Mapping Five Decades of Trip Generation Research: A Bibliometric Review for Sustainable Transportation Planning (1972–2024)

Background — Trip generation estimates the trips produced or attracted by land use, which is vital for transportation planning, guiding infrastructure, and policies [1]. Originally focused on passenger transport, recent studies now include freight, land use, and multimodal travel, shifting from static models to context-sensitive forecasts [2-4]. The field has progressed from regression to AI techniques, but it still lacks a comprehensive synthesis. Researchers increasingly use bibliometric methods to identify trends and influential contributors [5-8].

This study employs Kuhn’s theory to trace the evolution of trip generation from traditional models to machine learning and sustainability frameworks, indicating a paradigm shift [9]. Existing reviews often lack geographic diversity, especially from Asia and Latin America. While Scopus is a key source, broader inclusion recommends using Web of Science, Dimensions, and Google Scholar.

Problem Statement — Despite the increasing body of trip generation research, existing reviews often lack global coverage, theoretical depth, and methodological rigor, especially regarding sustainability and multimodal transportation. This study addresses these gaps through a systematic, theory-driven bibliometric analysis to map the field’s evolution and guide future research.

Objectives — This study aims to explore, explain, and forecast the trajectory of trip generation research by answering these questions:

1). What are the publication trends in trip generation research, and how have they evolved over decades?

2). Who are the most prolific authors, and what are their main contributions?

3). Which institutions lead in publication output, and how have they shaped the field?

4). Which countries and regions are most active, and how has their participation changed over time?

5). Which journals are key sources for disseminating trip generation research?

6). What are the most highly cited documents, and what explains their academic influence?

7). What are the dominant themes and keywords, and how have they evolved?

8). What new research directions are emerging, particularly?

This study provides the first comprehensive bibliometric analysis of trip generation research (1972–2024), offering an overview of past work, guidance for future studies, and practical insights for planners and researchers.

3.1 Document profiles

This section examines 394 documents from 1972 to 2024, focusing on productivity metrics, subject area distribution, publication formats, and language trends to highlight key development patterns in the field.

3.1.1 Citation and productivity metrics

Table 1 summarizes key indicators for 394 trip generation publications. Of these, 283 were cited, with a total of 3,900 citations (average 9.90 per paper; 13.78 for cited papers). Papers had an average of 2.61 authors and 3.80 citations per author. The h-index is 31, meaning 31 papers have at least 31 citations each; the g-index is 49, indicating that citation impact is concentrated in a few highly cited articles; and the m-index is 0.574, showing a gradual increase in academic influence over time. Overall, the field maintains steady interest but has room to improve its visibility through increased collaboration and interdisciplinarity.

Table 1. Citation metrics

3.1.2 Subject area distribution

Table 2 presents subject areas; most studies (68.53%) focus on engineering, followed by social sciences (40.36%), environmental science (13.45%), and computer science (8.63%). The social and environmental contributions indicate a growing interest in the sustainability of trip generation. The limited presence of computer science highlights a gap in advanced data analytics and machine learning, which will guide future research.

Table 2. Subject area

3.1.3 Document types and source outlets

Table 3 shows that most publications are journal articles (74.87%), followed by conference papers (20.56%) and others (4.56%). Table 4 indicates that 78.17% appeared in journals, 17.50% in conference proceedings, and a few in other outlets. These patterns are strong in journals and reveal opportunities for expanding to other platforms.

Table 3. Document type

Table 4. Source type

3.1.4 Language of publication

Table 5 shows the language distribution in publications. 90.61% are in English, emphasizing its importance in international academic communication. Other languages include Chinese (3.81%), Japanese (2%), Spanish (2%), and Persian (1%). This indicates the accessibility of global research and the underrepresentation of regional perspectives.

Table 5. Language

3.1.5 Synthesis and implications

The bibliometric landscape shows growth in trip generation research, with numerous studies focused on engineering in English. Although citation metrics indicate progress, low h- and m-index totals highlight the need for more impactful, interdisciplinary work. Future research should adopt diverse methods, especially machine learning and spatial analysis, and increase regional representation to foster inclusivity.

3.2 Publication trends

Figures 2 and 3 address the first research question: “What are the publication trends in trip generation research, and how have they evolved over decades?” This section examines the increase in publication activity, author participation, citation patterns, and the thematic development of trip generation research from 1972 to 2024.

