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Spatiotemporal data, and more specifically origin-destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin-destination flow information of individuals' whereabouts. In this paper, we propose a methodology to estimate origin-destination (O-D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhone-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users' unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O-D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O-D matrix carries similar estimations as those that can be obtained via travel surveys.

RailBeijing 2021, 9th International Conference on Railway Operations Modelling and Analysis (ICROMA), Pékin, CHINE, 03-/11/2021 - 07/11/2021; This professional paper presents the algorithmic framework for automatic timetable generation which is actually under development within the research project « Tools for mathematical optimization of strategic railway timetable models » funded by the Norwegian Railway Directorate. This framework is composed by two main components, which tackle the Train Timetabling Problem in a macroscopic model of infrastructure and operations. The first stage is a Multi-Objective Ant Colony Optimization (MOACO) algorithm which produces a Pareto Optimal Set of solutions. These are timetables optimized with respect to four main objectives, namely: the total travel time, the total energy consumption, the timetable robustness and the total number of scheduled trains. The MOACO treats conflict constraints as soft ones and produces strictly periodic traffic patterns. The second stage is a MILP formulation tackled with a commercial solver. It refines timetables produced by MOACO, looking for improvements in the neighbourhood of the solutions provided by the latter. MILP solutions are conflict-free timetables and can profit of given tolerances on trains periodicity. The MILP stage is used as a final refinement of the results as well as an intermediate local search during MOACO iterations.

We propose a traffic flow and control model for the train dynamics in a linear metro line without junction. The model takes into account time constraints such as minimum inter-station running times, minimum train dwell times at platforms, and minimum safe separation times between successive trains. Moreover, it includes a control law that sets the train dwell times at platforms based on the feedback of the train time-headways and of the passenger arrival rates at platforms. We show that the dynamic system converges to a stable stationary regime with a unique average growth rate, and derive, by numerical simulation, the traffic phases of the train dynamics. We compare the obtained traffic phases with the ones derived with an existing max-plus algebra traffic model, and derive the effect of the passenger travel demand on the train dynamics. Finally, we draw some conclusions and discuss perspectives of the proposed approach.

hEART 2020, 9th Symposium of the European Association for Research in Transportation - Virtual conference, Lyon, France, 03-/02/2021 - 04/02/2021; The present work proposes a framework to calibrate MFD models using mobile phone data. The three major components to calibrate in the present context include MFD shape, regional trip lengths and path flow distribution. Time dependent penetration rates are estimated by fusing the OD matrix and the Loop Detector Data (LDD). The estimated MFDs are stable with very low scatter. In the following step, macro-paths and their corresponding trip lengths are estimated. Dynamic evolution of trip lengths is demonstrated using the present data, which is otherwise very difficult to capture with other types of data sources. Another important component calibrated is the time dependent path flow distribution between the different macropaths for a given OD pair. The manuscript is concluded by presenting the time evolution of the User Equilibrium (UE) gap for different macroscopic OD pairs.

TRB 2021, Transportation Research Board 100th Annual Meeting - A Virtual Event, Washington DC, ETATS-UNIS, 05-/01/2021 - 29/01/2021; Departure time choice models have a crucial role in transportation network planning in order to provide planners with predictions about the optimal traffic load. This paper introduces a new dynamic departure time choice framework based on the so-called Mean-field Games (MFGs) theory proposed by Lasry and Lions. MFGs models aim to analyze large population differential games. This feature gives us a unique opportunity to design a departure time choice equilibrium model in a scale-free transportation system. The proposed framework is the combination of two components: (i) the reaction of travelers to the traffic congestion by choosing their departure time to optimize the travel cost; (ii) the aggregation of the actions of the travelers, which determines the congestion level of the system. The first component corresponds to a classical game theory model, while the second one captures the interactions of the travelers at the macroscopic level. We use the generalized bathtub model recently proposed by Jin to represent the traffic dynamics at the network level. First, we provide a continuous model to investigate the equilibria and then give a discrete approximation of the system from deterministic differential games models. Finally, we show on a real test case that the model can represent the departure time choice equilibrium for multiple preferred travel time with heterogeneous trip lengths wile the framework is capable of considering a large number of players.

hEART 2020, 9th Symposium of the European Association for Research in Transportation - Virtual conference, Lyon, FRANCE, 03-/02/2021 - 04/02/2021; This paper investigates the accuracy of accumulation estimation at different urban scales, mainly link and network levels. Different methods are compared depending on sensor types, probe vehicles or loop detectors, and historical data availability. Probe vehicles can provide an accurate estimation of the average travel times but only for a given vehicle sample. So, we resort to loops to calculate the fishing rate and scale up the observations to all vehicles. The fishing rate is the ratio of probes that are detected at the loop locations over the loop flow. Accurate accumulate estimation at the link level can only be obtained when probes and loop data are available in real-time. Methods based on spatially aggregated fishing rates when probe data are available but not loops show better results, but the errors are still significant at the link level. It is possible to obtain accurate accumulation estimations at the network level when combining loop and probe observations, even if few links capture both data sources. It requires applying proper aggregation technics and a careful definition of fishing rates at this scale.

Since the introduction of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorithms have been developed to implement MFD-based perimeter control over a specific urban region. A model-based controller consists of two components: a plant model that represents reality; and a prediction model used to determine optimal control actions. In most studies, the authors assume a constant average trip length for all drivers traveling within the same region, for the prediction model. In these studies about perimeter control and MFD traffic models, the controllers show a good performance because accumulations, i.e. traffic states, from the plant are used to reflect the initial state of the prediction model with a high frequency (about a few seconds). However, this average trip length changes over time as it depends on the Origin-Destination flow decomposition, playing an important role in real applications. The main contributions of this paper are twofold. First, we show that the assumption about constant trip lengths used in the prediction model deteriorates the controller's performance for low frequency updates of the optimal control actions. Second, we propose a methodological framework based on the Unscented Kalman Filter (UKF) for dynamically adjusting the average trip lengths and accumulations. Our test results on a real city network show that applying this methodological framework significantly improves the controller's performance.

