Understanding parking decisions with a Bayesian network

In this paper, a Bayesian network is developed to investigate three intertwining parking decisions, namely parking period, parking location, and parking duration, and the impacts of a number of parking-related factors on these decisions. With parking information from Beijing, China in 2005, the structure and parameter of a Bayesian network were learnt by employing the K2 algorithm and Bayesian parameter estimation method respectively. The results show that the decision on how long to park follows that on where to park, and both of them are affected by the decision of when to park. This suggests that parking policies aimed at intervening in one specific parking decision may have an indirect influence on other parking decisions, which embraces an integrated view in the development of parking policies. The findings facilitate the development of measures for regulating parking behavior by identifying important contributing factors.     

How do people respond to congestion mitigation policies? A multivariate probit model of the individual consideration of three travel-related strategy bundles

This study explores the relationships between adoption and consideration of three travel-related strategy bundles (travel maintaining/increasing, travel reducing, and major location/lifestyle change), linking them to a variety of explanatory variables. The data for this study are the responses to a fourteen-page survey returned by nearly 1,300 commuting workers living in three distinct San Francisco Bay area neighborhoods in May 1998. We first identified patterns of adoption and consideration among the bundles, using pairwise correlation tests. The test results indicate that those who have adopted coping strategies continue to seek for improvements across the spectrum of generalized cost, but perhaps most often repeating the consideration of a previously-adopted bundle. Furthermore, we developed a multivariate probit model for individuals’ simultaneous consideration of the three bundles. It is found that in addition to the previous adoption of the bundles, qualitative and quantitative Mobility-related variables, Travel Attitudes, Personality, Lifestyle, Travel Liking, and Sociodemographics significantly affect individual consideration of the strategy bundles. Overall, the results of this study give policy makers and planners insight into understanding the dynamic nature of individuals’ responses to travel-related strategies, as well as differences between the responses to congestion that are assumed by policy makers and those that are actually adopted by individuals.

Application of social force model to pedestrian behavior analysis at signalized crosswalk

Limited pedestrian behavior models shed light on the case at signalized crosswalk, where pedestrian behavior is characterized by group or individual evasion with surrounding pedestrians, collision avoidance with conflicting vehicles, and response to signal control and crosswalk boundary. This study fills this gap by developing a microscopic simulation model for pedestrian behavior analysis at signalized intersection. The social force theory has been employed and adjusted for this purpose. The parameters, including measurable and non-measurable ones, are either directly estimated based on observed dataset or indirectly derived by maximum likelihood estimation. Last, the model performance was confirmed in light of individual trajectory comparison between estimation and observation, passing position distribution at several cross-sections, collision avoidance behavior with conflicting vehicles, and lane-formation phenomenon. The simulation results also concluded that the model enables to visually represent pedestrian crossing behavior as in the real world.     

A network optimization-based approach for crowd management in large public gatherings

Effective crowd management during large public gatherings is necessary to enable pedestrians' access to and from the venue and to ensure their safety. This paper proposes a network optimization-based methodology to support such efficient crowd movement during large events. Specifically, a bi-level integer program is presented that, at the upper-level, seeks a reconfiguration of the physical layout that will minimize total travel time incurred by system users (e.g. evacuees) given utility maximizing route decisions that are taken by individuals in response to physical offerings in terms of infrastructure at the lower-level. The lower-level formulation seeks a pure-strategy Nash equilibrium that respects collective behavior in crowds. A Multi-start Tabu Search with Sequential Quadratic Programming procedure is proposed for its solution. Numerical experiments on a hypothetical network were conducted to illustrate the proposed solution methodology and the insights it provides.     

A three-stage evacuation decision-making and behavior model for the onset of an attack

Pedestrian behavior models have successfully reproduced human movement in many situations. However, few studies focus on modeling human behavior in the context of terrorist attacks. Terrorist attacks commonly occur in crowded public areas and result in a large number of casualties. This paper proposes a three-stage model to reproduce a series of complex behaviors and decision-making processes at the onset of an attack, when pedestrians generally do not have clear targets and have to deal with fuzzy information from the attack. The first stage of the model builds a Bayesian belief network to represent the pedestrians’ initial judgment of the threat and their evacuation decisions. The second stage focuses on pedestrians’ global assessment of the situation through an analogy with diffusion processes. The third stage uses a cost function to reproduce the trade-offs of distance, safety, and emotional impact when considering a path to take. The model is validated using a video from the November 2015 Paris attack. The behavioral characteristics and trajectories of three pedestrians extracted from the video are reproduced by the simulation results based on the model. The research can be used to set rules when performing risk analysis and strategic defensive resource allocation of terrorist attacks using agent-based simulation methods.     

