Analysis of passenger travel behavior based on public transportation OD data

2018 ◽  
Author(s):  
Zhen Hu ◽  
Liu Jingen ◽  
Haotian Bing
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Passenger Travel ◽  
Behavior Based

scholarly journals Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

2020 ◽  
Vol 10 (2) ◽  
pp. 628
Author(s):  
Di Lv ◽  
Yong Zhang ◽  
Jiongbin Lin ◽  
Peiyuan Wan ◽  
Yongli Hu
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Travel Behavior ◽  
Visual Exploration ◽  
Passenger Travel ◽  
Working Together ◽  
Transport Safety ◽  
Abnormal Behaviors ◽  
The Relationship ◽  
Pixel Matrix

More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnormal behaviors, such as stealing or begging. Thus, analyzing association between passengers is very important for management departments. It is very helpful to make operational plans, provide better services to passengers and ensure public transport safety. In order to quickly explore the association between passengers, we propose a multi-view interactive exploration method that provides five interactive views: passenger 3D travel trajectory view, passenger travel time pixel matrix view, passenger origin-destination chord view, passenger travel vehicle bubble chart view and passenger 2D travel trajectory view. It can explore the associated passengers from multiple aspects such as travel trajectory, travel area, travel time, and vehicles used for travel. Using Beijing public transportation data, the experimental results verified that our method can effectively explore the association between passengers and deduce the relationship.

scholarly journals The impacts of mediating the work environment on the mode choice in work trips

2021 ◽  
Vol 11 (1) ◽  
pp. 592-605
Author(s):  
Melchior Bria ◽  
Ludfi Djakfar ◽  
Achmad Wicaksono
Keyword(s):  
Work Environment ◽  
Public Transportation ◽  
Travel Behavior ◽  
Structural Equation ◽  
Mode Choice ◽  
Equation Modeling ◽  
Rapid Transit ◽  
Long Time ◽  
Socio Economic Variables ◽  
The Relationship

Abstract The impacts of work characteristics on travel mode choice behavior has been studied for a long time, focusing on the work type, income, duration, and working time. However, there are no comprehensive studies on the influence of travel behavior. Therefore, this study examines the influence of work environment as a mediator of socio-economic variables, trip characteristics, transportation infrastructure and services, the environment and choice of transportation mode on work trips. The mode of transportation consists of three variables, including public transportation (bus rapid transit and mass rapid transit), private vehicles (cars and motorbikes), and online transportation (online taxis and motorbike taxis online). Multivariate analysis using the partial least squares-structural equation modeling method was used to explain the relationship between variables in the model. According to the results, the mediating impact of work environment is significant on transportation choices only for environmental variables. The mediating mode choice effect is negative for public transportation and complimentary for private vehicles and online transportation. Other variables directly affect mode choice, including the influence of work environment.

scholarly journals Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase

2021 ◽  
Vol 18 (11) ◽  
pp. 6066
Author(s):  
Yanbing Bai ◽  
Lu Sun ◽  
Haoyu Liu ◽  
Chao Xie
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Behavior Patterns ◽  
Population Movements ◽  
Economic Level ◽  
Passenger Travel ◽  
Scale Population ◽  
Travel Behaviors ◽  
Railway Passenger ◽  
And Control

Large-scale population movements can turn local diseases into widespread epidemics. Grasping the characteristic of the population flow in the context of the COVID-19 is of great significance for providing information to epidemiology and formulating scientific and reasonable prevention and control policies. Especially in the post-COVID-19 phase, it is essential to maintain the achievement of the fight against the epidemic. Previous research focuses on flight and railway passenger travel behavior and patterns, but China also has numerous suburban residents with a not-high economic level; investigating their travel behaviors is significant for national stability. However, estimating the impacts of the COVID-19 for suburban residents’ travel behaviors remains challenging because of lacking apposite data. Here we submit bus ticketing data including approximately 26,000,000 records from April 2020–August 2020 for 2705 stations. Our results indicate that Suburban residents in Chinese Southern regions are more likely to travel by bus, and travel frequency is higher. Associated with the economic level, we find that residents in the economically developed region more likely to travel or carry out various social activities. Considering from the perspective of the traveling crowd, we find that men and young people are easier to travel by bus; however, they are exactly the main workforce. The indication of our findings is that suburban residents’ travel behavior is affected profoundly by economy and consistent with the inherent behavior patterns before the COVID-19 outbreak. We use typical regions as verification and it is indeed the case.

