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To reduce the congestion and pollution in urban cities, the political authorities encourage the modal shift from private cars in favor of sustainable trip behaviors such as intermodality (through combinations of private cars and public transport). Coercive decisions such as urban tolls are also an increasingly investigated solution. To avoid the cost of toll taxes, agents thus select intermodal transportation modes (private cars and public transport) by parking their vehicles in park-and-ride (PR) facilities at the entrance to the area toll. This paper proposes a methodology for an agent-based model (ABM), particularly a model called utility-based agent, to reproduce intermodal trip behaviors in a city and to assess the impact of an urban toll. In this context, we focus on multinomial logit models, coupled with the agent-and-activity simulation tool MATSim, is used to determine the modal choice for each agent. Based on open data (for European Metropolis of Lille, MEL), the simulation shows that 20 € ( 21.75 $) of toll tax is sufficient to reduce by 20 % the use of private vehicles. [ABSTRACT FROM AUTHOR]

The e-commerce sector's rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO2 emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts. [ABSTRACT FROM AUTHOR]

With improved technology and supported public policy, electric vehicles (EVs) are coming back in one-way carsharing from the 2010s. Although the addition of EVs offers a way to achieve carbon neutrality, the shorter vehicle range and longer charging time of EVs pose a greater challenge to the operation of one-way carsharing than fuel vehicles. Methods such as trial-and-error testing or mathematical models have difficulty in handling complex systems with mutual feedback between demand and supply. Therefore, this paper builds a one-way electric carsharing model and integrates it into an open-source multi-agent transport simulation platform (MATSim) to study its supply and demand relationship. A Shanghai baseline scenario was built to validate the model and test the impacts of vehicle range, charging rate, and power supply mode on carsharing demand. The results show that: (1) Vehicle range expansion and charging rate improvement have less impact on carsharing demand. The current vehicle range and charging rate can meet the daily use in Shanghai. (2) When the power supply mode changes from charging piles to battery swapping, the carsharing usage decreases slightly (-3%), while the carsharing trip characteristics remain almost the same. This means that operators could use battery swapping for power supply. This study provides suggestions for electric carsharing operators in Shanghai, as well as a simulation tool for more operators to test the supply and demand relationship of electric carsharing. [ABSTRACT FROM AUTHOR]

Abstract Urban settings require a thorough understanding of traffic patterns to best manage traffic, be prepared for emergency scenarios and to guide future infrastructure investments. In addition to analyzing collected traffic data, traffic modeling is an important tool that often requires detailed simulations that can be computationally intensive and time-consuming. A well-known comprehensive simulation framework is MATSim. On the other hand, simpler shortest-path routing systems that compute trips on an individual basis promise faster computations. The primary focus of this study is to assess the viability of a fast shortest path routing system as a method of traffic simulation. This study compares the MATSim with the Graphhopper routing system. Key metrics include travel time accuracy, congestion levels, route similarity, vehicle miles traveled, and average travel time. By analyzing these metrics, this study shows that a shortest-path routing system can serve as an effective and expedient approximation of more resource intensive simulation frameworks. This has significant implications for authorities and planners, as it offers a quick and efficient tool for traffic management and decision-making during critical events, enhancing their ability to respond quickly and effectively to dynamic traffic conditions.

Urban settings require a thorough understanding of traffic patterns to best manage traffic, be prepared for emergency scenarios and to guide future infrastructure investments. In addition to analyzing collected traffic data, traffic modeling is an important tool that often requires detailed simulations that can be computationally intensive and time-consuming. A well-known comprehensive simulation framework is MATSim. On the other hand, simpler shortest-path routing systems that compute trips on an individual basis promise faster computations. The primary focus of this study is to assess the viability of a fast shortest path routing system as a method of traffic simulation. This study compares the MATSim with the Graphhopper routing system. Key metrics include travel time accuracy, congestion levels, route similarity, vehicle miles traveled, and average travel time. By analyzing these metrics, this study shows that a shortest-path routing system can serve as an effective and expedient approximation of more resource intensive simulation frameworks. This has significant implications for authorities and planners, as it offers a quick and efficient tool for traffic management and decision-making during critical events, enhancing their ability to respond quickly and effectively to dynamic traffic conditions. [ABSTRACT FROM AUTHOR]

