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Design and Analysis of a MATSim Scenario From Open Data: The Messina City Use Case
Abstract
In the last years, our cities become more and more crowded due to the increasing number of cars into old city planes. So, even small/medium cities experience a travel time comparable with the bigger ones. To improve mobility management in modern cities, specific simulation tools can be used to analyze the impact of different mobility plans on mobility and, therefore to find the most suitable solution for each city. However, these tools are often hard to be used by city traffic managers without advanced computer skills. In this article, we used a multiagent transport simulation (MATSim) to provide a simple tool that can be easily used by end-users to better plan mobility strategies for both private and public transportation. In particular, starting from the open data provided by the city of Messina, we have implemented a software tool able to process MATSim events. Moreover, we propose a metric to estimate the safety of roads for cyclists. From the experimental results provided by the proposed software, we are able to discover the most overloaded links and estimate the travel time distribution by hour of departure time.
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Peer-to-peer car sharing is a service model in which car owners let others use their vehicle while it is not in use by themselves. To simulate such a service in the agent-based transport simulation framework MATSim, a novel synchronization concept is proposed that considers limited resources in the decision-making of the agents. The concept is introduced in detail and demonstrated for peer-to-peer car sharing on a conceptual use case and a large-scale simulation for Berlin. The concept is generalizable to other constrained resources, such as parking or micromobility.
Continuous improvement in computing power allowed for an increase of the scales micro-traffic models can be used at. Among them, agent-based frameworks are now appropriate for studying ordinary traffic conditions at city-scale, but remain difficult to adapt, especially for non-computer scientists, to more specific application contexts (e.g., car accidents, evacuation following a natural disaster), that require integrating particular behaviors for the agents. In this paper, we present a built-in model integrated into the GAMA open-source modeling and simulation platform, allowing the modeler to easily define traffic simulations with a detailed representation of the driver’s operational behaviors. In particular, it allows modelling road infrastructures and traffic signals, change of lanes by driver agents and less normative traffic mixing car and motorbike as in some South East Asian countries. Moreover, the model allows to carry out city-level simulations with tens of thousands of driver agents. An experiment carried out shows that the model can accurately reproduce the traffic in Hanoi, Vietnam.
In this study, an activity-based travel demand model of the Ústí nad Labem district (Czech Republic) is created. To do this, an advanced travel demand synthesis process is presented by utilizing the Eqasim framework, which is a pipeline-processing, initial raw data to simulation step. The framework is extensively modified and extended with several algorithms in order to utilize multiple data points for increasing realism in mobility for travel demand models. Two major extensions are provided. First, the pipeline framework is improved to estimate inbound and outbound trips of the study area, comprising a main city and 23 surrounding municipalities. The extended framework assigns synthetic gates for the study area as hubs for the inclusion of inbound and outbound trips. Second, the pipeline framework is advanced to provide a more compatible match of travel destination and activity location state. To do this, the extended framework assigns a capacity for each facility identified for the study area, the expected number of visitors to each facility, and the number of residents in each building. The resulting demand model is presented and the generated trips are evaluated based on locational, transport mode, and sociodemographic characteristics with origin–destination (OD) bundling. Additionally, distribution analyses of the present model are conducted to understand the matching results on a detailed level. The results demonstrate that the present model provides a reasonable output for transport researchers when testing different mobility scenarios and the provided extensions helps them to reduce implausible reflections of the distribution of travel and activity characteristics in household travel surveys while creating demand models, thus increasing realism. Lastly, open-source playground and code repository for further future improvement of synthetic travel synthesis methods are created, which enhances a deep understanding of the preparatory and methodological backgrounds required for complex activity-based simulations in order to inspire transport planners.
