Understanding seasonal variation in individual’s activity participation and trip generation by using four consecutive two-week travel diary

Termida, Nursitihazlin Ahmad; Susilo, Yusak O.; Franklin, Joel P.; Liu, Chengxi

doi:10.1016/j.tbs.2017.12.006

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…Büscher et al investigated the stability of the most common motifs over time [21], and Li et al investigated infrequent motif detection [23]. Travel diaries, and the trends or behaviours learnt from analysing them have also been widely researched [24][25][26][27]. Multiple studies claim that there are common days for various activities or tasks [24,26,28], with an increased stability of people's travel behaviours on work days [25].…”

Section: Related Workmentioning

confidence: 99%

“…Multiple studies claim that there are common days for various activities or tasks [24,26,28], with an increased stability of people's travel behaviours on work days [25]. Seasonal, geographical, economic, and cultural factors all have an impact on people's activity patterns [26][27][28], in addition to access to and availability of public transport and personal vehicles [29,30].…”

Section: Related Workmentioning

confidence: 99%

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Using Trajectory Subclustering to Improve Destination Prediction

Hinsbergh

Griffiths

Zhou

et al. 2022

Mobile Information Systems

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Destination prediction is an active area of research, especially in the context of intelligent transportation systems. Intelligent applications, such as battery management in electric vehicles and congestion avoidance, rely on the accurate prediction of the future destinations of a vehicle. Destination prediction methods can utilise mobility patterns and can harness the latent information within vehicle trajectories. Existing approaches make use of the spatial information contained within trajectories, but this can be insufficient to achieve an accurate prediction at the start of an unfolding trajectory, since several destinations may share a common start to their trajectories. To reduce the prediction error in the early stages of a journey, we propose the Destination Prediction by Trajectory Subclustering (DPTS) method for iteratively clustering similar trajectories into groups using additional information contained within trajectories, such as temporal data. We show in our evaluation that DPTS is able to reduce the mean distance error in the first 40–60% of journeys. The implication of reducing the distance error early in a journey is that location-aware applications could provide more accurate functionality earlier in a journey. In this article, we (i) propose the Destination Prediction by Trajectory Subclustering (DPTS) method by extending an existing destination prediction method through incorporating an iterative clustering stage to decompose groups of similar trajectories into smaller groups and (ii) evaluate DPTS against the baseline performance of the existing method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Trajectory Subclustering to Improve Destination Prediction

Hinsbergh

Griffiths

Zhou

et al. 2022

Mobile Information Systems

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“…Clifton et al [2] show that sensitivity of car trips to weather conditions may be presented on a higher aggregation level (level of precipitation intensity only, not taking into account the seasonal effects) in comparison to the sensitivity of bicycle trips (level of precipitation intensity separately for each season). Cyclists are directly exposed to weather, and Several studies [33][34][35] collected revealed preferences based on the panel research. In Böcker et al [33], there were 945 respondents who recorded travel data for two days during the summer, two days during the autumn and two days during the winter.…”

Section: Applied Modelling Proceduresmentioning

confidence: 99%

“…Days for conducting the survey were not determined by seasons, but by the combination of the analysed meteorological conditions. Termida et al [35] used a panel survey in the form of a longitudinal panel two-week travel diary of 67 individuals in four consecutive waves in four different seasons.…”

Section: Applied Modelling Proceduresmentioning

confidence: 99%

“…The tobit model is implemented mainly due to the non-negative value of the dependent variable and because there is a possibility that many respondents, for example, do not have any trips of specific purpose registered during the day [55]. Linear regression; cluster model; behavioural travel demand model; combining cluster model and travel demand model Pillat [54] Multiple regression analysis Haberl et al [6] Negative binomial Sabir et al [40], Sabir [19], Liu et al [39] Ordered logit model Rudloff et al [9] Panel mixed ordered probit models Liu et al [56] Simultaneous tobit models Liu et al [38], Termida et al [35] Structural equation models Liu et al [39] Tobit model Sabir et al [40], Sabir [19], Böcker et al [21], Böcker [55], Liu et al [39] Note: GEE -Generalized Estimating Equations sunshine hours had the highest impact on the traffic demand. Although the authors stressed the potential of integrating weather conditions to predict the traffic demand, the weather conditions were not considered in the demand model.…”

Section: Trip Generation Modellingmentioning

confidence: 99%

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Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

Petrović¹,

Ivanović²,

Djoric³

et al. 2020

PROMET

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This paper presents an overview of the applied research methodologies and developed travel demand models that take weather impact into account. The paper deals with trip generation and modal split as elements of travel demand that best describe changes in the travel behaviour in different weather conditions. The authors herein emphasize the importance of research in local conditions in all climate zones, especially in areas where climate and modal split characteristics are different from those in common research areas. This review is designed as a brief guide on how the impact of weather can be explored in order to encourage conducting research even in the countries where there is no systematic traffic and travel data collection. The stated adaptation technique followed by the panel household travel surveys may be particularly appropriate for those countries. It is concluded that small budgets should not be considered an obstacle, because it is possible to draw reliable conclusions based even on small samples. Moreover, modern research methods enable a cheaper survey process together with the possibility of obtaining higher quality of results. The increasing popularity of research in this field should contribute to the creation of more resilient transport systems all over the world. A special contribution of this paper is the review of research studies carried out in central, western and southern Europe and not mentioned in any review paper before.

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