Multiple factors can influence the public transport level of service. All take root in the network structure and the operating regime, i.e. how the bus lines are arranged atop the street network and how the service frequency is adjusted to meet urban mobility patterns. This is known as the bus network design problem and has been the subject of several studies. The problem is so challenging that most studies until now resort to strong assumptions such as a static description of the peak hour demand, homogeneous user behavior, and equal trip lengths. Potential effects of different types of user behavior and trip lengths patterns on the user and/or operator cost have not been investigated whatsoever. Moreover, none of the existing studies have considered the effects of bus network structure on private car users, the level of interactions between the modes, and the passenger mode choice that depends on traffic conditions. This paper aims to close this gap and provide a general framework considering multiple trip length patterns, two types of user behavior, and the effects that the bus network structure might have on the traffic performance and passenger mode choice. For modeling different trip length patterns, we use the trip length distribution as an intermediate level of abstraction. To capture complex modal interactions and quantify the operating speeds, we apply the recently proposed three-dimensional macroscopic fundamental diagram. We use the operating speed for each mode to determine the mode choice at the trip length level. This way, we are able to solve the optimal bus network design problem under the free-flow/saturated traffic conditions in an analytical way, while considering more realistic settings including a dynamic description of the peak hour demand, mixed traffic, and different mode choice decisions depending on trip lengths and walking preferences. Numerical analysis reveals that all the tested factors, including demand intensity, user behavior, and trip length patterns, have significant effects on the operator and user cost function. Results show that the probability of choosing any given mode follows certain distribution that varies across the trip length patterns, indicating the importance of modeling the mode choice at the trip length level. Furthermore, the results indicate that users can benefit if they are willing to adjust the number of transfers to minimize the walking distance at the origin and the destination. Moreover, we show that the optimal bus network design determined for the uniform trip pattern underestimates the number of required buses, which leads to passenger congestion at stops during the peak period. This, however, does not happen when we take into account the actual trip length distribution for the bus network design. A comparison with a simplified approach that considers the bus system only, reveals the value of accounting for the complex bi-modal interactions, especially for higher demand levels. Finally, we show that by allowing the design parameters to vary across cardinal directions we provide more flexibility for the bus system to serve the passenger demand while reducing the operator cost compared to the existing approaches. ISSN:0191-2615 ISSN:1879-2367

Over the past few decades, the digitalization of services and infrastructures has led to the emergence of a broad set of new information sources to characterize human mobility. These sources usually offer valuable significant population penetration rates but may also suffer from important temporal sparsity. Data generated by user activity, such as social networks or mobile phone data, especially fit this description. Although this temporal sparsity might prevent estimating individual travel speeds, we state that such low-frequency positioning data enable estimating the average urban traffic speed dynamics when considering an adequate network partitioning. In this sense, this article proposes a new method, based on the division of the urban area of a given city into regions and on the analysis of a limited set of basic characteristics of individual vehicle trips, such as the regional path. Our solution first involves estimating robust travel times from travelers sharing similar trip features and then jointly analyzing these travel times to deduce the underlying regional traffic speeds, using regression analysis. We apply this methodology on a set of trips derived from a large GPS dataset of vehicle tracks covering the city of Lyon. These data are purposely downsampled to reduce the sampling rate and reproduce bias and temporal features that are proper to sparser but larger-scale, mobility data sources dependent on user’s communication activities. Controlling the data downsampling process allows us to evaluate the impacts of the progressive information loss on the speed estimation, while the raw GPS data provide the ground truth speed reference against which to compare our results. Provided that the amount of observed individual trips is sufficient, the analysis returns satisfying speed estimation results, both at low and high downsampling levels. Thus, we successfully demonstrate that it is possible to estimate zonal traffic speeds from degraded trip data without evaluating individual travel speeds.

This work investigates network-related trajectory features to unravel trips that contribute most to system under-performance. When such trips are identified, feature analysis also permits determining the best alternatives in terms of routes to bring the system to its optimum. First, we define a combination of network-related trajectory features that helps us unravel the critical trips which contribute the most to the network under-performance, based on the literature review and a factor selection process. Second, based on supervised learning methods, we propose a two-step data-driven methodological framework to reroute a part of the users and make the system close to its optimum. The learning models are trained with trajectory features to identify which users should be selected, and which alternative routes should be assigned, thanks to the data and features obtained from two reference dynamic traffic assignment (DTA) simulations, under User-Equilibrium (UE) and System-Optimum (SO). We only focus on trajectory features that are accessible in real time, such as network features and regular travel time information, so that the methods proposed can be implemented without requiring cumbersome network monitoring and prediction. Finally, we evaluate the efficiency of the methods proposed through microscopic DTA simulations. The results show that by targeting 20% of the users according to our selection model and moving them onto paths predicted as optimal alternative paths based on our rerouting model, the total travel time (TTT) of the system is reduced by 5.9% in comparison to a UE DTA simulation. This represents 62.5% of the potential TTT reduction from UE to SO, when all the users choose their path under the SO condition.

One of the main challenges for multi-regional application of the aggregated traffic models based on the Macroscopic Fundamental Diagram, lies in the identification and characterization of the most prevailing paths chosen by drivers. In this paper, we propose a methodological framework, based on two distinct methods, to determine these prevailing paths. The first method requires the information about travel patterns in the urban network as well as the information about the city network partitioning. The second method is more parsimonious, and consists on the direct calculation of shortest-cost paths on the aggregated network. For this, we propose four impedance functions that utilize topological features of the urban network and its partitioning. We test the performance of this methodological framework for determining the most prevailing paths on a network representing the metropolitan area of Lyon (France). We consider a set of real trajectories (i.e. GPS data) of drivers in this network as a benchmark. We show that the proposed methods are able to identify the most prevailing paths as the ones chosen by drivers, as evidenced by a large similarity value between the sets of paths. Based on a maximum likelihood estimation, we also show that the Weibull distribution is the one that better reproduces the functional form of the network-wide distribution of travel distances. However, the characterization of the functional form of such distributions characteristic to each region defining a path is not trivial, and depends on the complex topological features of the urban network concerning the definition of its partitioning. We also show that the Euclidean distance metrics provides good estimates of the average travel distances. Interestingly, we also show that the most prevailing paths are not necessarily the ones that have the lowest average travel distances.

Recent empirical studies have found that travellers route choices deviate from perfect rationality, showing that urban trips do not necessary follow the shortest-time routes. However, there is no consent on how much the travellers' route choice behaviour deviates from the perfect rational assumption. The objective of this study is to contribute to the understanding on how travellers process travel time when making route choices, and to quantify to what extent users are strict travel time minimisers or if bounded rationality is observed. The question of whether travellers evaluate travel time differences in absolute or relative terms is also addressed, and the heterogeneity in the route choice behaviour of travellers investigated. The results of route choice experiments, focused on the choices in diverse OD pairs and traffic conditions, are analysed. In total, 496 participants recorded 5535 choices over 41 OD pairs. It was found that travellers evaluate relative rather than absolute differences in travel time. In 60.5% of the trips participants chose the fastest route, but this percentage is 80% when the travel time between the fastest and the rest of the alternatives is at least 30% higher. Only 10% of the individuals chose the fastest route in all trips, confirming the hypothesis of bounded rationality. Participants exhibited heterogeneous travel time indifference bands: the average participant was indifferent to relative travel time differences of less than 31%. A mixed logit model (MXL), considering heterogeneous indifference bands is proposed. The model shows a similar predictive accuracy compared to the classical MXL model.