This study employs the MATSim agent-based simulation model to analyse bike-sharing station locations by examining dynamic trip flows and individual behavioural changes. It explores demand on a microscopic scale, capturing the behaviour of multi-segment trips. A protocol was established to evaluate different station configurations' impact on profitability and demand. Findings suggest that optimising the station count from 219 to 66 strategic locations can significantly enhance both revenue and operational efficiency. The simulation produces data on the number of users arriving and departing from each station in different configurations, indicating the size of each station. This data allows stations to be classified into three types: generator, attractor, and interchange; displaying their changes across different configurations. This quantification offers operators insights for predicting bike distribution and planning operational strategies. Considering spatial and built environment factors, the findings underscore the potential of bike-sharing stations to evolve into mobility hubs, offering valuable insights for policymakers. [ABSTRACT FROM AUTHOR]

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

The adoption of agent-based models (ABMs) in transport research has highlighted their potential in digitally simulating travel behaviour from a bottom-up perspective. However, generating ABMs remains a complex multi-step process, often suffering from the lack of model representation or reliance on proprietary data, thereby limiting ABMs' transferability, especially in regions facing data scarcity. This research presents a systematic ABM generation algorithm for Brussels, Belgium, that generates high-quality ABMs more representative of real-life travel patterns, including incorporating external travel demand and allowing multiple destinations for the same activity types in activity chaining. Additionally, our algorithm's input data only contains social-spatial data and aggregated cellphone matrices. Due to the ubiquitous nature of such data worldwide, our algorithm demonstrates significant transferability to other regions for ABM generation while preserving individual privacy. We demonstrate the input data and the proposed algorithm step by step. The ABM generated using MATSim is validated against real-world data regarding mode share, distance share, travel time distribution and traffic counts. Overall, this research would serve as valuable guidance for ABM modellers in data collection and specific modelling setups, lowering the entry barrier of ABM research towards more efficient, representative and reproducible ABMs.

As shared autonomous vehicles (SAV) emerge as an economical and feasible mode of transportation in modern cities, effective optimization models are essential to simulate their service. Traditional optimization approaches, based on first-come-first-served principles, often result in sub-optimal outcomes and, more notably, can impact public transport (PT) operations by creating unnecessary competition. This study introduces four insertion strategies within the MATSim model of the Melbourne Metropolitan Area, addressing these challenges. Two strategies optimize SAV operations by considering overall network costs, and the other two make insertion decisions based on the available PT service in the network. The findings show that strategic insertions of the requests can significantly enhance SAV service quality by improving the vehicle load and decreasing vehicle and empty kilometers traveled per ride. The analysis indicates that these strategies are particularly effective for smaller fleet sizes, leading to an increased number of served rides and a more equitable distribution of wait times across the network, reflected in an improved Gini Index. The findings suggest that prioritization-based insertions significantly enhance service quality by prioritizing users with limited access to PT, ensuring that those with fewer PT options are served first, and encouraging a more integrated and sustainable urban transportation system. [ABSTRACT FROM AUTHOR]

The modeling and simulation of societies requires identifying the spatio-temporal patterns of people's activities. In urban areas, it is key to effective urban planning; it can be used in real estate projects to predict their future impacts on behavior in surrounding accessible areas. The work presented here aims at developing a method for making it possible to model the potential visits of the various equipment and public spaces of a district under construction by mobilizing data from census at the regional level and the layout of shops and activities as defined by the real estate project. This agent-based model takes into account the flow of external visitors, estimated realistically based on the pre-occupancy movements in the surrounding cities. To perform this evaluation, we implemented a multi-agent-based simulation model (MATSim) at the regional scale and at the scale of the future district. In its design, the district is physically open to the outside and will offer services that will be of interest to other residents or users of the surrounding area. To know the effect of this opening on a potential transit of visitors in the district, as well as the places of interest for the inhabitants, it is necessary to predict the flows of micro-trips within the district once it is built. We propose an attraction model to estimate the daily activities and trips of the future residents based on the attractiveness of the facilities and the urbanistic potential of the blocks. This transportation model is articulated in conjunction with the regional model in order to establish the flow of outgoing and incoming visitors. The impacts of the future district on the mobility of its surrounding area is deduced by implementing a simulation in the projection situation. [ABSTRACT FROM AUTHOR]