3.2.1 Annual growth in publications and citations

Figure 2 shows annual publications and citations, while Figure 3 depicts the growth of publications from 1972 to 2024. A second-order polynomial model was chosen because it provided the best fit for cumulative publications over time. This is indicated by its high coefficient of determination (R² = 0.988), outperforming both the linear and exponential models. Early research was limited, but a steady increase began in the early 2000s. Publications peaked at 25 in 2022, indicating growing interest. Citations also increased, reaching a high of 388 in 2019. These metrics emphasize the importance of trip generation studies in urban development. Including mobility in these studies in urban planning, policy, and sustainable development is essential for building resilient communities.

3.2.2 Author contributions and citation metrics

Table A1 shows the full list of publications by year, displays the Number of Contributing Authors (NCA), Average Citations per Publication (C/P), and Average Citations per Cited Publication (C/CP). The NCA steadily increased, reaching 76 in 2022, indicating wider academic participation, but decreased to 29 in 2024, suggesting increased specialization. The peak values of C/P = 35.13 and C/CP = 40.14 correspond to years of major methodological breakthroughs, such as machine learning models, multimodal trip integration, and person-trip concepts, underscoring the influence of pioneering work.

3.2.3 Evolution of trip generation research

The fifty-three-year evolution of trip generation research can be divided into three phases.

Foundational Phase (1972–1999): Developed baseline models using cross-classification, regression, and household surveys. Focused on household size, vehicle ownership, and land use, with static, vehicle-based metrics mainly guided by ITE.

Transitional Phase (2000–2010): Integrated socio-economic and land-use factors using data-driven methods such as neural networks and decision trees. Focused on mixed-use developments and freight trip generation; however, models struggled with urban complexities.

Contemporary Phase (2011–2024): Highlights sustainability, multimodal transportation, and advanced tools like AI and GIS. Broadens research to include pedestrian trips, accessibility, and the impact of the built environment, supported by large datasets (CDR, GPS, mobile data) for real-time modeling.

COVID-19 Impact (2020–2022): The pandemic shifted focus to behavioral changes, remote work, and decreased public transportation. Existing studies highlight health risks, psychological factors, and lasting shifts in demand, underscoring the need for adaptable, resilient trip generation models [17-19]. Overall, research has advanced from static land-use estimates to dynamic, interdisciplinary approaches that reflect the complexity of urban mobility while prioritizing sustainability and resilience.

3.3 Publications by authors

This section addresses the second research question: “Who are the most prolific authors in trip generation publications?” Table A2 lists the top 26 authors based on publications, citations, and author-level indices (h, g, m). Currans Kristina (Portland State University, United States) leads with 8 publications, 6 citations, and 101 total citations (C/P = 12.63; C/CP = 16.83; h = 5; g = 8; m = 0.238), demonstrating consistent influence. Following is Clifton Kelly (Portland State University, United States) with 7 publications, 4 citations, and 28 total citations (C/P = 4; C/CP = 7; h = 4; g = 5; m = 0.098), indicating sustained relevance. Overall, the metrics highlight both productivity and impact, with contributions from Asia, the Middle East, and North America shaping the field’s development.

3.4 Publications by institutions

To address the third research question: “What are the most influential institutions in trip generation publications, and how have they contributed to the development of the field?” Table A3 ranks the top 25 institutions by productivity and impact. Southeast University leads in both output and citations, supported by strong h- and g-indices. The University of California and Portland State University (United States) show consistent contributions with moderate impact. Parsons Brinckerhoff / WSP (Brazil), Institut Henri Fayol (France), and the University of Toronto (Canada) produce fewer publications but have high citation rates, reflecting quality-driven influence in areas such as multimodal mobility and machine learning. Others, including Tennessee Technological University, Rensselaer Polytechnic Institute, and Birla Institute of Technology and Science Pilani, demonstrate specialization, producing modest outputs but achieving strong citation impact. Overall, the data highlights a global research network with clusters in North America, Europe, and Asia, underscoring the need for greater collaboration and expanded research in developing countries on emerging themes such as non-motorized travel, pandemic effects, and AI in trip modeling.

