Distributed agent-based traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%

SimMobility Short-Term: An Integrated Microscopic Mobility Simulator

This paper presents the development of an integrated microscopic mobility simulator, SimMobility Short-Term (ST). The simulator is integrated because its models, inputs and outputs, simulated components, and code base are integrated within a multiscale agent- and activity-based simu- lation platform capable of simulating different spatiotemporal resolutions and accounting for different levels of travelers’ decision making. The simulator is microscopic because both the demand (agents and its trips) and the supply (trip realization and movements on the network) are microscopic (i.e., modeled individually). Finally, the simulator has mobility because it copes with the multimodal nature of urban networks and the need for the flexible simulation of innovative transportation ser - vices, such as on-demand and smart mobility solutions. This paper follows previous publications that describe SimMobility’s overall framework and models. SimMobility is an open-source, multiscale platform that considers land use, transportation, and mobility-sensitive behavioral models. SimMobility ST aims at simulating the high-resolution movement of agents (traffic, transit, pedestrians, and goods) and the operation of different mobility services and control and information systems. This paper presents the SimMobility ST modeling framework and system architecture and reports on its successful calibration for Singapore and its use in several scenarios of innovative mobility applications. The paper also shows how detailed performance measures from SimMobility ST can be integrated with a daily activity and mobility patterns simulator. Such integration is crucial to model accurately the effect of different technologies and service operations at the urban level, as the identity and preferences of simulated agents are maintained across temporal decision scales, ensuring the consistency and accuracy of simulated accessibility and performance measures of each scenario.Singapore. National Research Foundation (CREATE program)Singapore-MIT Alliance. Center. Future Urban Mobility Interdisciplinary Research Grou

Optimizing Transportation Systems with Information Provision, Personalized Incentives and Driver Cooperation

Poor performance of the transportation systems has many detrimental effects such as higher travel times, increased travel costs, higher energy consumption, and greenhouse gas emissions, etc. This thesis optimizes the transportation systems by addressing the traffic congestion problem and climate change impact resulting from the inefficient operation of these systems. I first focus on the key player of the transportation systems e.g., human being/traveler, and model travelers\u27 route choice behavior with real-time information. In this study, I define looking-ahead behavior in route choice as a traveler\u27s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Subjects participated in route-choice experiments in a driving simulator as well a PC-based environment. Three types of maps in increasing levels of complexity and information availability are used. Aggregate data analysis shows that network complexity negatively affects subjects\u27 ratio of choosing the risky route given an experiment environment. Higher cognitive load in the driving simulator results in a higher level of risk aversion than in the PC-based environment for the simplest map. I specify and estimate a mixed logit model with two latent classes, looking-ahead and myopic, taking into account the panel effect. The estimated latent class membership function suggests that some subjects can look ahead while others are myopic in making their route choices, and drivers learn to look ahead over time. The experiment environment plays a role in the risk attitude of myopic subjects. A bias against information is found for subjects who look ahead, however, is not significant among myopic subjects. I then shift my focus to influencing the travel patterns of individual travelers to reduce the energy and environmental impacts of the transportation sector. I present the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation systems. From the user\u27s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives each with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated to the available alternatives, in order to minimize the system-wide energy consumption, under a limited token budget. I present a method to solve this complex optimization problem and describe the system architecture, the multimodal simulation-based optimization model and the heuristic method for the on-line computation of the optimized token allocation. I then present the framework with the simulation results. Finally, I optimize the systems travel time by addressing the equity issue of congestion pricing. I propose an alternative approach to an equitable and Pareto-improving transportation systems based on cooperation among travelers assisted by defector penalty. Theoretical analysis shows the existence condition of the cooperative scheme for heterogeneous value of time (VOT) of travelers. I formulate a mathematical programming problem for the optimal cooperative scheme problem in a general network with Pareto-improving constraints and practical considerations on the length the cooperation cycle. I then conduct computational tests on a simple network and evaluate the solutions in terms of efficiency improvement (total system travel time) and equitability (Gini index)

A Software-Agnostic Agent-based Platform for Modelling Emerging Mobility Systems

Due to the rapidly accelerated innovation cycle in transport and the emergence of new mobility concepts and technologies, public authorities, policy makers, and transport planners are currently in need of the tools for sustainable spatial and transport planning in the new mobility era. In this paper, a new modular, software-agnostic and activity-based spatial and transport planning platform is designed, i.e, the HARMONY Model Suite, that facilitates a novel integration of new and existing spatial and transport modelling tools. The paper focuses on describing the architecture of the platform and its passenger mobility simulation framework, which integrates -in an interoperable manner- activity-based models, mobility service management, and traffic simulation tools for evaluating new mobility system dynamics. The service management controllers for new mobility concepts are discussed in more detail with regards to their functionality and applicability

Sustainable Travel Incentives Optimization in Multimodal Networks

Tripod, an integrated bi-level transportation management system, is a smartphone application from the potential user’s point of view which would be instantly accessed prior to performing the trip. Since there are constantly several alternatives for any trip, Tripod considers a series and combination of various parameters, including departure time, mode and route, and rewards for each alternative with a number of redeemable points for goods and services called “Tokens”. The framework responsible for computing the optimized number of tokens awarded to the set of available alternatives in order to minimize the system-wide energy consumption under a constrained Token budget, is the System Optimization (SO) implemented in Tripod. To do so, a higher number of tokens would be awarded to the alternatives, guaranteeing a larger energy saving, less energy consumption, alternatively. SO is multimodal whereby public transit, private car, carpooling, etc. are being considered as the potential travel modes. Furthermore, SO is dynamic, predictive and personalized: the same alternative is rewarded differently, depending on the current and predicted future condition of the network and on the individual’s profile. In order to solve this problem, a multimodal simulation-based optimization model will be elaborated

A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles

Time-sensitive parcel deliveries, shipments requested for delivery in a day or less, are an increasingly important research subject. It is challenging to deal with these deliveries from a carrier perspective since it entails additional planning constraints, preventing an efficient consolidation of deliveries which is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo-hitching. While there are various possible system designs, it is crucial that such solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of Mobility-On-Demand services to perform same-day parcel deliveries. For this purpose, we use SimMobility, a high-resolution agent-based simulation platform of passenger and freight flows, applied in Singapore. E-commerce demand carrier data are used to characterize simulated parcel delivery demand. Operational scenarios that aim to minimize the adverse effect of fulfilling deliveries with Mobility-On-Demand vehicles on Mobility-On-Demand passenger flows (fulfillment, wait and travel times) are explored. Results indicate that the Mobility-On-Demand services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle-kilometers-travelled without compromising the quality of Mobility On-Demand for passenger travel.Comment: 19 pages, 4 tables, 7 figures. Submitted to Transportation (Springer

Patterns to distribute mobility simulations

Travelers mobility simulation is a powerful tool to test strategies in a virtual environment, without impacting the quality of the real traffic network. However, existing mobility multiagent and micro-simulations can only consider a sample of the real volumes of travelers, especially for big regions. With distributed simulations, it would be easier to analyze and predict the status of nowadays networks. This kind of simulations requires big computational power and methods to split the simulation between several machines. This work describes how to achieve such a distribution in a microscopic simulation context, and compare our results with a previous work on macro-scopic simulation