For too long, many refined transportation models have focused solely on private and public transportation, assuming that bicycles only require simple models, such as bird flight distance or trips on horizontal tracks at a constant speed. This paper aims to study the impact of the road characteristics, such as road gradient, type of road and pavement surface of the road, on cyclists’ behavior using dedicated modules of MATSim. For that, we compare two approaches: a standard approach which does not consider the road characteristics, and a second approach that uses MATSim bicycle extension of Ziemke et al. The two approaches are analyzed over a sub-regional area around a district, focusing on a suburban city with an undulating relief made of average-to-steep hills. The focus is on the bicycle transportation model because the catchment area has a particularly challenging altitude profile and a large variety of roads, whether in type—from residential to national highway—or in pavement surface due to the number of green areas, such as parks and forests. This area is defined as a rather large 7 × 12 km, including five suburban cities in the South of Paris, France. A synthetic population of 126,000 agents was generated at a regional scale, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, with activity-time choice, location choice and modal choice. We wanted to know how accurately a standard model of bicycle travels can be made with a 2D flat Earth assumption by comparing it to an algorithm extension that explicitly considers road characteristics in cyclists’ route choices. Our finding is that the MATSim bicycle extension model impacts mainly the long trips. Otherwise, the differences are minimal between the two models in terms of travel time and travel distance.
This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.
In this paper, we present a software architecture that extends the MATSim mobility simulation framework by providing an external rebalancing server, which offers a set of rebalancing algorithms. This allows to implement complex rebalancing algorithms with minimal changes introduced into MATSim's code base. We are specifically interested in the potential of using algorithms based on reinforcement learning, we present and implement in our architecture a simple Q-learning rebalancing algorithm. Our initial results show the potential of this approach. Although they are not better than existing rebalancing algorithm in MATSim, this approach enables more developments in this area and the implementation of other more complex algorithms for mobility on-demand operation in MATSim.
As the level of passenger demand in rail transit systems increases, major railway stations in urban centres face serious capacity issues. Both analytical and simulation methods have been used to analyse complex station areas; however, prior efforts have only focused on either train or pedestrian movements with over-simplified assumptions that do not properly capture the impact of their interaction on capacity. This study applies an integrated crowd and transit simulation platform “Nexus” to simultaneously study the impact of pedestrian and train movements on the system performance of a complex railway station. Unlike other methods such as sequential simulation methods, the integrated simulation platform permits linkage between commercial-grade simulators. Instead of treating each simulator separately, this integrated method enables detailed modelling of how the train and crowd dynamic interact at station platforms. Such integration aims to explore the interactive effect on both types of movement and enable performance analysis possible only through this combination. To validate the model, a case study is performed on Toronto’s Union Station. Extensive data were collected, processed and input into railway and pedestrian models constructed using OpenTrack and MassMotion, respectively, and integrated via Nexus. Examining scenarios of increased levels of train and passenger volumes, a 9% drop in on-time performance of train operation is observed, while the level of service experienced by passengers on the platform deteriorates significantly due to crowding. Both length and variation in dwell time due to pedestrian movement are recognized as the main factors of performance deterioration, especially when the system approaches capacity limit. The simulation model produces estimates of the practical track-side capacity of the station and associated platform crowding levels, and helps identify locations where passengers experience severe overcrowding, which are not easily obtainable from mathematical models.
Synthetic populations of travelers and their detailed mobility behavior are an important basis for agent-based transport simulations, which are increasingly used in transport planning and research today. To date, research based on such simulations is rarely replicable as it is based on proprietary data and tools. To foster the discussion and steer research towards reproducible transport simulations, this paper introduces a process for generating a synthetic travel demand with individual households, persons, and their daily activity chains for Paris and its surrounding region Île-de-France — entirely based on open data and open software and replicable by any researcher. The resulting travel demand is published for others to use as a comprehensive data basis for agent-based transport simulations and as a test bed for population and demand synthesis algorithms. Furthermore, it is discussed how implicit correlation structures impact the potential use cases of the synthetic travel demand for simulation and analysis purposes and how the common practice of using population samples for downstream simulations affects the results.
Agent-based transport simulations rely on realistic representation of agents’ movements in the transport system, but also on the simulation of choice making processes that resemble reality well. The MATSim framework uses the scoring-based co-evolutionary algorithm to achieve this, based on the principle of utility maximization. Discrete choice models are another common tool in transport planning which rely on the same principle. Through a toy example, the paper at hand shows that the direct use of discrete choice parameters in MATSim is not a technically sound approach, but that a consistent integration can be achieved by generating deterministic pseudo-errors based on cryptographic hash functions. Further caveats with integrating choice parameters in MATSim scoring are discussed.