3.5 Publications by countries

This section addresses the fourth research question: “What are the most active countries in trip generation publications, and how does this vary across different regions and periods?” Table A4 and Figure 4 rank the top 20 countries in trip generation research. The US leads with 157 publications and 1954 citations, supported by the highest h-index (23) and g-index (44). China (37 papers) and India (28) follow, reflecting growing research capacity despite lower citation impact. Several countries with fewer publications, including the UK, Canada, and Japan, achieve high citation efficiency (C/P = 11.77 – 20.28; C/CP = 13.91 – 31.69), indicating quality-driven influence. Germany and Sweden exhibit strong citation profiles, while Indonesia, Brazil, and Iran contribute moderate publication outputs with comparatively lower citation impact. Overall, research is concentrated in a few nations, but citation efficiency highlights broader global influence. Building collaboration between leading and emerging countries is essential to improve the adaptability and transferability of trip generation models across diverse contexts.

3.6 Publications by source titles

This section addresses the fifth research question: “Which journals are key sources for disseminating trip generation research?” Table A5 highlights the leading outlets in this field. Transportation Research Record (47 papers, 898 citations, h = 17, g = 28), ITE Journal (36 papers), and the Journal of Urban Planning and Development (10 papers) are the most prolific outlets, with the latter offering practice-oriented insights. High-impact journals such as Transportation and Transportation Research Part A: Policy and Practice publish fewer papers but achieve much higher citation efficiency (C/P = 35.56 – 39.75; C/CP = 35.56 – 39.75). This indicates a dual publishing pattern: technical and practitioner-focused outlets maintain steady output, while high-impact journals influence theoretical and methodological advances. Increasing publication in interdisciplinary venues could boost methodological diversity and support themes like sustainability, emerging mobility, and post-pandemic travel.

3.7 Highly cited documents

Table A6 addresses the sixth research question: “What are the most highly cited documents in trip generation publications?" The 20 top-cited studies focus on trip generation across several key areas, including under-studied or vulnerable groups, freight trip generation, disaster response, shared mobility, and multimodal systems, reflecting applications to emerging contexts. Future research opportunities include developing dynamic, context-sensitive trip generation models that integrate real-time geospatial and socio-demographic data, expanding the use of ML/AI methods, and linking passenger and freight modeling to support sustainable, digitally enabled transport systems.

3.8 Trend topics

Figure 5 displays a term frequency analysis from 2014 to 2024. VOSviewer categorizes the frequency into three categories: 90 for high-frequency terms, 60 for medium-frequency terms, and 30 for emerging but relevant terms. The analysis highlights trip generation modeling with 90 mentions, followed by transportation planning with 60 mentions, and freight/land-use studies with 30 mentions each. These trends indicate a growing interest in data-driven, multimodal, and sustainability-focused approaches. The increased focus on land use and freight emphasizes the need for flexible models that can accurately reflect diverse urban and logistical patterns to support sustainable mobility and effective demand forecasting.

Figure 6 also illustrates the development of methodological groups within trip generation research across three periods (pre-2010, 2011–2014, and 2015–2024). The proportions of major methodological categories—Regression/Logit, ANN/ML, GIS, Simulation/Behavioral, and Trip Generation Modeling—change noticeably over time. Regression-based methods dominate early studies, while later periods show a gradual shift toward simulation/behavioral models and a modest rise in ANN/ML and GIS applications. The figure also highlights a clear gap: machine learning and GIS techniques remained limited for many years, only becoming more prominent in recent research.

Figure 6. The evolution of methodological groups in trip generation research over time

3.9 Co-occurrence analysis

This analysis was conducted using VOSviewer by Van Eck and Waltman [7] to map relationships among keywords and terms, highlighting research trends and thematic clusters. A minimum occurrence of two, fractional counting, and modularity-based clustering with the LinLog layout were used, resulting in clear, color-coded clusters that reveal conceptual structures and reduce noise in groupings.

3.9.1 Author’s keywords analysis

To answer the research question: “What are the most common author keywords in the literature on trip generation, and how have they evolved over time?”, we analyzed 122 keywords that appeared at least twice using VOSviewer. Figure 7 displays the co-occurrence network of these keywords, with circle sizes indicating frequency and connections showing co-occurrence strength [6]. The map revealed five distinct semantic clusters, each representing a coherent subfield within the trip generation literature.

