simultaneously role of cycling as a transportation mode has been largely absent from travel demand modelling which is applied to evaluate impacts of strategic land use and transportation operations. This thesis set out to research route choice preferences of Greater Helsinki cyclists. In addition, the study evaluates opportunities in applying route choice modelling to improve forecasting accuracy of cycling in regional travel demand modelling. Well-established research framework of choice theory was applied in this study. Factors with significant influence on cyclists’ route choice preferences were identified and applied in a stated preference survey for data collection. With the collected sample route choice utility models were generated and further incorporated into regional travel demand model HELMET for evaluation. Route choice results show cyclists to favour riding on routes with separated cycle paths, absence of hills and light traffic volume. Trip length and mixed traffic were instead found to be a cause of significant negative influence. Quantitative validation on assigned cycling volumes demonstrated route choice model to perform slightly worse than the current cycling model of HELMET. Further validation on tracing routes between pairs of origins and destinations displayed route choice model to forecast similar routes more frequently over present cycling model. Discovered route choice results are in line with literature and they can be further applied in regional cycling development. The generated route choice model was demonstrated to be applicable improvement in increasing behavioural factors in cycling in travel demand modelling. The model may be fully implemented at a later stage, but prior to this, further actions focusing on calibration and network updates are necessary.
  • Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway
    (2024-04) Li, Haizhen; Roncoli, Claudio; Zhao, Weiming; Ju, Yongfeng
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving (JAD) strategies on circular and linear scenarios have been evaluated. The manual vehicle is the intelligent driver model (IDM) and human driver model (HDM), respectively. The results suggest that on the ring road, the traffic performance of mixed traffic improves gradually with the increase of the proportion of CAVs under the ACC. Moreover, the traffic performance for the JAD strategy does not improve infinitely with the increase in the number of CAVs. Conversely, the FS strategy suppresses traffic waves at the cost of reducing traffic flow, and more CAVs are not beneficial for mixed traffic. It is interesting to note that under optimal performance in these three strategies, the FS strategy has the lowest number of CAVs, while the ACC strategy has the highest number of CAVs. For the linear road, it demonstrates that the JAD strategy exhibits a superior performance compared to the ACC. However, the FS strategy cannot dissipate traffic waves due to an insufficient buffer gap. Different models have varying effects on different strategies.
  • Assessing the performance of a hybrid max-weight traffic signal control algorithm in the presence of noisy queue information: An evaluation of the environmental impacts
    (2024-11) Liaquat, Muwahida; Vosough, Shaghayegh; Roncoli, Claudio; Charalambous, Themistoklis
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Max-weight (or max-pressure) is a popular traffic signal control algorithm that has been demonstrated to be capable of optimising network-level throughput. It is based on queue size measurements in the roads approaching an intersection. However, the inability of typical sensors to accurately measure the queue size results in noisy queue measurements, which may affect the controller's performance. A possible solution is to utilise the noisy max-weight algorithm to achieve a throughput optimal solution; however, its application may lead to decreased controller performance. This article investigates two variants of max-weight controllers, namely, acyclic and cyclic max-weight (with and without noisy queue information) in simulated scenarios, by examining their impact on the throughput and environment. A detailed study of multiple pollutants, fuel consumption, and traffic conditions, which are proxied by a total social cost function, is presented to show the pros and cons of each controller. Simulation experiments, conducted for the Kamppi area in central Helsinki, Finland, show that the acyclic max-weight controller outperforms a fixed time controller, particularly in avoiding congestion and reducing emissions in the network, while the cyclic max-weight controller gives the best performance to accommodate maximum vehicles flowing in the network. The complementary positive characteristics motivated the authors to propose a new controller, herein called the hybrid max-weight, which integrates the characteristics of both acyclic and cyclic max-weight algorithms for providing better traffic load and performance through switching.
