Mind the Gap – Passenger Arrival Patterns in Multi-agent Simulations

In most studies mathematical models are developed finding the expected waiting time to be a function of the headway. These models have in common that the proportion of passengers that arrive randomly at a public transport stop is less as headway in-creases. Since there are several factors of influence, such as social demographic or regional aspects, the reliability of public transport service and the level of passenger information, the threshold headway for the transition from random to coordinated passenger arrivals vary from study to study. This study's objective is to investigate if an agent-based model exhibits realistic passenger arrival behavior at transit stops. This objective is approached by exploring the sensitivity of the agents' arrival behavior towards (1) the degree of learning, (2) the reliability of the experienced transit service, and (3) the service headway. The simulation experiments for a simple transit corridor indicate that the applied model is capable of representing the complex passenger arrival behavior observed in reality. (1) For higher degrees of learning, the agents tend to over-optimize, i.e. they try to obtain the latest possible departure time exact to the second. An approach is presented which increases the diversity in the agents' travel alternatives and results in a more realistic behavior. (2) For a less reliable service the agents' time adaptation changes in that a buffer time is added between their arrival at the stop and the actual departure of the vehicle. (3) For the modification of the headway the simulation outcome is consistent with the literature on arrival patterns. Smaller headways yield a more equally distributed arrival pattern whereas larger headways result in more coordinated arrival patterns

Activity-Based Computation of Marginal Noise Exposure Costs : Implications for Traffic Management

In this paper, an innovative simulation-based approach is presented to calculate optimal dynamic, road- and vehicle-specific tolls on the basis of marginal traffic noise exposures. The proposed approach combines the advantages of an activity-based simulation with the economically optimal way of price setting. Temporal and spatial differences of traffic noise levels and population densities are considered. Moreover, noise exposures at work and educational activities are accounted for. The results of a case study for the area of Berlin showed that transport users avoided marginal noise cost payments by shifting to road stretches in areas with lower population densities, typically major roads. The simulation experiments indicated that the marginal cost approach could be used to improve the overall system welfare and to derive traffic control strategies

Agent-based Congestion Pricing and Transport Routing with Heterogeneous Values of Travel Time Savings

An existing agent-based simulation framework and congestion pricing methodology is extended towards a consistent consideration of non-linear, user- and trip-specific values of travel time savings (VTTS). The heterogeneous VTTS are inherent to the model and result from each agent's individual time pressure. An innovative approach is presented which accounts for the non-linear, user- and trip-specific VTTS (i) when converting external delays into congestion tolls and (ii) when generating new transport routes. The innovative pricing and routing methodology is applied to a real-world case study of the Greater Berlin area, Germany. The proposed methodology performs better than assuming a constant value of travel time savings or randomizing the routing relevant costs. The improved consistency of setting congestion toll levels, identifying transport routes and evaluating travel plans is found to result in a higher system welfare

On-road Air Pollution Exposure to Cyclists in an Agent-Based Simulation Framework

Bicycle is not only a sustainable mode of transport but also health benefits of bicycling due to increased physical activities are well cited. However, in urban agglomerations, on-road air pollution exposure to cyclists/pedestrians is a matter of concern which is understudied. This study proposes an approach to calculate the on-road air pollution exposure for drivers of different vehicles in an agent-based simulation framework. In the proposed approach, the breathing rate of different drivers, penetration rate, vehicle-occupancy and background concentration are taken into consideration. The approach is applied to a real-world scenario of Patna, India where non-motorized modes are in abundance. A comparison of total inhaled mass per trip for drivers of different vehicles is made and it is found that cyclists are most exposed user group. An analysis for various background concentrations for different days of the year shows that the contribution of the background concentration has a major effect on the air pollution exposure level. The outcome is spatially analyzed to identify the locations of most affected user groups mapped to their home locations. Further, the on-road air pollution exposure of business-as-usual scenario is compared with a policy case and it is found that a dedicated bicycle track can increase the exposure per trip to cyclists by 40 %

The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data

With more diverse transport policies being proposed and the advent of novel transport services and technologies, the transport system is becoming more individualized in many aspects. Transport models, the most important tool to assess policies and schemes, need to be sufficiently expressive to address these developments. Agent-based transport models, where travelers with individual properties and the ability to act and decide autonomously are resolved individually, allow to appropriately model and analyze such policies. This paper describes the MATSim Open Berlin Scenario, a transport simulation scenario for the Berlin metropolitan area implemented in the agent-based transport simulation framework MATSim. The scenario is solely based on open data and the demand for transport is created based on a fully synthetic procedure. Contrary to most transport simulation scenarios, no information from a travel diary survey is required as input. As such, the scenario generation procedure described in this study is spatially transferable and facilitates the creation of agent-based transport simulation scenarios for arbitrary regions