Trip Generation Modeling (Blue): planning, forecasting, traffic impact assessment, person-trip estimation, and developing-country studies (the field’s traditional core).

Freight Trip Generation and Machine Learning (Red): freight/logistics, machine learning, simultaneous equations, and mode-choice analysis (data-driven and specialized).

Land Use and Mobility (Yellow): spatial analysis, accessibility, socio-demographics, and equity—linking built form to travel behavior.

Neural Networks and Simulation (Purple): demand prediction, micro-simulation, regression models, and operational traffic forecasting tools.

General Modeling (Green): trip forecasting, residential development, artificial neural networks, and data analysis—serving as a bridge between classic regression and advanced neural methods.

3.9.2 Temporal evolution of research focus

Figure 8 shows a timeline overlay of the co-occurrence network, emphasizing how research focus areas evolved across five distinct phases:

Pre-2010: Concentrated on early studies of disaggregated spatial interaction models and principal component analysis, laying the groundwork for trip generation research.

2013-2014: Transitioned toward mode choice and activity-based modeling, highlighting behavioral factors and person-trip interactions.

2015-2016: Trip generation became a central element in traffic impact assessment (TIA) studies, underscoring its growing importance in regulations and planning.

2017-2018: Focused on urban transformation, land use, and developing countries, with researchers exploring contextualized modeling methods amid rapid urban growth and logistics expansion.

2019–present: The field is shifting toward advanced analytics, such as freight trip generation, artificial neural networks, and machine learning, driven by the need for precision and scalability in demand forecasting.

Figure 9 displays the temporal evolution of research themes across four periods (1972-1999, 2000-2011, 2012-2019, and 2020-2024). The earliest phase (1972-1999) is defined by foundational work focused on static trip rates and basic estimation methods. From 2000 to 2011, research shifts toward core trip-generation issues, reflecting growing interest in applying trip-generation techniques in practice. The period from 2012 to 2019 shows clear diversification, with studies addressing distribution centers, classification techniques, and advancements in data collection. In the most recent period (2020-2024), the thematic focus broadens to include land-use integration, improved estimation, and renewed regression-based refinements, aligning with broader trends toward behavioral, sustainability-focused, and multimodal perspectives highlighted by the thematic evolution map.

3.9.3 Themes analysis

To address the research question, “What are the key themes and topics emerging from co-occurrence analyses of author keywords and title/abstract terms in the literature on trip generation research?”, Figure 10 shows the co-occurrence clusters of author keywords in trip generation. These clusters are organized into five groups using modularity-based clustering to highlight common terms and their semantic connections.

Trip generation research can be categorized into five thematic groups, highlighting both methodological evolution and policy requirements.

Cluster 1: Innovative Techniques marks a shift from traditional models to data-driven tools such as machine learning, simulations, and spatial econometrics. Applications include freight and evacuation trips, as well as micromobility, with an emphasis on sustainable and resilient transportation.

Cluster 2: Sustainable Demand Forecasting focuses on low-carbon and equitable travel policies. Methods have evolved from regression and logit models to AI-based approaches that link land-use patterns, lifestyles, and emissions.

Cluster 3: Core Modeling for Urban Planning establishes the foundation for trip estimation through surveys, four-step models, and mobile data. It highlights the need for localized, behavior-sensitive models, especially in regions with limited data.

Cluster 4: Land Use and Accessibility studies how urban form, demographics, and accessibility influence mobility. Advanced spatial methods uncover equity gaps and support multimodal, neighborhood-scale planning.

Cluster 5: Freight and Logistics is an emerging area that addresses freight flows, last-mile delivery, and disruptions such as pandemics. It increasingly uses synthetic data, micro-simulation, and sustainable freight strategies. Together, these clusters shift from static models toward sustainability, technological integration, and spatial equity—signaling a maturing field that responds to contemporary urban challenges.

Trip generation research has shifted from static trip rates to adaptive, technology-driven methods that better reflect the complexity of urban mobility. Five thematic clusters define the field, with increasing focus on sustainability, equity, and machine learning. The United States and China lead in output, but contributions from developing countries remain limited, underscoring the need for greater global participation. Overall, the field is progressing toward more interdisciplinary, context-aware, and sustainable models that tackle current urban challenges.