MATSim is an agent-based transport simulation model. In contrast to a pure dynamic traffic assignment (DTA) model, MATSim can react to more choice dimensions than route choice, which is the only choice treated by a typical DTA. Simulating the mobility and activity participation of individuals during the whole day, MATSim can additionally represent mode choice, departure time choice, and other decisions and is, therefore, policy-sensitive in terms of these choice dimensions. This allows for the analysis of a wide scope of policies. MATSim can, however, not model the choice of the sequence of activity participation nor the choice of activity participation as such. Also, choices of locations are not typically part of the modeling scope. Interventions into the transport and land-use systems may, however, be substantial such that they can effect changes in behavior in terms of these choices. To allow for the assessment of such reactions, the FEATHERS activity-based demand model is coupled with MATSim. This paper explores different options of integration and describes the development steps of the integration of FEATHERS and MATSim.
Developing an effective tsunami evacuation plan is essential for disaster risk reduction in coastal regions. To develop effective tsunami evacuation plans, real transportation network, interaction among evacuees, and uncertainties associated with future tsunami events need to be considered in a holistic manner. This study aims to develop such an integrated tsunami evacuation approach using agent-based evacuation simulation and advanced stochastic tsunami hazard assessment. As a case study, a urban area in Padang, Indonesia, threatened by tsunamis from the Mentawai–Sunda subduction zone, is adopted. The uncertainty of the tsunami hazard is taken into account by generating 900 stochastic tsunami inundation maps for three earthquake magnitudes, i.e. 8.5, 8.75, and 9.0. A simplified evacuation approach considering the evacuees moving directly to evacuation areas (defined a priori) is compared with two more rigorous agent-based modeling approaches: (a) a two-destination-point tsunami evacuation plan developed by the local government and (b) a multiple-destination-point plan developed in this study. The improved agent-based stochastic tsunami evacuation framework with multiple destinations takes advantage of the extensive tsunami hazard analyses to define safe areas in a dynamic manner and is capable of capturing the uncertainty of future tsunami risk in coastal areas. In contrast, the results clearly show that the simplified approach significantly underestimates the evacuation time, and the existing tsunami evacuation routes identified by local authorities may be insufficient to save lives.
Shared Autonomous Electric Vehicles (SAEVs) are expected to enter the transportation market in the upcoming decades. In this paper, we describe the preparation of a MATSim model for Vienna in which we add this new service as a new transportation mode. We simulate different pricing schemes for various SAEV fleet sizes and analyze their impacts. Our focus is on the impacts in regards of socioeconomic heterogeneity. One main finding of our paper is that the number of SAEV trips does not necessarily decrease for higher fares. It is instead the average travel time of SAEV rides which decreases if the service gets more expensive. Our simulation results for higher pricing schemes show that many people switch from bike or walk mode to SAEV. Public transport is also highly cannibalized by this new service regardless of the price, whereas SAEVs would always replace no more than 10% of car trips. SAEVs help reduce travel times significantly. People who do not have a car available in their household experience the greatest savings in travel time. A similar high share of SAEV trips is done by people older than 35 years. In regards of gender, our results reveal that women tend to use SAEVs for shorter trips.
Agent-based transport models such as MATSim have been developed and used for the past few decades to simulate transportation systems in different cities and are capable of accurately reproducing travel patterns at different levels of aggregation. Given their disaggregate representation of both travel demand and supply as well as the high level of spatial and temporal detail, agent-based transport models also have the potential to allow for unprecedentedly detailed and disaggregated analysis.
When simulating large study areas, it is often common to increase the replanning rate to achieve a quicker convergence. However, this comes at the cost of causing substantial oscillations in link-level dynamics which render disaggregate analysis difficult, if not impossible.
This paper tries to reconcile both objectives: favour quicker convergence as with higher replanning rates while mitigating the strong oscillations of the link-level dynamics.
