Kyoto University · 사회과학
Wenzhe Sun 교수의 연구실은 대중교통 체계, 특히 버스 노선의 버스 밀집 현상과 이를 둘러싼 승객 행동을 중심으로 한 실시간 운행 예측 및 제어 전략 개발에 주력하고 있습니다. GPS 데이터 기반의 정량적 분석과 확률적 예측 모델을 활용해 승객 유동성, 경로 선택, 정류장에서의 이동 행동을 연구하며, 특히 대규모 트럭의 이동 제약과 도로 네트워크 특성에 따른 루트 선택 행동에 대해서도 탐구하고 있습니다. 이는 도시 이동성 향상과 공공교통 정책 수립에 기여하는 실용적인 연구입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Bus bunching is a well-known phenomenon on many bus routes where an initial delay to one service can disturb the whole schedule due to resulting differences in dwell times of subsequent buses at stops. This paper deals with the passenger behaviour when there is more than one bus serving the stop, focusing on their choices and possible switching actions from the queue of the bus they are waiting to board. A parameter γ is introduced to denote the percentage of passengers boarding the front bus of
This paper employs regression with ARIMA errors (RegARIMA) to quantify the impacts of multiple non-pharmaceutical interventions, daily new cases, seasonal and calendar effects, and other factors on activity trends across the timeline of the ongoing COVID-19 pandemic in Japan. The discussion focuses on two controversial policy sets imposed by the Japanese government that aim to contain the pandemic and to stimulate the recovery of the economy. The containing effect was achieved by stay-at-home re
The mobility of sizable trucks is often limited by their large size. They thus may have additional requirements on road types, road widths, and the turning radius at the intersection when traveling. Therefore, this study explores the unique needs and preferences of large truck drivers’ route choice with a focus on trip and road network characteristics. GPS trajectory data from the central Kansai area of Japan with numerous ports and freight terminals are used. Trajectories are considered having
Bus bunching resulting from initially small headway irregularities is a widely-known and studied problem. A variety of headway-prediction approaches, as well as corrective strategies, have been developed to identify and correct headway irregularity in real time. Instead of predicting an exact value for future headways, this study explores a probabilistic predictive methodology to forecast whether or not a bus will be bunched during its dwelling at a downstream stop, using a logistic regression m
This chapter discusses the various means of obtaining demand estimates for public transport planning. It starts by discussing the relevant models for obtaining a general knowledge on the network-level demand before the service starts operation. This is followed by consideration of demand estimation for existing services, focusing on the potential emerging from massive passive public transport (PT) data. Distinctive detailed levels of demand information (stop flows, leg-Origin Destination (OD) fl
Bus bunching is a well-known phenomenon on many bus routes where an initial delay to one service can disturb the whole schedule due to resulting differences in dwell times of subsequent buses at stops. This paper deals with the passenger behaviour when there is more than one bus serving the stop, focusing on their choices and possible switching actions from the queue of the bus they are waiting to board. A parameter γ is introduced to denote the percentage of passengers boarding the front bus of