Online calibration of an integrated framework for information‐based evacuation operations

This study seeks to online calibrate the parameters of aggregate evacuee behavior models used in a behavior‐consistent information‐based control module for determining information strategies for real‐time evacuation operations. It enables the deployment of an operational framework for mass evacuation that integrates three aspects underlying an evacuation operation: demand (evacuee behavior), supply (network management), and disaster characteristics. To attain behavior‐consistency, the control module factors evacuees' likely responses to the disseminated information in determining information‐based control strategies. Hence, the ability of the behavior models to predict evacuees' likely responses is critical to the effectiveness of traffic routing by information strategies. The mixed logit structure is used for the aggregate behavior models to accommodate the behavioral heterogeneity across the population. An online calibration problem is proposed to calibrate the random parameters in the behavior models by using the least square estimator to minimize the gap between the predicted network flows and unfolding traffic dynamics. Background traffic, an important but rarely studied issue for modeling evacuation traffic, is also accounted for in the proposed problem. Numerical experiments are conducted to illustrate the importance of the calibration problem for addressing the system consistency issues and integrating the demand, supply, and disaster characteristics for more efficient evacuation operations. Copyright © 2016 John Wiley & Sons, Ltd.     

Statistical and activity-based modeling of university student travel behavior

Abstract

This paper conducts a statistical analysis of student travel behavior at Virginia Commonwealth University (VCU). The data source is the ‘University NHTS’ project launched by the Virginia Department of Transportation (VDOT) in 2009. Through this empirical study, it has been found that university student travel behavior is different from that of the general population; urban universities have lower percentages of nonmotorized trips than college-town universities; undergraduate students are likely to make more daily trips than graduate students – similarly, on-campus students make more frequent trips than off-campus students; the most frequent student activities are home and academic activities; and student group categories have virtually no impact on daily activity profiles, though activity types do have a dramatic impact on daily activity profiles. Based on these research findings, the paper makes a series of recommendations regarding trip generation, trip distribution, mode choice, and activity-based modeling.     

管道超声导波检测技术

介绍了管道超声导渡检测技术的理论基础，指出了这一检测方法的技术关键；同时对现有已用于工程应用的导波检测设备与导渡检测实验系统作了描述和对比；总结了目前成熟的缺陷识别定位方法，在此基础上，对管道超声导波检测技术的广泛应用提出几点展望。     

Modal shift in Eastern Germany

The economic and political reunification of Germany in 1990 unleashed a transportation revolution in Eastern Germany. After forty years of public transport dominance under socialism, auto ownership and use skyrocketed with the transition to capitalism. In only three years, ridership on public transport fell by almost 50%, and auto registrations per 1,000 population rose by almost 60%. The main reason for the sudden shift in modal split is the large increase in real per-capita incomes of Eastern Germans. Their purchasing power rose dramatically thanks to massive financial aid from Western Germany and access to hard currency for the first time. In addition, the relative cost of auto use has fallen sharply since reunification because public transport fares rose ten-fold, while gasoline prices and auto prices fell. The massive shift from public transport to the auto has caused severe problems of pollution, safety, equity, and congestion in Eastern German cities, partly because of the suddenness of the modal shift. Urban transport policy in Eastern Germany should adopt some of the strategies used for years in Western Germany to tame the automobile, while at the same time allowing high levels of auto ownership. Such strategies include auto-free zones, traffic calming, extensive bicycle pathways, vehicle emission standards, and parking restrictions. Finally, large investments will have to be made in Eastern Germany's dilapidated roadway and public transport infrastructure.     

Applying the Value-Belief-Norm Theory to Marine Contexts: Implications for Encouraging Pro-Environmental Behavior

Encouraging pro-environmental behavior among protected area visitors and other stakeholders has become a priority for marine resource managers. However, there exists a lack of understanding of the human dimensions of resource management regarding the perceptions and attitudes of stakeholders. Using the value-belief-norm theory of environmentalism (VBN) as a framework, the purposes of this investigation were to: 1) test a model of variables that influence stakeholders' intentions to adopt pro-environmental behavior in two marine protected areas (i.e., the Great Barrier Reef Marine Park and the Florida Keys National Marine Sanctuary); and 2) using the relevant literature and information gleaned from the model, suggest techniques that managers can use to encourage pro-environmental intentions. Acceptable goodness-of-fit indices for both of the structural equation models (one for each protected area) provided empirical support for the usefulness of the VBN framework to guide both research in marine contexts and marine protected area management efforts to encourage stakeholders' intentions to engage in pro-environmental behavior. Specific suggestions include increasing stakeholders. knowledge about impacts via environmental education strategies, increasing their awareness of their impacts and their efficacy in mitigating those impacts (e.g., workshops), and developing interpersonal relationships among managers and stakeholders (e.g., implementation intention and follow-up strategies).