The modeling and simulation of societies requires identifying the spatio-temporal patterns of people’s activities. In urban areas, it is key to effective urban planning; it can be used in real estate projects to predict their future impacts on behavior in surrounding accessible areas. The work presented here aims at developing a method for making it possible to model the potential visits of the various equipment and public spaces of a district under construction by mobilizing data from census at the regional level and the layout of shops and activities as defined by the real estate project. This agent-based model takes into account the flow of external visitors, estimated realistically based on the pre-occupancy movements in the surrounding cities. To perform this evaluation, we implemented a multi-agent-based simulation model (MATSim) at the regional scale and at the scale of the future district. In its design, the district is physically open to the outside and will offer services that will be of interest to other residents or users of the surrounding area. To know the effect of this opening on a potential transit of visitors in the district, as well as the places of interest for the inhabitants, it is necessary to predict the flows of micro-trips within the district once it is built. We propose an attraction model to estimate the daily activities and trips of the future residents based on the attractiveness of the facilities and the urbanistic potential of the blocks. This transportation model is articulated in conjunction with the regional model in order to establish the flow of outgoing and incoming visitors. The impacts of the future district on the mobility of its surrounding area is deduced by implementing a simulation in the projection situation.

New transport technologies have historically influenced how cities are shaped, while urban form is known to influence travel behaviour. But this reciprocal relationship is seldom operationalized in practice due to the disciplinary gap between urban design and transport planning. The two are often linked through a 'predict and provide' workflow, which is problematic in the context of emerging technologies such as AVs. This paper argues for a more iterative design and transport simulation workflow, through design experiments. One design experiment is illustrated using Sketch MATSim, to investigate the impact of network design on the performance of shared automated vehicles. [ABSTRACT FROM AUTHOR]

A common task in today's transport simulation research is the assessment of on-demand mobility services on a large scale. A framework that is widely used for such tasks is MATSim, which has been applied frequently to study highly dynamic on-demand mobility services. To date, its efficient fleet management implementation does not yet support on-demand requests that are pre-booked in advance. This type of service, however, is seeing increased interest in the context of long-distance, rural, and inclusive mobility. Therefore, the present paper explores how prebooking can be integrated into MATSim and a use case for the city of Melun in France is presented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study introduces a methodology to model parking cost and residential parking zones within the agent-based simulation framework MATSim. An overview of related research and a description of the implementation are given. The results suggest that parking pricing has a larger impact on the share of motorized transport than longer access and egress walks to the activity. Raising parking costs could prove to be an effective tool to reduce motorized transport. [ABSTRACT FROM AUTHOR]

The paper deals with the extension of the logistics simulation within an agent-based simulation framework to be able to consider the delivery across several supply chains. The logistics simulation is placed in the context of the underlying freight transportation simulation. The use of multiple possible logistics chains addresses a key limitation of previous studies. Logistics chains represent the various transportation options of a Logistic Service Provider (LSP) and are made up of resources such as depots, logistics hubs and carriers that the logistics service provider can commission. When implementing the new functionality, the existing flow of the simulation process must be considered. In particular, changes need to be made to the assignment of freight requests to logistics chains, the iterative optimization of this assignment and scheduling. Appropriate assignment methods and rescheduling strategies are developed and presented. These innovations reveal compatibility problems within the existing simulation framework. The functionality developed is presented in constructed scenarios, highlighting the advantages of using multiple logistics chains. In addition, the applicability is demonstrated in a comprehensive real scenario that includes the delivery of supermarkets. [ABSTRACT FROM AUTHOR]

In this study, the existing agent-based simulation framework MATSim is extended towards a module for the simulation of trains and their interactions with the infrastructure. The proposed railway simulation module railsim offers several advantages over existing railway planning tools: railsim is open-source, easily adaptable, allows for a microscopic, mesoscopic or hybrid micro-mesoscopic simulation setup and is fully integrated in an existing simulation framework. Trains have a spatial dimension along several infrastructure segments. Trains accelerate and decelerate based on vehicle- and infrastructure-specific attributes as well as the available capacity ahead of the vehicle. The interaction of vehicles as well as the modeling of the infrastructure is adapted to the railway system. Trains are enabled to reserve infrastructure elements (fixed block segments, moving blocks), and reservation requests interact with each other. The illustrative Switzerland case study reveals that an overall unique capacity of 3 trains per section and 10 trains per station ensures most trains reach their destinations successfully, while lower capacities lead to insufficient infrastructure and train disruptions. [ABSTRACT FROM AUTHOR]

