scholarly journals Exploring Travel Patterns during the Holiday Season—A Case Study of Shenzhen Metro System During the Chinese Spring Festival

2020 ◽  
Vol 9 (11) ◽  
pp. 651 ◽  
Author(s):  
Jianxiao Liu ◽  
Wenzhong Shi ◽  
Pengfei Chen
Keyword(s):  
Travel Behavior ◽  
Chinese Spring ◽  
Latent Dirichlet Allocation ◽  
Travel Demand ◽  
Modern Society ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Metro Station ◽  
Spring Festival ◽  
Holiday Season

Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies of the Chinese Spring Festival (CSF) at the city level are even rarer. This paper adopts a text-mining model (latent Dirichlet allocation (LDA)) to explore the travel patterns and travel purposes during the CSF season in Shenzhen based on the metro smart card data (MSC) and the points of interest (POIs) data. The study aims to answer two questions—(1) how to use MSC and POIs inferring travel purpose at the metro station level without the socioeconomic backgrounds of the cardholders? (2) What are the overall inner-city mobility patterns and travel activities during the Spring Festival holiday-week? The results show that six features of the CSF travel behavior are found and nine (three broad categories) travel patterns and trip activities are inferred. The activities in which travelers engaged during the CSF season are mainly consumption-oriented events, visiting relatives and friends and traffic-oriented events. This study is beneficial to metro corporations (timetable management), business owners (promotion strategy), researchers (travelers’ social attribute inference) and decision-makers (examine public service).

scholarly journals Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility

2019 ◽  
Vol 8 (6) ◽  
pp. 271 ◽  
Author(s):  
Yuanxuan Yang ◽  
Alison Heppenstall ◽  
Andy Turner ◽  
Alexis Comber
Keyword(s):  
Social Media ◽  
Land Use ◽  
Smart Card ◽  
Temporal Variations ◽  
Time Profile ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Metro Station ◽  
Trip Purpose ◽  
Rate Measure

This study describes the integration and analysis of travel smart card data (SCD) with points of interest (POIs) from social media for a case study in Shenzhen, China. SCD ticket price with tap-in and tap-out times was used to identify different groups of travellers. The study examines the temporal variations in mobility, identifies different groups of users and characterises their trip purpose and identifies sub-groups of users with different travel patterns. Different groups were identified based on their travel times and trip costs. The trip purpose associated with different groups was evaluated by constructing zones around metro station locations and identifying the POIs in each zone. Each POI was allocated to one of six land use types, and each zone was allocated a set of land use weights based on the number of POI check-ins for the POIs in that zone. Trip purpose was then inferred from trip time linked to the land use at the origin and destination zones using a novel “land use change rate” measure. A cluster analysis was used to identify sub-groups of users based on individual temporal travel patterns, which were used to generate a novel “boarding time profile”. The results show how different groups of users can be identified and the differences in trip times and trip purpose quantified between and within groups. Limitations of the study are discussed and a number of areas for further work identified, including linking to socioeconomic data and a deeper consideration of the timestamps of POI check-ins to support the inference of dynamic and multiple land uses at one location. The methods and metrics developed by this research use social media POI data to semantically contextualise information derived from the SCD and to overcome the drawbacks and limitations of traditional travel survey data. They are novel and generalizable to other studies. They quantify spatiotemporal mobility patterns for different groups of travellers and infer how their purposes of their journeys change through the day. In so doing, they support a more nuanced and detailed view of who, where, when and why people use city spaces.

scholarly journals Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bhawat Chaichannawatik ◽  
Kunnawee Kanitpong ◽  
Thirayoot Limanond
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Peak Period ◽  
Intercity Travel ◽  
Departure Time ◽  
Departure Time Choice

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

scholarly journals Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

2019 ◽  
Vol 8 (10) ◽  
pp. 434 ◽  
Author(s):  
Tong Zhang ◽  
Jianlong Wang ◽  
Chenrong Cui ◽  
Yicong Li ◽  
Wei He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Card ◽  
Travel Behavior ◽  
Public Transit ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Learning Methods ◽  
Transit Network ◽  
Machine Learning Methods ◽  
Smart Card Data

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Exploratory Analysis of Weekend Activity Patterns in the San Francisco Bay Area, California

2005 ◽  
Vol 1926 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Allison M. Lockwood ◽  
Sivaramakrishnan Srinivasan ◽  
Chandra R. Bhat
Keyword(s):  
San Francisco ◽  
Travel Behavior ◽  
Transportation Planning ◽  
Travel Demand ◽  
San Francisco Bay ◽  
San Francisco Bay Area ◽  
Exploratory Analysis ◽  
Travel Patterns ◽  
Peak Period ◽  
Bay Area