This study reviews existing computational methods to calculate simulation-based dynamic network equilibrium. We consider a trip-based multi-modal approach for the dynamic network loading. Mode and path choices are carried out at the same level; therefore, travel times depend on the travel path and the mode attributes of travelers. This study develops a multi-class model with several parameters per class. Two different categories of algorithms (heuristic and meta-heuristic) are considered in order to solve the discrete DTA problem. Finally, we analyze the equilibrium in a large-scale multi-modal DTA test case (Lyon 6th + Villeurbanne) in order to investigate the performance of different optimization approaches to solve trip-based DTA. The results show that, in a multimodal and heterogeneous setting, the meta-heuristic methods provide better solutions than the heuristic methods in terms of optimality and computation time. These improvements are even more significant than in a homogeneous setting.

Can dynamic ride-sharing reduce traffic congestion? In this paper we show that the answer is yes if the trip density is high, which is usually the case in large-scale networks but not in medium-scale networks where opportunities for sharing in time and space become rather limited. When the demand density is high, the dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Sharing can compensate extra travel distances related to operating a mobility service. The situation is entirely different in small and medium-scale cities when trip shareability is small, even if the ride-sharing system is fully optimized based on the perfect demand prediction in the near future. The reason is simple, mobility services significantly increase the total travel distance, and sharing is simply a means of combating this trend without eliminating it when the trip density is not high enough. This paper proposes a complete framework to represent the functioning of the ride-sharing system and multiple steps to tackle the curse of dimensionality when solving the problem. We address the problem for two city scales in order to compare different trip densities. A city scale of 25 km 2 with a total market of 11,235 shareable trips for the medium-scale network and a city scale of 80 km2 with 205,308 demand for service vehicles for the large-scale network over a 4-hour period with a rolling horizon of 20 minutes. The solutions are assessed using a dynamic trip-based macroscopic simulation to account for the congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies on optimal fleet management that usually consider constant and fully deterministic travel time functions.

TRB 2021, Transportation Research Board 100th Annual Meeting - A Virtual Event, Washington DC, ETATS-UNIS, 05-/01/2021 - 29/01/2021; An excessive demand of vehicles to a motorway bottleneck leads to traffic jams. Motorbikes are narrow and can drive next to each other in a lane, or in-between lanes in low speeds. This paper analyses the resulting traffic characteristics and presents numerical scheme for a macroscopic traffic flow model for these two classes. The behavior included is as follows. If there are two motorbikes behind each other, they can travel next to each other in one lane, occupying the space of one car. Also, at low speeds of car traffic, they can go in between the main lanes, creating a so-called filtering lane. The paper numerically derives functions of class-specific speeds as function of the density of both classes, incorporating flexible lane usage dependent on the speed. The roadway capacity as function of the motorbike fraction is derived, which interesting can be in different types of phases (with motorbikes at higher speeds or not). We also present a numerical scheme to analyse the dynamics of this multi-class system. We apply the model to an example case, revealing the properties of the traffic stream , queue dynamics and class specific travel times. The model can help in showing the relative advantage in travel time of switching to a motorbike.

Analysing the properties of a network equilibrium can help to have a better view about network state, robustness, and the effect of any variation in the network. This study investigated the impacts of network design history on day-to-day multimodal user equilibrium. In particular, we analyze the long-term evolution of the network, including opening new multimodal options and its impacts on the final network equilibrium. First, the analysis focuses on static network loading with different successive configurations. Then, a more realistic setting is studied by simulation. A large-scale multimodal network with the flexible opening over time of three possible transport facilities shows that the final equilibrium is not unique; more importantly, significant differences can be observed in public transportation occupancy, while user equilibrium is enforced in all situations. Some solutions prove to be better from the collective viewpoint (shorter total travel time), thus giving new insight into public transport planning.

The estimation of trip lengths has been proven to be a key feature for the application of aggregated traffic models based on the Macroscopic Fundamental Diagram. The paths and distances to be traveled by vehicles in regional networks vary over time, due to changes in the traffic conditions. In this paper, we develop a methodological framework to explicitly determine traffic-dependent regional paths and estimate their travel distances. This framework is incorporated into a dynamic traffic assignment module designed to target the Deterministic and Stochastic User Equilibrium in regional networks. We first discuss how regional paths and their characteristic trip lengths are influenced by changes in the regional traffic dynamics. We then test the proposed methodology for estimating traffic-dependent travel distances on small and medium-sized networks, considering a simulation environment. We show that our methodology provides good estimations of the traffic-dependent trip lengths. Our results also shed light on the importance of how time-dependent trip lengths influence the traffic dynamics in the regions.

The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ride-sharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ride-sharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities., L'objectif principal de cette thèse est de répondre à l'une des questions essentielles sur les services de covoiturage dynamique : le covoiturage dynamique peut-il réduire la congestion ? Dans cette thèse, nous proposons un framework d'optimisation basé sur la simulation pour le covoiturage dynamique. Ensuite, à l'aide de ce framework, nous évaluons l'impact du covoiturage dynamique sur deux échelles de réseau différentes pour trouver la réponse à cette question. Lors de l'évaluation du problème du covoiturage dynamique, deux points importants doivent être pris en compte. Premièrement, comment le système de covoiturage répond à la demande du réseau et deuxièmement, comment le système de covoiturage est influencé par le réseau et en particulier par la congestion. Ensuite, nous pouvons évaluer l'impact d'un tel service sur le réseau. La plupart des approches existantes se concentrent sur le premier point, c'est-à-dire sur la conception de la correspondance de la demande, tout en utilisant des hypothèses de base pour le second point, principalement des temps de trajet constants. La méthode proposée dans cette thèse peut être plus performante que les méthodes existantes dans la littérature. L'algorithme d'optimisation peut fournir des solutions de haute qualité en peu de temps. Notre approche de solution est conçue pour être exacte pour les petits échantillons. Ensuite, pour pouvoir traiter les problèmes à grande échelle, elle est complétée par plusieurs heuristiques qui conservent la conception générale de la méthode de solution mais réduisent considérablement son temps de calcul. Dans la composante de simulation, un "Modèle d'usine" est appliqué sur la base du "Diagramme macroscopique fondamental (MFD) basé sur les déplacements" pour représenter la réalité de la dynamique du trafic et un "Modèle de prédiction" est appliqué sur la base de la vitesse moyenne à utiliser pendant le processus d'affectation. Nous réalisons une étude de simulation approfondie (basée sur des modèles de trafic réels) pour évaluer l'influence des systèmes de covoiturage dynamiques sur la congestion du trafic. À l'échelle moyenne (Lyon 6 + Villeurbanne), les résultats ont montré que le covoiturage ne pouvait pas améliorer de manière significative la situation du trafic. Des parts de marché élevées augmentent la distance et la durée des trajets et entraînent une augmentation du trafic sur le réseau. Dans les grandes villes, les résultats sont totalement différents de ceux des villes petites et moyennes. Dans les simulations à grande échelle (ville de Lyon en France), le système de covoiturage dynamique proposé peut améliorer considérablement les conditions de circulation, en particulier aux heures de pointe. L'augmentation de la part de marché et du nombre de covoiturages peut renforcer cette amélioration. Par conséquent, le système de covoiturage dynamique proposé est une option viable pour réduire la pression sur les transports publics existants et diminuer le trafic du réseau dans les villes peuplées et de grande taille.