  • Assessment of connected vehicle information quality for signalised traffic control
    (2021-06-16) Del Pino Verona, Hector; Mohammadi, Roozbeh; Roncoli, Claudio
     A4 Artikkeli konferenssijulkaisussa
    Connected vehicles (CVs) present a great opportunity to smooth and improve traffic flows at intersections thanks to their communication capabilities, which may allow a real-time flow of information with the controllers operating traffic signals. Therefore, it is reasonable to envision that, in the near future, CV data may complement or replace spot detector data that is currently used to operate traffic signals. However, CV data may be affected by errors, such as positioning error, which may depend on the technology that is employed for collecting such information. In this paper, we investigate the performances of different control strategies, namely a strategy that employs only aggregated information, such as queue lengths, and a strategy using disaggregated vehicle-based information, when they are operated with CV data, considering various realistic measurement accuracy settings. Our experiments, conducted via microscopic simulations, show that the disaggregated strategy features better performance and robustness in most of the tested scenarios.
  • Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data
    (2022-02) Agriesti, Serio; Roncoli, Claudio; Nahmias-Biran, Bat Hen
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.
  • A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models
    (2023) Agriesti, Serio; Kuzmanovski, Vladimir; Hollmen, Jaakko; Roncoli, Claudio; nahmias-biran, bat-hen
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors’ knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.
  • Big data for activity based transport models
    (2018-12-10) Hajduk, Petr
     Insinööritieteiden korkeakoulu | Master's thesis
    Our civilization needs to know as much information about itself as possible in order to keep running. One of the important fields is the field of transportation and since we could not measure all the movements happening on planet Earth, we need transport modelling. As of 2018, for the area of a metropolis the four-step model still seems to be a state of practice of modelling transportation. This comes with several disadvantages such as lack of detail (aggregation to zones) or oversimplifying of the travel demand phenomena (trips are not combined into daily schedules). To remedy these disadvantages, the scientific community came up with activity-based models that addressed those issues. The in-creased level of detail has however increased the demand for data. Nowadays the data is obtained from costly travel surveys that make the methodology less viable option for the practitioners. Therefore, in this thesis the focus are possible new sources of data for the model and using the open datasets to build an activity-based model. First, we examine the existing big data sources and evaluate their usefulness for the model. As a result of this evaluation, we carry on to create synthetic data handling the movements of the studied population, as no big data source related to movement of people was found useful for creating the data suitable for the model. We used the Capital region of Helsinki, Finland as a region for the case study to deal with the real data environment. The data is generated by disaggregation of statistical data aiming at preserving the variability in a maximum achievable way. Where needed, assumptions are used to forward the process. Using the synthetic big data a transport model was created. Despite the fact that the ac-curacy of the model in terms of error on link volumes does not reach the level of some other previously developed models, it is still surprisingly precise regarding the idea that solely open data and statistics were used. In the discussion possible synergies with other big datasets is described with respect to the experiences from the case study.
  • Bike users’ route choice behaviour: Expectations from electric bikes versus reality in Greater Helsinki
    (2024-12) Khavarian, Khashayar; Vosough, Shaghayegh; Roncoli, Claudio
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Electric pedal-assist bikes (e-bikes) are an emerging technology that aims to enhance cycling by incorporating battery-powered motors activated while pedalling. To promote cycling effectively, it is crucial to understand the factors that influence cyclists’ route choice behaviour. This study investigates individual route choice behaviour among cyclists, taking into account their bike type (i.e., e-bikes and regular bikes). Data collected through a stated preference (SP) survey in Finland is analysed using discrete choice models to compare the differences between e-bike and regular bike users’ route choice behaviour. The study also compares the outputs of multinomial and mixed Logit models for both e-bike and regular bike users to address the impact of error correlation in SP data. Furthermore, by employing a classification approach, the study examines the differences between the expected and actual behavioural changes upon using e-bikes, referred to as the expectation–reality gap, in terms of route choice behaviour. Our research findings highlight certain factors that consistently promote cycling among both regular bike and e-bike users, specifically, low interaction with traffic, fewer intersections, and the presence of separated bike facilities. Also, our findings imply that the SP survey is well-designed to capture the preferences of the individuals. Hence, the observations are not severely correlated, i.e., errors can be assumed to be independently and identically distributed. Furthermore, we show that regular bike and e-bike users with similar characteristics do not share similar beliefs regarding the effects of e-bikes on their cycling habits.