Computer hardware is steadily advancing; however, simulating real urban transportation systems that serve millions of individual travelers is still a difficult task. The Multi-Agent Transportation Simulation (MATSim) is the only agent-based traffic model that includes intrinsic downscaling - procedures of changing network parameters in order to simulate the dynamics of the system as a whole while activating only a fraction k of travelers. In this paper, we present the MATSim’s downscaling procedure and compare the dynamics of car traffic in the downscaled and full-scaled scenarios of the Sioux Falls test case. We compare aggregate and disaggregate statistics that represent Sioux Falls daily traffic, focusing on the morning peak. We conclude that downscaling up to k = 0.25 preserves all major statistics of urban traffic, within the interval of k between [0.1, 0.25]. Some of the statistics replicate well the statistics of the full-scaled runs, while downscaling below k = 0.1 can easily result in substantial deviations from the dynamics of the full-scale model.
Coupling activity-based models with dynamic traffic assignment appears to form a promising approach to investigating travel demand. However, such an integrated framework is generally time-consuming, especially for large-scale scenarios. This paper attempts to improve the performance of these kinds of integrated frameworks through some simple adjustments using MATSim as an example. We focus on two specific areas of the model—replanning and time stepping. In the first case we adjust the scoring system for agents to use in assessing their travel plans to include only agents with low plan scores, rather than selecting agents at random, as is the case in the current model. Secondly, we vary the model time step to account for network loading in the execution module of MATSim. The city of Baoding, China is used as a case study. The performance of the proposed methods was assessed through comparison between the improved and original MATSim, calibrated using Cadyts. The results suggest that the first solution can significantly decrease the computing time at the cost of slight increase of model error, but the second solution makes the improved MATSim outperform the original one, both in terms of computing time and model accuracy; Integrating all new proposed methods takes still less computing time and obtains relatively accurate outcomes, compared with those only incorporating one new method.
The work presented in this paper concerns a switching-based control formulation for multi-intersection and multiphase traffic light systems. A macroscopic traffic flow modeling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of adaptive dynamic programming (ADP). The main advantage of the switching-based formulation is its capability to determine both "when"' to switch and "which" mode to switch on without the need to use the cycle-based average flow approximation typical of state-of-the-art formulations. In addition, the framework can handle different cycle times across intersections without the need for synchronization constraints and, moreover, minimum dwell-time constraints can be directly enforced to comply with minimum green/red times in each phase. The simulation experiments on a multi-intersection and multiphase traffic light systems are presented to show the effectiveness of the method.
Conventional public transportation (CPT) services in rural areas are among others confronted with the accessibility problem and low traveler quantities. Demand responsive transportation (DRT) is an emerging mainstream concept, which aims to offer flexible mobility services to customers and also to solve the aforementioned problems of CPT providers. Studies and simulations about the comparison of CPT and DRT mostly focus on urban areas. Therefore, this paper is a case study of CPT vs. DRT services in the rural town of Colditz: A stop-based and a door-to-door DRT service is analyzed along with a CPT bus line.
With more diverse transport policies being proposed and the advent of novel transport services and technologies, the transport system is becoming more individualized in many aspects. Transport models, the most important tool to assess policies and schemes, need to be sufficiently expressive to address these developments. Agent-based transport models, where travelers with individual properties and the ability to act and decide autonomously are resolved individually, allow to appropriately model and analyze such policies. This paper describes the MATSim Open Berlin Scenario, a transport simulation scenario for the Berlin metropolitan area implemented in the agent-based transport simulation framework MATSim. The scenario is solely based on open data and the demand for transport is created based on a fully synthetic procedure. Contrary to most transport simulation scenarios, no information from a travel diary survey is required as input. As such, the scenario generation procedure described in this study is spatially transferable and facilitates the creation of agent-based transport simulation scenarios for arbitrary regions.
In recent years many authorities have considered heavy vehicle restrictions, or complete bans, in certain parts of the city to alleviate both congestion and other externalities. But such restrictions are often implemented without regard for the true impact, both intended and unintended. One reason is because anticipating the effects, through modelling, is not easily achieved. As but one consequence, such bans are often implemented but not well-enforced. In this paper we show how an agent-based simulation is used to study the efficacy of truck bans. That is, studying if the combination of the probability of being caught, and the size of penalty "if caught" violating the restriction, has the intended effect of getting heavy vehicles to abide by the restriction(s). The results show that the behaviour of heavy vehicles is more sensitive to enforcement efficiency than the size of the penalty.