KES AMSTA 2020, 14th International Conference on Agents and Multi-Agent Systems: Technology and Applications - Virtual conference, Split, CROATIE, 17-/06/2020 - 19/06/2020; Today policy makers face an ever more complex traffic system. While they need to ensure smooth traffic flows in the cities, at the same time an acceptable level of service must be provided in remote areas. Autonomous taxis (AT) offer the opportunity to manage car traffic at low operational cost and they can be appropriate alternatives for the driven vehicles. In this paper, we propose a multi-agent system to find the best dispatching strategy for a fleet of AT. In the dispatching process, we aim to satisfy both passengers and providers objectives and priorities. To be able to apply the method on large-scale networks, we introduce a clustering method to cluster the requests every minute and then solve the assignment problem for the requests inside each cluster. As the network congestion can have significant impacts on the vehicles speed and travel time especially considering the private cars that are driving in the system besides the taxis, an agent-based simulation platform is used to simulate the function of the AT fleet. We use the trip-based Macroscopic Fundamental Diagram (MFD) to simulate the time evolution of traffic flows on the road network and update the traffic situation in the system every second to represent the real traffic dynamics. We address the problem for a large city scale of 80 km2 (Lyon city in France) with more than 480,000 trips over 4 hours period containing the morning peak. The experimental results with real data show that the proposed multi-agent system is efficient in terms of serving all the requests in a short time satisfying both passengers and providers objectives.

TRB 2020, 99th Annual Meeting Transportation Research Board, Washington, ETATS-UNIS, 12-/01/2020 - 16/01/2020; This work focuses on the estimation ofMacroscopic Fundamental Diagram (MFD) and trip lengths for the city scale network using mobile phone data for the city of Dallas. The data used in the present work has different sampling intervals and in order to have an accurate estimation of travelled distance, a trip enhancement method is proposed based on map-matching scheme. Data from peak hour OD matrix and loop-detectors are fused together to obtain a typical full day OD matrix of Dallas city. Using the enhanced trip set and OD matrix data, penetration rates that depend on OD pair and time are estimated. The macroscopic variables computed from the data are expanded using the penetration rates. Finally, MFDs and trip lengths are estimated for the partitioned net work. An analysis on the trip lengths is presented to demonstrate the differences in trip length distributions that depend on the OD pair and macro-path.

In this paper, we revisit the concept of Prospect-basedUser Equilibrium in a dynamic context. We consider a mesoscopic Lighthill-Whitham-Richards(LWR) traffic model to determine time-dependent route costs. We propose a solution algorithm to determine the network equilibria. Monte Carlo simulations are used to account for the travel time distributions. We analyze the dynamic Prospect-basedUser Equilibrium compared to the benchmarks Deterministic and Stochastic User Equilibrium, on a synthetic Manhattan network. We set four endogenous reference points. We show that the setting of the reference point plays a very important role in the route flow patterns and on the network performance at an aggregated level, i.e. in terms of vehicles mean speed as well as internal and outflow capacities. Our results also enhance that the Prospect-basedUser Equilibrium is more sensitive to a change in the reference point than in the calibration of the users' risk-aversionand risk seekingparameters.

TRB 2020, 99th Annual Meeting Transportation Research Board, Washington, ETATS-UNIS, 12-/01/2020 - 16/01/2020; Traffic congestion and emission are the crucial problems due to its significant impact on the environment, economy, and health of the inhabitants. In this paper, we propose an approach to reduce network-wide emissions in an urban traffic network. The approach is comprised of two layers. The first layer (optimal green routing) which predicts the optimal splitting coefficient for all origin-destination (OD) pairs (only two alternatives are possible for each OD) crossing the city center or bypass it. As implementing optimal routing strategies on the field is almost impossible as we don't have direct control on the users decisions, we choose to balance travel time through gating at the city perimeter in order the usual dynamic user equilibrium (DUE) discipline matches the optimal splitting coefficient derived from the first layer. The second layer then adjusts inflows at the city gates to DUE based on the predictive travel time corresponds to the optimal routing strategy for each OD pair. The accumulation-based macroscopic fundamental diagram (MFD) model of a single reservoir city with seven atrial routes and six bypass alternatives is developed. Linear programming (LP) problem is formulated to determine the optimal splitting coefficients (first layer) and the nonlinear model predictive control (NMPC)-based gating control strategy is designed to track the optimal splitting coefficients (second layer). Combination of two layers indirectly track the network-wide emission levels by manipulating the traffic flows into the reservoir. The network wide emission control approach is compared with another usual gating strategy (perimeter control) aiming to optimize traffic conditions in the inner-city only. The comprehensive analysis conducted on proposed approach is presented. We compare the results of controlled case with respect to the uncontrolled case and with respect to each other. The comparison results show: (i) the proposed network-wide emission control approach outperforms congestion control approach in reducing the total emission, (ii) significant improvements in total time spent, mean speed, and emission are obtained when considering both the inner-city and the bypass traffic with the network-wide strategy and (iii) improvements come at the cost of increased complexity (need to solve two optimization problems).

This work attempts to validate the dynamic outputs of bi-modal MFD-based models, also referred to as 3D-MFD models, using empirical data. A previous study (Loder et al., 2017) gathered cars and public transport vehicles data in two different regions of Zurich city and showed that a well-defined 3D-MFD exists by proposing a functional form relating overall travel production to the accumulations of cars and public transport vehicles. This work aims to go one step further with the same data set to investigate if 3D-MFD embedded in dynamic conservation laws can predict the time evolution of the network traffic states. Two different approaches to estimate the inflow demand using outflow and mean speed evolutions are presented. The mean trip lengths are estimated using a network exploration technique. Accumulation-based, trip-based and accumulation-based with outflow delay models are considered in the validation study. It is concluded that a single bi-linear 3D-MFD fit is insufficient to predict the evolution of traffic states accurately. The current work proposes multi bi-linear 3D-MFD fits segregated depending on the time of the day. The proposed approach significantly improved the simulation results, where good correspondence with empirical data is obtained. Finally, it is shown that in multi-modal networks like the city of Zurich, it is essential to consider the effect of public transport vehicles, when considering aggregated simulations. It is also shown that using a 2D-MFD by treating public transport vehicles and private cars alike, result in poor accordance with the field observations.

