Multi-agent Transport Simulation Research Articles

Dynamic Calibration of a Carsharing System in Multiagent Transport Simulation

Dynamic Calibration of a Carsharing System in Multiagent Transport Simulation

Individual-level activity-based modeling and indicators for assessing construction sites noise exposure in urban areas

Construction site noise significantly contributes to urban noise pollution. To accurately assess the noise exposure from urban construction sites, it is essential to consider the variability and dynamic nature of the urban context. This paper proposes an innovative approach using individual-level activity-based modeling to tackle the given challenge. The methodology introduced encompasses: the modeling of construction site noise emission sources, individual-level activity-based modeling of zone of influences, a modeling chain for assessing individual noise exposure levels, and a novel dedicated exposure indicator. To illustrate the relevance of our methodology, a case study is conducted in Champigny-sur-Marne, France; this study involves the generation of a synthetic population of 1,344,394 individuals distributed among 591,153 households, with 4,854,597 associated activities, using the Eqasim simulation framework. A traffic model is then established using the multi-agent transport simulation software MATSim. NoiseModelling software is employed to compute noise maps both with and without the construction site. Subsequently, the noise exposure for each individual is assessed throughout the day for both configurations. Thanks to this complete modeling chain, the proposed indicator quantifying the cumulative amount of time of exposure above a given threshold can be compute, with the case study result being estimated at more than 3,300 person.quarter-hours above 65 dB(A).

A Comparative Analysis of Time-Based and Hybrid Pricing Models for Electric Vehicle Charging

The accelerated adoption of Electric Vehicles (EVs) necessitates innovative and effective pricing strategies for charging infrastructure. This study leverages the MATSim (Multi-Agent Transport Simulation) framework to meticulously evaluate the performance of two distinctive EV charging pricing models: time-based charging and a hybrid model integrating both usage-based and time-based components. Driven by the pivotal question of how EV charging should be optimally priced—whether contingent on energy consumption or charging duration—the research endeavors to conduct two simulations. These simulations aim to provide a comprehensive comparative analysis, evaluating metrics such as total power derived from grids, utility revenue, charging station queues and served vehicles. In the time-based charging scenario, EVs incur charges based on their plugged-in duration, reflecting a pricing approach that correlates directly with the time a vehicle remains connected to the charging station. In contrast, in the hybrid model, EVs undergo initial billing based on usage until a predetermined battery charge point is reached, such as achieving a full battery. Subsequently, time-based pricing takes effect until the user disconnects the vehicle. The findings indicate that, in the combined approach, utility owners have the potential to generate more revenue. Conversely, the time-based approach demonstrates a capacity to serve a higher number of electric EVs, with comparable queue lengths observed in both approaches. Importantly, using the results of this paper, policymakers can suggest pricing schemes that maximize benefits for utility owners, reduce queues at charging stations, and ensure the security of power grids.

Multi-Agent Multimodal Transportation Simulation for Mega-cities: Application of Los Angeles

This paper presents a comprehensive modeling framework for large-scale agent-based multimodal transportation simulation tailored to the complex urban environments of megacities, with a specific focus on the Greater Los Angeles (LA) area. The framework leverages the Multi-Agent Transport Simulation (MATSim) toolkit, which addresses the evolving challenges posed by urbanization and technology-driven shifts in human mobility patterns. The framework enables the generation of explicit travel trajectories for a representative 10% sample population of LA County, comprising over one million individuals. To ensure the model’s validity, an efficient network calibration algorithm is proposed with rigorous validation using publicly available traffic count data. This paper underscores the development and configuration of the agent-based simulation model for LA, providing a robust foundation for large-scale urban mobility simulations and advancing our understanding of the intricacies of megacities.

Integration of Level 3 probabilistic risk assessment for nuclear power plants with transportation simulation considering earthquake hazards