In other words, using the proverbial "stick" as a motivator instead of a carrot, just "using the stick" has more effect than the size of the stick, no matter how much you wave it around.
Among the new transportation services made possible by the introduction of automated vehicles, automated mobility-on-demand
(AMoD) has attracted a lot of attention from both industry and researchers. AMoD provides a service similar to
taxi or ride-sharing services, while being driverless. It is expected to attract a huge fraction of travelers currently using mass
transit or private vehicles and will have a disruptive effect on urban transportation. While most studies have focused on the
operational efficiency of the technology itself, our work aims to investigate its impact on urban mobility. Our contribution is
two-fold. First, we present a flexible AMoD modeling and simulation framework developed within a multi-modal agent-based
urban simulation platform (SimMobility). The framework allows the detailed simulation and assessment of different AMoD
operations together with an activity-based framework that accounts for changes in demand, such as activity participation,
trip making, mode, destination, or route choice decisions. Second, we focus our attention on the role of mass transit in a
futuristic urban system where AMoD is widely available. Mass transit is already challenged by current ride-sharing services,
for example, Uber and Lyft, which provide comparatively better and cheaper services. This trend will plausibly be exacerbated
with the introduction of AMoD, which may indirectly act as a replacement to mass transit. Our simulation results show that
mass transit is irreplaceable, despite the high efficiency of AMoD, in order to avoid congestion and maintain a sustainable
urban transportation system with acceptable levels of service.
The simulation of large-scale scenarios requires high-performance simulations. MATSim, an agent-based transport simulation, is increasingly reaching limits. The traditional approach is to scale the scenarios, i.e. simulating only 10% of a population instead of 100%. This paper suggests MacroSim, a macroscopic mobility simulation module for MATSim, to overcome the current per- formance limits within MATSim. It uses volume-delay functions to estimate travel times for links based on experienced usages of these links. This allows to decouple agents and thus allows a parallelization of the mobility simulation within MATSim per design. A preliminary implementation of MacroSim showed promising results (7 to 50 times faster than the current mobility simulation depending on the scenario size). Given its limitations - most important no back propagation of traffic congestion - MacroSim is suggested as a complementary mobility simulation to the current implementation for cases where scenario size and simulation performance are more important than precise traffic dynamics.
In many large cities, public transit systems have been carrying an ever-increasing burden of commuters. In such systems, service disruptions can negatively impact system performance and transit users well after they are resolved. Currently, transit agencies handle these disruption episodes in an ad-hoc fashion, largely due to the lack of adequate analytical tools to aid in analyzing and selecting appropriate response strategies. This paper presents a proof-of-concept case study of the Greater Toronto transit network using Nexus, a new crowd dynamics and transit network simulation platform. Nexus enables detailed simulation of all transit system actors using a novel method of linking together established simulators of surface transit, fully separated rail transit, and stations. Transit users, as agents in the model, move between the different simulators and have their routes determined by an external dynamic routing module. The case study focuses on interfacing Nexus with a commercial pedestrian simulator, MassMotion, to allow for detailed crowd simulation at key stations, and illustrating how the platform could be used for disruption management. To this end, the impact of disruptions of various lengths was analyzed, and a simple response strategy was implemented to provide an example of how the system could be used to test mitigating strategies.
Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale peoplecentric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual's day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-The-Art integrated agent-based demand and supply model-SimMobility-for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobilitysensitive behavioral models in a multiple time-scale structure comprising three simulation levels: (a) a long-Term level that captures land use and economic activity, with special emphasis on accessibility; (b) a midterm level that handles agents' activities and travel patterns; and (c) a shortterm level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-Term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.
The paper presents research on large-scale microscopic simulation of taxi services in Berlin and Barcelona based on floating car data collected by local taxi fleets. Firstly, Berlin’s and Barcelona’s taxi markets are shortly described and the demand and supply data obtained from FCD analysed. Secondly, the online taxi dispatching problem formulation for this specific case is given, followed by the definition of two real-time rule-based heuristics used to dispatch taxis dynamically within the simulation. Finally, the simulation setup in MATSim is described, and the results obtained with both heuristics are analysed and compared in terms of dispatching performance, proving the effectiveness of the second strategy at different demand and supply scales. This paper is an extended version of Maciejewski and Bischoff 2015, where only the Berlin case study was presented.
