A comparative study on travel mode share, emission, and safety in five Vietnamese Cities

The paper proposes a new discrete choice model, named the Heteroscedastic Polarized Logit (HPL) to investigate choice contexts with one or more alternatives with remarkably low market shares. The proposed model is used to investigate the factors influencing the choice of a bicycle as a travel mode in the National Capital Region (NCR) of Canada. Data from the latest household travel survey of the NCR are used to investigate the mode choices of bikeable trips. Bikeable trips are defined as trips with lengths shorter than 16 km as this is the observed maximum limit of a bicycle trip in the dataset. A large dataset with over 40,000 trip records is used for empirical investigation where the bicycle has the lowest mode share of 3%. The HPL model clearly shows its appropriateness and superiority over comparable models in such a context. The choice to walk is found to be more sensitive to trip length than the choice to cycle, yet walking is found to have three times larger market share than that of cycling. Similarly, motorized modes are found to have low sensitivity to travel time and other impedances and have larger market shares. Women and students are found not to prefer the bicycle as a travel mode. Cycling infrastructure is seen to be effective in increasing the choice of the bicycle as a travel mode, but it also becomes clear that additional soft policy initiatives would be necessary to increase the popularity of cycling among young people, students, and women.

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A data-driven travel mode share estimation framework based on mobile device location data

Transportation ◽

10.1007/s11116-021-10214-3 ◽

2021 ◽

Author(s):

Mofeng Yang ◽

Yixuan Pan ◽

Aref Darzi ◽

Sepehr Ghader ◽

Chenfeng Xiong ◽

...

Keyword(s):

Mobile Device ◽

Data Driven ◽

Travel Mode ◽

Location Data ◽

Mode Share ◽

Device Location

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Getting at Mode Share: Comparing 3 Methods of Travel Mode Measurement for School Travel Research

Journal of School Health ◽

10.1111/josh.12743 ◽

2019 ◽

Vol 89 (5) ◽

pp. 365-372 ◽

Cited By ~ 1

Author(s):

Stephanie Sersli ◽

Linda Rothman ◽

Meghan Winters

Keyword(s):

School Travel ◽

Travel Mode ◽

Mode Share

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

10.21203/rs.3.rs-455056/v1 ◽

2021 ◽

Author(s):

Mofeng Yang ◽

Yixuan Pan ◽

Aref Darzi ◽

Sepehr Ghader ◽

Chenfeng Xiong ◽

...

Keyword(s):

Mobile Device ◽

Travel Behavior ◽

Travel Demand ◽

Ground Truth ◽

Data Driven ◽

Travel Mode ◽

Location Data ◽

Mode Share ◽

Device Location ◽

Ground Truth Information

Abstract Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper targets at studying the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal 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 models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in a 95% recall rate in identifying trip ends and a 93% 10-fold cross-validation accuracy in imputing the five travel modes (drive, rail, bus, bike and walk) with a Random Forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.

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