The interaction effect of severe weather and non-weather factors on freeway travel volume

Yue, Xianfei; Yang, Xiaobao; Song, Dongdong; Yuan, Yali

doi:10.1016/j.scitotenv.2021.152057

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Existing studies investigating factors in uencing expressway passenger ow have relied on traditional econometric methods, such as descriptive analysis (Dehman & Drakopoulos, 2017) and regressions (Maze et al, 2006;Datla & Sharma, 2010;Roh et al, 2016;Yue et al, 2022;Angel et al, 2014;Roh, 2020). However, these traditional econometric models often face challenges in automatically identifying impact factors and analyzing their importance (Wen et al, 2021;Li et al, 2022).…”

Section: Explainable Arti Cial Intelligence (Xai) Modelmentioning

confidence: 99%

How Intercity Expressway Passenger Flow Varies? A View from Month, City Characteristics, and Weather at the Origin and Destination

Li,

Shi,

2023

Preprint

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This study aims to investigate the impact factors on intercity expressway passenger flow in China. A large-scale data set that integrated multiple data sources, including intercity passenger flow, city characteristics, and weather data, were constructed. To effectively handle and identify hidden patterns in the large-scale data, we employed Explainable Artificial Intelligence (XAI) models to analyze the associations between the impact factors and the intercity expressway passenger flow. The results show that among the XAI models used, the Explainable Extra Tree model that utilized SHAP (SHapley Additive exPlanations) values to explain the contributions of impact factors outperforms all others. In terms of the impact factors, we find that the intercity expressway passenger flow increased remarkably during certain months (such as February and September). In addition to city characteristics and weather variables that have been investigated by previous studies (e.g., GDP and temperature), the male-female ratio, the city tiers, and the temperature at the origin and destination cities were also found to be important contributors to expressway passenger flow. This study contributes to a deeper understanding of intercity expressway passenger flow patterns, offering valuable insights to policymakers for more effective management of intercity expressway passenger transportation.

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Section: Explainable Arti Cial Intelligence (Xai) Modelmentioning

confidence: 99%

How Intercity Expressway Passenger Flow Varies? A View from Month, City Characteristics, and Weather at the Origin and Destination

Li,

Shi,

2023

Preprint

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“…The primary scope of the study was focused on the development of regression-based trip generation models for trip attractions and trip productions by incorporating the use of secondary data such as Census data with details related to households in each zone as in investigations performed by Yue et al [10], Chmelik [11], and Hussein et al [12]. The present study also incorporates the development of a log-linear regression model, and its calibration and validation based on a smaller refined set of revealed preference (RP) obtained by eliminating erroneous data so that inter-zonal trips could be correctly synthesized.…”

Section: Introductionmentioning

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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Advancing and lagging effects of weather conditions on intercity traffic volume: A geographically weighted regression analysis in the Guangdong-Hong Kong-Macao Greater Bay Area

Lin,

Hong,

et al. 2024

International Journal of Transportation Science and Technology

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The interaction effect of severe weather and non-weather factors on freeway travel volume

Cited by 5 publications

References 19 publications

How Intercity Expressway Passenger Flow Varies? A View from Month, City Characteristics, and Weather at the Origin and Destination

How Intercity Expressway Passenger Flow Varies? A View from Month, City Characteristics, and Weather at the Origin and Destination

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

Advancing and lagging effects of weather conditions on intercity traffic volume: A geographically weighted regression analysis in the Guangdong-Hong Kong-Macao Greater Bay Area

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