Developing an activity-based trip generation model for small/medium size planning agencies

Molla, Mohammad M.; Stone, Matthew L.; Motuba, Diomo

doi:10.1080/03081060.2017.1314505

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…To facilitate statistics and calculations, the station impact range is represented as a circle with a uniform radius, and the circle's center is the rail station's location. Because passengers generally reach the endpoint by walking after arriving at a certain rail station or walking to fnd shared bikes and bus stops, only the acceptable range of walking connection is considered in the selection of the radius, and the value is generally 800 m as the researcher derived from the survey [28,29], and the overlapping part of the circular bufer is divided based on the Tyson polygon, which makes the fnal result without overlapping area. Te infuence range of the 800-meter radius of the Beijing rail station is shown in Figure 10.…”

Section: Calculation Results Of Impact Factor Indicatorsmentioning

confidence: 99%

Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)

et al. 2023

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Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environment data to be extracted. Therefore, this paper focuses on investigating the relationship between the built environment around the station and the station passenger flow and discusses whether the built environment data can be applied to the station passenger flow prediction. Firstly, the evaluation system of station passenger flow influencing factors is built based on multisource data. The inner relationship between built environment factors and station passenger flow is investigated using the Pearson correlation analysis. Based on this, a multilayer perceptron (MLP)-based passenger flow prediction model was developed to predict the passenger flow at key stations. The study results show that the built environment factors impact station passenger flow, and the MLP prediction model has better prediction accuracy and applicability. The results of the study can be applied to predict the passenger flow scale of rail stations without historical passenger flow data and thus are also applicable to new rail stations.

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Section: Calculation Results Of Impact Factor Indicatorsmentioning

confidence: 99%

Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)

et al. 2023

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“…The primary focus of ABM is to predict individual activity effectively. At the early stage, research explored integrating ABMs into the four‐step method to make full use of travel survey data and improve the explanatory ability of four‐step method [11, 12]. Such a prediction framework retains the forecasting steps of the aggregated model.…”

Section: Literature Reviewmentioning

confidence: 99%

Activity‐based model based on multi‐day cellular data: Considering the lack of personal attributes and activity type

Guo,

Yang,

Yan

et al. 2023

IET Intelligent Trans Sys

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Cellular data is a sequence of base station‐interaction data that records user ID, timestamp, location area code (LAC), and cell identity (CID). With long observation periods, the data allows traffic planners to analyze coarse‐granularity user travel behaviours at low costs. However, utilizing cellular data for urban planning is not an easy task as the data lacks user socioeconomic attributes due to privacy issues. The data is also challenging to recognize user activity types. This paper proposed an activity‐based model (ABM) with skeleton schedule constraints for multi‐day cellular data. The model first infers the activity pattern and home location. Then it predicts start time, duration, and locations separately for primary and secondary activities. Next, the model infers the travel mode and path considering user multi‐day travel behaviour, path non‐linear coefficient, and transfers. Finally, a time adjustment module is proposed to avoid time conflicts in consecutive activities. The proposed activity‐based model is validated at activity, travel, and path levels. Results show that the proposed model can effectively predict activities and has much higher stability than existing ABMs based on travel surveys.

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“…To explore the characteristics of multiple activity behaviours, Stopper et al [11] and Krizek [12] classified activities into three primary groupings according to whether the primary travel characteristics were fixed: mandatory activities (such as commuting and schooling), maintenance activities (such as shopping and banking), and optional activities (such as social activities and tourism). According to trip purposes and activity sequences, Hedau and Sanghai [13] classified the commuting-generated trip into five activity patterns and Molla et al [14] classified the randomly selected trip data into nineteen activity patterns. They all found that the majority of activity patterns are simple activity patterns such as Home-Work-Home (HWH), Home-Other-Home (HOH).…”

Section: Related Studiesmentioning

confidence: 99%

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

Cao

Yin

et al. 2021

PROMET

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This study explores the spatial distribution characteristics of travel activities and their relationship with land use, using data from the resident travel survey in 2015 of Xiaoshan District of Hangzhou City, China. A new classification method is proposed to classify the travel activity patterns into three groups: single-activity travel, multi-activity intermittent travel, and multi-activity continuous travel. The main findings are: (a) the length of activity chain and the proportion of multi-activity travels increase with the distance between residence and activity centre; (b) the non-home destinations of single-activity travel, multi-activity intermittent travel and multi-activity continuous travel agglomerate towards the activity centre, and the degree of agglomeration increases in this order; (c) the distribution density of Point Of Interest (POI) and activity destinations have strong positive correlations in space; (d) some attributes of POIs and demographics have significant influence on multi-activity continuous travels. These findings are useful in inducing the activities through reasonable combinations and spatial interconnections of POIs in urban planning.

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Developing an activity-based trip generation model for small/medium size planning agencies

Cited by 8 publications

References 2 publications

Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)

Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)

Activity‐based model based on multi‐day cellular data: Considering the lack of personal attributes and activity type

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

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