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• Passenger's non-compliance behavior is defined and observed. • Critical time indicators are defined to measure the alighting and boarding efficiency. • A simulation model is proposed to simulate passengers' alighting and boarding process at metro platform. • Effects of Passenger's non-compliance behaviors under different passenger volume conditions are analyzed. • Different alighting and boarding rules could be applied to increase alighting and boarding efficiency. Passengers' alighting and boarding process in metro stations has attracted increasing research attention since it has significant influence on the platform passenger distribution as well as the train dwell time. In this study, a field survey was firstly performed in a densely populated metro station at downtown area in Hong Kong to observe passengers' alighting and boarding characteristics and collect sample data. One commonly existing phenomenon is found that boarding passengers start to get aboard even when there are alighting passengers still inside the metro carriage. This is defined as passengers' non-compliance behaviors in this paper. In addition, time indicators are defined to measure the alighting and boarding efficiency. Then a microscopic pedestrian simulation model based on the Social Force Model is proposed to simulate the passengers' alighting and boarding patterns at metro platform. The verification result shows the good applicability of the proposed model to simulate the actual situation. Finally, several simulation tests are conducted to explore the impacts that passengers' non-compliance behaviors have on the alighting and boarding efficiency in different passenger volume conditions. The simulation result shows that higher level of passengers' non-compliance behaviors leads to longer passenger's alighting duration and boarding duration, but the influence on the overall transaction time is related to different passenger volume conditions. Thus, metro station facility could apply different alighting and boarding rules in different passenger volume conditions to increase alighting and boarding efficiency. [ABSTRACT FROM AUTHOR]

In highly motorised countries, some sectors of the population own and use cars despite struggling to afford their running costs, and so may be particularly vulnerable to motor fuel prices increases, whether market-led or policy-driven. This paper proposes a novel, disaggregated approach to investigating vulnerability to such increases at the household level. We propose a set of indicators of ‘car-related economic stress’ (CRES), based on individual household level expenditure data for the UK, to identify which low-income households spend disproportionately on running motor vehicles, and to assess the depth of their economic stress. By subsequently linking the dataset to local fuel price data, we are able to model the disaggregated price elasticities of car fuel demand. This provides us with an indicator of each household’s adaptive capacity to fuel price increases. The findings show that ‘Low Income, High Cost’ households (LIHC) account for 9% of UK households and have distinct socio-demographic characteristics. Interestingly, they are characterised by very low responses to fuel price increases, which may cause them to compromise on other important areas of their household expenditures. Simulations suggest that a 20% increase in fuel prices would substantially increase the depth, but not the incidence of CRES. Overall, the study sheds light on a sector of the population with high levels of vulnerability to fuel price increases, owing to high exposure, high sensitivity and low adaptive capacity. This raises challenges for social, environmental and resilience policy in the transport sector. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a population synthesiser based on the method of Iterative Proportional Fitting (IPF) algorithm developed for the new Danish national transport model system. The synthesiser is designed for large population matrices and allows target variables to be represented in several target constraints. As a result, constraints for the IPF are cross-linked, which makes it difficult to ensure consistency of targets in a forecast perspective. The paper proposes a new solution strategy to ensure internal consistency of the population targets in order to guarantee proper convergence of the IPF algorithm. The solution strategy consists in establishing a harmonisation process for the population targets, which combined with a linear programming approach, is applied to generate a consistent target representation. The model approach is implemented and tested on Danish administrative register data. A test on historical census data shows that a 2006 population could be predicted by a 1994 population with an overall percentage deviation of 5–6% given that targets were known. It is also indicated that the deviation is approximately a linear function of the length of the forecast period. [Copyright &y& Elsevier]

Abstract: This paper analyzes the optimal starting location of a high-occupancy-vehicle (HOV) lane for a linear monocentric urban area. Both travel time and carpooling costs are taken into account. The research proposes an analytical framework for the case with a continuum demand distribution along a highway corridor. The objective is assumed to maximize social welfare of the transportation system, which is the difference between the total user benefit and travel cost. Numerical analysis via simulation experiments was conducted to seek the existence of an optimal solution. Based on the results of a sensitivity analysis, we find a specific relationship between the carpooling cost and the optimal design of the starting point of an HOV lane. [Copyright &y& Elsevier]

Abstract: Choice set formation, location and mode preferences, coordinated scheduling, alternative utility valuations, and shared mobility resources are among the many activity-travel issues hypothesized to be significantly influenced by traveler interdependencies. Empirical evidence lags theory, particularly about the geography of social networks. A simulation tool is presented to let the experimenter construct and test hypothetical interdependencies between geography, socially-linked travelers, and activity-travel choices. The exploratory tool is integrated in the Multi-Agent Transportation Simulation Toolbox (MatSim-T). Initially, any social network can be constructed and embedded in geography. It can remain static, or be adapted to the travel patterns of the agents. The interactions and exchanges between agents influencing socializing and/or travel behavior can be defined in substance and in time/space. The reward for socializing or being socially linked can be varied. Finally, the co-dependence of social factors and travel behavior can be studied. This paper introduces the model and presents verification results which illustrate the coupling of extremely simplified socializing assumptions and travel behavior. [Copyright &y& Elsevier]

Abstract: Activity-based analysis has slowly shifted gear from the analysis of daily activity patterns to the analysis and modeling of dynamic activity-travel patterns. In this paper, we address one type of dynamics: the formation and adaptation of location choice sets under influence of dyad relationships within social networks. It extends the dynamic model developed in earlier work, which simulates habitual behavior versus exploitation and exploration as a function of discrepancies between dynamic, context-dependent aspiration levels and expected outcomes. Principles of social comparison and knowledge transfer are used in modeling the impact of social networks through information exchange, adaptations of spatial choice sets and formation of common aspiration levels. We demonstrate model properties using numerical simulation with a case study of shopping activities. [Copyright &y& Elsevier]

Abstract: Agent-based approaches to simulating long-term location and mobility decisions and short-term activity and travel decisions of households and individuals are receiving increasing attention in land-use and transportation interaction (LUTI) models to predict land-use changes and travel behaviour in mutual interaction. Social interactions between households and between individuals potentially have an influence on a wide range of the long-term and short-term choices involved in these systems. In this paper we identify the areas in which social interactions play a role and address the question how these influences can be modelled in the context of agent-based LUTI models. We distinguish impacts on activity participation (joint activity participation, support-and-help activities) and impacts on decision making (information exchange, social adaptation of preferences and aspirations) as the two main areas of social influence. A prototype of a LUTI model is proposed that accounts for impacts of the social network on longer-term mobility decision making through information exchange and social adaptation of preferences and aspirations. The model is demonstrated in a numerical simulation. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to verify/validate the results of an application of the Travel Activity Scheduler for Household Agents (TASHA) in the Greater Toronto Area (GTA), Canada. Activity generation and scheduling components of TASHA are validated using 1996 and 2001 travel survey data for the GTA. This validation proceeds in two parts: (a) verification that TASHA replicates the 1996 base case upon which the model was originally built; and (b) comparison of TASHA’s forecasts of 2001 daily travel behaviour with observed travel survey data for 2001. TASHA activity generation and scheduling model components replicate observed activities with good accuracy and precision for the base year. Although TASHA is not able to predict an observed increase in activity participation rate in a five year forecast, the distribution of activities in the day is forecast with greater success. Predictions of average travel distance are oversimulated in the base year by 0.9%. Increased average distances are underpredicted for school, shopping, and other activities, and are overpredicted for work and return home activities. These validation results are promising, although there exist opportunities to improve model performance, and to further validate other elements of the TASHA model. [Copyright &y& Elsevier]