The present work proposes a global framework to estimate all MFD model parameters using mobile phone data. The three major components that are estimated in the present context are MFD shapes, regional trip lengths and path flow distribution. A trip enrichment scheme based on the map matching process is proposed for the trips that have sparser records. Time dependent penetration rates are estimated by fusing the OD matrix and the Loop Detector Data (LDD). Two different types of penetration rates of vehicles are proposed based on the OD flow and the trips starting within an origin, respectively. The estimated MFDs based on two types of penetration rates are stable with very low scatter. In the following step, macro-paths and their corresponding trip lengths are estimated. This work is the first to present empirical evidences of the dynamic evolution of mean trip lengths over the day, which is very difficult to capture with other types of data sources. The last component is the time dependent path flow distributions between the different macro-paths for a given OD pair. The manuscript is concluded by presenting the time evolution of the User Equilibrium (UE) gap for different macroscopic OD pairs. It is noticed that UE principle holds true most of the time, except for OD pairs that have macro-paths transversing through congested reservoirs, especially during peak hours.

The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities. The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities.

MFTS 2020, The 3rd Symposium on Management of Future Motorway and Urban Traffic Systems - Virtual conference, Luxembourg, LUXEMBOURG, 06-/07/2020 - 08/07/2020; The development of Advanced Traveler Information Systems (ATIS) with enroute navigation systems has opened the door to new traffic regulation policies such as collective route guidance based on the minimization of total travel time. However, a critical element for their efficiency is the compliance of travelers with route recommendations. In this paper, we use a stated preference survey based on route choice situations from a real network to investigate traveler compliance for different levels of travel time sacrifice vs several social benefits. Two kinds of social benefits are considered: congestion alleviation and emission reduction. The data collected allows us to precisely quantify the travel time sacrifice that a given proportion of travelers would be ready to accept in order to take a different alternative that has a given level of social benefit. In line with the literature, our analysis confirms the decrease of compliance with the increase of sacrifice. Moreover, it suggests that the way the recommendation is intended could play a significant role in the level of compliance: the display of an advice message for the social path (in the congestion alleviation case) is shown to be more efficient than only showing additional information (in the emission reduction case).

In this paper, we propose a control and optimization framework to reduce network-wide emissions in an urban traffic network. The framework is comprised of two layers. The first layer (optimal green routing) predicts the optimal splitting coefficients for all Origin-Destination (OD) pairs crossing the city center or bypassing. As implementing optimal routing strategies on the field is almost impossible as we do not have direct ontrol over the user's decisions, we choose to balance travel time through gating at the city perimeter so that the usual Dynamic User Equilibrium (DUE) discipline matches the optimal splitting coefficients. The second layer then adjusts inflows at the city gates to the DUE solution based on the instantaneous travel times corresponding to the optimal routing strategy for each OD pair. The accumulation-based Macroscopic Fundamental Diagram (MFD) model of a single reservoir city with seven arterial routes and six bypass alternatives is developed. A Linear programming problem is formulated to determine the optimal splitting coefficients and a Nonlinear Model Predictive Control (NMPC)-based gating control strategy is designed to track the optimal splitting coefficients. The network-wide emission control framework is compared to three other gating strategies aiming to (i) optimize traffic conditions or (ii) minimize emissions, in the inner-city only, and (iii) optimize traffic conditions in the whole network. A comprehensive analysis conducted on all four approaches is presented. We compare the results of the controlled case with respect to the uncontrolled case and with respect to each other. The comparison results show that: (i) the proposed network-wide emission control strategy significantly outperforms the other two simpler control strategies focusing on inner-city only in reducing the total emission, (ii) but also improves the in total time spent and mean speed at the network-level.

Aggregated traffic flow models based on the Macroscopic Fundamental Diagram (MFD), also known as multi-reservoir or multi-region MFD models, have been developed for more than a decade for various applications. While being very appealing for simulating traffic dynamics at a city level, the outputs of these models were rarely confronted with real data measurements. Thus, this paper focuses on calibration and validation of an MFD simulation for a city partitioned into multiple reservoirs. The traffic predictions from the MFD simulation (total accumulation and mean speed) are compared with real data from loop and probe sensors. The questions addressed in this study include the influence of the city partitioning, the MFD and average trip length estimation, and the path flow distribution among reservoirs. This study is carried on the network of Lyon, France, composed by around 27,000 links that extend over an urban area of 80 km2. Two different partitioning cases are defined and compared, with respectively 5 and 10 reservoirs. Our results notably show that the proper estimation of three elements is critical for accurate traffic state prediction: (i) the total ?active? network length of each reservoir, (ii) the regional trip lengths in the reservoirs, and (iii) the path flow distribution at the regional network level. While the network equilibrium found in the 5-reservoir partitioning can be roughly approximated with Wardrop's principle, the 10-reservoir case is more complex and requires to design ad-hoc optimization process to derive regional path flow distributions that fit the data. The global equilibrium found in this latter case turns out to be hardly predictable with any traffic equilibrium principle.

TRB 2020, 99th Annual Meeting Transportation Research Board, Washington DC, ETATS-UNIS, 12-/01/2020 - 16/01/2020; Since the development of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorigthms have been developed to implement perimeter control over a specific urban region. In most cases, the typical assumption is to have a common average trip length for all vehicles traveling within the same region. However, the proper calibration of the trip length has been proved to be of crucial importance for ensuring accurate traffic predictions from MFD models. In this paper, we propose to investigate how the calibration of these travel distances influences the traffic dynamics and the performance of a perimeter control strategy using a Model Predictive Control (MPC) scheme. In particular, we quantify the effects of an improper estimation of the travel distances inMPC applications. We also propose a methodological framework based on the Unsected Kalmann Filter (UKF), to correct for these improper estimations of trip lengths and improve the efficiency of the controller. We perform the simulation tests using an accumulation based MFD model on a medium-sized real city network.

Demand estimation is an important step for multiple applications in urban studies. However, the level of accuracy required depends on the objective of the study. In dynamic traffic microsimulation, the estimation of demand needs to be accurate, as it is intended to describe individuals' trips on a very small-scale. In this case, poor estimation of trip initiation, path, and endings could result in the wrong estimation of the city-block scale's traffic state. Estimating demand at a large-scale with high-resolution is not only very challenging because it requires a large volume of data from multiple sources, but the underlying mathematical problem is considerable and thus hard to solve. In this paper, we address the issue of trip starts and ends when modeling large perimeters. We propose to enhance the location of trip initiation and termination by merging heterogeneous and large public datasets. To do so, we develop a series of algorithms that identify fine-mesh areas where trips could reliably start or end and we share the estimated demand within these sub-areas, following the distribution of trip purposes (Home, Work, Shop, etc.). The method is deployed in Lyon city, France, and validated on an extraction of it. Micro-simulation results show that the demand, once accurately distributed, changes the overall network's performance, confirming the significant influence of trip endings and starts on the overall traffic dynamics.