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation. This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently. Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable. [ABSTRACT FROM AUTHOR]

This work addresses the challenges of implementing shared e-scooter services (SSS) in urban areas. Despite their potential for sustainable mobility, issues like road safety and street cluttering persist. Policy regulation is crucial, and recent efforts have focused on free-floating e-scooter parking legislation. To assist decision-making, this paper proposes an agent-based framework to design SSS parking supply and evaluate its impact. The methodology is applied in Lyon, France, where the SSS is gaining more and more territory. The main outcomes show parking regulation can introduce conflicting objectives, with a reduction of SSS use due to an increase in the access and egress walking distance. [ABSTRACT FROM AUTHOR]

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future. • Behavioural models are integrated with agent-based microsimulation approach to predict BRT demand in the context of Dhaka, Bangladesh. • The demand for BRT is found to be notably influenced by both the fare structure and multimodal connectivity. • Simulation outputs demonstrate that BRT demand can vary between 0.7% and 25% depending on the adopted policies. • Policy measures to maximise BRT demand include making motorcycles less attractive and ensuring intermodal connectivity. • Customised services, incentives, and targeted planning, including gender-sensitive strategies, are vital for improving BRT ridership. [ABSTRACT FROM AUTHOR]

Shared, autonomous electric vehicles (SAEVs) are expected to enter the market in the coming decades. Using MATSim, we simulate a use case where SAEVs are introduced in multiple suburban zones at the outskirts of Vienna (Austria), which are characterized by relatively low population density, but have access to at least one rail-based public transport stop. For all combinations of different fleet sizes and fare levels, we find that a relatively small share of car trips by residents of these zones (7%–14%) are replaced by SAEVs, generating CO 2 emissions reductions of 5%–11%. Moreover, 23%–35% of trips previously undertaken by foot or bicycle are replaced by SAEVs, as well as 10%–20% of public transport trips. The potential of SAEVs to reduce the use and ownership of private vehicles in suburban areas therefore seems to be rather limited, which is also reflected in our finding that one SAEV usually replaces only 2–4 private vehicles. The potential becomes somewhat larger when the usage and ownership of private cars is assumed to become more expensive, leading to 17%–20% of car trips being replaced by SAEVs and generating CO 2 emissions reductions of up to 32%. • We simulate the introduction of SAEVs in suburban areas with public transport access. • Even with lare fleet sizes and low prices, only few car trips are replaced by SAEVs. • SAEVs mainly replace trips undertaken by foot, bicycle, or public transport. • Higher car usage & ownership costs increase switches from car to SAEV. • One SAEV replaces only 2–4 private vehicles, as little sharing takes place. [ABSTRACT FROM AUTHOR]

Mesoscopic, agent-based simulations efficiently model and assess entire regions’ daily activities and travel patterns, exemplified by smaller countries like Switzerland. The queue-based simulation represents a compromise between computational speed on the one hand and the necessity of detailed modeling infrastructure on the other hand. Thus, mesoscopic simulations enable an efficient and reasonably detailed analysis of the complex interplay between supply and demand in mobility research. Conversely, microsimulations excel at reproducing individual speed profiles and behavior by modeling the interactions between traffic participants, including pedestrians, bicycles, and scooters. Although allowing for more detailed system analysis, the downside is the high computational burden, which often prevents large-scale microscopic simulations from running in optimization or calibration loops. hybridPY, an extension of SUMOPy, aims to close the gap and benefit from both environments. The simulation suite allows the running of mesoscopic as well as microscopic traffic simulations based on the core idea: running a microscopic simulation in a smaller dedicated area, using the routes or mobility plans generated from a larger mesoscopic model. The main features of this software are: (i) import, editing and visualization of MATSim and BEAM CORE networks; (ii) conversion of MATSim plans to SUMO routes or plans within the SUMO area; (iii) configuring and running of MATSim simulations. The capability of hybridPY is demonstrated by two applications: the simulation of Schwabing, Germany, based on the MITO MATSim model, and the San Francisco municipality, USA, based on the mesoscopic BEAM CORE model of the entire San Francisco Bay area. Both scenarios demonstrate that the hybrid approach results in significant computational gains with respect to a pure microscopic approach.

