A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

Zhao, Pengxiang; Bucher, Dominik; Henry, Martin; Raubal, Martin

doi:10.1007/978-3-030-14745-7_5

Cited by 7 publications

(7 citation statements)

References 44 publications

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“…The cluster-based classification method is an accepted method for spatial and temporal travel behaviour analysis [68]. Previous studies conducted different cluster-based methods to analyse travel behaviour and travel mode choice [69][70][71][72][73]. A hotspot can be defined as an area that has a higher cluster level of activity/access.…”

Section: Hotspot Analysis (Getis-ord Gi* Statistic Method)mentioning

confidence: 99%

Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

2021

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The elderly population is increasing rapidly. Understanding travel behaviour for this group of commuters (in terms of the trip purpose and travel time) is necessary for future transport planning. Many researchers are working on travel’s spatial and temporal analysis to provide operational decision making and transport network planning. This research study’s primary purpose is to identify the influence of trip duration (using public transport), time of the day (usage of public transport), and public transport (PT) accessibility over public transport mode preference by elderly (over 65 years of age) commuters. The methodology of this study is divided into two parts as spatial analysis and temporal analysis. The research identified the dependency of trip duration, time of the day, geographical areas, and PT access over transport mode preference of elderly. The temporal study shows that transport mode preference can vary depending on trip purposes. However, for specific trip durations and times of the day, the elderly sometimes choose PT as a mobility mode. For instance, on shopping trips between 10:00 and 11:00 a.m., the elderly have a greater possibility of choosing public transport over private vehicles. Moreover, the results show the public transport mode preference based on different times of the day and trip purposes. Urban and transport planner can use the results to modify/plan public transport schedule, which can be easily accessible by the elderly population.

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Section: Hotspot Analysis (Getis-ord Gi* Statistic Method)mentioning

confidence: 99%

Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

2021

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“…An anomalous deviation of these features from one week to another can indicate a transition from one phase of behavior change to another and should be reflected within the supporting ICT. Additionally, identifying people in similar behavioral transition phases can be used for analytical purposes or to target individual groups with specific incentives (Zhao et al 2019).…”

Section: Detecting and Supporting Behavioral Changementioning

confidence: 99%

Geosmartness for Personalized and Sustainable Future Urban Mobility

2021

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Urban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

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“…Choices of transport modes differ widely, between individuals as well as between countries. For example, the work by Zhao et al [37] developed a clustering-based framework to understand individuals' travel mode choice behavior in multi-modal transportation. It is shown that the users exhibit different patterns in spending time by car and EV.…”

Section: Transport Mode Choicementioning

confidence: 99%

Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles

Bucher

Henry

Hamper

et al. 2020

AGILE GIScience Ser.

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Abstract. The adoption of electric vehicles has the potential to help decarbonizing the transport sector if they are powered by renewable energy sources. Limitations commonly associated with e-cars are their comparatively short ranges and long recharging cycles, leading to anxiety when having to travel long distances. Other factors such as temperature, destination or weekday may influence people in choosing an e-car for a certain trip. Using a unique dataset of 129 people who own both an electric vehicle (EV) as well as one powered by an internal combustion engine (ICE), we analyze tracking data over a year in order to have an empirically verified choice model. Based on a wide range of predictors, this model tells us for an individual journey if the person would rather choose the EV or the ICE car. Our findings show that there are only weak relations between the predictor and target variables, indicating that for many people the switch to an e-car would not affect their lifestyle and the related range anxiety diminishes when actually owning an electric vehicle. In addition, we find that choice behavior does not generalize well over different users.

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A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

Cited by 7 publications

References 44 publications

Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

Geosmartness for Personalized and Sustainable Future Urban Mobility

Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles

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