Big data assessment on the operating characteristics of on-line taxis and thoughts on relevant policies

A double-dimensional big data assessment method on the characteristics of on-line taxi traffic operation is proposed to provide a scientific basis for carrying out the taxi industry reform and standardizing the on-line taxi hailing management work. Taking Shenzhen as an example, multi- source data such as on-line taxi license plate data, plate identification data and taxi （including on-line taxis） operation data are combined with the results of the stated preference （SP） survey on taxi operating characteristics to assess the overall operation characteristics of on-line taxis. The results show that the current on-line taxis in Shenzhen can be divided into three categories, that is, full-time on-line taxis, non- active on-line taxis and part-time on-line taxis, accounting for 4%, 55%, and 41%, respectively, of the total quantity. In terms of the characteristics of space-time operations, full-time on-line taxis have similar operating characteristics as those of traditional taxis; the operation of non-active on-line taxis and part-time on-line taxis coincides with commuting requirements during morning and evening peak hours. However, part-time on-line taxis operate for a much longer time period at night. Due to the convenient hailing and favorable price, on-line taxis have a significant impact on trip modes of citizens; and the substitution eflbct of on-line taxis on traditional buses and cruising taxis is obvious. It is beneficial for helping the government departments to objectively understand the development law of the on-line taxi industry and providing decision reference for the formulation of relevant management policies during the critical development stage of on-line taxi industry.

Modeling customer satisfaction for bus rapid transit in Changzhou, China

This paper aims to develop a customer satisfaction model for bus rapid transit （BRT）. Both the socio-economic and travel characteristics of passengers were considered to be independent variables. Changzhou BRT was taken as an example and on which on-board surveys were conducted to collect data. Ordinal logistic regression （OLR） was used as the modeling approach. The general OLR-based procedure for modeling customer satisfaction is proposed and based on which the customer satisfaction model of Changzhou BRT is developed. Some important findings are concluded： Waiting sub-journey affects customer satisfaction the most, riding sub- journey comes second and arriving station sub-journey has relatively fewer effects. The availability of shelter and benches at stations imposes heavy influence on customer satisfaction. Passengers＇ socio-economic characteristics have heavy impact on customer satisfaction.