Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

Fatima, Kaniz; Moridpour, Sara; Saghapour, Tayebeh

doi:10.3390/su13094752

Cited by 7 publications

(3 citation statements)

References 69 publications

(72 reference statements)

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“…Although the time approach has been applied on several occasions [32,33], for older people, time-based accessibility index is not discussed widely. Many researchers around the world have worked on older travel-lers' active transport based on time-based measures [24,[34][35][36][37][38][39][40][41][42][43][44][45]. Some existing time-based index studies can be summarised as follows (Table 2).…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Public Transport Accessibility Index for Older Commuters: A Time-Based Approach

Fatima

Moridpour

Saghapour

2022

Journal of Advanced Transportation

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The development of a public transport accessibility index for older travellers using total travel time is not a subject of frequent discussion. This study proposes a public transport accessibility index (EPTAI) which considers older peoples’ travel time and the populations of the second-smallest statistical areas according to census data. EPTAI identifies the level of access of the elderly to public transport (train, tram, and bus) in an urban area. The time-based EPTAI includes different trip purposes, including shopping trips (trips to shopping centres), medical trips (travel to healthcare centres), education trips (travel to education centres), and recreation trips (e.g., restaurants, parks, and cafes). The developed index is validated using statistical validation methods, including Pearson’s chi-square, likelihood ratio, linear-by-linear association, Cramer’s V, contingency coefficient, and phi. In addition, the performance of the developed index is compared with household survey data and the public transport accessibility level (PTAL). The results indicate that older adults’ public transport access varies depending on travel time, population density, and travel destination. The proposed index can be used for future planning/expansion and modification of public transport networks in urban and regional areas to meet the travel demands of older travellers.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a Public Transport Accessibility Index for Older Commuters: A Time-Based Approach

Fatima

Moridpour

Saghapour

2022

Journal of Advanced Transportation

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“…We define context data as information that can provide perspective into a person or an event, such as culture and habits. These studies relate context information, such as individual characteristics [3], temporal factors [4], transportation supply [5], travel demands [6], and weather conditions [7], to existing or self-gathered travel behavior data.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Sustainability

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Over the years, researchers have been studying the effects of weather and context data on transport mode choices. Existing research studies are predominantly designed around travel surveys, but the accuracy of their findings relies on how travelers give accurate and honest answers. The proliferation of smartphones, however, now offers the possibility of utilizing GPS positioning data as an alternative information source, opening the potential to accurately model and better understand factors which influence transport mode choices, compared to travel surveys. The objective of this work is to develop a model to predict the transport mode choices based on GPS trajectories, weather and context data. We use 2671 GPS trajectories from the Geolife GPS trajectories dataset, weather data, such as temperature and air quality, and context data, such as rush hour, day/night time and onetime events, such as the Olympics. In the statistical analysis, we apply both descriptive and statistical models, such as the multinomial logit and probit models. We find that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27%, and the bike share reduces by 21%, which is line with the results from several survey-based studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous, as people are encouraged to use environmentally friendly transport mode choices, such as the bike instead of the bus or car, which are known CO2 emitters. Our conclusion is that GPS trajectories can be used as a means to model passenger behavior, e.g. the choice of transport mode, in a quantitative way, which will support transport mode operators and policy makers in their efforts to design and plan the transport mode infrastructure to best suit the passengers’ needs.

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“…We define context data as information that can provide perspective into a person or an event such as culture and habits. These studies relate context information such as individual characteristics [2], temporal factors [3], transportation supply [4], travel demands [5], and weather conditions [6] to existing or self-gathered travel behaviour data. Most of the works focus on weather effects on transport mode decisions across different cities [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Modelling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories From Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Preprint

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Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purposes.

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Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

Cited by 7 publications

References 69 publications

Development of a Public Transport Accessibility Index for Older Commuters: A Time-Based Approach

Development of a Public Transport Accessibility Index for Older Commuters: A Time-Based Approach

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Modelling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories From Beijing

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