Climate change is considered the most pressing environmental challenge of our time, being transport one of the major contributors. Consequently, transport models are required to test different urban mobility policies that can shift travel to more sustainable transport modes (e.g., active modes). This paper focuses on the development of a validated agent-based model (MATSim) applying a novel open-source methodology to generate the main input datasets, easily transferrable to any region in England. Required input datasets (synthetic population and network) are described with a high level of detail, identifying the datasets and tools used to develop them, with special interest in the simulation of cycling routes. A new attribute (quietness) ranking roads for cycling depending on their built-environment characteristics was incorporated into the MATSim bicycle extension. The results obtained in this paper show the baseline transport model of the Tyne and Wear region (England), where discrepancies up to 3.5% in transport mode shares and minimal differences in vehicle counts in urban areas were obtained, and a realistic representation of the routes chosen by the agents using bicycles is obtained. This provides the basis for the development of similar MATSim implementation in other UK regions.

The often-discussed issue of parental escorting of children to school and its potential disruption of traffic flow has been extensively examined in the literature. Still, the specific effects of traffic congestion remain understudied. To fill this gap, this study addresses the impacts of school trips on traffic congestion. An agent-based model is applied to simulate various scenarios and assess their impact. Our findings indicate that the traffic speed without school trips is higher speed during peak hours by around 8% and average travel time in the city is reduced significantly. We examine countermeasures: Firstly, flexible working hours can lead to a morning traffic speed improvement of approximately 1.9%. Secondly, staggered school schedules can result in a 6.9% increase in traffic speed at 7 a.m. Optimizing school loading spaces can also enhance road capacity, mitigating road lane blockages during drop-off and pick-up periods. This research sheds light on the impact of parent-escorting travel existence and offers potential solutions to alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.

With the introduction of automated vehicles, new operating regimes for public transport services will become possible. A station-based Automated Transit on Demand service could be an attractive alternative to the current modes of transportation. In this paper, the impact of this kind of service on the modal share for the city of Zurich, Switzerland, and its surrounding area is modeled using an agent-based approach. Different scenarios regarding the operating area, pricing scheme, and a cordon charge are tested on their potential to make use of the benefits of the new service while preventing an overflow of automated vehicles in the urban core. Results show that if left unconstrained the proposed service can substantially impact the demand for public transport. A pricing scheme that bases the pricing of the new service relative to the accessibility of the current public transport service is a promising solution to increase the accessibility of the rural areas while maintaining a high modal share for public transport in the city center. Finally, using an optimization algorithm we show that the total car-fleet and public parking space can be reduced at the cost of a slight increase in vehicle kilometers traveled. Moreover, we find that the cost coverage of the proposed transit service is potentially much higher in comparison to current public transport services.

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.

The majority of freight in Germany is carried out by trucks, resulting in emitting approximately 9% of Germany's carbon dioxide equivalent emissions. In particular, long-distance truck journeys contribute significantly to these emissions. This paper aims to explore the conditions and impacts of introducing E-Trucks in Germany by utilizing a microscopic traffic simulation approach. Therefore, five different electrification levels of the long-distance truck traffic are evaluated. The demand-oriented charging network dimensioning aims for a realistic and implementable design and is based on an average charging power of 720 k W. Additionaly, it considers the necessary infrastructure requirements at service and rest areas next to the motorway. The results of this research provide valuable insights in terms of usage, requirements and demand. For an electrification level of 1%, 177 chargers at 173 charging sites must be implemented, while 1296 chargers and 457 charging sites must be built for an electrification level of 20%. The increase in the electrification level leads to more efficient occupancy of the charging facilities; i.e., an increase from 1% to 5% improves the average occupation time ratio per charger by approximately 130%. Of the total energy consumed, 65% is recharged en-route at public chargers. Between Monday and Thursday, each 1% electrification level increase requires 2.68 G W   h more energy for the public recharging network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Transportation authorities have adopted more and more incentive measures (fare-free public transport, construction of park-and-ride facilities, etc.) to reduce the use of private cars by combining them with public transit. However, such measures remain difficult to assess with traditional transport models. This article proposes a different approach: an agent-oriented model. To reproduce realistic applications in an urban context (a metropolis), we investigate the preferences and choices of different agents based on utilities and focus on a modal choice performed through a multinomial logit model. Moreover, we propose some methodological elements to identify the individuals' profiles using public data (census and travel surveys). We also show that this model, applied in a real case study (Lille, France), is able to reproduce travel behaviors when combining private cars and public transport. Moreover, we focus on the role played by park-and-ride facilities in this context. Thus, the simulation framework makes it possible to better understand individuals' intermodal travel behavior and assess its development policies. [ABSTRACT FROM AUTHOR]

