Abstract

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.

Abstract

RelStat12, 12th Annual International Conference on Reliability and Statistics in Transportation and Communications, Riga, LETTONIE, 17-/10/2012 - 20/10/2012; Along a transit line, vehicle traffic and passenger traffic are jointly subject to variability in travel time and vehicle load hence crowding. The paper provides a stochastic model of passenger physical time and generalized time, including waiting on platform and in-vehicle run time from access to egress station. Five sources of variability are addressed: (i) vehicle headway which can vary between the stations provided that each service run maintains its rank throughout the local distributions of headways; (ii) vehicle order in the schedule of operations; (iii) vehicle capacity; (iv) passenger arrival time; (v) passenger sensitivity to quality of service. The perspective of the operator, which pertains to vehicle runs, is distinguished from the user's one at the disaggregate level of the individual trip.

Abstract

It is increasingly recognized that travel time reliability is important to travellers and goods, and hence there is an increasing demand to include reliability in the evaluation and appraisal of transport projects and programs, as a separate factor in its own right. A number of the policy options already in use as congestion mitigation policies have also positive impacts on the travel time reliability. It is therefore important to evaluate impacts of any remedial action separately both for average travel time (congestion) and for variability of that travel time (reliability). The impacts are generally assessed with an observational before/after study. As many things, independent from the system to be assessed, change between the periods before and after the installation of the system, observational before/after studies take also into account a reference site, where the effect of these changes is identified. In a previous paper Bhouri and Aron, (2013) presented the reliability impacts of a managed lane case consisting in a Dynamic Hard Shoulder Running (HSR) during rush hours on a French urban motorway. The study was conducted by comparing reliability indicators based on data collected in 2002 before the implementation of the system and in 2006 after this implementation. Jacques Chirac, former president of France, launched in 2003 an important campaign for road safety and against speeding. This modified the travel time distribution, differently according to the lane. As reliability is based on the percentiles (and not only on averages), it was necessary, in order not to confound the effect of this campaign with the HSR effect, to develop the observational before/after study method for determining that time effect by level of travel time: to do this, the before and after travel time distributions of the reference site are established, then their difference is analyzed as a function of the travel time, taking into account the percentiles. This method and the results are presented in this paper. Document type: Conference object

Abstract

TRB 2014 - 93rd Transportation Research Board Annual Meeting, WASHINGTON DC, ETATS-UNIS, 12-/01/2014 - 16/01/2014; The travel time variability may significantly influence the driver's route choice. Estimating travel time reliability is thus really appealing to urban infrastructure managers. This could provide better information to drivers, encourage their reassignment on the network, and optimize its use. In this paper, we study any arterial in under-saturated conditions with known fixed-time traffic signals. The kinematic wave model is chosen to simply represent traffic dynamics. A semi-analytical (grid-free) method is applied to determine the separation between vehicle groups at each signal of the arterial. The main contribution of this paper is to present an aggregated diagram that describes the functioning of this arterial. It graphically provides the direct assessment of vehicle travel times with respect to their departure and the traffic flow. This tool only depends on signal settings and is cyclic and invariant for fixed-time signals. We show how to use this diagram to generate probabilistic travel time distributions when some input parameters are uncertain. Furthermore, this diagram appears insightful when the traffic flow is not accurately known.

Abstract

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.

Abstract

Increasing mobility and congestion results in an increase in travel time variability and in a decrease in reliability. Reliability becomes an important performance measure for transportation facilities. A variety of performance measures have been proposed to quantify it. Many of these indicators are based on percentiles of travel time. The knowledge of the distribution of travel time is needed to properly estimate these values. Congestion distorts the distribution and particular statistical distributions are needed. Different distributions have been proposed in the literature. In a previous paper, we presented a comparison of six statistical distributions used to model travel time. These six distributions are the Lognormal, Gamma, Burr (extended by Singh-Maddala), Weibull, a mixture of two Normal distributions and a mixture of two Gamma distributions. In this paper a probabilistic modeling of travel time which takes into account the levels-of-service is given. Levels of service are identified, then travel time distributions are modeled by level of service. This results in a very good fit between the empirical and modeled distributions Moreover, the adjustment was improved, thanks to the calibration of “Bureau of Public Roads” functions, linking the travel time to the traffic flow by level of service. The superiority of the Singh-Maddala distribution appears in many cases. This has been validated, thanks to travel time data from the same site at another period. However the parameters of the distributions vary from one year to another, due to changes in infrastructure. The transferability of the approach, not performed, will be based on travel time data on another site. Document type: Conference object

Abstract

Traffic Granular Flow, DELFT, PAYS-BAS, 28-/10/2015 - 30/10/2015; This paper presents a generic data assimilation framework based on a mesoscopic-LWR model formulated in Lagrangian-space coordinates and using both Lagrangian observations. This is a challenge work since probe trajectories are not directly related to specific vehicle/platoon indexes in the simulation model. Therefore, we develop a method to incorporate probe information and to further estimate states. The proposed method has been validated on a homogeneous road stretch, and it provides promising results for further extension of the framework.