We present an algorithm for optimal guidance of users in road networks. It is a "stochastic-on-time-arrival (SOTA)"-like algorithm which calculates optimal guidance strategies with reliable paths, for road network origin-destination pairs. Our contribution consists here in extending an existing SOTA algorithm, in order to include robustness of the guidance strategy, towards path failures. The idea of SOTA algorithms is to calculate the maximum probability of reaching a destination node, starting from any node of a road network, and given a time budget. This calculus gives the optimal path for every origin-destination pair of nodes in the network, with an associated optimal adaptive guidance strategy. We propose here an extension of this approach in order to take into account the existence and the performance of alternative detours of the selected paths, in the calculus of the guidance strategy. We take into account the fact that one or many links of the selected optimal path may fail during the travel. We then consider that users may be sensitive to path changing. That is to say that they may prefer paths with efficient alternative detours, with respect to paths without, or with less efficient detours, even with a loss in the average travel time, and/or in its reliability. In order to take into account such behaviors, we propose a model that includes the existence as well as the performance of detours for selected paths, in the calculus of the travel time reliability. This new way of calculating travel time reliability guarantees a kind of robustness of the guidance strategies., 9 pages, 3figures, conference

Mobil.TUM 2019, International Scientific Conference on Mobility and Transport , Munich, ALLEMAGNE, 11-/09/2019 - 12/09/2019; Since the development of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorigthms have been developed to implement perimeter control over a specific urban region. In most cases, the typical assumption is to have a common average trip length for all vehicles traveling within the same region. However, the proper calibration of the trip length has been proved to be of crucial importance for ensuring accurate traffic predictions from MFD models. In this paper, we propose to investigate how the calibration of these travel distances influences the traffic dynamics and the performance of a perimeter control strategy using a Model Predictive Control (MPC) scheme. In particular, we quantify the effects of an improper estimation of the travel distances inMPC applications. We also propose a methodological framework based on the Unsected Kalmann Filter (UKF), to correct for these improper estimations of trip lengths and improve the efficiency of the controller. We perform the simulation tests using an accumulation based MFD model on a medium-sized real city network.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; One of the key questions for Macroscopic Fundamental Diagram traffic models lies in the calibration of the trip lengths, i.e. the travel distances of vehicles in the regions. Few studies in the literature have attempted to propose methodological frameworks to calibrate the trip lengths. Batista et al. (2019) propose a framework to explicitly calculate trip length distributions. However, they do not consider the influence of the traffic conditions on the trip lengths. In this paper, we propose to extend their methodological framework to determine time-varying trip length distributions according to the changes in the traffic conditions.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; This work investigates network-related trajectory features to unravel trips that the most contribute to the system under-performance. When such trips are identified, features analysis also permits to identify the best alternatives in terms of routes to make the system to its optimum. First, data mining is carried out on trajectories obtained from reference dynamic traffic assignment (DTA) simulations in a real-world network, based on User-Equilibrium (UE) and System-Optimum (SO). This helps us (i) to target the trajectories to be changed, and (ii) to identify their main features (trip lengths, experienced travel time, path marginal costs, and network-related features such as betweenness centrality and traffic light parameters, etc.). Similarity analysis based on Longest Common Subsequence, Principle Component Analysis are the main methods that are performed to carry out descriptive analysis of trajectories. Supported Vector Machine is then used to determinate the features with regards to their contribution to better network performance.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; In recent years, intelligent transportation systems made it possible for operators to adapt in real-time the transportation supply to travel demand via new mobility services. Among these services, ride-sharing is becoming popular. The dynamic ride-sharing problem involves two sub-problems: (1) How to serve the upcoming trips (Optimal fleet management) and (2) How to accurately predict the travel times to determine vehicles availability and pick up/drop off times. In this paper, we express the optimal fleet management problem as a constrained multi objective integer linear programming. Our aim is to find the global optimal solution for the ride-sharing problem without uncertainty in demand to have a vision of these services performance in optimal situation. Then we compare the results and experiments with the exact optimal condition. We have designed an algorithm to find the exact solution for matching problem based on the branch and bound algorithm. To solve the second sub-problem, we define two different models to assess the impact of traffic conditions on the dynamic ride-sharing system performance for large-scale problems.

TRISTAN X, The Tenth Triennial Symposium on Transportation Analysis, Hamilton Island, AUSTRALIE, 17-/06/2019 - 21/06/2019; In the recent past, the Macroscopic Fundamental Diagram (MFD) proved to be an attractive alternative to describe the traffic states at the network level. But although complex formulations were proposed for MFD-based simulation in the literature, very few detailed validations were carried on real networks. Hence, this work focuses on a thorough validation of MFD simulators on the real field. In particular, we aim at (i) investigating the accuracy of the MFD multi-reservoir trip-based and accumulation-based models for a real large scale network by comparing the simulation results with real traffic data; and (ii) going a step further in defining proper calibration methods for the key parameters of MFD models. The network studied is the city of Lyon, which has the second greatest urban area of France, with more than 2 million inhabitants. This area is manually split into 5 reservoirs exhibiting a well defined MFD. The demand scenario was estimated for a typical weekday in a previous study based on household trip surveys and socio-demographic data. The traffic data consists of GPS trajectories of taxi fleets in Lyon, and all the loop detectors available in the area. The taxi trip data allows to determine the mean speed of each reservoir. The loop data provides a measure of the mean flow of each equipped link, which is then scaled up to each reservoir level by assuming that the measured mean flow is also representative of the non-observed links (homogeneity assumption). The average distances traveled are estimated by using shortest path calculations on the empty network (a method proposed by some of the authors in another study). A first comparison between the accumulation-based MFD simulation and the real data is presented with the evolution of accumulation in each reservoir. While providing a reliable estimation of the accumulation level in reservoir 1, the MFD simulation under-estimates the number of circulating vehicles in the other reservoirs. Different sources of errors are identified, but more investigations are still required for a better calibration of the MFD, the trip lengths and the distribution of flows.

KES-AMSTA 2019, 13th KES International Conference Agents and Multi-agent Systems: Technologies and Applications, Saint Julian's, MALTE, 17-/06/2019 - 19/06/2019; Sharing rides can be an effective solution for traffic management in populated urban areas. Real-time ride-sharing is a dynamic and complex optimization problem. Indeed, the problem data are not known a priori in a dynamic context. However, most of the approaches in the literature consider that the missing data concerns the travelers, which are revealed online. Very few consider traffic changes during optimization or execution. More precisely, they assume that the predicted travel times used during optimization remain the same when executing the vehicle schedule, which is usually not the case in practice. In this paper, we propose a multi-agent system to solve the real-time ride-sharing problem. In this system, two models are defined to deal with dynamic traffic conditions. On the one side, the currently observed average speed in the network is used to predict travel times when calculating the optimal schedule for the ride-sharing fleet. On the other side, the traffic situation is updated every 10 seconds using a simulator as the plant model to represent the real traffic dynamics. The experimental results with real data on the city of Lyon show that the proposed multi-agent system is efficient in terms of congestion reduction, especially during peak hours and if sufficient rides are shared. The system can also reduce the providers cost with a small increase in passengers waiting time and travel time.

