A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice

Tang, Jinjun; Wang, Yiwei; Hao, Wei; Liu, Fang; Huang, Helai; Wang, Yinhai

doi:10.1109/tits.2019.2905579

Cited by 27 publications

(16 citation statements)

References 26 publications

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“…Dou et al proposed a heuristic line piloting method that a taxi deviating from the typical route would raise an alert when malfunction took place or even hijacked by criminals [56]. Tang et al proposed a customer-search model based on route choice behavior analysis to help taxi drivers find next passengers in urban road networks [57]. Although taxi companies and governments have already made great efforts, there are still plenty of service improving problems to be settled.…”

Section: Taxi Service Improving Problemmentioning

confidence: 99%

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Gui

2020

Journal of Advanced Transportation

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Aggravating energy shortages and increasing labor costs have become global problems and have garnered special importance in recent years in the transportation sector, especially in taxi markets. Automatic vehicles have a bright future, however, there is an equal amount of skepticism and concern about safety for all the optimism. To unlock the potential of automatic vehicles in intelligent transportation systems, a lot more data and testing are required to promote safety level as far as possible and achieve the organizational aim of reducing accidents to zero tolerance. And it is vital to provide accurate models for vehicle movement analyses. In this study, Modified Digital Elevation (MDE) model and Spherical Bilinear Interpolation (SBI) model were proposed for vehicle movement analyses considering altitude. And the experimental data of 9,990 GPS-enabled taxis in Sanya, Zhengzhou, and Liaoyang were adopted to support comparisons. Measurement results showed that MDE model had over 99% less disparity with direct solution than original model and SBI model could further improve the effects. It indicated that the application of MDE model and SBI model could improve both accuracy and efficiency of vehicle movement analyses and it had a bright future in the field of automatic vehicles. Future directions could be improving models and expanding data.

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Section: Taxi Service Improving Problemmentioning

confidence: 99%

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Gui

2020

Journal of Advanced Transportation

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“…In this paper, travel demand is set as the dependent variable, and is estimated by the total number of taxi pick-ups in different blocks. Due to different patterns, destinations and times taken in every trip, we further separately divide travel demand into two parts corresponding to weekdays and the weekend [3,34].…”

Section: Explanatory Variablesmentioning

confidence: 99%

“…For socioeconomic and demographic factors, demographic variables reflect the characteristics of urban travel and are the main factors affecting taxi travel demand; these include Hispanic Population and people with bachelor's degree or higher (BS) population [3]. In addition, employment is more significant than mean income [39].…”

Section: Explanatory Variablesmentioning

confidence: 99%

“…On one hand, they provide uninterrupted door-to-door services for passengers; on the other hand, they are also an important connection for other transportation modes such as buses, subways and light rail [1,2]. Recently, the development of the intelligent transportation system (ITS) and mobile devices makes it possible to easily and comprehensively obtain large-scale data to analyze the spatial-temporal variations of travel demand [3]. In addition, the analysis of travel demand based on taxi Global Positioning System (GPS) trajectories may help to investigate urban travel patterns and regularity [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The geographically weighted regression (GWR) is one of the most widely used methods to understand the variations of urban travel demand, and is also applied in transportation and economic areas [3,24]. In addition, many spatio-temporal data can be expressed as time series data.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

et al. 2019

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Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

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A turning point-based offline map matching algorithm for urban road networks

Zhang

Dong

Guo

2021

Information Sciences

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A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice

Cited by 27 publications

References 26 publications

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

A turning point-based offline map matching algorithm for urban road networks

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