This research improves the realism of Level 3 probabilistic risk assessment (PRA) for nuclear power plants (NPPs) to avoid subjective expert judgement by modeling the evacuation behavior of the residents and making two advancements. First, evacuation speed taken from a transportation simulation code was used as an input to the Level 3 PRA code, i.e., the MELCOR Accident Consequence Code System (MACCS). The transportation simulation code uses the Multiagent Transport Simulation (MATSim) evacuation extension software. MATSim is interfaced with MACCS, by generating input-output communication through evacuation routes and speed distributions. Integrating the agent-based transportation simulation with the Level 3 PRA code enables the explicit incorporation of spatiotemporal evacuation processes into NPP offsite risk assessment. Second, this research is the first to explicitly incorporate the probability of seismic damage to roadway bridges into Level 3 PRA of NPPs. The likelihood of seismic bridge damage, given an earthquake, is estimated using the HAZUS program developed by the Federal Emergency Management Agency (FEMA). The road closure due to seismic damage, estimated by HAZUS, is used as input to the agent-based transportation simulation, where evacuation routes and speed distributions are calculated considering the impact of seismic damage. These advancements can improve the realism of Level 3 PRA for NPPs by enhancing the evacuation analysis resolution and reducing the reliance on subjective judgment to characterize evacuation routes, speed, and seismic impact on Level 3 PRA. Integrating the transportation simulation and the natural hazard risk assessment with the Level 3 PRA code can also provide more in-depth causal insights for offsite risk management, such as recommending optimal evacuation routes in an emergency response plan. These contributions are demonstrated using the Sequoyah NPP case study adopted from the state-of-the-art reactor consequence analyses (SOARCA) conducted by the U.S. Nuclear Regulatory Commission (NRC).

Evaluation of Low-Traffic Neighborhoods and Scale Effects: The Paris Case Study

This paper investigates the socio-economic impacts of the driving restriction zone policy for the Paris region, using the multi-agent transport simulation MATSim. Based on a previous work which has calibrated a first instantiation of the mobility model to the Paris region, we extend it by modeling and simulating two forms of intermodality: Park-and-Walk and Park-and-Ride. After re-calibrating the model, we evaluate two policy scenarios featuring different scales of the restriction zone, using indicators related to mobility, traffic emissions, and social cost. The analysis puts special attention on how modal shift and emissions vary across space, that is, inside and outside the policy zone. We find that the small-scale scenario with a restriction zone limited to the Paris center leads to very limited modal shift, whereas the larger-scale policy scenario encompassing the whole of Paris shifts significantly more trips (about 1.4% of total trips) from cars to public transport and other modes. In both scenarios, traffic emissions decline in the restriction zone and its near surroundings. Far from the restriction zone, however, emissions increase in the small-scale scenario but decrease in the large-scale scenario. These differences are caused by the traffic detour, modal shift, and rebound effects, the magnitudes of which vary depending on the scale of the restriction zone. The driving restrictions also cause the social cost to increase by around 10% in both scenarios. This ultimately implies a trade-off between reducing emissions and increasing user costs, with a substantially greater efficiency (larger reduction for similar cost) in the large-scale policy scenario.

Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, also known as ride-pooling services, accommodates requests of unrelated parties with origins and destinations along the same route who agree to share the same vehicle, usually at a discounted fare. Uber and Lyft offer ride-pooling services in select markets. Compared to individual ride requests, ride-pooling services hold better promise toward easing urban congestion by reducing the number of automobiles on the road. However, their impact on traffic operations is still not fully understood. Using Birmingham, AL as a case study, this research evaluated the impact that ride-pooling services have on traffic operations using a Multi-Agent Transport Simulation (MATSim) model of the Birmingham metro area. Scenarios were developed to simulate baseline conditions (no TNC service) and ride-pooling availability with two types of ride-pooling services, namely door-to-door (d2d) and stop-based (sB) service and three fleet sizes (200, 400, and 800 vehicles). The results indicate that when TNC vehicles are added to the network, the Vehicle Kilometers Traveled (VKT) decrease by up to 5.78% for the door-to-door (d2d) service, and up to 2.71% for stop-based (sB) services, as compared to the baseline scenario (no TNC service). The findings also suggest that an increase in the size of the ride-pooling fleet results in a rise in total ride-pooling service VKT, network-wide total VKT, and detour distance. However, increasing the size of the ride-pooling fleet also results in a decrease in the ride request rejection rates, thus benefiting the customers and decreasing the vehicle empty ratio which, in turn, benefits the TNC drivers. The results further suggest that a fleet of 200 ride-pooling vehicles can meet the current demand for service in the Birmingham region at all times, thus it is the optimal ride-pooling TNC fleet size for a medium-sized city such as Birmingham.