Microscopic traffic simulators are the most advanced tools for representing the movement of vehicles on a transport network. However, the energy spent in traffic microsimulation has been mainly oriented to cars. Little interest has been devoted to more sophisticated models for simulating transit systems. Commercial software has some options to incorporate the operation of transit vehicles, but they are insufficient to properly consider a real public transport system. This paper develops an Application Programming Interface, called MIcroscopic Simulation of TRANSIT (MISTRANSIT), using the commercial microsimulator PARAllel MICroscopic Simulation. MISTRANSIT makes advances in three ways: public transport vehicles can have new characteristics; passengers are incorporated and traced as individual objects; and specific models represent the interaction between passengers and vehicles at stops. This paper presents the modelling approach as well as various experiments to illustrate the feasibility of MISTRANSIT for studying policy operations of transit systems.
Several recent mass evacuations, including those in advance of Hurricane Katrina in New Orleans and Hurricane Rita in Houston, have demonstrated the effects of limited planning for carless populations. The lack of planning left a significant portion of the mobility-limited population of both these cities unable to flee in advance of the storms. Since 2005, however, both of these cities (as well as others across the United States) have developed transit-assisted mass evacuation plans at various levels of detail. Because these plans are relatively recent and do not have a history of experience on which to base their performance, it is difficult to know how well, or even if, they will work. This article describes one of the first attempts to systematically model and simulate transit-based evacuation strategies. In it, the development of and the results gained from an application of the TRansportation ANalysis and SIMulation System (TRANSIMS) agent-based transportation simulation system to model assisted evacuation plans of New Orleans are described. In the research, average travel time and total evacuation time were used to compare the results of a range of conditions over a two-day evacuation period, including two alternative transit evacuation routing plans and four alternative network loading scenarios. Among the general findings of the research was that the most effective scenarios of transit-based evacuation were those that were carried out during time periods during which the auto-based evacuation was in its “lull” (nonpeak/overnight) periods. These conditions resulted in up to a 10% reduction in overall travel time and up to 45% reduction in the total evacuation time when compared to peak evacuation conditions. It was also found that routing buses to alternate arterial routes reduced the overall travel time by up to 52% and the total evacuation time by up to 14%.
Urban transportation system is a large complex nonlinear system. It consists of surface-way networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and pedestrians. Traffic congestions occur frequently, which affect daily life and pose all kinds of problems and challenges. Alleviation of traffic congestions not only improves travel safety and efficiencies but also reduces environmental pollution. Among all the solutions, traffic signal control (TSC) is commonly thought as the most important and effective method. TSC algorithms have evolved quickly, especially over the past several decades. As a result, several TSC systems have been widely implemented in the world, making TSC a major component of intelligent transportation system (ITS). In TSC and ITS, many new technologies can be adopted. Computational intelligence (CI), which mainly includes artificial neural networks, fuzzy systems, and evolutionary computation algorithms, brings flexibility, autonomy, and robustness to overcome nonlinearity and randomness of traffic systems. This paper surveys some commonly used CI paradigms, analyzes their applications in TSC systems for urban surface-way and freeway networks, and introduces current and potential issues of control and management of recurrent and nonrecurrent congestions in traffic networks, in order to provide valuable references for further research and development.
In contrast to aggregated macroscopic models of traffic simulation, multi-agent microscopic models, such as MATSim, enable modeling of individual behavior and facilitate more detailed traffic analysis. However, such detailed modeling also leads to an increased computational burden, such that simulation performance becomes critical.
This paper looks specifically at the MATSim simulation framework and proposes several ways to improve its performance. This is achieved through a combination of several approaches, including reducing disk access, decoupling computational tasks, and making use of parallel computing. Additionally, for the traffic simulation, an event-based model is adopted instead of a fixed-increment time advance approach.
Experiments show that by applying these methods, a simulation speedup of four times and more is achieved (depending on the scenario) when compared to the current Java micro-simulation in MATSim.
Initial simulation experiments on a high-resolution navigation network of Switzerland – containing around one million roads and 7.3 million agents – demonstrate that real-world scenarios can now be executed in around one-and-a-half weeks using the improved model. Ways to further shorten the computational time of MATSim are also described.
