A comprehensive review of trip generation models based on land use characteristics

https://doi.org/10.1016/j.trd.2022.103340Get rights and content

Highlights

  • Existing researches on the impact of land use on trip generation has been explored.

  • Advanced data collection techniques for travel data collection were investigated.

  • Numerous approaches to trip generation modelling have been discussed.

Abstract

To assess the impact of any proposed development, it is necessary to estimate the trips that are likely to be generated because of such development. In case of developing countries, land use pattern varies significantly and it becomes very challenging to model the trip rates. In view of this, an effort has been made to synthesize the available literature related to modelling of trip generation rates. This paper provides a comprehensive review of studies related to impact of land use on trip rates, data collection techniques and modelling approaches. Major challenges associated with modelling of trip generation includes the unavailability of standard reference databases for multimodal trip generation, consideration of land use evolvement, less explored machine learning modelling approaches, and the feasibility of using emerging data collection techniques in travel surveys. Finally, a framework is proposed for modelling multimodal trip generation rates considering the above identified issues.

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.

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