Analysis of Suburbanites' Travel Behavior Based on Big Data

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Xiaozhe Wu ◽  
Kai Zhang ◽  
Jinping Guan ◽  
Bokui Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Travel Behavior ◽  
Behavior Based

scholarly journals Understanding Electric Bicycle Users’ Mode Choice Preference under Uncertainty: A Case Study of Shanghai

2022 ◽  
Vol 14 (2) ◽  
pp. 925
Author(s):  
Feifei Xin ◽  
Yifan Chen ◽  
Yitong Ye
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Mode Choice ◽  
Long Distance ◽  
Choice Preference ◽  
Transportation Modes ◽  
Electric Bicycle ◽  
Private Car ◽  
Public Transportation System

The electric bicycle is considered as an environmentally friendly mode, the market share of which is growing fast worldwide. Even in metropolitan areas which have a well-developed public transportation system, the usage of electric bicycles continues to grow. Compared with bicycles, the power transferred from the battery enables users to ride faster and have long-distance trips. However, research on electric bicycle travel behavior is inadequate. This paper proposes a cumulative prospect theory (CPT) framework to describe electric bicycle users’ mode choice behavior. Different from the long-standing use of utility theory, CPT considers travelers’ inconsistent risk attitudes. Six socioeconomic characteristics are chosen to discriminate conservative and adventurous electric bicycle users. Then, a CPT model is established which includes two parts: travel time and travel cost. We calculate the comprehensive cumulative prospect value (CPV) for four transportation modes (electric bicycle, bus, subway and private car) to predict electric bicycle users’ mode choice preference under different travel distance ranges. The model is further validated via survey data.

Periodic Timetabling with Integrated Routing: Toward Applicable Approaches

2020 ◽  
Vol 54 (6) ◽  
pp. 1714-1731
Author(s):  
Philine Schiewe ◽  
Anita Schöbel
Keyword(s):  
Real World ◽  
Public Transportation ◽  
Computation Time ◽  
Upper And Lower Bounds ◽  
Problem Size ◽  
Classic Problem ◽  
Passenger Travel ◽  
Periodic Timetabling ◽  
Public Transportation Planning ◽  
Passenger Routing

Periodic timetabling is an important, yet computationally challenging, problem in public transportation planning. The usual objective when designing a timetable is to minimize passenger travel time. However, in most approaches, it is ignored that the routes of the passengers depend on the timetable, so handling their routing separately leads to timetables that are suboptimal for the passengers. This has recently been recognized, but integrating the passenger routing in the optimization is computationally even harder than solving the classic periodic timetabling problem. In our paper, we develop an exact preprocessing method for reducing the problem size and a heuristic reduction approach in which only a subset of the passengers is considered. It provides upper and lower bounds on the objective value, such that it can be adjusted with respect to quality and computation time. Together, we receive an approach that is applicable for real-world problems. We experimentally evaluate the performance of the approach on a benchmark example and on three close-to-real-world instances. Furthermore, we prove that the ratio between the classic problem without routing and the problem with integrated routing is bounded under weak and realistic assumptions.

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

scholarly journals Effect of socioeconomic and latent variables in vehicle ownership: A case study of Agartala city, India

2017 ◽  
Vol 7 (1.1) ◽  
pp. 472
Author(s):  
Amitabha Acharjee ◽  
Partha Pratim Sarkar ◽  
Joyanta Pal
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Latent Variables ◽  
Structural Equation ◽  
Car Ownership ◽  
Vehicle Ownership ◽  
Socioeconomic Variables ◽  
Equation Modelling ◽  
Two Wheeler

On the whole, car ownership is regarded as an imperative variable in travel behavior research. Car and motorcycle ownership are increasing rapidly in developing countries leading to an unsustainable developments. Using a data of 584 respondents from the Agartala city randomly collected, a model has been prepared to understand vehicle ownership for both car and motorized two wheeler mode (MTW). Latent variables along with socioeconomic variables such as monthly income, gender, age were used for modeling vehicle ownership using structural equation modelling. Latent variables used in this study, flexibility (Motorized Two wheeler), Negative public transportation perception and comfort (car) were found to be significant in the model. Our result suggests apart from socioeconomic variables, latent variables also explains vehicle ownership model.

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