This paper explores the integration of freight traffic into an agent-oriented simulation system for large scale traffic simulations. An agent type for freight operators is described, and it is implemented with a minimal interface to the existing simulation system. Simulating freight traffic with passenger traffic requires to consider different temporal planning horizons, as well as a traffic flow model with heterogeneous vehicle fleets.
This paper presents a transit simulation model designed to support evaluation of operations, planning and control, especially in the context of Advanced Public Transportation Systems (APTS). Examples of potential applications include frequency determination, evaluation of real-time control strategies for schedule maintenance and assessing the effects of vehicle scheduling on the level of service. Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems taking into account the stochasticity due to interactions with road traffic. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world high-demand bus line in the Tel-Aviv metropolitan area under various scenarios. The headway distributions at two stops are compared with field observations and show good consistency between simulated and observed data.
Strategic traffic management is crucial for combating traffic congestion at the macroscopic level. However, such a field is still relatively unexplored, particularly for microscopic control objects, such as intersections and coordinated intersection groups. This article proposes a human-in-the-loop recommendation system for strategic urban traffic management, which follows an agent-based structure. A regional agent dispatcher is defined to assign agents for operation whenever “operation on-demand” is required. Such a requirement is identified by a daily-dependent operational mode on strategic traffic operations at a control object level. The strategic management scheme for each control object is guided by a strategic agent (customized), which is essentially a deep recommender model with a specific architecture. By featuring the multiagent design, a customized operational scheme can be generated at the intersection level, which instructs the corresponding controller to take specific operations. The utility of the recommendation system is demonstrated via a case study using real-world traffic data. In both offline and online evaluations, the system performs consistently at traffic operational recommendations in different scenarios and has the potential to provide more reasonable traffic operational strategies than a human-operated system.
Large scale simulations of transportation networks can yield highly valuable insights into the design decisions needed to deliver the best possible transportation service. Current technologies, however, are generally unable to provide support for large scale simulations as they often require impractical amounts of time to compute results. To address this issue, we have developed Hermes, a novel simulation core for MATSim designed as an alternative to QSim. Using an event-driven approach, as well as numerous optimizations to the data structures and the simulation logic, Hermes is capable of completing simulations of larger scale scenarios within feasible time frames. We demonstrate Hermes’ capabilities through extensive experiments as well as through production level results obtained from the Swiss Federal Railways (SBB), where Hermes is currently being used in production.
With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.
Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.
Activity-based models appeared as an answer to the limitations of the traditional trip-based and tour-based four-stage models. The fundamental assumption of activity-based models is that travel demand is originated from people performing their daily activities. This is why they include a consistent representation of time, of the persons and households, time-dependent routing, and microsimulation of travel demand and traffic. In spite of their potential to simulate traffic demand management policies, their practical application is still limited. One of the main reasons is that these models require a huge amount of very detailed input data hard to get with surveys. However, the pervasive use of mobile devices has brought a valuable new source of data. The work presented here has a twofold objective: first, to demonstrate the capability of mobile phone records to feed activity-based transport models, and, second, to assert the advantages of using activity-based models to estimate the effects of traffic demand management policies. Activity diaries for the metropolitan area of Barcelona are reconstructed from mobile phone records. This information is then employed as input for building a transport MATSim model of the city. The model calibration and validation process proves the quality of the activity diaries obtained. The possible impacts of a cordon toll policy applied to two different areas of the city and at different times of the day is then studied. Our results show the way in which the modal share is modified in each of the considered scenario. The possibility of evaluating the effects of the policy at both aggregated and traveller level, together with the ability of the model to capture policy impacts beyond the cordon toll area confirm the advantages of activity-based models for the evaluation of traffic demand management policies.