Analysis of cost-efficient urban air mobility systems: Optimization of operational and configurational fleet decisions

With the introduction of low-noise and low-emission electric vertical take-off and landing vehicles, passenger air transportation in urban areas is becoming increasingly important. Previous studies have designed vehicle concepts based on reference mission profiles, however, without considering strategic decisions about fleet operations, such as charging infrastructure, range and battery capacity. Comprehensive cost analyses for urban air mobility fleets have been largely neglected. In this paper, we develop an optimization model to analyze the cost-efficient operation of urban air mobility systems. Strategic decisions on vehicle concepts, battery capacity, and charging infrastructure are incorporated and evaluated using a total cost of ownership approach. We consider state-of-the-art modeling approaches, including vehicle-specific parameters, for accurate calculation of energy consumption. The optimization model is applied to the multi-agent transport simulation MATSim in a case study of the Ruhr (Germany) scenario to evaluate key parameter variations.The study results indicate the feasibility of an urban air mobility system in the study region with trip costs ranging from 27 US-$ to 46 US-$ per requested trip, depending on the scenario settings. Based on parameter variations, we find that a high cruising speed has a detrimental effect on the total cost. A medium charging power level is sufficient and energy costs account for only a moderate share. The latter is in contrast to previous research results, but can be attributed to the more detailed modeling approach of vehicle-specific parameters. We propose a cost incentive system as the ground handling time of the vehicles outweighs the recharging and flight time. The overall results provide a promising basis for model extensions.

The impact of dispatching logic on the efficiency of Urban Air Mobility operations

The application of electrified propulsion systems is expected to facilitate the future operation of small aircraft in urban and suburban areas. However, in addition to the safe design of vehicles, efficient operation must also be achieved by matching passenger requests to unoccupied and suitable vehicles. In the agent-based transportation simulation MATSim (Multi-Agent Transport Simulation), a rule-based approach has been followed by assigning the nearest available vehicle to each passenger request. Sophisticated considerations such as pooling multiple passengers with identical trip requests in terms of time and location or the urgency of a request were only partially considered or not considered at all. To decrease operating costs, especially by reducing the number of passenger flights and avoiding relocation flights, appropriate dispatching is essential for efficient assignment of available vehicles to trip requests. For this purpose, the suitability of the Bipartite Matching Dispatcher (BMD), already proven for ground-based on-demand mobility concepts, was investigated for application to UAM systems by demonstrating its performance on an existing data set using a mock-up simulation and comparing it to the initial dispatcher in MATSim. Studies have not only shown the similarity between UAM and ground-based on-demand operations, but also the compatibility of BMD for high load traffic scenarios with undersized fleets. Insights on the impact of available information, for example, the prioritization of early-submitted requests over urgent requests, on the operator’s point of view can be concluded. For an efficient vehicle assignment, the consideration of passengers’ attitude towards possible comfort restrictions due to longer waiting times and incentives through price adjustments is of crucial importance.

Tenerife’s Infrastructure Plan for Electromobility: A MATSim Evaluation

According to the Canarian government’s plans, a complete decarbonization of the Canary Islands economy is foreseen from 2040 onwards, which includes the electrification of land transport in the archipelago. However, due to the current low penetration rate of electric vehicles (EVs) on the islands, the number of EVs in circulation is expected to grow significantly in the coming years. Despite this, the network of charging points in Tenerife is currently totally insufficient, which is why it is essential to carry out a study to design the network of charging points in such a way that it can absorb the entire fleet of EVs that is expected to be in place by 2040. To this end, there are studies on the capacity, in terms of parking space, available for the installation of these charging points, but to date there are no studies on this subject supported by mobility data. For this reason, a simulation of traffic in Tenerife in 2040 has been carried out using MATSim (Multi-Agent Transport Simulation) to determine the ideal places to install these charging points and to find the number of charging points needed for the network.

Analysis of Charging Infrastructure for Private, Battery Electric Passenger Cars: Optimizing Spatial Distribution Using a Genetic Algorithm

