Hazard-based Models For Analyzing Departure Time Choice Of Urban Shopping

With the development of society and economy and the increase of the consumer demand, the proportion of shopping trips has growing trend. Departure time is one of the important parameters of traffic prediction in Intelligent Traffic System; It is affected by the personal attributes, family property, travel mode, and many other factors. Compared with commuting, shopping trip has great flexibility in time. The time distribution of shopping is more complex. Therefore, it has important practical significance to study the distribution and affecting factors of urban shopping trip. Based on the survey data of Jinan city resident, this paper uses hazard model in survival analysis. It establish the departure time choice model of shopping, and analysis the effects of different social economy on shopping departure time, then predict change in departure time. The research results can provide predictive information for the Intelligent Transportation System more intuitive, accurate.At the same time; it provides the scientific basis for traffic inducement and management of intelligent system. The main work is as follows:(1) The overall departure time distributions of urban shopping were explored. Based on the residents travel investigation data, a hazard model of departure time of urban shopping was proposed. Their departure time was estimated. The different risk rate of social economic variables was explored. The results showed that before7:00in morning, the survival rate changed slowly. From the morning7:00continued to10:00, survival rate decreased dramatically. About58.5%.of travelers have gone shopping. After16:00, with the time increasing, the trend of hazard rate is upward.(2)Cox hazard-based models and the influence factors of departure time were investigated. Cox hazard-based models of departure time were proposed. Based on the surveyed data, the model parameters were estimated. The significant factors on departure time were analyzed. The results show that gender, age, whether to have children less than6years of age, medium income, and travel mode have significantly influence on departure time behavior. Overall, in departure time, male are earlier than the female, above60years old start the earliest than other age, middle income people are earlier than other income levels, family with children under6years start earlier than family without children. Walking start the earliest than other travel modes. Test Cox-Shell residual plot results show that the Cox model fitting effect is good.(3)Departure time of urban shopping was proposed by using parameter theory and, accelerated hazard models method. Based on the empirical data, the optimal parameter model was chosen. And key factors affecting the departure time were investigated. The results show that the log-logistic model is the most suitable to fit departure time of urban shopping. In the analysis of influencing factors, gender and family whether with children less than6, age, travel mode are important factor.