A comprehensive review of trip generation models based on land use characteristics
Introduction
Human mobility has always been a very challenging field for researchers, and an understanding of mobility patterns is essential to predict the travel patterns of individuals in order to plan for adequate transport infrastructure. Travel Demand Modelling (TDM) has been widely used as a means to forecast the traffic demand associated with any study area or Traffic Analysis Zone (TAZ). The modelling is carried out in four stages: Trip Generation, Trip Distribution, Mode Spilt, and Traffic Assignment. Trip generation is the first stage and is the main pillar to the success of this approach. Trip generation aims at modelling the total number of trips that are expected to be generated or attracted to each zone of the study area. To predict the traffic impact assessment (TIA) of any proposed development, it is necessary to predict the total number of trips that are likely to be generated because of such proposals. Travel behavior of the residents gives an idea of the numbers of trips that are likely to be generated.
Travel behaviour is largely influenced by the socio economic attributes of the traveller, which mainly include household size, vehicle ownership, and income. (Rashidi et al., 2010, Shay and Khattak, 2012, Wang, 2013, Chang et al., 2017, Sun and Yang, 2017). Many previous studies have also shown the relevance of accessibility and street configuration in understanding mobility patterns (Paul, 2012, Chang et al., 2017). Another major factor affecting trip generation is land use. As different types of land use are associated with different sorts of activities, significant variation in trip rates is expected across different land use types. Hence, Land use is considered one of the salient factors to understand the trip generation and can be better predictor compared to socioeconomic characteristics (Mirmoghtadaee, 2012). Traditionally, the planning process considers travel demand and land use separately. If land-use planning is considered after transport planning, then the benefits of feedback and interaction doesn’t get included. Thus, it is necessary to account for the land use characteristics in the planning stage itself so that better outcomes can be achieved. In the case of developing countries, land use varies significantly, and mixed land use is generally found, which results in reduced trip rates. However, only a few studies have focused upon the effect of land use in a developing country context (Sarkar and Chunchu, 2016, Jayasinghe et al., 2017, Ahmed et al., 2020). The dynamic nature of land use characteristics is not much explored.
Trip generation modelling has traditionally relied largely upon trip diaries, intercept surveys, and household interview surveys to obtain data about mobility patterns. Though these techniques facilitate collection of detailed trip and traveller information, they are often limited by high costs, non-representative samples, misreporting, and fatigue (Sarmiento et al., 2013). Rapid technological advancements in recent years have made it possible to collect a massive amount of mobility data with ease. Mobile phone positioning (MPP), call record data (CDR), global positioning system (GPS), and web-based application data are examples of cutting-edge technologies that can generate massive amounts of data at a much lower cost. Many studies have stated the need for emerging technologies to overcome the shortcomings of traditional survey methods; however, they cannot completely replace traditional methods and should be used as a supplement to them (Roorda et al., 2011; Chiao et al., 2011; Richard and Rabaud, 2018). However, the majority of these studies are limited to developed nations, and the applicability of such technologies in the case of developing nations has not yet been worked out. Major barriers to adoption of such technologies include low response rates, reliability of obtained data, expertise in such technologies, etc.
Keeping in view the need to incorporate the effect of land use and latest technologies in development of trip generation models for developing countries, this paper primarily focuses upon providing comprehensive review on:
- •
Use of advanced technologies for travel data collection.
- •
Providing insights into the available studies related to the development of trip generation models based on land use characteristics.
- •
Recent methodological developments related to trip generation models.
