Multimodal Travel Mode Imputation based on Passively Collected Mobile Device Location Data

Passively collected mobile device location (PCMDL) data contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, PCMDL data have larger spatial, temporal and population coverage while lack of ground truth information. This study proposes a framework to identify trip ends and impute travel modes from the PCMDL data. The proposed framework firstly identify trip ends using the Spatio-temporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning methods. A PCMDL dataset with ground truth information is used to calibrate and validate the proposed framework, resulting in 95% accuracy in identifying trip ends and 93% accuracy in imputing five travel modes using the Random Forest (RF) classifier. The proposed framework is then applied to two large-scale PCMDL datasets, covering Maryland and the entire U.S. The mode share results are compared against travel surveys at different geographic levels

Activity-Based Household Travel Survey Through Smartphone Apps in Tennessee

RES 2020-19Activity-based household travel surveys (HTS) are one of primary data sources for many research fields at Tennessee Department of Transportation (TDOT). Traditional HTS methods are often costly, time-consuming, less scalable, and difficult to achieve high quality and accuracy. Recent years have witnessed a fast-growing interest in conducting HTS through smartphone apps to address survey issues and improve quality of collected survey data. A research project on activity based HTS through smartphone apps for both Android and iOS has been performed. The overarching goal of this research project is to develop an effective, economical, scalable HTS solution for TDOT. To achieve this goal, with the guidance and support from TDOT, the research team has 1) developed a smartphone-based effective, scalable, and secure application for household travel surveys that can span from days to months, 2) integrated fine-grained location information in submitted travel data by leveraging smartphone built-in sensor technologies, and 3) validated the developed HTS application by running a pilot HTS with the application. The pilot survey lasted three months. During the survey study, over 800 people downloaded the mobile apps and registered an account. Over 200 participants have been given a reward for completing the survey. Over 1,800 trips were submitted by those rewarded participants. This research project brings the following benefits to TDOT: 1) A tested, comprehensive smartphone app based HTS solution, 2) Important findings about smartphone app based HTS gained from running the pilot survey study, and 3) An anonymized survey dataset for research exploration obtained from the pilot survey study. A number of key findings as well as recommendations are also generated from this research project and they will help TDOT conduct HTS more effectively and generate more research results in the future

Developing and validating a statistical model for travel mode identification on smartphones

Smartphone travel surveys are able to capture accurate details about individuals' travel behavior. However, extracting the required information (e.g., travel mode and purpose) from the data captured by smartphone applications is relatively complex, particularly when relying on the computational power of smartphones and limiting the communications between these applications and third parties [e.g., geographic information systems (GIS)]. These limitations are mainly enforced to enable passive data collection through smartphones by automatically recognizing the mode and purpose of trips. Furthermore, limited data transfer between the application and third parties ensures the privacy protection of survey participants and facilitates real-world travel surveys with large sample sizes. Accordingly, the objective of this paper is to develop a model of travel mode identification, which can be integrated with smartphone travel surveys without using GIS data or interacting with participants. Most existing models and algorithms are either inaccurate or computationally complex, and require extensive processing power. A smartphone travel survey, namely, the Advanced Travel Logging Application for Smartphones II (ATLAS II), has been used to collect individuals' travel data across New Zealand and Queensland, Australia. A detailed algorithm is put forward to clean the captured data, segment trips into single modal trips, and develop multiple statistical models for comparison, using the data collected from New Zealand. The preferred approach, which is adapted for the integration with smartphone travel survey applications, is validated using the two separate data sets from New Zealand and Australia. The resulting mode identification model (i.e., a nested logit model with eight variables) could detect travel modes with the accuracy of 97% for New Zealand after preprocessing (i.e., data cleaning and trip segmentation) and 79.3% for Australia without any preprocessing