برآورد ارزش زمان سفر برای مسافران شهری حمل و نقل عمومی بر اساس بررسی اعلام اولویت

Quantitative study on the travel costs of urban transit passengers has great significance for scientifically evaluating social benefits of public transportation system. Analysis of travel time values is one of the most important parts of traveler's travel cost estimation. According to the stated preference (SP) survey data for Beijing residents, factors that influence public transport values of travel time are analyzed and a Logit-based model is used. An improved SP survey based model is proposed, in which traveler's income is introduced as a variable. The parameters estimating the travel time values under the trip purposes of work/business and leisure are calibrated. Then the values of travel time under different conditions in Beijing are obtained (i.e. different trip purposes and with/without transferring). The results indicate that the travel time values for work are generally higher than those for leisure. The waiting time values are higher than transferring time values and in-vehicle time values under any circumstances, and the waiting time values are higher with transferring than those without transferring.

Travel time value analysis plays an important role in the economic benefit evaluation of transportation projects. Travel time savings are the principal benefit of public transport investment projects that can be quantified. However, there are different perceptions of travel time values for different regions,eras and trip purposes[1]. The research on travel time values for public transport system is one of the most important and difficult tasks. Many studies on travel time values have been conducted recently by domestic and international scholars. After a review of the literature in China and foreign countries, three widely used methods to calculate travel time values emerged. They are the product method, the income method and the willingness-to-pay method, which are based on the Probit model or the Logit model[2,3]. Nowadays, Stated Preference(SP) survey has been used extensively in the field of transportation because of its capability to make good use of data, its high efficiency and its low cost[4]. For example,Wang[5] discussed the definition of travel time value and its influencing factors in depth. He established a simplified travel time value model for Beijing residents. SP survey was adopted to collect data and calibrate model parameters. The travel time values for residents in Beijing were obtained. Fu et al[6].quantified the travel time values of Beijing residents with a simplified Multinomial Logit (MNL) model. The time values of urban public transport system in Beijing were calculated based on an analysis of transit travelers characteristics. Travel time, travel cost, income and other factors were also considered. Hess et al. [7] discussed some of the issues that arose with the computation of the implied values of traveltime savings in the case of discrete choice models allowing for random taste heterogeneity. The coefficients of Multinomial Logit (MNL) and Mixed Multinomial Logit (MMNL) models for travel time values estimation were calculated, and distributions of travel time values were presented. Steimetz and Brownstone[8] applied the multiple imputation method to estimate the travel time values of commuters (full-time workers and part-time workers), which varied with trip distance.In general, progress has been made in the studies of travel time valuations after many years of research. However, the existing studies concentrate on the relationship between travel time values and costs rather than on the income. The impact of the travel process of transit travelers on estimating travel time values has not been analyzed in detail. To this end, the Logit model has been chosen as a basic model in this paper and traveler’s income is introduced as a new variable. The number of transfers is also considered to estimate the waiting time values, in-vehicle time values and transferring time values under different trip purposes.

SP survey is used to obtain travel behavior data from Beijing residents, and an improved time value model for the public transport systems was proposed in this paper. Moreover,the transferring time values, the waiting time values and the in-vehicle time values for various conditions in Beijing were obtained, which provide a support for the analysis of travel costs for the public transport system. The results indicate that time values for work purposes are generally higher than those for leisure purposes, and time values increase with the increase of travelers’ incomes. Meantime, waiting time values are higher than in-vehicle time values and transferring time values because travelers are more anxious when waiting than when in-vehicle or transferring. Transferring time is valued less than in-vehicle time for work and business purposes with transferring. This is because trips for work and business purposes occur during peak hours with heavy traffic flow and crowded buses or subways. Travelers are more anxious when they are in-vehicle than when they are transferring from one vehicle to another because they feel uncomfortable because of crowded circumstances and traffic congestion. Transferring time values are nearly the same as in-vehicle time values for leisure purposes (such as shopping or entertainment) with transferring. Compared with the route for work and business purposes, the route and the transferring time for of leisure purposes are more flexible. At the same time, leisure trips typically occur during off-peak hours, and the vehicle is comfortable because it is less crowded. Furthermore,in-vehicle time values for work and business purposes are higher than those for leisure purposes, with or without transfer.Comfort in vehicle is one of the reasons for this, not to mention less time stress for leisure trips. The bus or subway is less crowed during off-peak hours because work and business trips occur at peak hours, while leisure trips take place during off-peak hours.The research on travel time values involves many areas,such as traffic engineering, economics and statistics. There are a great many factors affecting travel time values. Further studies can be carried out to analyze additional factors that may influence travel time values in the future.