MT-ITS 2019, 6th International Conference on Models and Technologies for Intelligent Transportation Systems, Cracovie, POLOGNE, 05-/06/2019 - 07/06/2019; Graph-based analysis has proven to be a good approach to study topological vulnerabilities of road networks through specific metrics, such as betweenness centrality (BC). Even though BC of unweighted, undirected graphs has been widely adopted to identify critical road segments and intersections, given the very high number of potentially highly-traversed paths flowing through them, congestion and vulnerability are strongly influenced also by static and dynamic context factors, such as road capacity, speed limits, travellers' behaviors, accidents, social gatherings and maintenance operations. In this paper, we focus on the analysis of BC on dynamically weighted graphs, used as a model of a road network and associated dynamic information (e.g. travel time). The aim is to discover correlations between the centrality metric and vehicle flows, both in space and in time. The analysis proves the existence of relevant spatio-temporal correlations that provide useful information about the characteristics of road networks and the behavior of drivers. In particular, we identify the existence of anti-correlations that point out forecasting properties of BC when computed on dynamic graphs.These properties justify the usage of the metric for the implementation of next-generation proactive, data-driven urban monitoring systems. These systems are expected to empower urban planners and traffic operators with novel intelligent solutions to reduce traffic congestion and vulnerability risks, therefore contributing to implement the vision of a more resilient and sustainable city.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington DC, ETATS-UNIS, 13-/01/2019 - 17/01/2019; When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ride-sharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Most of the existing approaches focus on the first point, i.e. designing the demand matching while using basic assumptions for the second point, mainly constant travel times. Furthermore, most assume that predicted travel times used for the demand-matching are observed when executing the vehicle schedule, which is usually not the case in practice. In this paper two models are defined to deal with dynamic traffic conditions: current mean speed in the network is used over the next 10 minutes to predict travel times when calculating the optimal schedule for the ride-sharing fleet. This fleet is assumed composed of autonomous cars to avoid considering constraints about the drivers. Then, cars travels are simulated and the traffic situation is updated every 10 seconds using a trip-based MFD model as the plant model to represent the traffic dynamics. Some important details are discussed: improvements in the objective functions and also traffic conditions with different values for the number of sharing, the market-rate, and pickup/drop off time window. We find out that the proposed system is really efficient in terms of reducing congestion, especially in peak hours if sufficient sharing happens. Also it can reduce the providers cost while it has small increase in passengers waiting time and travel time.

TRB 2019, 98th Annual Meeting Transportation Research Board, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; In this paper, we propose a regional dynamic traffic assignment framework for macroscopic fundamental diagram (MFD) models that explicitly accounts for trip length distributions. The proposed framework considers stochasticity on both the trip lengths and the regional mean speed. Consequently, we can define utility functions to assess the cost on alternatives, depending on which terms are considered stochastic. We propose a numerical resolution scheme based on Monte Carlo simulations and use the method of successive averages to solve the network equilibrium. Based on our test scenarios, we show that the variability of trip lengths inside the regions cannot be neglected. Moreover, it is also important to consider the stochasticity on the regional mean speeds to account for correlation between regional paths. We also discuss an implementation of the proposed dynamic traffic assignment framework on the sixth district of the Lyon network, where trip lengths are explicitly calculated. The traffic states are modeled by considering the accumulation-based MFD model. The results highlight the influence of the variability of trip lengths on the predicted traffic states.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; Multi-reservoir systems based on theMacroscopic Fundamental Diagram (MFD) look appealing to simulate traffic states at large urban scales. Nevertheless, flow limitation due to spillbacks between reservoirs is still not fully understood yet. Recently, we analyzed flow exchange properties and formulated new merging and diverging models, different from the ones previously proposed in the literature. However, both the latter and our approach received very little support from aggregated link-scale data (real or simulated one). The contribution of this study is to validate different MFD-based modeling approaches by using microscopic simulation. To this end, we focus on an artificial Manhattan network crossed by two regional paths (West-East and North-South). We then analyze the aggregated accumulation, inflow and outflow of these paths for different congestion scenarios, and compare the results with the outputs predicted by the MFD-based models. It is notably found that the outflow diverging scheme is critical to fairly reproduce microsimulation results. During congestion on-set, the widely used approach of a decreasing outflow demand with independent partial outflow treatment provides good estimation of transient states, but fails to predict reliable network unloading during congestion off-set. The modified approach we developed in our previous study takes advantage of setting the reservoir outflow demand to maximum and applying inter-dependency relationships between outflows to overcome this issue. To a lesser extend, using a demand pro-rata merging scheme, as it is usually done in the literature, would be the best option to determine partial inflows properly.

KES-AMSTA-2019, 13th KES International Conference Agents and Multi-agent Systems: Technologies and Applications 2019, Saint Julian's, MALTE, 17-/06/2019 - 19/06/2019; Sharing rides can be an effective solution for traffic management in populated urban areas. Real-time ride sharing is a dynamic and complex optimization problem. Indeed, the problem data are not known a priori in a dynamic context. However, most of the approaches in the literature consider that the missing data concerns the travelers, which are revealed online. Very few consider traffic changes during optimization or execution. More precisely, they assume that the predicted travel times used during optimization remain the same when executing the vehicle schedule, which is usually not the case in practice. In this paper, we propose a multi-agent system to solve the real-time ride-sharing problem. In this system, two models are defined to deal with dynamic traffic conditions. On the one side, the currently observed average speed in the network is used to predict travel times when calculating the optimal schedule for the ride-sharing fleet. On the other side, the traffic situation is updated every 10 s using a simulator as the plant model to represent the real traffic dynamics. The experimental results with real data on the city of Lyon show that the proposed multi-agent system is efficient in terms of congestion reduction, especially during peak hours and if sufficient rides are shared. The system can also reduce the provider's cost with a small increase in passengers waiting time and travel time.

This paper investigates how network and traffic heterogeneities influence the accuracy of a simulation based on the Macroscopic Fundamental Diagram (MFD). To this end, the MFD modeling of a simple grid network is compared to the outputs of a mesoscopic kinematic wave model simulating traffic in the same network. Heterogeneous distributions of demand and supply at the boundaries are set to the local entries and exits of the mesoscopic model to generate heterogeneous network loadings. These boundary conditions challenge the MFD simulation, as significant discrepancies are observed between both modeling approaches in steady state. While the accurate calibration of the MFD and the average trip length can reduce the discrepancies for heterogeneous demand settings, no simple solution exists for heterogeneous supply settings, because they may drive very different internal congestion patterns in the network. We propose a correction method to adjust the MFD model outputs in such a case.

In this paper, we propose a regional dynamic traffic assignment framework for macroscopic fundamental diagram (MFD) models that explicitly accounts for trip length distributions. The proposed framework considers stochasticity on both the trip lengths and the regional mean speed. Consequently, we can define utility functions to assess the cost on alternatives, depending on which terms are considered stochastic. We propose a numerical resolution scheme based on Monte Carlo simulations and use the method of successive averages to solve the network equilibrium. Based on our test scenarios, we show that the variability of trip lengths inside the regions cannot be neglected. Moreover, it is also important to consider the stochasticity on the regional mean speeds to account for correlation between regional paths. We also discuss an implementation of the proposed dynamic traffic assignment framework on the sixth district of the Lyon network, where trip lengths are explicitly calculated. The traffic states are modeled by considering the accumulation-based MFD model. The results highlight the influence of the variability of trip lengths on the predicted traffic states.

