A Multi-threaded Execution Model for the Agent-Based SEMSim Traffic Simulation

Abstract. An efficient simulation execution engine is crucial for agent-based traffic simulation. Depending on the size of the simulation sce-nario the execution engine would have to update several thousand agents during a single time step. This update may also include route calcula-tions which are computationally expensive. The ability to dynamically re-calculate the route of agents is a feature often not required in classical microscopic traffic simulations. However, for the agent-based traffic sim-ulation which is part of the Scalable Electro-Mobility Simulation (SEM-Sim) platform, the routing ability of agents is an important feature. In this paper, we describe a multi-threaded simulation engine that explic-itly supports routing capabilities for every agent. In addition, we analyse the efficiency and performance of our execution model in the context of a Singapore-based simulation scenario.

Change of Scale and Forecasting with the Control-Function Method in Logit Models

Endogeneity is a model misspecification that precludes the consistent estimation of the model parameters. The control-function method is the most suitable tool to address endogeneity for several discrete choice models that are relevant in transportation research. However, the estimators obtained with the control-function method are consistent only up to a scale. In this paper, we first depict the determinants of this change of scale by adapting an existing result for omitted orthogonal attributes in logit models. Then, we study the problem of forecasting under these circumstances. We show that a procedure proposed in previous literature may lead to significant biases, and we suggest novel alternatives to be used with synthetic populations. We use Monte Carlo experimentation and real data on residential location choice to illustrate these results. The paper finishes by summarizing the findings of this investigation and suggesting future lines of research in this area.MIT-Portugal Progra

Incorporating social interaction into hybrid choice models

The aim of this paper is to develop a methodological framework for the incorporation of social interaction effects into choice models. The developed method provides insights for modeling the effect of social interaction on the formation of psychological factors (latent variables) and on the decision-making process. The assumption is based on the fact that the way the decision maker anticipates and processes the information regarding the behavior and the choices exhibited in her/his social environment, affects her/his attitudes and perceptions, which in turn affect her/his choices. The proposed method integrates choice models with decision makers’ psychological factors and latent social interaction. The model structure is simultaneously estimated providing an improvement over sequential methods as it provides consistent and efficient estimates of the parameters. The methodology is tested within the context of a household aiming to identify the social interaction effects between teenagers and their parents regarding walking-loving behavior and then the effect of this on mode to school choice behavior. The sample consists of 9,714 participants aged from 12 to 18 years old, representing 21 % of the adolescent population of Cyprus. The findings from the case study indicate that if the teenagers anticipate that their parents are walking lovers, then this increases the probability of teenagers to be walking-lovers too and in turn to choose walking to school. Generally, the findings from the application result in: (a) improvements in the explanatory power of choice models, (b) latent variables that are statistically significant, and (c) a real-world behavioral representation that includes the social interaction effect

Spatial accessibility and social inclusion: The impact of Portugal's last health reform

Health policies seek to promote access to health care and should provide appropriate geographical accessibility to each demographical functional group. The dispersal demand of health‐careservices and the provision for such services atfixed locations contribute to the growth of inequality intheir access. Therefore, the optimal distribution of health facilities over the space/area can lead toaccessibility improvements and to the mitigation of the social exclusion of the groups considered mostvulnerable. Requiring for such, the use of planning practices joined with accessibility measures. However,the capacities of Geographic Information Systems in determining and evaluating spatial accessibility inhealth system planning have not yet been fully exploited. This paper focuses on health‐care services planningbased on accessibility measures grounded on the network analysis. The case study hinges on mainlandPortugal. Different scenarios were developed to measure and compare impact on the population'saccessibility. It distinguishes itself from other studies of accessibility measures by integrating network data ina spatial accessibility measure: the enhanced two‐stepfloating catchment area. The convenient location forhealth‐care facilities can increase the accessibility standards of the population and consequently reducethe economic and social costs incurred. Recently, the Portuguese government implemented a reform thataimed to improve, namely, the access and equity in meeting with the most urgent patients. It envisaged,in terms of equity, the allocation of 89 emergency network points that ensured more than 90% of thepopulation be within 30 min from any one point in the network. Consequently, several emergency serviceswere closed, namely, in rural areas. This reform highlighted the need to improve the quality of the emergencycare, accessibility to each care facility, and equity in their access. Hence, accessibility measures becomean efficient decision‐making tool, despite its absence in effective practice planning. According to anapplication of this type of measure, it was possible to verify which levels of accessibility were decreased,including the most disadvantaged people, with a larger time of dislocation of 12 min between 2001 and 2011

Modeling Recreation Demand When the Access Point Is Unknown

Not observing where an individual enters a geographically large recreation area complicates the task of modeling recreation demand. Traditionally, analysts have arbitrarily defined distances on the basis of the midpoint of a river or beach segment or on the basis of the nearest access point. In this article, we draw on the aggregation literature to generate a consistent framework for incorporating information on site characteristics and travel costs gathered at a finer level than that used to obtain trip counts. We use Monte Carlo experiments to illustrate the performance of the traditional midpoint and nearest access point approximations. Our results suggest that, while the nearest access point approach often provides a good approximation to underlying preferences, use of the midpoint approach can lead to significant bias in the travel cost parameter and corresponding welfare calculations. Finally, we use our approach to model recreation demand for the major river systems in Iowa using data from the 2009 Iowa Rivers and River Corridors Survey

Modelling long-distance route choice using mobile phone call detail record data: A case study of Senegal

The growing mobile phone penetration rates have led to the emergence of large-scale call detail records (CDRs) that could serve as a low-cost data source for travel behaviour modelling. However, to the best of our knowledge, there is no previous study evaluating the potential of CDR data in the context of route choice behaviour modelling. Being event-driven, the data are discontinuous and only able to yield partial trajectories, thus presenting serious challenges for route identification. This paper proposes techniques for inferring the users' chosen routes or subsets of their likely routes from partial CDR trajectories, as well as data fusion with external sources of information such as route costs, and then adapts the broad choice framework to the current modelling scenario. The model results show that CDR data can capture the expected travel behaviour and the derived values of travel time are found to be realistic for the study area

Choice-Based Demand Management and Vehicle Routing in E-Fulfillment

Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared with current industry practice