To enable the deployment of battery electric vehicles (BEVs) as passenger cars in the private transport sector, suitable charging infrastructure is crucial. In this paper, a methodology for the efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a 100% market penetration of BEVs of (around 1.3 million vehicles) in Berlin, Germany. The goal of the evaluated methodology is the development of various charging infrastructure scenarios—including public and private charging—which are suitable to cover the entire charging demand. Therefore, these scenarios are investigated in detail with a focus on the number of public charging points, their spatial distributions, the available charging power, and the necessary capital costs. For the creation of these charging infrastructure scenarios, a placement model is developed. As input, it uses the data of a multi-agent transport simulation (MATSim) scenario of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital costs of the charging points and the mean detour car drivers must undertake are used as the optimization criteria. Using these criteria, we expect to generate cost-efficient infrastructure solutions that provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found, and suitable solutions can be selected by subsequently using other criteria. Besides the generated charging scenarios for Berlin, the main goal of this paper is to provide a valid methodology, which is able to use the output data of an agent-based, microscopic transport simulation of an arbitrary city or area (or even real driving data) and calculate different suitable charging infrastructure scenarios regarding the different optimization criteria. This paper shows a possible application of this method and provides suggestions to improve the significance of the results in future works. The optimized charging infrastructure solutions for the Berlin scenario show capital costs of between EUR 624 and 2950 million. Users must cover an additional mean detour of 254 m to 590 m per charging process to reach an available charging point. According to the results, a suitable ratio between the charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50 kW) of less than ten percent seems to be sufficient if it is situated at the main traffic routes and highly frequented places.

Are we getting vehicle emissions estimation right?

Simulation is a valuable prediction tool to evaluate the impact of climate change interventions prior to disruptive implementations that may yield unintended and unwanted consequences. Advances in agent-based simulation allow us to estimate the dynamic emissions produced by vehicles at a fine resolution, with each vehicle modelled individually. The scalability of these models allow decision-makers to evaluate large-scale scenarios. But how good are these models? In this paper, the authors apply a state-of-the-art emissions model in the Multi Agent Transport Simulation (MATSim) framework to simulate individual vehicle emissions. Simulation results are compared with real driving emissions tests, using a portable emissions measurement device on a light and heavy vehicle. Quantifying the gap between macro-level simulation and actual emissions, the paper contributes by showing that pollutants are significantly underestimated, especially for heavy vehicles. This confirms prior micro-scale studies and magnifies the detrimental impact of using uncorrected aggregated models to inform environmental policy affecting transport. • Quantifying the gap in emission estimates on large-scale traffic simulations. • City-scale activity-based transport model coupled with vehicle-specific emissions. • Variation in real driving emissions is much larger than in randomised simulations. • Macro-level models underestimate heavy vehicle emissions more than light vehicles.

FABILUT: The Flexible Agent-Based Integrated Land Use/Transport Model

Integrated land-use transport models are often accused of being too complex, too coarse or too slow. We tightly couple the microscopic land use model SILO (Simple Integrated Land Use Orchestrator) with the agent-based transport simulation model MATSim (Multi-Agent Transport Simulation). The integration of the two models is person-centric. It means, firstly, that travel demand is generated microscopically. Secondly, SILO agents can query individualized travel information to search for housing or jobs (and to choose among available modes). Consequently, travel time matrices (skim matrices) are not needed anymore. Travel time queries can be done for any time of the day (instead of for one or few time periods), any x/y coordinate (instead of a limited number of zones) and take into account properties of the individual. This way, we avoid aggregation issues (e.g., large zones that disguise local differences) and we can account for individual constraints (e.g., nighttime workers who cannot commute by public transport for lack of service). Therefore, the behavior of agents is represented realistically, which allows us to simulate their reaction to novel policies (e.g., emission-class-based vehicle restrictions) and to extract system-wide effects. The model is applied in two study areas: a toy scenario and the metropolitan region of Munich. We simulate various transport and land use policies to test the model capabilities, including public transport extensions, zones restricted for private cars and land use development regulations. The results demonstrate that the increase of the model resolution and model expressiveness facilitates the simulation of such policies and the interpretation of the results.

Agent-Based Simulation Approach to Determine Safety Impacts of Demand-Responsive Transport (DRT) in Wayne County, Michigan

Road safety is one of the major concerns in transportation system management. Safety performance analyses usually assess crash frequencies and the impacts of countermeasures on the number of crashes. However, the advent of new mobility solutions makes safety evaluation more challenging; data tend to be sparse, and the impacts of such services on demand and the performance of the broader network is not completely understood. This paper attempts to fill this gap by creating a novel method to estimate the safety impacts of Demand-Responsive Transport (DRT) services. Using an agent-based mesoscopic model in the Multi-Agent Transport Simulation (MATSim) Toolkit, we simulate DRT as a new mobility solution in Wayne County, Michigan, and obtain spatial and temporal distributions of traffic volume and operating speed to predict the frequency and severity of crashes. We tested for different DRT service designs, such as fleet size, detour tolerance, and initial placement of the fleet. Our findings indicate that introducing a DRT service on its own increases vehicle kilometers traveled (VKT) by 22% and consequently the number of crashes by 17%. However, this impact could be ameliorated by increasing the detour tolerance, which can significantly increase the number of shared rides and decreases the crash frequency (10.8%) and crash severity as a result of a lower VKT (15%). The output of the proposed framework serves as a starting point of a safety performance evaluation using an agent-based simulation approach to achieve a safe and successful implementation of shared mobility services in large-scale urbanized areas.