This paper contributes to literature by covering all the aspects which are indeed important for trip generation modelling. Further, methodological framework for obtaining trip generation rates is also proposed addressing various issues identified from literature. To fulfil these objectives, a literature review of pertinent publications is carried out. The existing research database search has been adopted based on the web of science documents. Two-cycle search techniques were adopted to gather literature. In first cycle, literature related to “trip generation” and “trip generation modelling” were gathered which was followed by “travel data collection” and “GPS based travel survey” in the subsequent cycle. The search was limited to studies carried out after 2005 and for English-language publications, which may have confined the amount of literature reviewed. After performing an initial overview of each publication, additional relevant literature was associated through “backward referencing” followed by “forward referencing”. A total of 97 publications were reviewed that were significantly relevant to the defined objectives. Of the 97 publications reviewed, 49 focused on trip generation based on land use characteristics and 44 on the use of advanced technologies for travel data collection, providing the main basis for the literature review. Fig. 1 presents the various keywords used by the researchers in studies related to trip generation. The following keywords are mostly used, such as land use development, travel behaviour, mobile phone positioning, GPS, travel demand, personal trip data, origin destination matrix, trip identification, mode detection, panel surveys, household travel surveys, travel diaries, by linking with the trip generation. It can be observed that many recent studies have focused upon land use in developing trip generation models, but these studies are limited to developed countries only. From bibliography analysis, it is found that almost 65% of the studies are confined to the United States and China. Country wise breakdown of studies related to trip generation incorporating land use is shown in Fig. 2.
Thus, the reviewed studies related to trip generation based on land use characteristics, and emerging technologies for travel surveys are recent and confined to developed countries only, and much needs to be explored in the context of developing countries. The next section provides insight on the studies related to travel survey data collection, trip generation based on land use characteristics, and various available trip generation modelling approaches. Section 3 discusses various research gaps identified from the reviewed literature. Section 4 summarizes the overall study based on which conclusion is drafted and conceptual framework for modelling trip generation rates is also proposed.
Section snippets
Literature review
This section covers the brief overview and research carried out in the discipline of trip generation modelling. Firstly, reviews of studies related to travel data collection techniques are presented, followed by trip generation based on land use, data processing techniques, and lastly, an overview of various modelling techniques available in literature.
Research gaps in the existing literature
The majority of the existing studies on the development of trip generation models have explored the effects of land use, socio-economic, and built environment measures in the case of automobile-oriented nations. In the context of developing countries, the effect of land use is not much explored by researchers. In developing nations, land-use conditions change tremendously over time due to rapid developmental activities, which needs to be investigated. Though many studies have proposed various
Summary
The main objective of this paper was to provide a comprehensive review of the trip generation model associated with land use characteristics and advanced technologies in travel data collection. Further, various modelling approaches used in the literature were examined. Though socio-demographic, built environment, and land use characteristics had an influence on the trip generation rates, it was found that land use characteristics were better predictors of trip generation rates. The majority of
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References (107)
- et al.
Smartphone based travel diary collection: experiences from a field trial in Stockholm
Transp. Res. Proc.
(2017) - et al.
Advanced Trip Generation/Attraction Models
Proc. - Social Behav. Sci.
(2014) - et al.
Trip Attraction Model Using Radial Basis Function Neural Networks
Proc. Eng.
(2015) - et al.
Transport survey methods - in the era of big data facing new and old challenges
Transp. Res. Proc.
(2018) - et al.
Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands
Transport. Res. Part C: Emerg. Technol.
(2009) - et al.
Modelling trip generation using mobile phone data: A latent demographics approach
J. Transp. Geogr.
(2019) - et al.
Land use inference from mobility mobile phone data and household travel surveys
Transportation Research Procedia
(2020) - et al.
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example
Transport. Res. Part C: Emerg. Technol.
(2013) - et al.
Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study
Transport. Res. Part A: Policy Pract.
(2010) - et al.
Exploring ITE’s Trip Generation Manual: Assessing age of data and land-use taxonomy in vehicle trip generation for transportation impact analyses
Transport. Res. Part A: Policy Pract.
(2018)
Activities and Daily trips of University Students in a CBD area (Case Study: Amirkabir University of Technology)
Transp. Res. Procedia
Evaluating the biases and sample size implications of multi-day GPS-enabled household travel surveys
Transp. Res. Proc.
Trip and Parking Generation at Transit-oriented Developments: Five US Case Studies
Landscape Urban Plann.
Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies
Proc. – Soc. Behav. Sci.
Identification of activity stop locations in GPS trajectories by DBSCAN-TE method combined with support vector machines
Transp. Res. Proc.