Over the past few years, electric vehicles (EVs) have become a popular mode of individual transportation due to their significant benefits over internal combustion engine vehicles, resulting in significant growth in their penetration rate. However, the current flat electricity charging pricing and rapid diffusion of EVs may impose challenges to both transportation systems and power grids. It is well-accepted that the implementation of charging pricing schemes is a promising solution for changing and controlling EV users' charging behavior. Nevertheless, in most research studies that have examined pricing schemes, the charging logic is defined in a way that agents will charge their vehicles while performing their daily activities. This will make the application of pricing schemes unfeasible, as agents will not have the freedom to unplug their EVs whenever they want, to reduce their billing costs. Accordingly, this paper provides an agent and activity-based framework for charging pricing schemes by decoupling activity and charging start and end times. Following this, three different charging pricing schemes have been introduced, including time of use (TOU), non-linear and zonal pricing and tested on the Montreal scenario. [ABSTRACT FROM AUTHOR]

We employ calibrated and validated MATSim multi-modal model of the Jerusalem Metropolitan Area traffic for assessing the effectiveness of Demand-Responsive-Transport (DRT) with ridesharing as a possible game-changer of the existing equilibrium between public transport and private cars. We investigate the combined effect of congestion charges, parking prices, and shared DRT services on the modal split and demonstrate that the DRT is effective when introduced together with congestion charges or parking prices. The policy effectiveness depends on the choice of the deterrence mechanism – given the overall payment for entering the controlled area, the effect of congestion charges depends on the size of the area. At the same time, parking prices are equally effective for an area of any size and, thus, seem advantageous. [ABSTRACT FROM AUTHOR]

Conventional wisdom is that a person's attitude towards an issue dictates their behaviour. In contrast, self-perception theory accounts for how a person forms their attitude. In the context of this paper, the theory asserts that a person can, in the absence of prior experience, establish an attitude towards cycling based on observing their own, hopefully, positive experience. The adaptive agent-based model, MATSim, allows one to test the self-perception theory. The case study in Cape Town, South Africa, demonstrates that as much as 7.8% of people introduced to cycling will experience it as positive, opening the door for adoption. [ABSTRACT FROM AUTHOR]

A new open-source, web-based, configurable data visualization platform is presented that is specifically designed to support large-scale transportation simulations including MATSim and ActivitySim. It produces a wide array of interactive charts, maps, animations and analysis dashboards that are generally useful in the transportation domain. Interactive visualizations can be created and viewed locally on an analyst's laptop, or public web-based dashboards can be published for viewing on the open Internet. The details of software design are provided along with several examples of implementation at public agencies. User feedback shows the platform is found to be very flexible, while the straightforward configuration approach enables efficient development and deployment of web-based interactive visualizations. While it is not intended to replace geographic information systems or commercial software packages, the smaller curated set of capabilities is found by users to warrant its current adoption at several public agencies. Further work is needed to add more useful features, improve the platform's quality and user experience, and extend documentation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simulation studies on Shared Autonomous Vehicles (SAVs) could offer several insights to transport policymakers about future mobility. Case studies using the agent-based approach have recently emerged worldwide to test the impacts of SAVs on local networks. However, as the most densely populated area in Belgium, a relevant SAV study for Brussels is currently lacking. In this paper, we have simulated the impacts of substituting Brussels private car trips for people who either only travel in Brussels or do not travel to external places by car. The simulation was carried out with various SAV fleet sizes implementing vehicle relocation strategy. This paper describes how we transferred certain car trips to SAVs, and the respective SAV parameters used for simulation. The results suggest that under a 10% current travel demand, if all current studied drivers groups are willing to carpool as long as the additional travel times do not exceed a certain threshold, 1000 SAVs will be sufficient to replace these car trips in Brussels with the average daily request waiting time of around 5 minutes. By deploying SAVs with the proposed operational scheme, agents' overall travel time and traffic congestion in Brussels can also be significantly reduced. [ABSTRACT FROM AUTHOR]

