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Dr. Yang Zhou
Tongji University

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Research Keywords & Expertise

0 Transportation
0 travel behavior
0 Travel surveys
0 Transport and Mobility
0 TRANSPORTATION DATA MODELING AND SIMULATION

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Journal article
Published: 31 March 2021 in Travel Behaviour and Society
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Pattern clustering is an effective method for exploring the regularities of human mobility scheduling and daily activities. There still remains the challenge of measuring the similarity between pairs of activity patterns that are in the form of categorical time series sequences. Existing studies measured similarity using binary vector or edit distance, but these methods were insufficient to characterize routine arrangement and time scheduling of daily activities. To address this issue, we cluster daily activities and identify regular patterns using a Markov-chain-based mixture model, which captures features of activity scheduling by Markov transition matrix as well as measures similarity with probability distribution. Logistic regression models are further built to test hypothetical relationships between activity patterns and socio-demographic characteristics. Results show there are three main human activity patterns in terms of daily routine arrangement and activity scheduling: working-education-oriented (WE-oriented), recreation-shopping-oriented (RS-oriented), and schooling-drop-off/pick-up-oriented (SDP-oriented). People in the WE-oriented pattern mainly engage with regular home-based commuting trips, while people in the RS-oriented pattern are involved in home-based shopping and entertainment events. With regard to the SDP-oriented pattern, people plan their trips under a restricted scheduling of schooling pickup/drop-off. Each pattern clearly indicates long-term regularity of daily activity behaviors and corresponds to specific socio-demographics. Distinguishing three categories of residents with distinct life styles, this research would help accommodate travel demand from different groups of people in urban transportation planning.

ACS Style

Yang Zhou; Quan Yuan; Chao Yang; Yinhai Wang. Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model. Travel Behaviour and Society 2021, 24, 102 -112.

AMA Style

Yang Zhou, Quan Yuan, Chao Yang, Yinhai Wang. Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model. Travel Behaviour and Society. 2021; 24 ():102-112.

Chicago/Turabian Style

Yang Zhou; Quan Yuan; Chao Yang; Yinhai Wang. 2021. "Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model." Travel Behaviour and Society 24, no. : 102-112.

Journal article
Published: 01 December 2020 in Sustainability
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The rapid aging of the population has posed significant challenges to society and raised new demand for transportation services. Understanding travel needs of the elderly is crucial to making effective strategies for accommodating their demand in many newly motorized cities in developing countries such as China. Using a Markov-chain-based mixture model, we identify two main activity patterns of the elderly: recreation-shopping-oriented (RS-oriented) pattern and schooling-drop-off/pick-up-oriented (SDP-oriented) pattern. Elderly people in the RS-oriented pattern enjoy a cozy life with much time spent on recreation and shopping activities, while those in the SDP-oriented pattern take responsibility of sending grandchildren to school and taking them back home. The RS-oriented elderly people are faced with spatial constraints to access the sparsely distributed recreational sights; however, the SDP-oriented group is subject to temporal constraints when making daily trips. These results would encourage policy makers to reconsider the role of transportation in aged people’s lives and better accommodate their demand through designing safer walking and cycling environment and improving the quality of transit services.

ACS Style

Yang Zhou; Quan Yuan; Chao Yang. Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints. Sustainability 2020, 12, 10024 .

AMA Style

Yang Zhou, Quan Yuan, Chao Yang. Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints. Sustainability. 2020; 12 (23):10024.

Chicago/Turabian Style

Yang Zhou; Quan Yuan; Chao Yang. 2020. "Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints." Sustainability 12, no. 23: 10024.

Research article
Published: 08 September 2020 in Journal of Advanced Transportation
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Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.

ACS Style

Xiaolei Ru; Xiangdong Xu; Yang Zhou; Chao Yang. Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network. Journal of Advanced Transportation 2020, 2020, 1 -10.

AMA Style

Xiaolei Ru, Xiangdong Xu, Yang Zhou, Chao Yang. Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network. Journal of Advanced Transportation. 2020; 2020 ():1-10.

Chicago/Turabian Style

Xiaolei Ru; Xiangdong Xu; Yang Zhou; Chao Yang. 2020. "Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network." Journal of Advanced Transportation 2020, no. : 1-10.

Articles
Published: 12 October 2019 in Transportation Planning and Technology
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Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3 min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.

ACS Style

Yang Zhou; Chao Yang; Rongrong Zhu. Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai. Transportation Planning and Technology 2019, 42, 739 -756.

AMA Style

Yang Zhou, Chao Yang, Rongrong Zhu. Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai. Transportation Planning and Technology. 2019; 42 (8):739-756.

Chicago/Turabian Style

Yang Zhou; Chao Yang; Rongrong Zhu. 2019. "Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai." Transportation Planning and Technology 42, no. 8: 739-756.