EVALUATION OF THE IMPACTS OF AUTONOMOUS VEHICLES ON THE MOBILITY OF USER GROUPS BY USING AGENT-BASED SIMULATION

An agent-based transport simulation model is used to examine the impacts of Autonomous Vehicles (AVs) on the mobility of certain groups of people. In the state of the art, it has been found that the researchers primarily have simulation studies focusing on the impacts of AVs on people regardless of certain groups. However, this study focuses on assessing the impacts of AVs on different groups of users, where each group is affected variously by the introduction of different penetration levels of AVs into the market. The Multi-Agent Transport Simulation (MATSim) software, which applies the co-evolutionary algorithm and provides a framework to carry out large-scale agent-based transport simulations, is used as a tool for conducting the simulations. In addition to the simulation of all travellers, 3 groups of users are selected as potential users of AVs, as follow: (1) long commuters with high-income, (2) elderly people who are retired, and (3) part-time workers. Budapest (Hungary) is examined in a case study, where the daily activity plans of the households are provided. Initially, the existing daily activity plans (i.e., the existing condition) of each group are simulated and assessed before the introduction of AVs into the market. After that, the AVs are inserted into the road network, where different fleet sizes of AVs are applied based on the demand of each group. The marginal utility of the travel time spent in case of a transport mode, the AV fleet size, and the cost of the travel are the key variables that determine the use of a transport mode. The key variables are set based on the characteristics of the case study (i.e., demand) and the AVs. The results of the simulations suggest that the AVs have different degrees of influences on certain groups as demonstrated in the occurred changes on the modal share. The value of changes depends on the Value of Travel Time (VOT) of people and the used fleet size of AVs. Moreover, the influence of the traveller’s characteristics on the AVs is manifested, such as different values of fleet utilization. Furthermore, the study demonstrates that an increase in the fleet size of AVs beyond 10% of the demand does not significantly raise the modal share of AVs. The outcome of this paper might be used by decision-makers to define the shape of the AVs’ use and those groups who are interested in using AVs.

Fuel cell drive for urban freight transport in comparison to diesel and battery electric drives: a case study of the food retailing industry in Berlin

The option of decarbonizing urban freight transport using battery electric vehicle (BEV) seems promising. However, there is currently a strong debate whether fuel cell electric vehicle (FCEV) might be the better solution. The question arises as to how a fleet of FCEV influences the operating cost, the greenhouse gas (GHG) emissions and primary energy demand in comparison to BEVs and to Internal Combustion Engine Vehicle (ICEV). To investigate this, we simulate the urban food retailing as a representative share of urban freight transport using a multi-agent transport simulation software. Synthetic routes as well as fleet size and composition are determined by solving a vehicle routing problem. We compute the operating costs using a total cost of ownership analysis and the use phase emissions as well as primary energy demand using the well to wheel approach. While a change to BEV results in 17–23% higher costs compared to ICEV, using FCEVs leads to 22–57% higher costs. Assuming today’s electricity mix, we show a GHG emission reduction of 25% compared to the ICEV base case when using BEV. Current hydrogen production leads to a GHG reduction of 33% when using FCEV which however cannot be scaled to larger fleets. Using current electricity in electrolysis will increase GHG emission by 60% compared to the base case. Assuming 100% renewable electricity for charging and hydrogen production, the reduction from FCEVs rises to 73% and from BEV to 92%. The primary energy requirement for BEV is in all cases lower and for higher compared to the base case. We conclude that while FCEV have a slightly higher GHG savings potential with current hydrogen, BEV are the favored technology for urban freight transport from an economic and ecological point of view, considering the increasing shares of renewable energies in the grid mix.

