A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data

12/08/2020
by   Chenfeng Xiong, et al.
10

While benefiting people's daily life in so many ways, smartphones and their location-based services are generating massive mobile device location data that has great potential to help us understand travel demand patterns and make transportation planning for the future. While recent studies have analyzed human travel behavior using such new data sources, limited research has been done to extract multimodal travel demand patterns out of them. This paper presents a data-driven analytical framework to bridge the gap. To be able to successfully detect travel modes using the passively collected location information, we conduct a smartphone-based GPS survey to collect ground truth observations. Then a jointly trained single-layer model and deep neural network for travel mode imputation is developed. Being "wide" and "deep" at the same time, this model combines the advantages of both types of models. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines and therefore enhance the imputation accuracy. To showcase the applications of the introduced framework in answering real-world planning needs, a separate mobile device location data is processed through trip end identification and attribute generation, in a way that the travel mode imputation can be directly applied. The estimated multimodal travel demand patterns are then validated against typical household travel surveys in the same Washington D.C. and Baltimore Metropolitan Regions.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

page 7

page 9

page 14

page 17

page 19

page 20

page 22

06/17/2020

A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data

Mobile device location data (MDLD) contains abundant travel behavior inf...
05/16/2018

A Framework to Integrate Mode Choice in the Design of Mobility-on-Demand Systems

Mobility-on-Demand (MoD) systems are generally designed and analyzed for...
09/17/2021

An open GPS trajectory dataset and benchmark for travel mode detection

Travel mode detection has been a hot topic in the field of GPS trajector...
12/24/2019

Mining User Behaviour from Smartphone data, a literature review

To study users' travel behaviour and travel time between origin and dest...
01/22/2019

Perturbation Privacy for Sensitive Locations in Transit Data Publication: A Case Study of Montreal Trajet Surveys

Smartphone based travel data collection has become an important tool for...
08/19/2021

Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach

Urban rail transit (URT) system plays a dominating role in many megaciti...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.