Spatial context and the complexity of daily travel patterns: an international comparison

2003 ◽  
Vol 11 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Harry Timmermans ◽  
Peter van der Waerden ◽  
Mario Alves ◽  
John Polak ◽  
Scott Ellis ◽  
...  
Keyword(s):  
International Comparison ◽  
Spatial Context ◽  
Travel Patterns ◽  
Daily Travel

scholarly journals Understanding Daily Travel Patterns of Subway Users – An Example From the Beijing Subway

2020 ◽  
Vol 32 (1) ◽  
pp. 13-23
Author(s):  
Hainan Huang ◽  
Jian Rong ◽  
Pengfei Lin ◽  
Jiancheng Weng
Keyword(s):  
Data Mining ◽  
Travel Behaviour ◽  
Travel Patterns ◽  
System A ◽  
Activity Duration ◽  
Different Types ◽  
Weekly Sample ◽  
Mining Procedure ◽  
Daily Travel ◽  
Beijing Subway

The daily travel patterns (DTPs) present short-term and timely characteristics of the users’ travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.

Using Longitudinal Mobile Phone Data to Understand the Stability of Individual Travel Patterns

2017 ◽  
Vol 2643 (1) ◽  
pp. 166-177 ◽  
Author(s):  
Zhengyu Duan ◽  
Chun Wang ◽  
H. Michael Zhang ◽  
Zengxiang Lei ◽  
Haifeng Li ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Travel Patterns ◽  
Trip Chain ◽  
Demand Models ◽  
The Stability ◽  
Daily Trip ◽  
Daily Travel

Most travel demand models assume that individuals’ daily travel patterns are stable or follow a fixed routine. This hypothesis is being questioned by more and more researchers. In this study, longitudinal mobile phone data were used to study the stability of individual daily travel patterns from three aspects, including activity space, activity points, and daily trip-chain patterns. The activity space was represented by the number of nonhome activity points, the radius of nonhome activity points, and the distance from home. The visitation pattern of activity points was analyzed by entropy and predictability measures. The stability of trip-chain patterns was described by the number of distinct trip chains, the typical trip chain, and the typical trip-chain ratio. Analysis of 21 days of mobile phone data from three communities in Shanghai, China, revealed that individuals’ daily travel patterns showed considerable variation. Although individuals’ visitation patterns to activity points were very regular, the day-to-day variations of individual trip-chain patterns were quite significant. On average, an individual exhibited about eight types of daily trip chains during the 21-day period. The daily travel patterns of residents in the outskirts were more stable than those of residents in the city center. Individuals’ travel patterns on weekdays were more complex than those on weekends. As individuals’ activity spaces increased, the stability of their travel patterns decreased.

Impact of Neighborhood Built Environments on Shopping Travel Modes in Shanghai, China

2019 ◽  
Vol 2673 (8) ◽  
pp. 669-681
Author(s):  
Hao Wu ◽  
Yong Chen ◽  
Junfeng Jiao
Keyword(s):  
Travel Behavior ◽  
Primary Schools ◽  
Multinomial Logistic Regression ◽  
Built Environments ◽  
Travel Patterns ◽  
Total Data ◽  
Research Gap ◽  
The Impact ◽  
Daily Travel ◽  
Shopping Travel

Walking is one of the most widely used means of transport. Neighborhood built environments have a direct influence on individuals’ daily commuting, recreational travel patterns, and shopping travel behavior. In Chinese cities, shopping activities are among the most frequent reasons for daily travel. Yet, research on the impact of neighborhood built environments on people’s shopping travel activities in high-density cities is limited. To fill this research gap, this study investigates how neighborhood built environments might affect pedestrians’ shopping travel activities in Shanghai, China. The data, which includes shopping travel patterns, perceived environmental characteristics, and individual socioeconomic status, were collected from a survey of 21 randomly selected neighborhoods in Shanghai in 2011. In total, data from 2,838 samples (participants) were collected. Multinomial logistic regression was used to investigate how neighborhood built environments affect residents’ choice of travel mode for shopping, that is, the likelihood of taking transit, driving, or biking vs. walking. Results showed that nearly half of people surveyed (43.3%) used walking as their primary shopping mode. Road network density, presence of primary schools, and average sidewalk width were positively correlated with the likelihood of using walking as the primary shopping mode. Gender, age, and car ownership were also significant in the model.

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