This paper shows an application of the multiagent, activity-based transport simulation MATSim to evaluate equity effects of a congestion charging scheme. A cordon pricing scheme was set up for a scenario of the city of Zurich, Switzerland, to conduct such an analysis. Equity is one of the most important barriers toward the implementation of a congestion charging system. After the challenges posed by equity evaluations are examined, it is shown that agent-based simulations with heterogeneous values of time allow for an increased level of detail in such evaluations. Such detail is achieved through a high level of disaggregation and with a 24-h simulation period. An important difference from traditional largescale models is the low degree of correlation between travel time savings and welfare change. While traditional equity analysis is based on travel time savings, MATSim shows that choice dimensions not included in traditional models, such as departure time changes, can also play an important role in equity effects. The analysis of the results in light of evidence from the literature shows that agent-based models are a promising tool to conduct more complete equity evaluations not only of congestion charges but also of transport policies in general.
The car still represents the bulk of urban transport in large cities. However the weight of the car tends to be reduced as a result of public policies, the emergence of new transport alternatives and the increasing cost of use of private vehicles. Public transport are not enough to resolve the problems of urban mobility and soft modes (walking and cycling) being relevant only for short distances and mild weather conditions. Nowadays, the collaborative models (carpooling and car-sharing) are increasingly popular and appear among the possible solutions. In this paper, we present new extensions of MATSim to support modeling and simulating carpooling and car-sharing trips. The new modules are totally integrated in MATSim and we carried out a validation study of our results with real data collected in the Grater Region.
The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
The transportation sector is one of the major energy consumers in most energy systems and a large portion of the energy demand is linked to road transport and personal vehicles. It accounted for 32.8% of the final energy consumption of Croatia in 2011 making it the second most energy demanding sector. Because of their higher efficiency, a modal switch from conventional ICE (internal combustion engines) to EVs(electric vehicles ) has the potential to greatly reduce the overall energy demand of the transport sector. Our previous work has shown that a transition to EVs in a combination with a modal split from air and road to rail transport can reduce the energy consumption in Croatia by 99 PJ, which is approximately 59%, by the year 2050 when compared to the business as usual scenario. The goal of this paper is to model the hourly distribution of the energy consumption of EVs and use the calculated load curves to test their impact on the Croatian energy system. The hourly demand for the transport sector has been calculated using the agent-based modelling tool MATSim on a simplified geographic layout. The impact EVs have on the energy system has been modelled using EnergyPLAN.
The agent-based microsimulation modeling technique for transportation planning is rapidly developing, is being applied in practice, and is attracting considerable attention. Along with the conventional four-step modeling technique, MATSim and EMME/2 represent two genres of traffic assignment. They are built on different theoretical bases: dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment, respectively. A study was done of the models' application with data from the Greater Toronto and Hamilton area network in Canada. Given the actual demand data, the models' assignment results are compared and validated on the basis of four indicators of the road network-travel time, travel distance, link volume, and link speed-to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results show that numerical outputs produced by MATSim are not only compatible with those by EMME/2 but are also more realistic from a temporal point of view. The agent-based microsimulation model can be an appropriate alternative to the conventional model for transportation planning. Therefore, agent-based microsimulation models reflect a promising direction of next-generation transportation planning models.
The activity-based multiagent simulation toolkit MATSim adopts a coevolutionary approach to capturing the patterns of people's activity scheduling and participation behavior at a high level of detail. Until now, the search space of the MATSim system was formed by every agent's route and time choice. This paper focuses on the crucial computational issues that have to be addressed when the system is being extended to include location choice. This results in an enormous search space that would be impossible to explore exhaustively within a reasonable time. With the use of a large-scale scenario, it is shown that the system rapidly converges toward a system's fixed point if the agents' choices are per iteration confined to local steps. This approach was inspired by local search methods in numerical optimization. The study shows that the approach can be incorporated easily and consistently into MATSim by using Hager-strand's time-geographic approach. This paper additionally presents a first approach to improving the behavioral realism of the MATSim location choice module. A singly constrained model is created; it introduces competition for slots on the activity infrastructure, where the actual load is coupled with time-dependent capacity restraints for every activity location and is incorporated explicitly into the agent's location choice process. As expected,. this constrained model reduces the number of implausibly overcrowded activity locations. To the authors' knowledge, incorporating competition in the activity infrastructure has received only marginal attention in multiagent simulations to date, and thus, this contribution is also meant to raise the issue by presenting this new model.
Creating an open MATSim scenario from open data: The case of Santiago de Chile
- B Kickhöfer
- D Wesemeyer
- K Turner
- A Tirachini