Abstract
The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual’s activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities).
Highlights
Nowadays, quick and easy transportation has been an essential part of modern society, but traffic congestion will become worse without big changes in citizen’s travel behavior towards more efficient, sustainable and environmental travel alternatives [1].Different solutions have been explored for solving those issues, and it is important to know what is causing the traffic congestion
We explore a solution to combine the above services in a single Collaborative Mobility (CM) system
We propose an indicator for enhancing collaborative mobility that can be used by a travel advisor to pro-actively recommend different actions towards an environmentally-friendly and sustainable travel behavior
Summary
Quick and easy transportation has been an essential part of modern society, but traffic congestion will become worse without big changes in citizen’s travel behavior towards more efficient, sustainable and environmental travel alternatives [1].Different solutions have been explored for solving those issues, and it is important to know what is causing the traffic congestion. Interesting to note, according to [2], the average number of passengers per car (taking into account regular vehicles with five passengers, including the driver) for the European countries is approximately 1.45 passengers. This means that vehicles are often running in low occupancy, only 29% occupancy, and sometimes even unoccupied. The results showed that 55% of the respondents did not carpool because of the difficulty in finding someone with a similar location and schedule, and 45% prefer the flexibility of solo driving Another survey made by [9] showed that the poor carpooling schedule and trust level between strangers are two major obstructions for carpool activities. The question that arises in this context is how we can solve those issues in order to attract more people towards collaborative mobility
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