The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

Autonomous electric vehicles are set to replace most conventional vehicles in the near future. Extensive research is being done to improve efficiency at the individual and fleet level. There is much potential benefit in optimizing the deployment and rebalancing of Autonomous Electric Taxi Fleets (AETF) in cities with dynamic demand and limited charging infrastructure. We propose a Fleet Management System with an Online Optimization Model to assign idle taxis to either a region or a charging station considering the current demand and charging station availability. Our system uses real-time information such as demand in regions, taxi locations and state of charge (SoC), and charging station availability to make optimal decisions in satisfying the dynamic demand considering the range-based constraints of electric taxis. We integrate our Fleet Management System with MATSim, an agent-based transport simulator, to simulate taxis serving real on-demand requests extracted from the San Francisco taxi mobility dataset. We found our system to be effective in rebalancing and ensuring efficient taxi operation by assigning them to charging stations when depleted. We evaluate this system using different performance metrics such as passenger waiting time, fleet efficiency (taxi empty driving time) and charging station utilization by varying initial SoC of taxis, frequency of optimization and charging station capacity and power.

Autonomous electric vehicles are set to replace most conventional vehicles in the near future. Extensive research is being done to improve efficiency at the individual and fleet level. There is much potential benefit in optimizing the deployment and rebalancing of Autonomous Electric Taxi Fleets (AETF) in cities with dynamic demand and limited charging infrastructure. We propose a Fleet Management System with an Online Optimization Model to assign idle taxis to either a region or a charging station considering the current demand and charging station availability. Our system uses real-time information such as demand in regions, taxi locations and state of charge (SoC), and charging station availability to make optimal decisions in satisfying the dynamic demand considering the range-based constraints of electric taxis. We integrate our Fleet Management System with MATSim, an agent-based transport simulator, to simulate taxis serving real on-demand requests extracted from the San Francisco taxi mobility dataset. We found our system to be effective in rebalancing and ensuring efficient taxi operation by assigning them to charging stations when depleted. We evaluate this system using different performance metrics such as passenger waiting time, fleet efficiency (taxi empty driving time) and charging station utilization by varying initial SoC of taxis, frequency of optimization and charging station capacity and power.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

This paper analyzes how an agent-based simulation model of Stockholm can be used for water transitplanning. A new route for commuters by boat was added to the model of Stockholm’s existingtransport system and evaluated. By comparing results from the model and statistic data fromTrafikförvaltningen Region Stockholm during the morning rush-hour, a scale factor was calculated.The scale factor was later used to adjust the number of travelers on the added new water transit routeas the model underestimate the number of persons who use the available public transport by boat. Thelarge size of the calculated factor made the results uncertain when trying to predict any effects the newroute would have, e.g. on congestion in the system. Simulations of the new transit line resulted in amajority of short trips, between stations were the other public transit options took longer routes. Thetransit stops close to the city centre were used the most and most trips were conducted between them.To gain an improved ability to simulate water transit, continued studies of people’s preferencesregarding mode choice appear to be crucial.

Transport simulation models play a crucial role in transportation planning, design, and operations, allowing for the replication of various scenarios through the incorporation of real-world data and parameters. Recently, agent-based transport models have gained prominence for their ability to simulate intricate metropolitan transport systems. These models take into account the distinct characteristics, decision-making processes, and interactions of individual agents. Among the array of agent-based transport models, MATSim stands out as a potent and adaptable tool for modeling transportation systems. A critical aspect of MATSim’s input preparation involves assigning activity location points using land use raster data. However, the characteristics of land use raster data present limitations in certain urban case studies such as Stockholm. In response, some researchers have turned their attention to buildings shapefile data, a commonly used geospatial data format. This study aims to improve the activity location assignment model by developing an evaluation workflow of model uncertainty for different geospatial input data in MATSim and empirically analyzing their impacts on simulation outcomes. Despite acknowledging data availability and activity representation limitations, the study’s results demonstrate that utilizingbuildings shapefiles as input data yields more consistent outcomes with reduced uncertainty. This suggests the promising potential of buildings shapefiles as a favorable data source for transportation modeling and planning within the studied scenarios.

Past political regimes and socio-economic imbalances have led to the formation of a transport system in the Republic of South Africa (RSA) that is unique to the developing world. Affluent communities in metropolitan cities are situated close to economic activity, whereas the people in need of public transport are situated on the periphery of the cities. This demographic structure is opposite to that of developed countries and complicates both the provision of transport services and the planning process thereof. Multi-Agent Transport Simulation (MATSim) has been identified as an Agent-Based Simulation (ABS) approach that models individual travellers as autonomous entities to create large scale traffic simulations. The initial implementation of MATSim in the RSA successfully simulated private vehicle trips between home and work in the province of Gauteng, proving that there is enough data available to create a realistic multi-agent transport model. The initial implementation can be expanded to further enhance the simulation accuracy, but this requires the incorporation of additional primary and secondary activities into the initial transport demand. This study created a methodology to expand the initial implementation in the midst of limited data, and implemented this process for Gauteng. The first phase constructed a 10% synthetic population that represents the demographic structure of the actual population and identified various socio-demographic attributes that can influence an individual's travel behaviour. These attributes were assigned to the synthetic agents by following an approach that combines probabilistic sampling and rule-based models. The second phase used agents' individual attributes, and census, National Household Travel Survey (NHTS) and geospatial data to transform the synthetic population into a set of daily activity plans - one for every agent. All the agents' daily plans were combined into a plans.xml file that was used as input to MATSim, where the individuals' activity plans were executed simultaneously to model the transport decisions and behaviour of agents. Data deficiencies were overcome by contemplating various scenarios and comparing the macroscopic transport demand patterns thereof to the results of the initial implementation and to actual counting station statistics. This study successfully expanded the initial home-work-home implementation of MATSim by including additional non-work activities in the transport demand. The addition of non-work activities improved the simulation accuracy during both peak and off-peak periods, and the initial demand therefore provides an improved representation of the travel behaviour of individuals in Gauteng. / Dissertation (MEng)--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

Public transit is becoming an increasing important field of study to combat global issues such as traffic congestion and climate change. Accurate simulation of public transit is therefore likewise vital, as it is an important tool for understanding potential impacts of public transit policies. The research presented in this thesis describes the implementation of a multimodal, dynamic, agent-based supply-side simulation model of public transit implemented in the open-source platform MATSim for the city of Toronto. Transit schedule data was converted from Google Transit Feed Specification (GTFS) and map-matched to a region-wide road network to obtain a congestion-based multimodal assignment for transit. Volume-based results from the assignment showed under-prediction of subway volumes and slight over-prediction of bus volumes, but were generally comparable with static EMME/3 assignment for the same data. Travel time analysis indicated that further calibration of network specification is needed.