Inferring driving trajectories based on probabilistic model from large scale taxi GPS data

Tang, Jinjun; Liang, Junbin; Shen, Zhang; Huang, Helai; Liu, Fang

doi:10.1016/j.physa.2018.04.073

Cited by 77 publications

(34 citation statements)

References 39 publications

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“…To determine whether there are significant differences between parameter estimates for drivers with and without aggressive driving behaviors, a likelihood ratio test was performed [37][38][39][40]. The ratio test determines the transferability of aggressive driving behavior model's coefficients developed in the aggressive driving behavior model to the appropriate driving behavior model and whether there is a significant difference between the two groups.…”

Section: Likelihood Ratio Testmentioning

confidence: 99%

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Hao

Xiang

et al. 2018

Journal of Advanced Transportation

117

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The effect of aggressive driving behavior on driver's injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers' injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers' high level injury severity for drivers with aggressive driving behaviors.

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Section: Likelihood Ratio Testmentioning

confidence: 99%

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Hao

Xiang

et al. 2018

Journal of Advanced Transportation

117

View full text Add to dashboard Cite

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“…Conventional travel survey data cannot be compared in these respects. With the development of big data technology, travel behavior of every traffic participant could be revived by using passively collected data (PCD), such as mobile phone data [2,3], in-vehicle GPS data [4,5], transit smart card [6,7], loop detector and remote sensor data [8][9][10][11][12], etc.…”

Section: Introductionmentioning

confidence: 99%

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Zhang

Wang

et al. 2019

Sustainability

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Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

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“…On the contrary, public transit, with uncertain waiting time and fixed routes, has a limitation to undertake multiple activities in a tour [21,22]. Therefore, the complex trip chains may increase the dependence of travelers on automobiles, which leads to the problems related to auto route choice and optimization, as well as transportation safety [23][24][25][26][27][28][29][30][31]. In order to verify this conclusion, this paper also takes the mode choice of commuters as one of the independent variables.…”

Section: Introductionmentioning

confidence: 94%

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

Guan

Shi

et al. 2019

Sustainability

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This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters' outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters' non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters' behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.

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Inferring driving trajectories based on probabilistic model from large scale taxi GPS data

Cited by 77 publications

References 39 publications

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

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