Comparative Study of Online and In-person Questionnaire Surveys in Cordon Pricing

Document Type : Original Article

Authors
Alireza Gholami 1
Fatemeh Haghverdi 2
Amir Reza Mamdoohi 3
1 M.Sc., Grad., Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
2 M.Sc., Grad., Institute for Management and Planning Studies, Tehran, Iran.
3 Associate Professor, Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran; & Adjunct Professor, Department of Civil, Geological & Mining Eng., Polytechnique Montréal, Canada.
10.22034/road.2025.521191.2394
Abstract
Behavioral studies in transportation planning require accurate and reliable data, typically obtained through various survey methods. Although the use of online questionnaires has increased in recent years, systematic comparisons between online and in-person surveys in the context of travel behavior analysis remain scarce. Online surveys offer notable advantages, including speed, cost-effectiveness, and broad accessibility, making them increasingly popular in research. In contrast, in-person surveys often yield higher-quality data from certain demographic groups. To address the gap in empirical comparative research, this study conducts a field-based analysis of how survey mode—online versus in-person—affects the assessment of private car users' responses to cordon pricing policies in Tehran's traffic control zone. A total of 3,691 online and 1,029 in-person responses were collected, from which 493 and 385 cases related to mode shift behavior were selected for modeling. Descriptive statistics reveal structural similarities in response patterns across the two modes, although differences are evident in the selection of alternatives such as changes in travel time, route, or mode. Inferential statistical tests, including chi-square analysis, show significant differences between online and in-person respondents across most variables. Multinomial logit modeling indicates that variables such as trip destination within the restricted zone, gender (female), marital status, and trip purpose are significant predictors in both models. Model prediction accuracy is satisfactory: 45.23% for the online sample and 44.99% for the in-person sample. The findings suggest that integrating data from both survey modes can provide a complementary and robust basis for evaluating the effectiveness of traffic demand management policies.
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Keywords
Online Survey
Congestion Pricing
Multinomial Logit
Comparative Study
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