Behavioral-Based Pedestrian Modeling Approach: Formulation, Sensitivity Analysis, and Calibration

Pedestrians are among the travelers most vulnerable to collisions that are associated with high fatality and injury rates. The increasing rate of urbanization and mixed land-use construction make walking (along with other non-motorized travel) a predominant transportation mode with a wide variety of behaviors expected. Because of the inherent safety concerns seen in pedestrian transportation infrastructures, especially those with conflicting multimodal movements expected (crosswalks, transit platforms, etc.), it is important that pedestrian behavior is modeled as a risk-taking stochastic behavior that may lead to errors and thus collision formation. In previous work, the complexity and cost associated with building pedestrian models in a cognitive-based environment weighted down the construction of simulation tools that can capture pedestrian-involved collisions, including those seen in shared space environments. In this paper, a tool that will help evaluate the safety of pedestrian traffic is initiated: an extended modeling framework of pedestrian walking behavior is adopted while incorporating different physiological, physical, and decision-making elements. The focus is on operational decisions (i.e., path choices defined by longitudinal and lateral trajectories) with a pre-specified set of origins and destinations. The model relies on the prospect theory paradigm where pedestrians evaluate their acceleration and directional alternatives while considering the possibility of colliding with other “particles.” Using a genetic algorithm method, the new model is calibrated using detailed trajectory data. This model can be extended to model the interactions between a variety of different modes that are present in different mixed land-use environments.

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Trajectory Data Analysis on the Spatial and Temporal Influence of Pedestrian Flow on Path Planning Decision

Sustainability ◽

10.3390/su122410419 ◽

2020 ◽

Vol 12 (24) ◽

pp. 10419

Author(s):

Jeongyun Kim ◽

Sehyun Tak ◽

Michel Bierlaire ◽

Hwasoo Yeo

Keyword(s):

Path Planning ◽

Path Selection ◽

Dynamic Walking ◽

Pedestrian Flow ◽

Trajectory Data ◽

Walking Behavior ◽

Planning Decision ◽

Walking Environment ◽

Pedestrian Modeling ◽

The Relationship

The modeling of walking behavior and design of walk-friendly urban pathways have been of interest to many researchers over the past decades. One of the major issues in pedestrian modeling is path planning decision-making in a dynamic walking environment with different pedestrian flows. While previous studies have agreed that pedestrian flow influences path planning, only a few studies have dealt with the empirical data to show the relationship between pedestrian flow and path planning behavior. This study introduces a new methodology for analyzing pedestrian trajectory data to find the dynamic walking conditions that influence the path planning decision. The comparison of the pedestrians’ path shows that the higher proportion of opposite flows are, the greater they influence the path selection decision. In this study, we investigate the relationship between the opposite flow changes and path planning behavior and find the spatial and temporal ranges of the opposite flow that affects the path planning behavior. Lastly, we find the ratio of pedestrians that update their paths with respect to the opposite flow rate.

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SENSITIVITY OF FLUVIAL SOURCE CONTRIBUTIONS IN A MIXED LAND-USE ENVIRONMENT: OTTER CREEK, FORT KNOX, KENTUCKY

10.1130/abs/2019sc-327124 ◽

2019 ◽

Author(s):

Cara Peterman ◽

◽

Alan Fryar ◽

Dwayne Edwards ◽

Lillian Gorman-Sanisaca ◽

...

Keyword(s):

Land Use ◽

Source Contributions ◽

Mixed Land Use

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Distinguishing Wastewater Contamination From On-site Systems In Mixed Land Use Watersheds

10.13031/2013.24021 ◽

2007 ◽

Author(s):

Phillip M Geary ◽

Steven A Lucas ◽

Richard H Dunstan ◽

Peter J Coombes

Keyword(s):

Land Use ◽

Mixed Land Use

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Assessing CO2 Emissions from Passenger Transport with the Mixed-Use Development Model in Shenzhen International Low-Carbon City

Land ◽

10.3390/land10020137 ◽

2021 ◽

Vol 10 (2) ◽

pp. 137

Author(s):

Xianchun Tan ◽

Tangqi Tu ◽

Baihe Gu ◽

Yuan Zeng ◽

Tianhang Huang ◽

...

Keyword(s):

Land Use ◽

Co2 Emissions ◽

Travel Demand ◽

Development Model ◽

Low Carbon ◽

Passenger Transport ◽

Transport Accessibility ◽

Mixed Use ◽

Mixed Land Use ◽

Low Carbon City

Assessing transport CO2 emissions is important in the development of low-carbon strategies, but studies based on mixed land use are rare. This study assessed CO2 emissions from passenger transport in traffic analysis zones (TAZs) at the community level, based on a combination of the mixed-use development model and the vehicle emission calculation model. Based on mixed land use and transport accessibility, the mixed-use development model was adopted to estimate travel demand, including travel modes and distances. As a leading low-carbon city project of international cooperation in China, Shenzhen International Low-Carbon City Core Area was chosen as a case study. The results clearly illustrate travel demand and CO2 emissions of different travel modes between communities and show that car trips account for the vast majority of emissions in all types of travel modes in each community. Spatial emission differences are prominently associated with inadequately mixed land use layouts and unbalanced transport accessibility. The findings demonstrate the significance of the mixed land use and associated job-housing balance in reducing passenger CO2 emissions from passenger transport, especially in per capita emissions. Policy implications are given based on the results to facilitate sophisticated transport emission control at a finer spatial scale. This new framework can be used for assessing the impacts of urban planning on transport emissions to promote sustainable urbanization in developing countries.

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Relationship between Land Use Mix and Walking Choice in High-Density Cities: A Review of Walking in Seoul, South Korea

Sustainability ◽

10.3390/su13020810 ◽

2021 ◽

Vol 13 (2) ◽

pp. 810

Author(s):

Eun Yeong Seong ◽

Nam Hwi Lee ◽

Chang Gyu Choi

Keyword(s):

Land Use ◽

Neighborhood Environment ◽

Single Family ◽

Housing Type ◽

General Belief ◽

Factors Affecting ◽

Land Use Mix ◽

Family Housing ◽

Mixed Land Use ◽

Major Factors

This study confirmed the general belief of urban planners that mixed land use promotes walking in Seoul, a metropolis in East Asia, by analyzing the effect of mixed land use on the travel mode choice of housewives and unemployed people who make non-commuting trips on weekdays. Using binomial logistic regression of commuting data, it was found that the more mixed a neighborhood environment’s uses are, the more the pedestrians prefer to walk rather than drive. The nonlinear relationship between the land use mix index and the choice to walk was also confirmed. Although mixed land use in neighborhoods increased the probability of residents choosing walking over using cars, when the degree of complexity increased above a certain level, the opposite effect was observed. As the density of commercial areas increased, the probability of selecting walking increased. In addition to locational characteristics, income and housing type were also major factors affecting the choice to walk; i.e., when the residents’ neighborhood environment was controlled for higher income and living in an apartment rather than multi-family or single-family housing, they were more likely to choose driving over walking.

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Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

Remote Sensing ◽

10.3390/rs13040631 ◽

2021 ◽

Vol 13 (4) ◽

pp. 631

Author(s):

Kyle D. Woodward ◽

Narcisa G. Pricope ◽

Forrest R. Stevens ◽

Andrea E. Gaughan ◽

Nicholas E. Kolarik ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Resource Use ◽

Training Data ◽

Systems Science ◽

Participatory Mapping ◽

Land Use Patterns ◽

Modeling Framework ◽

Use Patterns ◽

Human Habitation

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

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