Modeling and simulating travelers mode choice is of great importance when evaluating novel transportation modes, policy measures or other changes to the overall transport system. The agent-based transportation framework MATSim has been widely used to study such scenarios and evaluate potential changes in the modal split. The default mode choice behavior in MATSim lets agents explore all their mode options randomly. A scoring-based co-evolutionary algorithm is applied to maximize the utility over a large number of iterations and arrive at consistent modes shares. In this paper, we present a new mode choice approach, that resembles a more rational decision-making process. First, the utilities of all available modes are estimated and used as costs to build a graph with all trips of the day. A k-shortest-path algorithm is applied to compute the most promising k combinations of modes for the day. Additionally, we formulate a pruning methodology that allows to remove unpromising modes. Finally, a selection model based on a multinomial logit is used to choose one of the plan candidates. The scale parameter of the selection model is annealed over the course of iterations to predict deterministically to the candidate with the highest estimate utility. Our simulation experiments show that convergence speed compared to random mode choice is significantly increased and only 25% of iterations are required to achieve overall better scores. [ABSTRACT FROM AUTHOR]

Shared micro-mobility services are increasingly becoming popular in many cities. These services can help shape sustainable cities and reduce car dependency. To unlock their full potential, a deep understanding of these services' operation, adoption, and integration within existing mobility systems is required. This paper uses a newly introduced agent-based simulation framework to study the implications of shared bikes and e-scooters in Lyon, France. The paper describes data collection, processing, and input data generation for the simulation of shared bikes and e-scooters. Simulation outcomes evaluate the impact of introducing these services on existing travel modes. The introduction of shared bikes and e-scooters can compete against public transit and walking. The simulation framework can be used to assess various aspects of shared bikes and e-scooters and help policy-makers take advantage of their full potential to improve urban mobility. [ABSTRACT FROM AUTHOR]

Innovative mobility solutions are constantly emerging with the advent of information and communication technologies, offering travellers multidimensional choices. Without a fully agent-based and dynamic travel demand microsimulation modelling system, it is impossible to evaluate such emerging mobility solutions/options realistically. The network modelling component is the most critical roadblock of such a modelling system. The paper presents an operational network model (GTASim) in an agent-based microsimulation framework, MATSim for the Greater Toronto and Hamilton Area, to capture the network dynamics necessary for a Comprehensive Utility maximizing System of Travel Option Modelling (CUSTOM). GTASim is developed using open-source network data and a large-scale regional household travel survey data. GTASim microstimulates network flows for all modes, including different transit services in the region. The modelled transit network has 26,125 transit stop facilities and 820 transit routes with more than 64,350 departures. The CaDyTS-based calibration is specified using peak hour traffic volumes at over 100 cordon count stations in major highways, regional streets, and numerous transit stations/stops. This is the first-ever developed fully agent-based dynamic multimodal transportation network microsimulation for the region. Thus, the model, with further integration with CUSTOM will equip the authorities to make conclusive decisions considering users' level of behavioural adaptation with intricate policies in megacities like GTHA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

On-demand ride-pooling systems have gained increasing attention in science and practice in recent years. Simulation studies have shown an enormous potential to reduce fleet sizes and vehicle kilometers traveled if private car trips are replaced with ride-pooling services. However, existing simulation studies assume operation with autonomous vehicles, with no restrictions on operational tasks required when the vehicles are operated by manual drivers. In this article, we simulate and evaluate the operational challenges of non-autonomous ride-pooling systems through driver shifts and breaks and compare their capacity and efficiency to autonomous on-demand services. Based on the existing ride-pooling service MOIA in Hamburg, Germany, we introduce shift and break schedules and implement a new hub return logic to perform the respective tasks at different types of vehicle hubs. This way, currently operating on-demand services are modeled more realistically and the efficiency gains of such services through autonomous vehicles are quantified. The results suggest that operational challenges substantially limit the ride-pooling capacity in terms of served rides with a given number of vehicles. While results largely depend on the chosen shift plan, the presented operational factors should be considered for the assessment of current operational real-world services. The contribution of this study is threefold: From a technical perspective, it is shown that the explicit simulation of operational constraints of current services is crucial to assess ride-pooling services. From a policy perspective, the study shows the operational challenges of a ride-pooling service with non-autonomous vehicles and the potential of future autonomous services. Lastly, the paper adds to the literature a practical ride-pooling simulation use case based on observed real-world demand and shift data. [ABSTRACT FROM AUTHOR]