The innovative concept of intermittent bus lanes may lead to an important increase of bus system performance while limiting the reduction of the capacity devoted to general traffic. The main idea is that a general traffic lane can be intermittently converted to an exclusive bus lane. Frequently studied by analytical papers, practical demonstrations of the intermittent bus lane strategy are not numerous. Especially, the results of the two previous field tests are very specific to the test sites and are hardly transposable. This paper tries to fill this gap by proposing the results and the lessons learned of a new real-field demonstration in Lyon, France. After a detailed presentation of the 350-m case study, effects of an intermittent bus lane strategy on traffic conditions are evaluated. Then, analyses of the impacts on the bus systems performance are carefully performed and also compared to more classical bus operations: a transit signal priority strategy. The results show that an intermittent bus lane can be a promising strategy especially when it is combined with transit signal priority. The median travel time of the buses is significantly reduced whereas the regularity of the line increases.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; This work focuses on the application of so-called accumulation-based and trip-based MFD approaches to real transportation networks and discusses the calibration of the MFD shape and trip lengths estimation using a thorough validation of the network dynamics with micro-simulation data. As network state observations are often scattered close to the saturation, this work not only investigates a classical unimodal approach to fit the production MFD, but also a bimodal MFD curve. A method to integrate the production hysteresis into the MFD-based approach is briefed. Different methods of calibrating trip lengths in the reservoir are introduced to study the influence of trip lengths estimation on the accuracy of MFD models. MFD models are validated against micro-simulations that are carried out using the real OD matrix and demand that are estimated from the data of Lyon city in France. Two different scenarios namely, a free flow scenario and a network saturation scenario are considered to study the influence of calibration of production hysteresis and trip length estimation. The proposed bimodal production MFD curve captures the hysteresis in the production MFD to a good extent. Following, it is shown that the refined description of trip lengths gives more accurate estimates of accumulation evolution for the trip-based approach, whereas a single trip with mean trip length produces good evolution of state variables in accumulation-based model. Finally, a test case is presented with a modified OD matrix to study the effect of OD matrix changes on accuracy of MFD simulations.

TRISTAN X, The Tenth Triennial Symposium on Transportation Analysis, Hamilton island, AUSTRALIE, 17-/06/2019 - 21/06/2019; Ride-sharing is becoming one of the most important transportation modes in many cities due to its convenience and accessibility. But it is important to assess the impact of network on the ride-sharing performance and vice versa. This paper proposes a complete framework to represent the ride-sharing system functioning. We address the problem for a city scale of 25 km2, a total market of 11,235 shareable trips over a 4 hours period with a rolling horizon of 20 minutes. Furthermore, the solutions are assessed using a dynamic trip-based macroscopic simulation to account for congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies about optimal fleet management that usually consider constant and fully deterministic travel time functions. Then we assess the network performance under different levels of ride-sharing.

Large-scale traffic flow models based on the Network Macroscopic Fundamental Diagram (MFD) are usually grounded on the bathtub analogy and a conservation equation for vehicle accumulation inside a given urban area. Recent studies have proposed a different approach where the MFD defines the spatial mean speed that is shared by all vehicles in a region while their traveling distance is tracked individually. The former approach is also referred to as accumulation-based while the latter is usually named trip-based. While extensive studies of both model properties have been carried out for the single reservoir case (a unique region), the multi-reservoir setting still requires some research effort in particular to clearly understand how inflow merge at a reservoir entry and outflow diverge at exit should be managed. These two components play a significant role in the evolution of the whole system, when flows are exchanged between multiple reservoirs. One of the crucial questions is to ensure that congestion properly propagates backwards through a succession of reservoirs when oversaturated situations are observed. In this paper, we propose a thorough analysis of how to handle congestion propagation in the accumulation-based framework with several trip lengths or categories, e.g. internal and external trips. This allows to derive a congestion propagation model for the trip-based approach in a multi-reservoir setting. Based on theoretical considerations and simulation studies, we develop a consistent framework to restrict the inflow and adapt to oversaturated traffic conditions in a reservoir including several trip lengths. Two inflow merging schemes are investigated. The first one is inspired from the existing literature and shares the available supply based on the demand flow ratio at the entry boundary. It is called exogeneous in contrast to the second endogenous scheme, which shares the supply with respect to the internal accumulation ratio on the different routes. At the reservoir exit, a new outflow diverging scheme is also introduced to better reproduce the effect of queuing vehicles that are prevented from exiting the reservoir when congestion spills back from neighboring reservoirs. Compared to the conventional outflow model, our new approach proves to avoid unrealistic gridlocks when the reservoir becomes oversaturated. Both entry and exit flow models are investigated in details considering the accumulation-based and trip-based frameworks. Finally, the most consistent approach is compared with two other existing MFD models for multiple reservoirs. This demonstrates the importance of properly handling entry and exit flows at boundaries.

The calibration of trip lengths is an important challenge for multi-regional MFD-based applications, as it can influence the dynamics of regional densities (and speeds). Existing research has not paid significant attention in the topic, giving opportunities for answering some fundamental questions. In this paper, we propose an original methodology to explicitly determine trip length distributions based on a subset of trips in the city network and its partitioning. Since the full description of all-realized trips is-difficult-to-estimate for a large city network, we propose to define a set of virtual trips corresponding to a full coverage of potential local origin-destination pairs and shortest-paths in distance. We investigate how different levels of information can influence the accuracy of the multi-regional dynamic MFD-based models, through the estimated trip length distributions. This information ranges from regional trip lengths without any information of origin, destination, previous or next regions up to the specific regional path associated to each trip. We test this methodology on a simulated environment with a significant amount of real information for a network with 757 links that corresponds to the 6th district of Lyon. We first provide guidance on how to properly define the set of virtual trips. We show that a single average trip length is not representative of all possible trip lengths estimated by the most detailed levels of information for one region. We also highlight that the first level of information assigns similar trip lengths for regional paths that are composed by the same regions, but in a reverse ordered sequence. Nevertheless, this is not the case for the most detailed levels of information since they capture the directed links city network topology. We propose a procedure to update the trip lengths when an update of the regional Origin Destination matrix changes, without a need for a full recalculation. We also investigate the influence of the trip length tuning on the traffic dynamics of the regional network. The traffic states are modeled by a trip-based MFD model. We show that the setting of the trip lengths influences the traffic dynamics in the regions. Moreover, the traffic conditions predicted by the two most detailed levels of information are close. (C) 2019 The Authors. Published by Elsevier Ltd.