Noise Shielding in an Agent-Based Transport Model Using Volunteered Geographic Data

This paper describes an improved noise modeling approach for the agent-based transport simulation MATSim. In contrast to previous versions, the new implementation takes into account the shielding of noise at building facades. The simplified approach is based on German noise modeling guidelines. As a proof of concept, a comparative calculation of noise immissions for a use case in the city of Munich with and without the consideration of buildings reveals more realistic immission values when shielding is taken into account. While uncovered areas are not affected by the updated calculation, backyards and areas behind larger building blocks show a major reduction in immissions. When looking at noise exposure costs in a dense area, ignoring the effect of shielding seems to significantly overestimate costs by up to 20%. The presented approach is a step forwards incorporating environmental aspects in an agent-based integrated land use/transport modeling suite.DFG, 5051013, Implementation and application of a tightly integrated behavioural land use and transport modelEC/FP7/291763/EU/TUM-IAS Fellowships for the cooperative development of high risk new fields in technology and science/RiskingCreativit

The impact of pricing and service area design on the modal shift towards demand responsive transit

In this study, an agent-based transport simulation is used to look into different design concepts for demand responsive transit (DRT). In different simulation experiments for a real-world case study of the Greater Berlin area, the DRT service area is either set to the inner-city center area or the entire city area, and the DRT pricing scheme is varied. The existing simulation framework is extended by an iterative approximation approach to improve the computational performance. The simulation results show that a small service area and too low prices may result in an unwanted mode shift effect from walk and bicycle to DRT. For higher fares, the unwanted mode shift effect is reduced and fewer users switch from bicycle and walk to DRT. The simulation experiments also show that a larger DRT service area contributes towards an increase of the desired mode shift effect from car to DRT.BMVI, 16AVF2160, AVÖV - Räumlich und zeitlich hochauflösende Evaluation und Optimierung automatisierter und vernetzter Bedienkonzepte im öffentlichen Verkeh

Using real-world traffic incident data in transport modeling

This study incorporates real-world traffic incident data into a transport simulation and analyzes the impact of roadworks, accidents and other incident types on the transport system. Traffic incidents are modeled as a reduction in road capacity to which transport users can react by adjusting their transport routes. Depending on the type of traffic incident, i.e. long-term vs. short-term effect, a different behavioral reaction is implemented which reflects a different assumption regarding the transport users level of knowledge. Simulation experiments for the Greater Berlin area indicate that traffic incidents cause an increase in average travel time per car trip of 5-7 minutes. Also, over a long period of time, traffic incidents have a significant effect on the transport system: On an average working day, for almost half of all car trips, transport users either travel on a road (segment) which is affected by a traffic incident or bypass such a road (segment). Overall, this study highlights the importance to account for traffic incidents in transport modeling. Accounting for traffic incidents allows to quantify the effects from roadworks, accidents and other incident types. Furthermore, the simulation of traffic incidents makes the model more realistic and allows for an improved policy evaluation and decision-making

Simultaneous internalization of traffic congestion and noise exposure costs

manuscipt submitted under different title: “Simultaneous optimization of traffic congestion and noise exposures“This study elaborates on the interrelation of external effects, in particular road traffic congestion and noise. An agent-based simulation framework is used to compute and internalize user-specific external congestion effects and noise exposures. The resulting user equilibrium corresponds to an approximation of the system optimum. For traffic congestion and noise, single objective optimization is compared with multiple objective optimization. The simulation-based optimization approach is applied to the real-world case study of the Greater Berlin area. The results reveal a negative correlation between congestion and noise. Nevertheless, the multiple objective optimization yields a simultaneous reduction in congestion and noise. During peak times, congestion is the more relevant external effect, whereas, during the evening, night and morning, noise is the more relevant externality. Thus, a key element for policy making is to follow a dynamic approach, i.e. to temporally change the incentives. During off-peak times, noise should be reduced by concentrating traffic flows along main roads, i.e. inner-city motorways. In contrast, during peak times, congestion is reduced by shifting transport users from the inner-city motorway to smaller roads which, however, may have an effect on other externalities