TOYOTA - Analysis of individual mobility data to understand the driving patterns of users (DIT.AD004.108)

Thematic area

Engineering, ICT and technologies for energy and transportation

Project area

Structure responsible for the research project

Institute of information science and technologies "Alessandro Faedo" (ISTI)

Project manager

MIRCO NANNI
Phone number: 0506212843
Email: mirco.nanni@isti.cnr.it

Abstract

The project objective is the analysis of individual mobility data to understand the driving patterns of users. (1) Spatial driving patterns analysis: (a) (Spatial) Where do users drive: start, end and intermediate stops (b) (Spatial) What are the main locations for each driver (Home, work, school, etc.) (c) (Spatial) Which locations are visited after being in a particular location, with which frequency/probability (d) (Spatial) What routes they follow (e) (Temporal) When do people drive (probability distributions): start, end, travel durations, stay durations, including confidence measures for temporal predictability on each location. (f) (Temporal) Flexibility measure, expressed as the amount of time the user spends staying at home or work, provided both as overall aggregate and per location type (home and work). (2) Prediction and simulation: (a) (Position probabilities) What is the probability of being at a certain location according to the time of the day and the day of the week? (b) (One-day prediction) For each user, predict the most likely mobility of a typical day. (c) (Multiple-days simulation) For each user, simulate a realistic mobility for several consecutive days.

Goals

The project objective is the analysis of individual mobility data to understand the driving patterns of users. (1) Spatial driving patterns analysis: (a) (Spatial) Where do users drive: start, end and intermediate stops (b) (Spatial) What are the main locations for each driver (Home, work, school, etc.) (c) (Spatial) Which locations are visited after being in a particular location, with which frequency/probability (d) (Spatial) What routes they follow (e) (Temporal) When do people drive (probability distributions): start, end, travel durations, stay durations, including confidence measures for temporal predictability on each location. (f) (Temporal) Flexibility measure, expressed as the amount of time the user spends staying at home or work, provided both as overall aggregate and per location type (home and work). (2) Prediction and simulation: (a) (Position probabilities) What is the probability of being at a certain location according to the time of the day and the day of the week? (b) (One-day prediction) For each user, predict the most likely mobility of a typical day. (c) (Multiple-days simulation) For each user, simulate a realistic mobility for several consecutive days.

Start date of activity

25/02/2020

Keywords

mobility data, driving patterns

Last update: 04/10/2024