Exhaust and non-exhaust emissions from today’s and future road transport: A simulation-based quantification for Berlin

The full decarbonization of the transport sector is one of the greatest challenges of the coming decades. In this study, both greenhouse gases and local air pollutant emissions are quantified for the today’s situation (reference case), a full decarbonization scenario and a transition scenario. The quantification uses the Handbook Emission factors for road transport (HBEFA, version 4.1) and the simulation framework MATSim (Multi-Agent Transport simulation). In the today’s situation, for an average car user who lives in Berlin, the CO2 footprint amounts to 3.3 tons per year. In contrast, for an average car user who lives in the Greater Berlin area, including Berlin, the CO2 footprint amounts to 4.9 tons per year. In the full decarbonization scenario, greenhouse gases are eliminated. Nevertheless, non-exhaust emissions are still present. In the full decarbonization case, PM10 is found to decrease by 13-15%, PM2.5 by 26-31% and Black Carbon by 63-68% compared to the reference case (year 2020). The applied simulation approach allows for a detailed investigation of further transition scenarios towards full decarbonization.

The Influence of Spent Time at Park-and-Ride Facility on the Travel Behavior of Workers and Shoppers

In this paper, the Park-and-Ride (P&R) facilities are considered as short time activities in the daily activity plans of travelers. The purpose of integrating the P&R system into the daily activity plans is its benefits of reducing the undesirable effects of using private vehicles, such as pollution and traffic congestion, in the city center. Previous works did not extensively study the influence of the P&R operational strategy on the travel behavior of workers and shoppers; while this study not only covers this underexamined area of research but focuses on the impact of extending the duration of the P&R facility on traveler mobility. The study is conducted by integrating 13 P&R facilities in Budapest, Hungary into the daily activity plans belonging to the traveler groups of workers and shoppers. The study points out the changes in the travel pattern when P&R is enforced to be used by travelers to hinder travelers from entering the city center or park their cars on street. Besides, a comparison is made with a previously published work to study the impact of changing the P&R activity time and the existing condition where no P&R is provided. The multi-agent transport simulation (MATSim) software is used to conduct the simulation. The results support that using the P&R system increases the total travel time, decreases the number of Vehicle-Miles-Traveled (VMT), and changes the travel pattern. Moreover, the results reveal 5.75 minutes increments in the average trip time, when the P&R duration is increased from 4.5 minutes to 10 minutes. The result led to the conclusion that the operational strategy of the P&R facilities impacts the mobility of travelers, and the P&R system changes the travel pattern, such as the duration of peak periods and the number of vehicles en route.

Potential Benefits of Increased Public Transit Ridership in Medium Sized Cities: A Case Study

At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infrastructure investments that increased road capacity further facilitated the increase in automobile use at the expense of reduced public transit ridership. With the increase of dependency on automobiles and the continuing growth of private automobile ownership and use, various problems became major challenges in big cities of USA. These include traffic congestion, air pollution, road and parking infrastructure costs, energy consumption, traffic safety, fewer mobility options for the non-drivers, and a decline in the image and use of public transit. This study uses a medium sized city, Birmingham as a case study to investigate the potential of public transit to reduce automobile trips and in turn improve the overall performance of the road network by addressing the abovementioned challenges. An agent-based simulation model was developed for the Birmingham metropolitan region using the Multi-agent Transport Simulation (MATSim) platform. Three scenarios were considered with gradually increased transit ridership to identify the benefits of increased public transit. Traffic volume, network average speed, and travel times were used as performance measures for the evaluation of the designated scenarios. Results suggest that modal shifts toward public transit and reduction in travel demand for an automobile can result in improvements in speed and travel time for all users. Therefore, investments for improving transit quality and frequency of service, as well as campaigns to improve the image of public transit and make it a mode of choice for transportation users can increase transit ridership and, in turn, improve network operations, thus are deemed worthy for medium sized cities.

Simulation of On-Demand Vehicles that Serve both Person and Freight Transport

Serving both passenger and freight demand with the same vehicle fleet is an ambition that led to the development of several innovative vehicle concepts [10, 26]. This study proposes a simulation-based methodology to investigate the execution of freight tours with an On-Demand fleet of autonomous, modular vehicles while giving priority to passenger transport. Based on assumptions regarding the operational scheme, tour pricing and delivery time windows, the software Multi-Agent Transport Simulation (MATSim) is extended. The developed methodology is then applied to a Berlin-wide freight delivery scenario. The results show that when using a relatively large vehicle fleet, passenger waiting time statistics barely change. However, it has to be noted that the study should be repeated with different (smaller) fleet sizes. A rough cost analysis for the freight operator suggests that there is a large saving potential when using autonomous On-Demand vehicles instead of an owned fleet. Because of the uncertainty of price composition, further studies to quantify this saving potential have to be made.