The relationship between land use and intrazonal trip making behaviors: Evidence and implications
Transport. Res. Part D: Transport Environ.
Multiple classification analysis in trip production models
Transp. Policy
Examining the relationship between different urbanization settings, smartphone use to access the Internet and trip frequencies
Journal of Transport Geography
Measuring density and diversity to model travel behavior in Indian context
Land Use Policy
Application for developing countries: Estimating trip attraction in urban zones based on centrality
J. Traffic Transport. Eng. (Engl. Ed.)
Analysis of Household Survey Sample Size in Trip Modelling Process
Transp. Res. Proc.
SmartITS: Smartphone-based identification and tracking using seamless indoor-outdoor localization
Journal of Network and Computer Applications
An evaluation of emerging data collection technologies for travel demand modeling: from research to practice
Transport. Lett.
Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai
Landscape Urban Plann.
Daily travel behaviour in Beijing, China: An analysis of workers trip chains, and the role of socio-demographics and urban form
Habitat Int.
Workshop Synthesis: Household travel surveys in an era of evolving data needs for passenger travel demand
Transportation Research Procedia
Supporting large-scale travel surveys with smartphones – A practical approach
Transport. Res. Part C: Emerg. Technol.
On detection of emerging anomalous traffic patterns using GPS data
Data Knowl. Eng.
Modelling urban freight generation: A case study of seven cities in Kerala, India
Transport Policy
Trip Generation by Transportation Mode of Private School, Semi-private and Public. Case Study in Merida-venezuela
Transp. Res. Proc.
French household travel survey: The next generation
Transp. Res. Proc.
Spatial distribution of urban trips in recently expanded Surat city through Fuzzy Logic with various clustering Techniques: A case study of typical metropolitan city in India
Transportation Research Procedia
A process for trip purpose imputation from Global Positioning System data
Transport. Res. Part C: Emerg. Technol.
Vehicle Ownership And Trip Generation Modelling
IATSS Res.
Travel behavior of low-income residents: studying two contrasting locations in the city of Chennai, India
J. Transp. Geogr.
Household travel surveys: Where are we going?
Transport. Res. Part A: Policy Pract.
Search for a global positioning system device to measure person travel
Transport. Res. Part C: Emerg. Technol.
The Challenge of Obtaining Ground Truth for GPS Processing
Transp. Res. Procedia
A walk trip generation model for Portland, OR
Transport. Res. Part D: Transport Environ.
On data processing required to derive mobility patterns from passively-generated mobile phone data
Transport. Res. Part C: Emerg. Technol.
Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example
Transport. Res. Part C: Emerg. Technol.
A pre-processing and network analysis of GPS tracking data
Spatial Econ. Analy.
Using Smartphones and Sensor Technologies to Automate Collection of Travel Data
Transport. Res. Record: J. Transport. Res. Board
Trip Generation Rates of Land Uses in a Developing Country City
Transport. Res. Record: J. Transport. Res. Board
Creating Trip Generation Models for Unplanned Cities
International Journal of Scientific and Engineering Research
Forecasting Travel Demand with Alternatively Structured Models of Trip Frequency
Transport. Plann. Technol.
Modeling External Trips: Review of Past Studies and Directions for Way Forward
Journal of Transportation Engineering, Part A: Systems
How to combine survey media (web, telephone, face-to-face): Lyon and Rhône-Alps case study
Transp. Res. Proc.
Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data
Transport. Res. Record: J. Transport. Res. Board
Household Travel Surveys with GPS
Transport. Res. Record: J. Transport. Res. Board
Cited by (6)
Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation
2023, Ain Shams Engineering JournalIntegrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits
2024, Transportation LettersA System Dynamics Model for Assessing Land-Use Transport Interaction Scenarios in Chennai, India
2023, Sustainability (Switzerland)Trip Attraction Rates of Banking Services in Developing Countries' Cities
2023, Civil Engineering Journal (Iran)Sustainable land use as panacea for efficient households’ trips in Osun State Nigeria
2023, Cogent Engineering