  • Boost I&E concept for urban mobility education
    (2022) Sayrol, Elisa; Roncoli, Claudio; Aba, Attila; Banfi, Miklos; Bloemer, Alexander; Bragós, Ramon; Estrada, Miquel; Macario, Rosario; Marques da Costa, Nuno; Minner, Stefan; Mohammadi, Roozbeh; Pinhas, Nathan; Shiftan, Yoram; Ribas, Imma; Witlox, Frank
     A4 Artikkeli konferenssijulkaisussa
    Modern higher education needs to provide skills needed in working life, such as entrepreneurship, besides the more traditional technological competence. The Boost I&E Project2 was developed in 2020 and 2021 with the aim of generating a set of guidelines for innovation and entrepreneurship challenge-driven projects for master's programmes. The added value was created by collaborating and exchanging best practices among higher education institutions in seven different countries, with the aim of developing students' skills with an international perspective and exposure to the knowledge triangle. The implementation of Boost I&E would allow learning about the advantages and disadvantages of different approaches in a practical way, while courses on urban mobility were provided. The activities involved more than 100 students over two years. The experience concluded with the adoption of a set of guidelines based on best practices, covering several aspects. Most emphasis was placed on the methodology of the course, on sharing activities and on finding best practices and implications for stakeholders. Our experience can be useful for universities that want to open up their students to I&E.
  • Centralized and Distributed Multi-Region Traffic Flow Control
    (2020-05) Boufous, Omar; Roncoli, Claudio; Charalambous, Themistoklis
     A4 Artikkeli konferenssijulkaisussa
    In this paper, centralized and distributed multi-region perimeter flow control approaches are proposed for congestion avoidance in urban networks. First, multi-region network dynamics are modeled with Macroscopic Fundamental Diagrams (MFDs) and necessary stability conditions are derived using Lyapunov stability theory for a centralized perimeter controller. Later, an optimization problem is formulated, solved and the desired optimal states are reached by means of an algorithm based on Model Predictive Control (MPC). Finally, the paper combines the centralized controller for perimeter control as a first layer controller and a distributed controller managing the inter-transfers between regions, thus optimizing the overall state of the network. Simulations show that the distributed control scheme leads to good results maximizing the output of the traffic network, similar to the MPC controller.
  • Comparing and identifying the barriers of the electrification of last mile delivery
    (2023-08-21) Desreumaux, Valentine Sophie Anne Catherine
     Insinööritieteiden korkeakoulu | Master's thesis
    The thesis researches the barriers of the electrification of last mile delivery for the light commercial vehicles’ segment. Although electrification offers advantages such as reduced environmental impact and lower operational cost, some challenges hinder its implementation. Key challenges include high purchase costs, limited range, and inadequate charging infrastructure. This research aims to determine whether the size of logistics companies influences the adoption and the use of electric vehicles. In order to compare impacts, given the size of the companies, logistics operators were interviewed about their operations and thoughts on EVs. The comparison of the responses revealed that smaller companies can be more impacted by the financial and operational barriers. In fact, smaller companies have less resources which makes them more affected by the uncertainties and their perception of EVs. On the other hand, larger companies have more capital to purchase the vehicles and to try to find the best use for them. The main reason for larger companies to use EVs are to meet the sustainability goals they are setting for themselves. In fact, it seems like EVs are still at an early stage in the last mile delivery market, and the barriers are impeding its wide use.
  • A computational framework for revealing competitive travel times with low carbon modes based on smartphone data collection
    (2020) Bagheri Majdabadi, Mehrdad; Mladenovic, Milos; Kosonen, Iisakki; Nurminen, Jukka K.; Roncoli, Claudio; Ylä-Jääski, Antti
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment's preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions.
