A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

Yang, Fan; Li, Fulin; Ding, Fan; Tan, Huachun

doi:10.3390/su12187688

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…This test is used to determine whether or not the model is stationary. It represents that whether the explanatory components in the model have a consistent correlation with the dependent variable in both geographic and data space (Mitchel and Griffin, 2005;Yang et al, 2020).…”

Section: Model Stationarymentioning

confidence: 99%

Spatial analysis of factors influencing bacterial leaf blight in rice production

Ahmad

Haider

Randhir³

et al. 2023

Braz. J. Biol.

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Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial leaf blight that is a major threat to rice production. Crop losses in extreme situations can reach up to75%, and millions of hectares of rice are affected each year. Management of the disease required information about the spatial distribution of BLB incidence, severity, and prevalence. In this study, major rice-growing areas of Pakistan were surveyed during 2018-2019 for disease occurrence, and thematic maps were developed using geographic information system (GIS). Results showed that Narowal district had highest percentage of disease incidence (54-69%), severity (42-44%), and prevalence (72-90%) meanwhile Jhung district had the lowest incidence (21-23%), severity (18-22%), and prevalence (45-54%). To understand the environmental factors contributing to this major rice disease, the research analyze, the spatial relationships between BLB prevalence and environmental variables. Those variables include relative humidity (RH), atmospheric pressure (A.P), minimum temperature, soil organic carbon, soil pH, and elevation, which were evaluated by using GIS-based Ordinary Least Square (OLS) spatial model. The fitted model had a coefficient of determination (R2) of 65 percent explanatory power of disease development. All environmental variables showed a general trend of positive correlation between BLB prevalence and environmental variables. The results show the potential for disease management and prediction using environmental variable and assessment.

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Section: Model Stationarymentioning

confidence: 99%

Spatial analysis of factors influencing bacterial leaf blight in rice production

Ahmad

Haider

Randhir³

et al. 2023

Braz. J. Biol.

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show abstract

“…Another reason for using Big Data is the enormous increase in the amount of passively collected location data that are available. Research on the use of LBSN data for supply-demand transportation is advancing and ongoing [6], including trip generation and [7] trip distribution modeling [8], as well as trip destination selection [9]. It is clear, though, that research on the use of LBSN data for transportation system modeling is not yet comprehensive and in-depth.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The point radius number is expressed in latitude, longitude, and radius and is entered in the data collection program script. Twitter limits the data capture radius to less than 25 miles (40.2 km) [7]. Since there are two techniques for retrieving data, another question arises: whether streaming data or historical data alone are sufficient, or do both methods need to be used to provide enough data for the modeling?…”

Section: Literature Reviewmentioning

confidence: 99%

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling

Rayat,

Dwicaksono,

Putro

et al. 2023

Applied Sciences

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This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data.

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“…This approach was considered less tedious when compared to the time and effort spent on traffic data collection. Yang et al [36] demonstrated the use of a random forest regression approach to model trip generation for resident and non-resident trip-makers of 36 zones in Nanjing city in China using cell phone signals collected from 76,061 signal towers. The study indicated that the trip-making characteristics of resident and non-resident trip-makers of the city varied significantly and that mixed land use influenced trip generation.…”

Section: Mustafa and Zhongmentioning

confidence: 99%

Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS

Anand

Varghese

2022

Civ Eng J

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The efficiencies of urban transport systems in several cities are drastically affected due to difficulties imposed by rapid urbanization and the proliferation of private modes of transport. The conventional four-stage travel demand modeling approach provides an ideal platform to formulate strategies to rectify problems in urban transport. Trip generation is the first stage in this exercise (where trip production and trip attractions are modelled), followed by trip distribution in the second stage. The present work related to the development of models for trip generation and trip distribution necessitated the use of census data related to the number of households in each zone since the available revealed preference (RP) data compiled based on household interview surveys was partially incorrect. A review of the literature indicated that studies on the use of sparsely available and partially inaccurate data such as revealed preference and zone-specific secondary data on trip generation and trip distribution were limited. In the present study, the use of the initial trip generation regression models developed based on existing household survey data resulted in prediction errors ranging between 26% and 32%. Modeling efforts after applying corrections to zone-specific characteristics based on secondary data and the use of trip rate per household later resulted in prediction errors of less than ±5%. In the latter phase of work related to trip distribution modeling, a log-linear regression model was developed based on a smaller refined set of the revealed preference data obtained by eliminating erroneous data in a stage-wise manner. The use of the calibrated and validated model ensured that the errors in predicted trip frequencies were less than 0.6%. Here, the information on the inter-zonal aerial distances that formed part of the trip distribution model was obtained using GIS approaches that employed the moment area method, which considered the intensity of land use at the sub-zone level. The combined strategy incorporates the use of GIS-based approaches to determine inter-zonal aerial distances, and the use of the refined relationship between trip interchanges and the inter-zonal aerial distances in the development of a reliable log-linear regression model for trip distribution contributed towards attaining higher accuracies in travel demand estimation. The modeling approaches described herein do not rely on the use of sophisticated technology, and time-consuming data processing. The study will provide the basic framework for transport planners to formulate better strategies for travel demand modeling where available data is noisy and less reliable. Doi: 10.28991/CEJ-2022-08-09-013 Full Text: PDF

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A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

Cited by 8 publications

References 26 publications

Spatial analysis of factors influencing bacterial leaf blight in rice production

Spatial analysis of factors influencing bacterial leaf blight in rice production

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling

Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS

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