计量经济分析 = Econometric analysis

The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

/pdf/understanding-individual-human-mobility-patterns-3k1tyob274.pdf

Understanding individual human mobility patterns

The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Climate change 2014 - Mitigation of climate change

https://www.rug.nl/staff/e.m.steg/abrahamsestegvlekrothengatterreview.pdf

A review of intervention studies aimed at household energy conservation

Gaining a better understanding of the factors that affect the likelihood of a vehicle crash has been an area of research focus for many decades. However, in the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes - the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period. This paper provides a detailed review of the key issues associated with crash-frequency data as well as the strengths and weaknesses of the various methodological approaches that researchers have used to address these problems. While the steady march of methodological innovation (including recent applications of random parameter and finite mixture models) has substantially improved our understanding of the factors that affect crash-frequencies, it is the prospect of combining evolving methodologies with far more detailed vehicle crash data that holds the greatest promise for the future.

http://ceprofs.tamu.edu/dlord/Papers/Lord-Mannering_Review.pdf

The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

SimTRAVEL: Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land

Pricing policies are increasingly being considered in urban areas around the world to better manage travel demand, mitigate adverse impacts of automobile travel, and raise financial resources for much needed infrastructure development. This paper demonstrates the feasibility of applying an integrated microsimulation model of activity-travel demand and dynamic traffic assignment for analyzing the impact of pricing policies on the entire range of activity-travel choices. The model system is based on a dynamic integration framework wherein the activity-travel simulator and the dynamic traffic assignment model communicate with one another along the continuous time axis so that trips are routed and simulated on the network as and when they are generated. This framework is applied to the analysis of a systemwide pricing policy for a small case study site to demonstrate how the model responds to various levels of pricing. A generalized travel time measure that accounts for the pricing effect and reflects population heterogeneity in values of time is used to simulate activity-travel choices, including both activity generation and destination choice, in a prism-constrained activity-travel simulation paradigm. Case study results show that trip lengths, travel time expenditures, and vehicle miles of travel are impacted to a greater degree than activity-trip rates and activity durations as a result of pricing policies. Measures of change output by the model are found to be consistent with elasticity estimates reported in the literature, suggesting that the model is able to reflect adjustments in activity-travel behavior in response to pricing policies.

Reflecting Impacts of Systemwide Pricing Strategies in an Integrated Continuous-Time Prism-Constrained Activity-Travel Simulator of Demand and Supply

The health and well-being of individuals is related to their activity-travel patterns. Indeed, there is increasing interest in drawing connections between activity-travel indicators and public health outcomes. Many of the indicators related to physical activity participation, sedentary activity participation (such as watching television or sitting at the computer for extended periods), and extent of bicycling and walking are measures that public health professionals would be interested in connecting to health outcomes such as body mass index (BMI), blood pressure, and overall state of health. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, despite the widespread recognition of the importance of attitudes and lifestyle preferences on activity engagement patterns and mode use, activity-based models fail to include such variables in the model specification.  Engagement in physical activities, and the use of bicycle and walk modes, are likely to be influenced by the lifestyle preferences and attitudes of individuals.  But, such lifestyle preferences and attitudes are rarely, if ever, measured in surveys rendering it difficult to explicitly include such measures in activity model specifications.  This study constitutes an initial attempt to fill this gap by adopting Bhat's (2014) Generalized Heterogeneous Data Model (GHDM) model system in which latent constructs that describe an individual's health consciousness and physical activity propensity are modeled as a function of observed socio-economic and demographic characteristics.  The resulting latent constructs, together with socio-economic and demographic variables, are then used to predict a number of activity engagement outcomes (describing frequency and duration of participation in various types of activities - both physically active and sedentary) and health outcomes (such as body mass index, self-reported health well-being, and blood pressure).  The entire system of equations is estimated jointly through the use of the maximum approximate composite marginal likelihood (MACML) estimation approach that greatly simplifies the evaluation of the likelihood function and brings about computational efficiency in the estimation of simultaneous equations model systems that involve a mixture of dependent variable types. The model system is applied using the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample. The findings of the paper show that latent constructs, health consciousness and physical activity propensity, are related to socio-economic and demographic variables. These latent constructs play a significant role in shaping activity-travel and mode use patterns, with those who are more health conscious or inclined towards physically active lifestyles reporting higher levels of physical activity engagement and better health outcomes.  Given the significance of the latent variables in explaining activity engagement and mode use, activity-based microsimulation models may be enriched in terms of the model specification through the inclusion of such latent variables that are themselves functions of observed socio-economic and demographic variables collected in travel surveys.  There has been a reluctance historically to include attitudinal and lifestyle preference variables in model specifications because such variables are not typically measured in travel surveys, and more importantly, they are difficult to forecast into the future.  However, the approach proposed in this paper, where latent variables are functions of observed variables and can be included in models of activity-travel behavior, offers a mechanism by which such latent attitudinal and lifestyle constructs can be included in models of activity-travel demand. The study is not without its limitations. Due to the nature of the study, the survey data set used for this modeling effort had to include both activity-travel indicators as well as health indicators.  The 2005-2006 National Health and Nutrition Examination Survey (NHANES) offered such a data set, but this data set suffered from the drawback that it did not include any built environment, contextual, or network level of service variables - all of which invariably affect activity-travel indicators and possibly health outcomes as well.  Future research and data collection efforts should attempt to include all of the variables of interest so that contextual variables may be accounted for in the model specification.  Normal 0 false false false EN-US KO X-NONE /* Style Definitions */
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/pdf/an-integrated-latent-construct-modeling-framework-for-3komi4q53n.pdf

An Integrated Latent Construct Modeling Framework for Predicting Physical Activity Engagement and Health Outcomes

Understanding time-space prism constraints for modeling weekend travel behavior

This paper presents a methodology to distribute the Traffic Analysis Zone (TAZ) level synthesized households and their members to parcels according to the household and parcel attributes. Three Multinomial Logit (MNL) models are estimated to represent the residence location association of households and land parcels. The estimated models are then used in an algorithm that assigns three different types of households to locations in the Los Angeles County. Daily Vehicle Miles Traveled (VMT) of each household is assigned in this way to the parcel each household is assigned to using the algorithm. The method illustrated here shows the feasibility of doing this assignment using millions of parcels and households. It also shows the results are reasonable and the authors are able to estimate VMT at specific locations and for spatially disaggregate jurisdictions enabling the assessment of policies at very fine levels of resolution. In addition, these findings and related maps challenge the claim that central city residents travel less miles and suburban residents travel more.

https://www.caee.utexas.edu/prof/bhat/ABSTRACTS/ResidentialAssignment_Nov2013.pdf

Residential Geolocation of Households in a Large-Scale Activity-Based Microsimulation Model and Development of a High Definition Spatial Distribution of Vehicle Miles Traveled

Ram M. Pendyala

Papers

SimTRAVEL: Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land

Reflecting Impacts of Systemwide Pricing Strategies in an Integrated Continuous-Time Prism-Constrained Activity-Travel Simulator of Demand and Supply

An Integrated Latent Construct Modeling Framework for Predicting Physical Activity Engagement and Health Outcomes

Understanding time-space prism constraints for modeling weekend travel behavior

Residential Geolocation of Households in a Large-Scale Activity-Based Microsimulation Model and Development of a High Definition Spatial Distribution of Vehicle Miles Traveled