  • Computationally efficient dynamic assignment for on-demand ridesharing in congested networks
    (2021-06-16) Zhou, Ze; Roncoli, Claudio
     A4 Artikkeli konferenssijulkaisussa
    On-demand ridesharing service has been recognized as an effective way to meet travel needs while significantly reducing the number of required vehicles. However, most previous studies investigating dynamic assignment for ridesharing systems overlook the effects on travel times due to the assignment of requests to vehicles and their routes. To better assign the ridesharing vehicles while considering network traffic, we propose a framework that incorporates time-dependent link travel time into the request-vehicle assignment. Furthermore, we formulate an optimal assignment problem that considers multiple path options and that accounts for the congestion potentially caused by assigned routes. A set of simulations reveals that using an appropriate congestion avoidance ridesharing strategy can remarkably reduce passenger average travel and waiting time by alleviating traffic congestion in the network.
  • A constrained spectral clustering method for lane identification using trajectory data
    (2023-10) Zhao, Weiming; Roncoli, Claudio
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The rapid development of information and communications technologies acts as an enabler for the successful implementation of vehicle automation and advanced traffic management applications. In particular, the appearance of new sources of high-resolution trajectory data, such as videos obtained from drones, provides an opportunity to build accurate maps and enrich applications in traffic research at an unprecedented resolution. However, existing methods cannot handle certain features, such as, among others, accurate of road lane identification. This paper proposes a constrained spectral clustering method to identify lane information from high-resolution trajectory data. Contrary to state-of-the-art methods, such as the Gaussian mixture model, the proposed method is directly applicable to two-dimensional trajectory data, without assuming a constant number of lanes characterised by the same lane width. The trajectory data is clustered by taking into account the neighbourhood distances and prior knowledge via defining so-called must-link and cannot-link constraints, which significantly improve the clustering results, especially in cases where the number of lanes or the lane width changes. The proposed method has been evaluated through numerical experiments using data obtained from drone videos, and the results indicate that the method performs well on complex road segments, even in the presence of a varying number of lanes or lane-changing manoeuvres.
  • A cooperative framework for Universal Basic Mobility System: Mobility credits approach
    (2019-12) Mladenovic, Milos; Abbas, Montasir M.; Roncoli, Claudio; Bozorg Chenani, Sanaz
     A4 Artikkeli konferenssijulkaisussa
    Development of integrated mobility and traffic management strategies is an important aspect of the ongoing transition of urban mobility systems. Extending from existing credit schemes, this research presents a system design and evaluation of a framework based on the principle of Universal Basic Mobility. In particular, using premises of long-term cooperation and hierarchical self-organization, the system design includes user-based Mobility Credits interrelated with Priority Levels. To complement the cooperation framework, system architecture is formulated in line with the distributed ledger technology. The proposed framework is tested using web-based interaction in the form of stated-preference experiment. Results are analyzed through statistical distributions and a discrete-choice model of user decision-making within the proposed framework. This research concludes that this framework could nudge uses towards reciprocity and altruism in their travelling behavior. In addition, experiment participants have provided a range of comments related to positive features, potential for failure, and further development. Finally, the paper ends by raising several implications for wider citizen participation in the integrated mobility system design and evaluation.
  • Cooperative Rerouting to Redistribute the Load of Connected and Automated Vehicles in Urban Networks
    (2023) Vitale, Francesco; Roncoli, Claudio
     A4 Artikkeli konferenssijulkaisussa
    In this paper, we propose a novel distributed algorithm for cooperative rerouting of Connected and Automated Vehicles (CAVs) in urban networks, in which each intersection unit manages the portion of the network for which they are in charge by sending updated routes to follow to the CAVs therein, while communicating among each other to be updated on the situation of the (in general non-homogeneous) roads of the network. The proposed approach allows to decompose the problem into subproblems, which are resolved distributively with little information exchange. The problems are constructed to obtain a fair compromise between user equilibrium and system performance. We show the results we obtained on a simulated urban network with CAVs and compared them with a baseline scenario.