Research on travel demand modeling has predominantly focused on weekday activity–travel patterns, with studying the effects of commute travel on peak period traffic congestion as a major objective. Few studies have examined the weekend activity–travel behavior of individuals. However, weekend travel volume has been increasing over time and is comparable to weekday travel volumes. Hence, weekend activity–travel patterns warrant careful attention in transportation planning. This paper focuses on presenting a comprehensive exploratory analysis of weekend activity–travel patterns and contrasting weekday and weekend activity participation characteristics. Data from the 2000 San Francisco Bay Area Travel Survey, California, are used in the analysis. A comparative analysis of several aggregate activity–travel characteristics indicates that, although weekday and weekend travel volumes are comparable, there are several key differences in activity–travel characteristics. Specifically, weekend activity–travel is predominantly leisure oriented and undertaken during the midday period. Average trip distances are longer on weekends. Transit shares are lower but occupancy levels in personal automobiles are higher on weekends. The weekend activity sequencing and trip-chaining characteristics explored in this study provide further insights into individuals’ activity organization patterns on weekend days. This paper highlights the importance of studying weekend activity–travel behavior for transportation planning and air-quality modeling. Insights from this exploratory analysis can form the basis for comprehensive weekend activity–travel modeling efforts.

scholarly journals Mobility Patterns of Students: Evidence from Tricity Area, Poland

2021 ◽  
Vol 11 (2) ◽  
pp. 522
Author(s):  
Agnieszka Szmelter-Jarosz ◽  
Michał Suchanek
Keyword(s):  
Factor Analysis ◽  
Young Adults ◽  
Public Transport ◽  
Travel Behavior ◽  
Online Survey ◽  
Complex Analysis ◽  
Travel Demand ◽  
Future Research ◽  
Mobility Patterns ◽  
Active Commute

Generational change is one of the vital socioeconomic forces affecting the global economic environment. In many studies, the youngest generations are presented as the ones changing the market trends. This can also be observed in areas of travel demand and mobility patterns. However, research on those topics in many countries, for many societies, is scarce. This study aimed to examine the travel behavior of Polish young adults, namely students living in the Tricity area. Factor analysis and ANOVA were used to analyze the data gathered via an online survey assessing the characteristics of mobility patterns of students born between 1981 and 1999. Factor analysis allowed grouping the attitudes towards traveling among those young adults (Y Generation, Y’s, Y Gen). Three factors were identified, and they were associated with luxury and self-expression, freedom and comfort, safety and environmental friendliness. The driver’s characteristics were the least consistent with the classic image of typical Y’s, and those using the active commute—the most. In turn, the largest group were people using public transport, which partially presented convergent opinions with drivers and users of the active commute. It turned out that the car drivers, active commuters and respondents utilizing public transport differed not only in their behavior and presentation of Y Gen characteristics but also in their attitude towards categories such as comfort, desire for luxury, economy or ecology. This study is a complex analysis of the mobility patterns of students in the Tricity area. It presents the set of variables influencing the travel demand of the chosen age group. The study also compares the presented travel choices with those declared by representatives of other nations. Finally, it indicates the next research problems to be addressed in future research.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

Gendered Experiences of Mobility

Transfers ◽  
2013 ◽  
Vol 3 (3) ◽  
pp. 79-98 ◽  
Author(s):  
Shahnaz Huq-Hussain ◽  
Umme Habiba
Keyword(s):  
Middle Class ◽  
Travel Behavior ◽  
Weather Conditions ◽  
Capital City ◽  
Mobility Patterns ◽  
Security Issues ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
The City ◽  
Detailed Picture

This article examines the travel behavior of middle-class women in Dhaka, the capital city of Bangladesh and one of the world's largest and most densely populated cities. In particular, we focus on women's use of non-motorized rickshaws to understand the constraints on mobility for women in Dhaka. Primary research, in the form of an empirical study that surveyed women in six neighborhoods of Dhaka, underpins our findings. Our quantitative and qualitative data presents a detailed picture of women's mobility through the city. We argue that although over 75 percent of women surveyed chose the rickshaw as their main vehicle for travel, they did so within a complex framework of limited transport options. Women's mobility patterns have been further complicated by government action to decrease congestion by banning rickshaws from major roads in the city. Our article highlights the constraints on mobility that middle-class women in Dhaka face including inadequate services, poorly maintained roads, adverse weather conditions, safety and security issues, and the difficulty of confronting traditional views of women in public arenas.

Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

2020 ◽  
pp. 036119812096479
Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Management Strategies ◽  
Land Development ◽  
Future Research ◽  
Alternative Mode ◽  
Travel Demand Management ◽  
Development Level ◽  
Automobile Demand

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

2020 ◽  
Vol 2674 (6) ◽  
pp. 363-371
Author(s):  
Jungin Kim ◽  
Ikki Kim ◽  
Jaeyeob Shim ◽  
Hansol Yoo ◽  
Sangjun Park
Keyword(s):  
Travel Behavior ◽  
Household Income ◽  
Travel Demand ◽  
Behavioral Model ◽  
Income Group ◽  
Air Travel ◽  
Jeju Island ◽  
Demand Model ◽  
Air Travel Demand ◽  
Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

scholarly journals From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior

2017 ◽  
Vol 11 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Rolf Moeckel ◽  
Leta Huntsinger ◽  
Rick Donnelly
Keyword(s):  
Travel Behavior ◽  
Processing Time ◽  
Travel Demand ◽  
Computer Processing ◽  
Professional Judgment ◽  
Trip Generation ◽  
Household Type ◽  
Demand Models ◽  
Generation Mode ◽  
Definition Of

Background: In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective: This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method: Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results: This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.

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